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Because of their large size, intact proteins can be difficult to study using analytical techniques, such as mass spectrometry. Consequently, it is often desirable to break a large polypeptide down into smaller pieces. Proteases are enzymes that typically break peptide bonds by binding to specific amino acid sequences in a protein and catalyzing their hydrolysis. Chemical reagents, such as cyanogen bromide, which cleaves peptide bonds on the C-terminal side of a methionine residue can also be used to cut larger proteins into smaller peptides. Common proteins performing this activity are found in the digestive system and are shown below. • Subtilisin - C-terminal side of large uncharged side chains • Chymotrypsin - C terminal side of aromatics (Phe, Tyr, Trp) • Trypsin - C-terminal side of lysine and arginines (not next to proline) • Carboxypeptidase - N-terminal side of C-terminal amino acid • Elastase - Hydrolyzes C-side of small AAs (Gly, Ala) • Cyanogen Bromide (chemical) - Hydrolyzes C-side of Met Determining mass and protein sequence Mass spectrometry, as its name suggests, is a method that can be used to determine the masses of molecules. Once limited to analyzing small molecules, it has since been adapted and improved to allow the analysis of biologically important molecules like proteins and nucleic acids. Mass spectrometers use an electrical field to accelerate an ionized molecule toward a detector. The time taken by an ionized molecule to move from its point of ionization to the detector will depend on both its mass and its charge and is termed its time of flight (TOF). MALDI-TOF MALDI-TOF (Matrix-assisted Laser Desorption Ionization - Time of Flight) is an analytical technique allowing one to determine the molecular masses of biologically relevant molecules with great precision. It is commonly used in proteomics and determination of masses of large biomolecules, including nucleic acids. The development of MALDI, which permits the production of ionic forms of relatively large molecules, was crucial to the successful use of mass spectrometry of biomolecules. Figure 8.46 shows a compact MALDI-TOF system. The MALDI-TOF process involves three basic steps. First, the material to be analyzed is embedded in solid support material (matrix) that can be volatilized in a vacuum chamber by a laser beam. In the second part of the process, a laser focused on the matrix volatilizes the sample, causing the molecules within it to vaporize and, in the process, to form ions by either gaining or losing protons. Third, the ions thus created in the sample are accelerated by an electric field towards a detector. Their rate of movement towards the detector is a function of the ratio of their mass to charge (m/z). An ion with a mass of 100 and a charge of +1 will move twice as fast as an ion with a mass of 200 and a charge of +1 and at the same rate as an ion with a mass of 200 and a charge of +2. Thus, by precisely determining the time it takes for an ion to go from ionization (time zero of the laser treatment) to being detected, the mass to charge ratio for all of the molecules in a sample can be readily determined. Ionization may result in destabilization of larger molecules, which fragment into smaller ones in the MALDI-TOF detection chamber. The size of each of the sub-fragments of a larger molecule allows one to determine its identity if this is not previously known. This fragmentation can be intentionally enhanced by having the accelerated ions collide with an inert gas, like argon. Fragmentation of a molecule may also be carried out prior to analysis, as for example, by cleaving a protein into smaller peptides by the use of enzymes or chemical agents. The amino acid sequence of a protein may be determined by using MALDI-TOF by analyzing the precise molecular masses of the many short peptide fragments obtained from a protein. When one amino acid, for example, fragments from a larger peptide, this can be detected as the difference in mass between the fragment with and without the amino acid, since each amino acid will have a characteristic molecular mass. By peptide mass fingerprinting and analysis of smaller fragments of individual peptides, the entire sequence of a polypeptide can, thus, be determined. 8.12: Membrane Dynamics (FRAP) Understanding the dynamics of movement in the membranes of cells is the province of the Fluorescence Recovery After Photobleaching (FRAP) technique (Figure 8.47). This optical technique is used to measure the two dimensional lateral diffusion of molecules in thin films, like membranes, using fluorescently labeled probes. It also has applications in protein binding. In the method, a lipid bilayer is uniformly labeled with a fluorescent tag (Figure 8.47, Step A) and then a subset of the tag is bleached using a laser (Step B). The spread of the bleached molecules is followed using a microscope (Step C). Information obtained in this manner provides data about the rate of lateral diffusion occurring in a lipid bilayer (Step D). 09: Chapter 10 This page is a placeholder created because the page was deleted, but has sub-pages. 10: Chapter 11 This page is a placeholder created because the page was deleted, but has sub-pages.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_For_All_(Ahern_Rajagopal_and_Tan)/08%3A_Basic_Techniques/8.11%3A_Protein_Cleavage.txt

In this chapter we introduce the subject and talk about the scientific aspects of the most important and most abundant liquid on the face of Earth - water. 01: Cells Water and Buffers Biochemistry is a relatively young science, but the rate of its expansion has been truly impressive. This rapid pace of discoveries, which shows no signs of slowing, is reflected in the steady increase in the size of biochemistry text books, most of which top a thousand pages and undergo revisions every couple of years to incorporate new findings. These full-scale texts offer an enormous amount of information and serve as invaluable resources. Those who need the greater level of detail and broader coverage that these books provide have many choices available in any good bookstore. As certified (some might say, certifiable) biochemistry nerds and unrepentant lovers of corny jokes, we firmly believe that students can have fun while learning the subject. Toward this end, we have sprinkled each chapter with rhymes and songs that we hope will have you learning biochemistry happily. The format of the book as available for the iPad, allows readers to click on figures to enlarge them, watch video lectures relevant to each topic, listen to the songs in the book, like the one above, and link out to the internet to find more information simply by clicking on any term. If you are using a PDF version of this book, you will still be able to use the links to the video lectures. Also, though you cannot listen to the songs by clicking on them in the PDF version, you can download them HERE. We hope you find these features useful and that they help you learn biochemistry. 1.02: Cells- The Bio of Biochemistry Biochemistry happens inside organisms and possibly, the most obvious thing about living organisms is their astounding diversity. If living things are so varied, it seems reasonable to ask whether their chemistry is, too. The invention of the microscope opened up a whole new world of microscopic organisms while also providing the first clue that living organisms had something in common-all living things are made up of cells. Some cells are “lone rangers” in the form of unicellular entities, such as bacteria and some protists. Cells are also the building blocks of more complex organisms (like humans, wombats, and turnips). As increasingly powerful microscopes became available, it was possible to discern that all cells fell into one of two types- those with a nucleus and other sub-cellular compartments like mitochondria and lysosomes, termed eukaryotes, and those that lack such internal compartmentation, the prokaryotes. Some eukaryotes, such as yeast, are unicellular, while others, including animals and plants are multicellular. The prokaryotes may be divided into two very broad categories, the bacteria and the archaeans. One can find living cells almost everywhere on earth - in thermal vents on the ocean floor, on the surface of your tongue and even in the frozen wastes of the Antarctic. Some cells may have even survived over two years on the moon. Yet, despite their diversity of appearance, habitat, and genetic composition, cells are not as different from each other as you might expect. At the biochemical level, it turns out that all cells are more alike than they are different. A great simplifying feature of biochemistry is that many of the reactions are universal, occurring in all cells. For example, most bacteria process glucose in the same 10-step pathway that plant, animal, and fungal cells do. The genetic code that specifies the amino acids encoded by a nucleic acid sequence is interpreted almost identically by all living cells, as well. Thus, the biochemical spectrum of life is (mercifully) not nearly as broad or as complicated as he evolutionary spectrum. Where cells differ significantly in processes/reactions, we will note these differences. 1.03: Water Water Everywhere Click here for Kevin's Introductory Lecture on Youtube Vital for life, water is by far the most abundant component of every cell. To understand life, we must, therefore, understand the basics of water, because everything that happens in cells, even reactions buried deep inside enzymes, away from water, is influenced by water’s chemistry. We start with simple properties. The molecule has a sort of wide ‘V’ shape (the H-O-H angle is 104°) with uneven sharing of electrons between the oxygen and the hydrogens. The hydrogens, as a result, are described as having a partial positive charge and the oxygen has a partial negative charge. These tiny partial charges allow the formation of what are described as hydrogen bonds, which occur when the partial positive charge of one atom is attracted to the partial negative of another. In water, that means the hydrogen of one water molecule will be attracted to the oxygen of another. Hydrogen bonds play essential roles in proteins, DNA, and RNA, as well, as we shall see.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/01%3A_Cells_Water_and_Buffers/1.01%3A_Introduction.txt

Water can ionize to a slight extent ($10^{-7}\; M$ is about 6 molecules per 100 million of pure water) to form $H^+$ (proton) and $OH^-$ (hydroxide). We measure the proton concentration of a solution with pH, which we define as the negative log of the proton concentration. $pH = -\log[H^+] \label{1.4.1}$ If the proton concentration, $[H^+]= 10^{-7}\; M$, then the pH is 7. We could just as easily measure the hydroxide concentration with the pOH by the parallel equation, $pOH = -\log[OH^-]$ In pure water, dissociation of a proton from it creates a hydroxide, so the pOH of pure water is 7, as well. This also means that $pH + pOH = 14 \label{1.4.2}$ Now, because protons and hydroxides can combine to form water, a large amount of one will cause there to be a small amount of the other. Why is this the case? In simple terms, if I dump 0.1 moles of $H^+$ into a pure water solution, the high proton concentration will react with the relatively small amount of hydroxides to create water, thus reducing hydroxides. Similarly, if I dump excess hydroxide (as $NaOH$, for example) into pure water, the proton concentration falls for the same reason. Chemists use the term “acid” to refer to a substance which has protons that can dissociate (come off) when dissolved in water. They use the term “base” to refer to a substance that can absorb protons when dissolved in water. Both acids and bases come in strong and weak forms. Strong acids, such as HCl, dissociate completely in water. If we add 0.1 moles of $HCl$ to a solution to make a liter, it will have 0.1 moles of $H^+$ and 0.1 moles of Cl-. There will be no remaining $HCl$ when this happens. A strong base like NaOH also dissociates completely into $Na^+$ and $OH^-$. Weak acids and bases differ from their strong counterparts. When you put one mole of acetic acid (HAc) into pure water, only about 4 in 1000 HAc molecules dissociate into $H^+$ and $Ac^-$. Thus, if I start with 1000 $HAc$, I will end up with 996 $HAc$ and 4 each of $H^+$ and $Ac^-$. Clearly, weak acids are very different from strong acids. Weak bases behave similarly, except that they accept protons, rather than donate them. You may wonder why we care about weak acids.You may never have thought much of weak acids when you were in General Chemistry. Your instructor described them as buffers and you probably dutifully memorized the fact that “buffers are substances that resist change in pH” without really learning what it meant. We will not allow that to happen here. Weak acids are critical for life because their affinity for protons causes them to behave like a UPS. We’re not referring to the UPS that is the United Parcel Service®, but instead, to the encased battery backup systems for computers called Uninterruptible Power Supplies that kick on to keep a computer running during a power failure. Your laptop battery is a UPS, for example. We can think of weak acids as Uninterruptible Proton Suppliers within certain pH ranges, providing (or absorbing) protons as needed.Weak acids thus help to keep the H+ concentration (and thus the pH) of the solution they are in relatively constant. Consider the acetic acid (acetate) system. Here is what happens when $HAc$ dissociates $HAc \rightleftharpoons H^+ + Ac^- \label{1.4.3}$ As noted, about 4 in 1000 $HAc$ molecules come apart. However, what if one started adding hydroxyl ions (by adding a strong base like $NaOH$) to the solution with the $HAc$ in it? As the added $OH$ ions reacted with the $H^+$ ions to make water, the concentration of $H^+$ ions would go down and the pH would go up. However, in contrast to the situation with a solution of pure water, there is a backup source of $H^+$ available in the form of $HAc$. Here is where the UPS function kicks in. As protons are taken away by the added hydroxyl ions (making water), they are partly replaced by protons from the $HAc$. This is why a weak acid is a buffer. It resists changes in pH by releasing protons to compensate for those “used up” in reacting with the hydroxyl ions.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/01%3A_Cells_Water_and_Buffers/1.04%3A_Buffers_Keep_the_Cellular_Environment_Stable.txt

It is useful to be able to predict the response of the $HAc$ system to changes in $H^+$ concentration. The Henderson-Hasselbalch equation defines the relationship between pH and the ratio of $Ac^-$ and $HAc$. It is as follows $pH = pK_a + \log \left(\dfrac{[Ac^-]}{[HAc]}\right) \label{1.5.1}$ This simple equation defines the relationship between the pH of a solution and the ratio of Ac- and HAc in it. The new term, called the pKa, is defined as $\text{pKa} = -\log \text{Ka} \label{1.5.2}$ just as $\text{pH} = -\log [\text{H}^+] \label{1.5.3}$ The Ka is the acid dissociation constant and is a measure of the strength of an acid. For a general acid, HA, which dissociates as $\text{HA} \leftrightharpoons \text{H}^+ + \text{A}^- \label{1.5.4}$ $\text{Ka} = [\text{H}^+][\text{A}^-] / \text{[HA]} \label{1.5.5}$ Thus, the stronger the acid, the more protons that will dissociate from it and the larger the value its Ka will have. Large values of Ka translate to lower values of pKa. As a result, the lower the pKa value is for a given acid, the stronger the acid is. Please note that pKais a constant for a given acid. The pKafor acetic acid is 4.76. By comparison, the pKafor formic acid is 3.75. Formic acid is therefore a stronger acid than acetic acid. A stronger acid will have more protons dissociated at a given pH than a weaker acid. Now, how does this translate into stabilizing pH? The previous figure shows a titration curve. In this curve, the titration begins with the conditions at the lower left (very low pH). At a this pH, the HAc form predominates, but as more and more OH- is added (moving to the right), the pH goes up, the amount of Ac- goes up and (correspondingly), the amount of HAc goes down. Notice that the curve “flattens” near the pKa (4.76). What this tells us is that the pH is not changing much (not going up as fast) as it did earlier when the same amount of hydroxide was added. The system is resisting a change in pH (not stopping the change, but slowing it) in the region of about one pH unit above and one pH unit below the pKa. Thus, the buffering region of the acetic acid/acetate buffer is from about 3.76 to 5.76. It is maximally strong at a pH of 4.76. Now it starts to become apparent how the buffer works. HA can donate protons when extras are needed (such as when $OH^-$ is added to the solution. Similarly, A- can accept protons when extra $H^+$ are added to the solution (adding HCl, for example). The maximum ability to donate or accept protons comes when $[\text{A}^-] = \text{[HA]} \label{1.5.6}$ To understand how well a buffer protects against changes in pH, consider the effect of adding .01 moles of HCl to 1.0 liter of pure water (no volume change) at pH 7, compared to adding it to 1.0 liter of a 1M acetate buffer at pH 4.76. Since HCl completely dissociates, in 0.01M ($10^{-2}$ M) HCl you will have 0.01M $H^+$. For the pure water, the pH drops from 7.0 down to 2.0 (pH = -log(0.01M)). By contrast, the acetate buffer’s pH is 4.74. Thus, the pure water solution sees its pH fall from 7 to 2 (5 pH units), whereas the buffered solution saw its pH drop from 4.76 to 4.74 (0.02 pH units). Clearly, the buffer minimizes the impact of the added protons compared to the pure water. It is important to note that buffers have capacities limited by their concentration. Let’s imagine that in the previous paragraph, we had added the 0.01 moles HCl to an acetate buffer that had a concentration of 0.01M and equal amounts of Ac- and HAc. When we try to do the math in parallel to the previous calculation, we see that there are 0.01M protons, but only 0.005M A- to absorb them. We could imagine that 0.005M of the protons would be absorbed, but that would still leave 0.005M of protons unbuffered. Thus, the pH of this solution would be approximately $\text{pH} = -\log 0.005\text{M} = 2.30 \label{1.5.7}$ Exceeding buffering capacity dropped the pH significantly compared to adding the same amount of protons to a 1M acetate buffer. Consequently, when considering buffers, it is important to recognize that their concentration sets their limits. Another limit is the pH range in which one hopes to control proton concentration. Now, what happens if a molecule has two (or more) ionizable groups? It turns out, not surprisingly, that each group will have its own pKa and, as a consequence, will tend to ionize at different pH values. The figure above right shows the titration curve for a simple amino acid, alanine. Note that instead of a single flattening of the curve, as was seen for acetic acid, alanine displays two such regions. These are individual buffering regions,each centered on the respective pKa values for the carboxyl group and the amino group. If we think about alanine, it can have three possible charges: +1 (alpha carboxyl group and alpha amino group each has a proton), 0 (alpha carboxyl group missing a proton and alpha amino group has a proton) and -1 (alpha carboxyl group and alpha amino group each lacking a proton). How does one predict the charge at a given pH for an amino acid? A good rule of thumb for estimating charge is that if the pH is more than one unit below the pKa for a group (carboxyl or amino), the proton is on. If the pH is more than one unit above the pKa for the group, the proton is off. If the pH is NOT more than one or less than one pH unit from the pKa, this simple assumption will not work. Further, it is important to recognize that these rules of thumb are estimates only. The pI (pH at which the charge of a molecule is zero) is an exact value calculated as the average of the two pKa values on either side of the zero region. It is calculated at the average of the two pKa values around the point where the charge of the molecule is zero.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/01%3A_Cells_Water_and_Buffers/1.05%3A_Henderson-Hasselbalch_Approximation.txt

Living organisms are made up of cells, and cells contain many biochemical components such as proteins, lipids, and carbohydrates. But, living cells are not random collections of these molecules. They are extraordinarily organized or "ordered". By contrast, in the nonliving world, there is a universal tendency to increasing disorder. Maintaining and creating order in cells takes the input of energy. Without energy, life is not possible. It is therefore important that we consider energy first in our attempt to understand biochemistry. Where does energy come from? Photosynthetic organisms can capture energy from the sun, converting it to chemical forms usable by cells. Heterotrophic organisms like ourselves get our energy from the food we eat. How do we extract the energy from the food we eat? • 2.1: Oxidative Energy • 2.2: Oxidation vs Reduction in Metabolism Catabolic processes are often oxidative in nature and energy releasing. Some, but not all of that energy is captured as ATP.  Not all of the energy is captured as ATP, and the rest is released as heat and it is for this reason that we get hot when we exercise. By contrast, synthesizing large molecules from smaller ones (for example, making proteins from amino acids) is referred to as anabolism. Anabolic processes are often reductive in nature and require energy input. • 2.3: Energy Coupling The addition of phosphate to a sugar is a common reaction that occurs in a cell. By itself, this process is not very energetically favorable (that is, it needs an input of energy to occur). Cells overcome this energy obstacle by using ATP to “drive” the reaction. The energy needed to drive reactions is harvested in very controlled conditions in the confines of an enzyme. This involves a process called ‘coupling’. • 2.4: Entropy and Energy • 2.5: Gibbs Free Energy • 2.6: Cellular Phosphorylations Formation of triphosphates is essential to meet the cell’s immediate energy needs for synthesis, motion, and signaling. In a given day, an average human being uses more than their body weight in triphosphates. Since triphosphates are the “currency” that meet immediate needs of the cell, it is important to understand how triphosphates are made. There are three phosphorylation mechanisms – 1) substrate level; 2) oxidative; and 3) photophosphorylation. We consider them here individually. • 2.7: Energy Efficiency • 2.8: Metabolic Controls of Energy • 2.9: Molecular Backups for Muscles • 2.10: Summary 02: Energy Living organisms are made up of cells, and cells contain many biochemical components such as proteins, lipids, and carbohydrates. But, living cells are not random collections of these molecules. They are extraordinarily organized or "ordered". By contrast, in the nonliving world, there is a universal tendency to increasing disorder. Maintaining and creating order in cells takes the input of energy. Without energy, life is not possible. It is therefore important that we consider energy first in our attempt to understand biochemistry. Where does energy come from? Photosynthetic organisms can capture energy from the sun, converting it to chemical forms usable by cells. Heterotrophic organisms like ourselves get our energy from the food we eat. How do we extract the energy from the food we eat? In this series, the most reduced form of carbon is on the left. The energy of oxidation of each form is shown above it. Fatty acids are more reduced overall than sugars. This can also be seen by their formulas. Palmitic acid = \(C_{16}H_{34}O_2\) Glucose = \(C_6H_{12}O_6\) Palmitic acid only contains two oxygens per sixteen carbons, whereas glucose has six oxygen atoms per six carbons. Consequently, when palmitic acid is fully oxidized, it generates more ATP per carbon (128/16) than glucose (38/6). It is because of this that we use fat as our primary energy storage material.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/02%3A_Energy/2.01%3A_Oxidative_Energy.txt

Biochemical processes that break things down from larger to smaller are called catabolic processes. Catabolic processes are often oxidative in nature and energy releasing. Some, but not all of that energy is captured as ATP. If not all of the energy is captured as ATP, what happens to the rest of it? The answer is simple. It is released as heat and it is for this reason that we get hot when we exercise. By contrast, synthesizing large molecules from smaller ones (for example, making proteins from amino acids) is referred to as anabolism. Anabolic processes are often reductive in nature and require energy input. By themselves, they would not occur, as they are reversing oxidation and decreasing entropy (making many small things into a larger one). To overcome this energy ‘barrier’, cells must expend energy. For example, if one wishes to reduce CO2 to carbohydrate, energy must be used to do so. Plants do this during the dark reactions of photosynthesis. The energy source for the reduction is ultimately the sun. The electrons for the reduction ultimately come from water, and the CO2 comes from the atmosphere and gets incorporated into a sugar. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University) 2.03: Energy Coupling The addition of phosphate to a sugar is a common reaction that occurs in a cell. By itself, this process is not very energetically favorable (that is, it needs an input of energy to occur). Cells overcome this energy obstacle by using ATP to “drive” the reaction. The energy needed to drive reactions is harvested in very controlled conditions in the confines of an enzyme. This involves a process called ‘coupling’. In coupled reactions, an enzyme binds both a high energy molecule (usually ATP) and the other molecule(s) involved in the reaction. Hydrolysis of ATP provides energy for the enzyme to stimulate the reaction on the other substance(s). Hexokinase, for example, catalyzes the phosphorylation of glucose to form glucose-6-phosphate. In the absence of ATP, the reaction has a fairly positive ΔG°’ (described later), but hydrolysis of ATP provides excess energy, giving the coupled reaction a fairly negative ΔG°’ value. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University) 2.04: Entropy and Energy Most students who have had some chemistry know about the principle of the Second Law of Thermodynamics with respect to increasing disorder of a system. Cells are very organized or ordered structures, leading some to mistakenly conclude that life somehow violates the second law. In fact, that notion is incorrect. The second law doesn’t say that entropy always increases, just that, left alone, it tends to do so, in an isolated system. Cells are not isolated systems, in that they obtain energy, either from the sun, if they are autotrophic, or food, if they are heterotrophic. To counter the universal tendency towards disorder on a local scale requires energy. As an example, take a fresh deck of cards which is neatly aligned with Ace-King-Queen . . . . 4,3,2 for each suit. Throw the deck into the air, letting the cards scatter. When you pick them up, they will be more disordered than when they started. However, if you spend a few minutes (and expend a bit of energy), you can reorganize the same deck back to its previous, organized state. If entropy always increased everywhere, you could not do this. However, with the input of energy, you overcame the disorder. The cost of fighting disorder is energy. There are, of course, other reasons that organisms need energy. Muscular contraction, synthesis of molecules, neurotransmission, signaling, thermoregulation, and subcellular movements are examples. Where does this energy come from? The currencies of energy are generally high-energy phosphate-containing molecules. ATP is the best known and most abundant, but GTP is also an important energy source (required for protein synthesis). CTP is involved in synthesis of glycerophospholipids and UTP is used for synthesis of glycogen. In each of these cases, the energy is in the form of potential chemical energy stored in the multi-phosphate bonds. Hydrolyzing those bonds releases the energy in them. Of the triphosphates, ATP is the primary energy source, acting to facilitate the synthesis of the others by action of the enzyme NDPK. ATP is made by three distinct types of phosphorylation – oxidative phosphorylation (in mitochondria), photophosphorylation (in chloroplasts of plants), and substrate level phosphorylation (in enzymatically catalyzed reactions).

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/02%3A_Energy/2.02%3A_Oxidation_vs_Reduction_in_Metabolism.txt

Most of the time, ATP is the “storage battery” of cells (See also ‘Molecular Battery Backups for Muscles below). In order to understand how energy is captured, we must first understand Gibbs free energy and in doing so, we begin to see the role of energy in determining the directions chemical reactions take. Wikipedia defines Gibbs free energy as “a thermodynamic potential that measures the "useful" or process-initiating work obtainable from an isothermal, isobaric thermodynamic system,” and further points out that it is “the maximum amount of non-expansion work that can be extracted from a closed system; this maximum can be attained only in a completely reversible process.” Mathematically, the Gibbs free energy is given as $G = H – TS$ where H is the enthalpy, T is the temperature in Kelvin, and S is the entropy. At standard temperature and pressure, every system seeks to achieve a minimum of free energy. Thus, increasing entropy will reduce Gibbs free energy. Similarly, if excess heat is available (reducing the enthalpy), the free energy can also be reduced. Cells must work within the laws of thermodynamics, as noted, so all of their biochemical reactions, too, have limitations. Now we shall consider energy in the cell. The change in Gibbs free energy (ΔG) for a reaction is crucial, for it, and it alone, determines whether or not a reaction goes forward. $ΔG = ΔH – TΔS,$ There are three cases • ΔG < 0 - the reaction proceeds as written • ΔG = 0 - the reaction is at equilibrium • ΔG > 0 - the reaction runs in reverse For a reaction aA <=> bB (where ‘a’ and ‘b’ are integers and A and B are molecules) at pH 7, ΔG can be determined by the following equation, $ΔG = ΔG°’ + RT\ln \dfrac{([B]^b}{[A]^a}$ For multiple substrate reactions, such as aA + cC <=> bB + dD $ΔG = ΔG°’ + RT\ln \dfrac{[B]^b[D]^d}{[A]^a[C]^c}$ The ΔG°’ term is called the change in Standard Gibbs Free energy, which is the change in energy that occurs when all of the products and reactants are at standard conditions and the pH is 7.0. It is a constant for a given reaction. In simple terms, if we collect all of the terms of the numerator together and call them {Products} and all of the terms of the denominator together and call them {Reactants}, $ΔG = ΔG°’ + RT \ln \dfrac{Products}{Reactants}$ For most biological systems, the temperature, T, is a constant for a given reaction. Since ΔG°’ is also a constant for a given reaction, the ΔG is changed almost exclusively as the ratio of {Products}/ {Reactants} changes. If one starts out at standard conditions, where everything except protons is at 1M, the RTln({Products}/{Reactants}) term is zero, so the ΔG°’ term determines the direction the reaction will take. This is why people say that a negative ΔG°’ indicates an energetically favorable reaction, whereas a positive ΔG°’ corresponds to an unfavorable one. Increasing the ratio of {Products}/{Reactants} causes the value of the natural log (ln) term to become more positive (less negative), thus making the value of ΔG more positive. Conversely, as the ratio of {Products}/{Reactants} decreases, the value of the natural log term becomes less positive (more negative), thus making the value of ΔG more negative. Intuitively, this makes sense and is consistent with Le Chatelier's principle – a system responds to stress by acting to alleviate the stress. If we examine the ΔG for a reaction in a closed system, we see that it will always move to a value of zero (equilibrium), no matter whether it starts with a positive or negative value. Another type of free energy available to cells is that generated by electrical potential. For example, mitochondria and chloroplasts partly use Coulombic energy (based on charge) from a proton gradient across their membranes to provide the necessary energy for the synthesis of ATP. Similar energies drive the transmission of nerve signals (differential distribution of sodium and potassium) and the movement of some molecules in secondary active transport processes across membranes (e.g., H+ differential driving the movement of lactose). From the Gibbs free energy change equation, $ΔG = ΔH – TΔS$ it should be noted that an increase in entropy will help contribute to a decrease in ΔG. This happens, for example when a large molecule is being broken into smaller pieces or when the rearrangement of a molecule increases the disorder of molecules around it. The latter situation arises in the hydrophobic effect, which helps drive the folding of proteins.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/02%3A_Energy/2.05%3A_Gibbs_Free_Energy.txt

Formation of triphosphates is essential to meet the cell’s immediate energy needs for synthesis, motion, and signaling. In a given day, an average human being uses more than their body weight in triphosphates. Since triphosphates are the “currency” that meet immediate needs of the cell, it is important to understand how triphosphates are made. There are three phosphorylation mechanisms – 1) substrate level; 2) oxidative; and 3) photophosphorylation. We consider them here individually. Substrate Level Phosphorylation The easiest type of phosphorylation to understand is that which occurs at the substrate level. This type of phosphorylation involves the direct synthesis of ATP from ADP and a reactive intermediate, typically a high energy phosphate-containing molecule. Substrate level phosphorylation is a relatively minor contributor to the total synthesis of triphosphates by cells. An example substrate phosphorylation comes from glycolysis. \[\ce{Phosphoenolpyruvate (PEP) + ADP <=> Pyuvate + ATP}\] This reaction has a very negative \(ΔG^{o'}\) (-31.4 kJ/mol), indicating that the PEP contains more energy than ATP, thus energetically favoring ATP’s synthesis. Other triphosphates can be made by substrate level phosphorylation, as well. For example, GTP can be synthesized by the following citric acid cycle reaction \[\ce{Succinyl-CoA + GDP + P_i <=> Succinate + GTP + CoA-SH}\] Triphosphates can be interchanged readily in substrate level phosphorylations catalyzed by the enzyme Nucleoside Diphosphate Kinase (NDPK). A generalized form of the reactions catalyzed by this enzyme is as follows: \[\ce{XTP + YDP <=> XDP + YTP}\] where \(\ce{X}\) can be adenosine, cytidine, uridine, thymidine, or guanosine and \(\ce{Y}\) can be any of these as well. Last, an unusual way of synthesizing ATP by substrate level phosphorylation is that catalyzed by adenylate kinase \[\ce{2 ADP <=> ATP + AMP}\] This reaction is an important means of generating ATP when the cell doesn’t have other sources of energy. The accumulation of AMP resulting from this reaction activates enzymes, such as phosphofructokinase, of glycolysis, that will catalyze reactions to give the cell additional, needed energy. Electron Transport/Oxidative Phosphorylation Mitocohondria are called the power plants of the cell because most of a cell’s ATP is produced there, in a process referred to as oxidative phosphorylation. The mechanism by which ATP is made in oxidative phosphorylation is one of the most interesting processes in all of biology. It has three primary considerations. The first is electrical – electrons from reduced energy carriers, such as NADH and FADH2, enter an electron transport system via protein complexes containing iron. As seen in the figure on the following page,electrons move from one complex to the next, not unlike the way they might move through an electrical circuit. The next consideration arises as a secondary phenomenon. When electrons pass through complexes I, III, and IV, protons are moved from the mitochondrial matrix (inside of mitochondrion) and deposited in the intermembrane space (between the inner and outer membranes of the mitochondrion). The effect of this redistribution is to increase the electrical and chemical potential across the membrane. Students may think of the process as “charging the battery.” Just like a charged battery, the potential arising from the proton differential across the membrane can be used to do things. This is the third consideration. In the mitochondrion, the “thing” that the proton gradient does is create ATP from ADP and Pi (inorganic phosphate). This process requires energy and is accomplished by movement of protons through a protein complex in the inner mitochondrial membrane. The protein complex is an enzyme that has several names, including Complex V, PTAS (Proton Translocating ATP Synthase), and ATP Synthase. Central to its function is the movement of protons through it (from outside back into the matrix). Protons will only move through ATP Synthase if their concentration is greater outside the inner membrane than in the matrix. In summary, the electron transport system charges the battery for oxidative phosphorylation by pumping protons out of the mitochondrion. The intact inner membrane of the mitochondrion keeps the protons out, except for those that re-enter through ATP Synthase. The ATP Synthase allows protons to re-enter the mitochondrial matrix and harvests their energy to make ATP. ATP Synthase The ATP Synthase itself is an amazing nanomachine that makes ATP using a gradient of protons flowing through it from the intermembrane space back into the matrix. It is not easy to depict in a single image what the synthase does. The figure at the right illustrates the multi-subunit nature of this membrane protein, which acts like a turbine at a hydroelectric dam. The movement of protons through the ATP Synthase causes it to spin like a turbine, and the spinning is necessary for making ATP. In ATP Synthase, the spinning component is the membrane portion (c ring) of the F0 stalk. The c ring proteins are linked to the gamma-epsilon stalk, which projects into the F1 head of the mushroom structure. The F1 head contains the catalytic ability to make ATP. The F1 head is hexameric in structure with paired alpha and beta proteins arranged in a trimer of dimers. Movement of the gamma protein inside the alpha-beta trimer causes each set of beta proteins to change structure slightly into three different forms called Loose, Tight, and Open (L,T,O). Each of these forms has a function. The Loose form binds \(\ce{ADP}\) and \(\ce{P_i}\). The tight form “squeezes” them together to form the ATP. The open form releases the ATP into the mitochondrial matrix. Thus,as a result of the proton excess in the intermembrane space, ATP is made. Photophosphorylation The third type of phosphorylation to make ATP is found only in cells that carry out photosynthesis. This process is similar to oxidative phosphorylation in several ways. A primary difference is the ultimate source of the energy for ATP synthesis. In oxidative phosphorylation, the energy comes from electrons produced by oxidation of biological molecules. In the case of photosynthesis, the energy comes from the light of the sun. Photons from the sun interact with chlorophyll molecules in reaction centers in the chloroplasts of plants or membranes of photosynthetic bacteria. A schematic of the process is shown above. The similarities of photophosphorylation to oxidative phosphorylation include: • an electron transport chain • creation of a proton gradient • harvesting energy of the proton gradient by making ATP with the help of an ATP synthase. Some of the differences include: • the source of the electrons – \(\ce{H2O}\) for photosynthesis versus \(\ce{NADH/FADH2}\) for oxidative phosphorylation • direction of proton pumping – into the thylakoid space of the chloroplasts versus outside the matrix of the mitochondrion • movement of protons during ATP synthesis – out of the thylakoid space in photosynthesis versus into the mitochondrial matrix • nature of the terminal electron acceptor – \(\ce{NADP^{+}}\) in photosynthesis versus \(\ce{O2}\) in oxidative phosphorylation. Electron Transport in Chloroplasts vs. Mitochondria In some ways, the movement of electrons in chloroplasts during photosynthesis is opposite that of electron transport in mitochondria. In photosynthesis, water is the source of electrons and their final destination is \(\ce{NADPH}\). In mitochondria, \(\ce{NADH/ FADH2}\) are electron sources and \(\ce{H2O}\) is their final destination. How do biological systems get electrons to go both ways? It would seem to be the equivalent of going to and from a particular place while always going downhill, since electrons will move according to potential. The answer is the captured energy of the photons, which elevates electrons in photosynthesis to an energy where they move “downhill” to their \(\ce{NADPH}\) destination in a Z-shaped scheme. The movement of electrons through this scheme in plants requires energy from photons in two places to “lift” the energy of the electrons sufficiently. Last, it should be noted that photosynthesis actually has two phases, referred to as the light cycle (described above) and the dark cycle, which is a set of chemical reactions that captures \(\ce{CO2}\) from the atmosphere and “fixes” it, ultimately into glucose. The dark cycle is also referred to as the Calvin Cycle.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/02%3A_Energy/2.06%3A_Cellular_Phosphorylations.txt

Cells are not 100% efficient in energy use; nothing that we know of is. Consequently, cells do not get as much energy out of catabolic processes as they put into anabolic processes. A good example is the synthesis and breakdown of glucose, something liver cells are frequently doing. The complete conversion of glucose to pyruvate in glycolysis (catabolism) yields two pyruvates plus 2 NADH plus 2 ATPs. Conversely, the complete conversion of two pyruvates into glucose by gluconeogenesis (anabolism) requires 4 ATPs, 2 NADH, and 2 GTPs. Since the energy of GTP is essentially equal to that of ATP, gluconeogenesis requires a net of 4 ATPs more than glycolysis yields. This difference must be made up in order for the organism to balance everything. It is for this reason that we eat. In addition, the inefficiency of our capture of energy in reactions results in the production of heat and helps to keep us warm. 2.08: Metabolic Controls of Energy It is also noteworthy that cells do not usually have both catabolic and anabolic processes for the same molecules (for example, breakdown of glucose and synthesis of glucose, shown on the previous page) occurring simultaneously inside of them because the cell would see no net production of anything but heat and a loss of ATPs with each turn of the cycle. Such cycles are called futile cycles and cells have controls in place to limit the extent to which they occur. Since futile cycles can, in fact, yield heat, they are sources of heat in some types of tissue. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University) 2.09: Molecular Backups for Muscles For plants, the needs for energy are different than for animals. Plants do not need to access energy sources as rapidly as animals do, nor do they have to maintain a constant internal temperature. Plants can neither flee predators, nor chase prey. These needs of animals are much more immediate and require that energy stores be accessible on demand. Muscles, of course, enable the motion of animals and the energy required for muscle contraction is ATP. To have stores of energy readily available, muscles have, in addition to ATP, creatine phosphate and glycogen for quick release of glucose from glycogen. The synthesis of creatine phosphate is a prime example of the effects of concentration on the synthesis of high energy molecules. For example, creatine phosphate has an energy of hydrolysis of -43.1 kJ/mol whereas ATP has an energy of hydrolysis of -30.5 kJ/mol Creatine phosphate, however, is made from creatine and ATP in the reaction shown below. How is this possible? Creatine + ATP <=> Creatine phosphate + ADP The ΔG°’ of this reaction is +12.6 kJ/mol, reflecting the energies noted above. In a resting muscle cell, ATP is abundant and ADP is low, driving the reaction to the right, creating creatine phosphate. When muscular contraction commences, ATP levels fall and ADP levels climb. The above reaction then reverses and proceeds to synthesize ATP immediately. Thus creatine phosphate acts like a battery, storing energy when ATP levels are high and releasing it almost instantaneously to create ATP when its levels fall. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University) 2.10: Summary In summary, energy is needed for cells to perform the functions that they must carry out in order to stay alive. At its most basic level, this means fighting a continual battle with entropy, but it is not the only need for energy that cells have. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University)

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/02%3A_Energy/2.07%3A_Energy_Efficiency.txt

Function flows from structure. In order to understand the function of biomolecules, we must first understand their structures. • 3.1: Introduction to Structure & Function If we hope to understand function in biological systems, we must first understand structure. • 3.2: Building Blocks Biological macromolecules are all polymers of a sort, even fats, in which the fatty acids can be thought of as polymers of carbon. The remaining categories of biological macromolecules include proteins, nucleic acids, and polysaccharides. The building blocks of these, respectively, are amino acids, nucleotides, and monosaccharides (sugars). Of these, the most diverse collection of chemical properties is found among the amino acids. • 3.3: Proteins Whereas nucleotides all are water soluble and have the same basic composition (sugar, base, phosphate) and the sugars also are water soluble and mostly contain 5 or 6 carbons (a few exceptions), the amino acids (general structure below) are structurally and chemically diverse. • 3.4: Nucleic Acids The DNA molecule is a polymer of nucleoside monophosphates with phosphodiester bonds between the phosphate and the 5’ end of one deoxyribose and the 3’ end of the next one. In the B form the DNA helix has a repeat of 10.5 base pairs per turn, with sugars and phosphate forming the covalent “backbone" of the molecule and the adenine, guanine, cytosine, and thymine bases oriented in the middle where they form the now familiar base-pairs that look like the rungs of a ladder. • 3.5: Carbohydrates The last class of macromolecules we will consider structurally here is the carbohydrates. Built of sugars or modified sugars, carbohydrates have several important functions, including structural integrity, cellular identification, and energy storage. • 3.6: Lipids and Membranes Lipids are a broad class of molecules that all share the characteristic that they have at least a portion of them that is hydrophobic. The class of molecules includes fats, oils (and their substituent fatty acids), steroids, fat-soluble vitamins, prostaglandins, glycerophospholipids, and sphingolipids. Interestingly, each of these can be derived from acetyl-CoA. Thumbanil: An antibody molecule. The two heavy chains are colored red and blue and the two light chains green and yellow. (Public Domain; TimVickers). 03: Structure Function If we hope to understand function in biological systems, we must first understand structure. At a simple level, we can divide molecules up according to their affinities for water – hydrophobic (limited solubility in water), hydrophilic (soluble in water) and amphiphilic (have characteristics of both hydrophobicity and hydrophilicity). Hydrophobicity in biological molecules arises largely because carbon-hydrogen bonds have electrons that are fairly evenly shared (not unlike carbon-carbon bonds). By contrast, the electrons between the oxygen and hydrogen of water are not equally shared. Oxygen has a greater electronegativity, so it holds them closer than hydrogen does. As a consequence, oxygen has what we call a partial negative charge and hydrogen has a partial positive charge. Virtually all of life on Earth is built upon the biochemistry that arises from the molecular properties described in the preceding paragraph. The biomolecules referred to as lipids are largely water insoluble because they have predominantly carbon-hydrogen bonds with few ionic or hydrogen bond characteristics. 3.02: Building Blocks Biological macromolecules are all polymers of a sort, even fats, in which the fatty acids can be thought of as polymers of carbon. (We will consider fatty compounds - fats, glycerophospholipids, sphingolipids, isoprenoids/terpenoids separately). The remaining categories of biological macromolecules include proteins, nucleic acids, and polysaccharides. The building blocks of these, respectively, are amino acids, nucleotides, and monosaccharides (sugars). Of these, the most diverse collection of chemical properties is found among the amino acids.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/03%3A_Structure__Function/3.01%3A_Introduction_to_Structure__Function.txt

Whereas nucleotides all are water soluble and have the same basic composition (sugar, base, phosphate) and the sugars also are water soluble and mostly contain 5 or 6 carbons (a few exceptions), the amino acids (general structure below) are structurally and chemically diverse. Though all of the amino acids are, in fact, soluble in water, the interactions of their side chains with water differ significantly. This is important, because it is only in the side chains (R-groups) that amino acids differ from each other. Based on side chains, we can group the 20 amino acids found in proteins as follows: • Aromatic (phenylalanine, tyrosine, tryptophan) • Aliphatic (leucine, isoleucine, alanine, methionine, valine) • Hydroxyl/Sulfhydryl (threonine, serine, tyrosine, cysteine) • Carboxyamide (glutamine, asparagine) • R-Acids (glutamic acid, aspartic acid) • R-Amines (lysine, histidine, arginine) • Odd (glycine, proline) Note that tyrosine has a hydroxyl group and fits into two categories. Note also that biochemistry books vary in how they organize amino acids into categories. Amino acids are joined to each other by peptide bonds. This introduces a slight simplifying aspect to the structure of proteins – one need only consider the positioning of the R-groups around each peptide bond when determining protein structure schematically. Proteins that are in aqueous environments, such as the cytoplasm of the cell, have their amino acids arranged so that those with hydrophilic side chains (such as threonine or lysine) predominate on the exterior of the protein so as to interact with water. The hydrophobic amino acids in these proteins are found predominantly on the interior. When one examines the structure of proteins in non-aqueous environments, such as the interior of a lipid bilayer, the arrangement is flipped – hydrophobics predominate on the outside where they can interact with the hydrophobic side chains of membrane fatty acids and the hydrophilic amino acids are arranged anyplace where they can contact water. For a protein like porin, which provides an interior channel through which water can pass, this is where the hydrophilics are found. For transmembrane proteins, which project through both sides of the membrane, the hydrophilics are found at each point where the polypeptide chain emerges from the membrane. Primary Structure How do proteins obtain such arrangements of amino acids? As we shall see, the structures of all proteins ultimately arise from their amino acid sequences. The amino acid sequence is referred to as the primary structure and changes in it can affect every other level of structure as well as the properties of a protein. The primary structure of a protein arrived at its current state as a result of mutation and selection over evolutionary time. On a more immediate time scale, 3D protein structure arises as a result of a phenomenon called folding. Protein folding results from three different structural elements beyond primary structure. They are referred to as secondary, tertiary, and quaternary structures, each arising from interactions between progressively more distant amino acids in the primary structure. Secondary Structure Interactions between amino acids within about ten units of each other give rise to regular repeating structures. These secondary structures include the well known alpha-helix and beta strands. Both were predicted by Linus Pauling, Robert Corey, and Herman Branson in 1951. Each structure has unique features. We use the terms rise, repeat, and pitch to describe the parameters of a helix. The repeat is the number of residues in a helix before it begins to repeat itself. The rise is the distance the helix elevates with addition of each residue. The pitch is the distance between the turns of the helix. Alpha Helix The alpha helix (Figure 3.1.3) forms as the result of interactions between amino acids separated by four residues. Interesting;y, the side chains of the amino acids in an alpha helix are all pointed outwards from the axis of the helix. Alpha helices have a repeat of 3.6 amino acid residues per turn of the helix, meaning that four turns of the helix have approximately 14 amino acid residues. Hydrogen bonds occur between the C=O of one amino acid and the N-H of another amino acid four residues distant and these help to stabilize the structure (note that the C=O and N-H involved are part of the polypeptide backbone, not the R-groups). Some amino acids have high helix forming tendencies. They include methionine, alanine, leucine, uncharged glutamate, and lysine. Others, such as proline, glycine, and negatively charged aspartate, disfavor its formation. Beta Strands Beta strands are the most fundamental helix, having essentially a 2D backbone of 'fold' like those of the pleats of a curtain. Indeed, beta strands can be arranged together to form what are called beta sheets. Other regular structures are also known. What determines whether a given stretch of a protein is in a helical or other structure? Here is where the shape and chemistry of the side chains play a role. Fibrous Proteins Not all proteins have significant amounts of tertiary or quaternary structure. (As we shall see, these last two levels of structure arise from 'bends' in polypeptide chains and interactions between separate polypeptide chains, respectively.) Alpha keratin, for example, is what we refer to as a fibrous protein (also called scleroprotein). Alpha keratin has primary structure and secondary structure, but little tertiary or quaternary structure. Consequently, alpha keratin exists mostly as long fibers, such as are found in hair. Beta-keratin is a harder fibrous protein found in nails, scales, and claws. It is made up mostly of beta sheets. Proline, which is the least flexible amino acid, due to attachment of the side chain to the alpha-amino grip, is less likely to be found in alpha helices, but curiously it is found abundantly in the fibrous protein known as collagen. Collagen (previous page) is the most abundant protein in the human body and is the 'glue' that literally sticks us together. How does the inflexibility of proline permit it to be in a helix? The answer is probably the parallel abundance in collagen of glycine, which contains the smallest side group and therefore has the greatest flexibility. As an interesting sidelight of the presence of proline in collagen is the chemical modification of prolines, by the addition of hydroxyl groups, after the protein is made. Such 'post-translational modifications' are not uncommon. Threonine, serine, and tyrosine frequently have their hydroxyl side-chains phosphorylated. Lysines in collagen too are hydroxylated post-translationally. The hydroxylated prolines and lysines play a role in the formation of interchain hydrogen bonds and crosslinking of triple helices during the assembly of collagen fibrils. These bonds provide structural integrity to the collagen. The enzymes that add hydroxyls to proline and lysine require vitamin C (ascorbic acid) for their activity. Lack of vitamin C leads to the production of weakened collagen fibrils, resulting in a condition called scurvy. The carbonyl oxygen of the peptide bond can exist in resonance with the C-N bond, giving the peptide bond characteristics of a double bond and imposing limitations for rotation around it. If we trat the peptide bond as a double bond, then the arrangements of adjacent carbon bonds around it can be thought of as being in the cis or trans configurations. In proteins, not surprisingly, the preferred arrangement of these groups is strongly trans (1000/1). Of the 20 amino acids, the one that favors peptide bonds in the cis configuration most commonly is proline, but even for proline, the trans isomer is strongly preferred. Figure 3.2.5: Collagen. Ramachandran Plots Another consequence of considering the peptide bond as a double bond is that it reduces the number of variable rotational angles of the polypeptide backbone. The terms phi and psi refer to rotational angles about the bonds between the N-alpha carbon and alpha carbon-carbonyl carbon respectively (previous page). Given the bulkiness of R-groups, the phenomenon of steric hindrance and the tendency of close side chains to interact with each other, one might expect to find a bias in the values of phi and psi. Indeed, that is exactly what is observed. Dr. G.N. Ramachandran proposed such a result and, in a plot that bears his name, depicted the theoretical likelihood of each angle appearing in a polypeptide. More recent observations of actual phi and psi angles in data from the PDB protein database bear out Dr. Ramachandran's predictions. In the plot above, beta strands fit nicely in the darker blue section at the top and alpha helices fit in the yellow section near the middle. Tertiary Structure In contrast to secondary structures, which arise from interactions between amino acids close in primary structure, tertiary structure arises from interactions between amino acids more distant in primary structure. Such interactions are not possible in an endlessly stretching fiber because each amino acid placed between two amino acids causes them to be moved farther away from each other in what is essentially the two dimensions of a secondary structure. For distant amino acids to interact, they must be brought into closer proximity and this requires bending and folding of the polypeptide chain. Proteins with such structures are referred to as ‘globular’ and they are, by far, the most abundant class of proteins. Indeed, it is in globular proteins that we have the most vivid images of the results of folding. “Folds" in polypeptides arise as a result of ‘bends’ between regions of secondary structure (such as alpha helix or beta strands). Such structures may be preferred due to incompatibility of a given amino acid side chain for a secondary structure formed by the amino acids preceding it. Bends occur commonly in proteins and proline is often implicated. Bends do not have the predictable geometry of alpha helices or beta strands and are often referred to as random coils. Thus, even though protein structure can be described easily as regions of secondary structure separated by bends, the variability of bend structures makes prediction of tertiary structure from amino acid sequence enormously more difficult than identifying/predicting regions of secondary structure. Hydrophobic Effect It is at the level of tertiary structure that the characteristic arrangement of hydrophobic and hydrophilic amino acids in a protein occurs. In an aqueous environment, for a protein to remain soluble, it must have favorable interactions with the water around it, hence, the positioning of hydrophilic amino acids externally. Another impetus for the folding phenomenon is a bit harder to understand. It is known as the hydrophobic effect. At a chemical level, it makes sense – hydrophobic amino acids will ‘prefer’ to interact with each other internally and away from water. The driving force for this phenomenon, though, is a bit more conceptually difficult. Consider a bottle containing oil and water. As everyone knows, the two liquids will not mix and instead will form separate layers. A reasonable question might be why they do this instead of one existing as tiny globules inside of the other. The answer to that question, as well as the positioning of hydrophobic amino acids in the interior of water soluble proteins, is the hydrophobic effect. To understand the hydrophobic effect, perform the followingexperiment – take the water-oil mixture and shake it vigorously. This will force the layers to mix and one will observe that tiny globules of both water and oil can, in fact, be found initially in the layer of each. Over time, though, the tiny globules break up and merge with the appropriate layer. This is due to the phenomenon of entropy and consideration of surface area. First, the sum of the surface area of the embedded tiny globs is far greater than the area of the region between the two layers after mixing is over. The smaller the globs, the more the surface area of interaction between the oil and the water. The minimum possible surface area of interaction occurs when there are no globs at all – just two layers and nothing else. How does this relate to entropy? Interactions between the water-hydrophobic layers causes the molecules at the interface to arrange themselves precisely/regularly so as to minimize their interactions. Ordering thus occurs at the layer interfaces. The maximum amount of ordering occurs when the maximum surface areas of oil and water interact. Small globules give rise to more exposed surface area between the water and hydrophobic layers and, as a consequence, more ordering. Since entropy in a closed system tends to increase, it will tend to reduce the amount of ordering, if left alone. Thus, one can increase the ordering on a nanoscopic scale (forming globules) by applying energy in the form of shaking. When left alone, however, the system will increase its disorder by reducing the interactions between hydrophobic groups and hydrophilic ones. In the oil water mixture, this causes the tiny globs to break up and produce the two layers we are familiar with because this is the minimum surface area that can be made between the two layers and thus the least ordering. In proteins, hydrophobic amino acid side chains are ‘shielded’ from water by placement internal to the protein, thus also reducing interfaces between hydrophobic residues and water. In both cases, entropy is increased, due to the reduced organization of the layers. Once formed, the interactions between the hydrophobic amino acid side chains helps to stabilize the overall protein structure. Quaternary Structure The last level of protein structure we will consider is that of quaternary structure. In order to have quaternary structure, a protein must have multiple polypeptide subunits because the structure involves the arrangement of those subunits with respect to each other. Consider hemoglobin, the oxygen-carrying protein of our blood. It contains two identical subunits known as alpha and two other identical ones known as beta. These are arranged together in a fashion as shown on the previous page. By contrast, the related oxygen storage protein known as myoglobin only contains a single subunit. Hemoglobin has quaternary structure, but myoglobin does not. Multiple subunit proteins are common in cells and they give rise to very useful properties not found in single subunit proteins. In the case of hemoglobin, the multiple subunits confer the property of cooperativity – variable affinity for oxygen depending on the latter’s concentration. In the case of enzymes, it can impart allosterism – the ability to have the activity of the enzyme altered by interaction with an effector molecule. We will discuss allosterism in detail in the next chapter. Other Protein Structural Features Not everything found in a protein is an amino acid. Proteins frequently have other chemical groups, known as prosthetic groups, bound to them, that are necessary for the function of a protein. Examples include the porphyrin ring of heme in myoglobin and hemoglobin that carries an iron so that oxygen can be bound. Metals are frequently employed by enzymes in their catalysis. Several vitamins (referred to as coenzymes), such as thiamine (B1) and riboflavin(B2) are modified and chemically bound to enzymes to help them perform specific catalytic functions. Cooperativity An interesting and important aspect of some proteins is the phenomenon of cooperativity. Cooperativity refers to the fact that binding of one ligand molecule by a protein favors the binding of additional molecules of the same type. Hemoglobin, for example, exhibits cooperativity when the binding of an oxygen molecule by the iron of the heme group in one of the four subunits causes a slight conformation change in the subunit. This happens because the heme iron is attached to a histidine side chain and binding of oxygen ‘lifts’ the iron along with the histidine ring (also known as the imidazole ring). Since each hemoglobin subunit interacts with and influences the other subunits, they too are induced to change shape slightly when the first subunit binds to oxygen (a transition described as going from the T-state to the R-state). These shape changes favor each of the remaining subunits binding oxygen, as well. This is very important in the lungs where oxygen is picked up by hemoglobin, because the binding of the first oxygen molecule facilitates the rapid uptake of more oxygen molecules. In the tissues, where the oxygen concentration is lower, the oxygen leaves hemoglobin and the proteins .ips from the R-state back to the T-state. Cooperativity is only one of many fascinating structural aspects of hemoglobin that help the body to receive oxygen where it is needed and pick it up where it is abundant. Hemoglobin also assists in the transport of the product of cellular respiration (carbon dioxide) from the tissues producing it to the lungs where it is exhaled. Let us consider these individually. Bohr Effect The Bohr Effect was first described over 100 years ago by Christian Bohr. Shown graphically (above left), the observed effect is that hemoglobin’s affinity for oxygen decreases as the pH decreases and/or as the concentration of carbon dioxide increases. Binding of the protons by histidine helps to facilitate structural changes in the protein and also with the uptake of carbon dioxide. Physiologically, this has great significance because actively respiring tissues (such as contracting muscles) require oxygen and release protons and carbon dioxide. The higher the concentration of protons and carbon dioxide, the more oxygen is released to feed the tissues that need it most. 2,3 BPG Another molecule affecting the release of oxygen by hemoglobin is 2,3 bisphosphoglycerate (also called 2,3 BPG or just BPG). Like protons and carbon dioxide, 2,3 BPG is produced by actively respiring tissues, as a byproduct of glucose metabolism. The 2,3 BPG molecule fits into the ‘hole of the donut’ of adult hemoglobin. Such binding of 2,3 BPG favors the T (tight) state of hemoglobin, which has a reduced affinity for oxygen. In the absence of 2,3 BPG, hemoglobin can exist in the R (relaxed) state, which has a high affinity for oxygen. Fetal Hemoglobin Adult hemoglobin releases oxygen when it binds 2,3 BPG. This is in contrast to fetal hemoglobin, which has a slightly different configuration (α2Γ2) than adult hemoglobin (α2ß). Fetal hemoglobin has a greater affinity for oxygen than maternal hemoglobin, allowing the fetus to obtain oxygen effectively from the mother’s blood. Part of the reason for fetal hemoglobin’s greater affinity for oxygen is that it doesn’t bind 2,3 BPG. Another significant fact about 2,3 BPG is that its concentration is higher in the blood of smokers than it is of non-smokers. Consequently, hemoglobin in a smoker’s blood spends more time in the T state than the R state. That is a problem when it is in the lungs, where being in the R state is necessary to maximally load the hemoglobin with oxygen. A high blood level of 2,3 BPG is one of the reasons smokers have trouble breathing when they exercise – they have reduced oxygen carrying capacity. Last, though it is not related directly to 2,3 BPG, smokers have another reason why their oxygen carrying capacity is lower than that of non-smokers. Cigarette smoke contains carbon monoxide and this molecule, which has almost identical dimensions to molecular oxygen, competes effectively with oxygen for binding to the iron atom of heme. Part of carbon monoxide’s toxicity is due to its ability to bind hemoglobin and prevent oxygen from binding. Denaturation For proteins, function is dependent on precise structure. Loss of the precise, folded structure of a protein is known as denaturation and is usually accompanied by loss of function. Anyone who has ever worked to purify an enzyme knows how easy it is for one to lose its activity. A few enzymes, such as ribonuclease, are remarkably stable under even very harsh conditions. For most others, a small temperature or pH change can drastically affect activity. The reasons for these differences vary, but relate to 1) the strength of the forces holding the structure together and 2) the ability of a protein to refold itself after being denatured. Let us consider these separately below. Forces Stabilizing Structures Amino acids are linked one to the other by peptide bonds. These covalent bonds are extraordinarily stable at neutral pHs, but can be broken by hydrolysis with heat under acidic conditions. Peptide bonds, however, only stabilize primary structure and, in fact, are the only relevant force responsible for it. Secondary structure, on the other hand, is generally stabilized by weaker forces, including hydrogen bonds. Hydrogen bonds are readily disrupted by heat, urea, or guanidinium chloride. Forces stabilizing tertiary structure include ionic interactions, disulfide bonds, hydrophobic interactions, metallic bonds, and hydrogen bonds. Of these, the ionic interactions are most sensitive to pH changes. Hydrophobic bonds are most sensitive to detergents. Thus, washing one’s hands helps to kill bacteria by denaturing critical proteins they need to survive. Metallic bonds are sensitive to oxidation/reduction. Breaking disulfide bonds requires either a strong oxidizing agent, such as performic acid or a strong reducing agent on another disulfide, such as mercaptoethanol or dithiothreitol. Quaternary structures are stabilized by the same forces as tertiary structure and have the same sensitivities. Refolding Denatured Proteins All of the information for protein folding is contained in the primary structure of the protein. It may seem curious then that most proteins do not refold into their proper, fully active form after they have been denatured and the denaturant is removed. A few do, in fact, refold correctly under these circumstances. A good example is bovine ribonuclease (also called RNase). Its catalytic activity is very resistant to heat and urea. However, if one treats the enzyme with mercaptoethanol (which breaks disulfide bonds) prior to urea treatment and heating, activity is lost, indicating that the covalent disulfide bonds help stabilize the overall enzyme structure. If one allows the enzyme mixture to cool back down to room temperature, over time some enzyme activity reappears, indicating that ribonuclease can re-fold under the proper conditions. Irreversible Denaturation Most enzymes, however, do not behave like ribonuclease. Once denatured, their activity cannot be recovered to any significant extent. This may seem to contradict the idea that folding information is inherent to the sequence of amino acids in the protein. It does not. The reason most enzymes can’t refold properly is due to two phenomena. First, normal folding may occur as proteins are being made. Interactions among amino acids early in the synthesis are not “confused" by interactions with amino acids later in the synthesis because those amino acids aren’t present as protein synthesis starts. In many cases, the proper folding of newly made polypeptides is also assisted by special proteins called chaperones. Chaperones bind to newly made proteins, preventing interactions that might result in misfolding. Thus, early folding and the assistance of chaperones eliminate some potential “wrong-folding" interactions that can occur if the entire sequence was present when folding started. Denatured full-length polypeptides have many more potential wrong folds that can occur. A second reason most proteins don’t refold properly after denaturation is probably that folding, like any other natural phenomenon, is driven by energy minimization. Though the folded structure may have a low energy, the path leading to it may not be all downhill. Like a chemical reaction that has energies of activation that must be overcome for the reaction to occur, folding likely has peaks and valleys of energy that do not automatically lead directly to the proper fold. Again, folding during synthesis leads the protein along a better-defined path through the energy maze of folding that denatured full-length proteins can’t navigate. Prions and Misfolding Folding and the stability of folded proteins is an important consideration for so-called “infectious" proteins known as prions. These mysterious proteins, which are implicated in diseases, such as mad cow disease and the related human condition known as Creutzfeldt-Jakob disease, result from the improper folding of a brain protein known as PrP. The misfolded protein has two important properties that lead to the disease. First, it tends to aggregrate into large complexes called amyloid plaques that damage/destroy nerve cells in the brain, leading ultimately to dementia and loss of brain function. Second, and probably worse, the misfolded protein “induces" other copies of the same protein to misfold as well. Thus, a misfolded protein acts something like a catalytic center and the disease progresses rapidly. The question arises as to how the PrP protein misfolds to begin with, but the answer to this is not clear. There are suggestions that exposure in the diet to misfolded proteins may be a factor, but this is disputed. An outbreak of mad cow disease in Britain in the 1980s was followed by a rise in the incidence of a rare form of human Creutzfeld-Jakob disease called variant CJD (v-CJD), lending some credence to the hypothesis. It is possible that misfolding of many proteins occurs sporadically without consequence or observation, but if PrP misfolds, the results are readily apparent. Thus, Creutzfeld-Jakob disease may ultimately give insights into the folding process itself. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University)

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/03%3A_Structure__Function/3.03%3A_Proteins.txt

Determination of the structure of the most common form of DNA, known as the B form, was one of the most important scientific advances of the 20th century. Using data from Rosalind Franklin, James Watson and Francis Crick initiated the modern era of molecular biology with their paper in the April 25, 1953 issue of Nature. Arguably, that single page paper has had more scientific impact per word than any other research article ever published. Today, every high school biology student knows the double helical structure in which G pairs with C and A pairs with T. The DNA molecule is a polymer of nucleoside monophosphates with phosphodiester bonds between the phosphate and the 5’ end of one deoxyribose and the 3’ end of the next one. In the B form the DNA helix has a repeat of 10.5 base pairs per turn, with sugars and phosphate forming the covalent “backbone" of the molecule and the adenine, guanine, cytosine, and thymine bases oriented in the middle where they form the now familiar base-pairs that look like the rungs of a ladder. Hydrogen bonds help to hold the base pairs together, with two hydrogen bonds per A-T pair and three hydrogen bonds per G-C pair. The two strands of a DNA duplex run in opposite directions. The 5’ end of one strand is paired with the 3’ end of the other strand and vice-versa at the other end of the duplex. The B form of DNA has a prominent major groove and a minor groove tracing the path of the helix (shown at left). Proteins, such as transcription factors bind in these grooves and access the hydrogen bonds of the base pairs to “read" the sequence therein. Other forms of DNA besides the B form are known. One of these, the ‘A’ form, was identified by Rosalind Franklin in the same issue of Nature as Watson and Crick’s paper. Though the A structure is a relatively minor form of DNA and resembles the B form, it turns out to be important in the duplex form of RNA and in RNA-DNA hybrids. Both the A form and the B form of DNA have the helix oriented in what is termed the right-handed form. These stand in contrast to another form of DNA, known as the Z form. Z-DNA, as it is known, has the same base-pairing rules as the B and A forms, but instead has the helices twisted in the opposite direction, making a left-handed helix (Figure \(3\)). The Z form has a sort of zig-zag shape, giving to the name Z DNA. In addition, the helix is rather stretched out compared to the A and B forms. Why are there different forms of DNA. The answer relates to both superhelical tension and sequence bias. Sequence bias means that certain sequences tend to favor the “flipping" of B form DNA into other forms. Z DNA forms are favored by long stretches of alternating Gs and Cs. Superhelicity Short stretches of linear DNA duplexes exist in the B form and have 10.5 base pairs per turn. Double helices of DNA in the cell can vary in the number of base pairs per turn they contain. There are several reasons for this. For example, during DNA replication, strands of DNA at the site of replication get unwound at the rate of 6000 rpm by an enzyme called helicase. The effect of such local unwinding at one place in a DNA has the effect increasing the winding ahead of it. Unrelieved, such ‘tension’ in a DNA duplex can result in structural obstacles to replication. Such adjustments can occur in three ways. First, tension can provide the energy for ‘flipping’ DNA structure. Z-DNA can arise as a means of relieving the tension. Second, DNA can ‘supercoil’ to relieve the tension. In this method, the strands of the duplex can cross each other repeatedly, much like a rubber band will coil up if one holds one section in place and twists another part of it. Third, enzymes called topoisomerases can act to relieve or, in some cases, increase the tension by adding or removing twists in the DNA. RNA Structures With respect to structure, RNAs are more varied than their DNA cousins. Created by copying regions of DNA, cellular RNAs are synthesized as single strands, but they often have self-complementary regions leading to “fold-backs" containing duplex regions. The structure of tRNAs and rRNAs are excellent examples. The base-pairing rules of DNA are the same in RNA (with U in RNA replacing the T from DNA), but in addition, base pairing between G and U can also occur in RNA. This latter fact leads to many more possible duplex regions in RNA that can exist compared to single strands of DNA. RNA structure, like protein structure, has importance, in some cases, for catalytic function. Like random coils in proteins that give rise to tertiary structure, single-stranded regions of RNA that link duplex regions give these molecules a tertiary structure, as well. Catalytic RNAs, called ribozymes, catalyze important cellular reactions, including the formation of peptide bonds. DNA, which is usually present in cells in strictly duplex forms (no tertiary structure, per se), is not known to be involved in catalysis. RNA structures are important for reasons other than catalysis. The 3D arrangement of tRNAs is important for enzymes that attach amino acids to them to do so properly. Small RNAs called siRNAs found in the nucleus of cells appear to play roles in both gene regulation and in cellular defenses against viruses. The key to the mechanisms of these actions is the formation of short fold-back RNA structures that are recognized by cellular proteins and then chopped into smaller units. One strand is copied and used to base pair with specific mRNAs to prevent the synthesis of proteins from them. Denaturing Nucleic Acids Like proteins, nucleic acids can be denatured. Forces holding duplexes together include hydrogen bonds between the bases of each strand that, like the hydrogen bonds in proteins, can be broken with heat or urea. (Another important stabilizing force for DNA arises from the stacking interactions between the bases in a strand.) Single strands absorb light at 260 nm more strongly than double strands (hyperchromic effect), allowing one to easily follow denaturation. For DNA, strand separation and strand hybridization are important aspects of the technique known as the polymerase chain reaction (PCR). Strand separation of DNA duplexes is accomplished in the method by heating them to boiling. Hybridization is an important aspect of the method that requires complementary single strands to “find" each other and form a duplex. Thus, DNAs (and RNAs too) can renature readily, unlike most proteins. Considerations for efficient hybridization (also called annealing) include temperature, salt concentration, strand concentration, and magnesium ion levels.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/03%3A_Structure__Function/3.04%3A_Nucleic_Acids.txt

The last class of macromolecules we will consider structurally here is the carbohydrates. Built of sugars or modified sugars, carbohydrates have several important functions, including structural integrity, cellular identification, and energy storage. Monosaccharides Simple sugars, also known as monosaccharides, can generally be written in the form \(C_x(H_2O)_x\). It is for this reason they are referred to as carbo-hydrates. By convention, the letters ‘ose’ at the end of a biochemical name flags a molecule as a sugar. Thus, there are glucose, galactose, sucrose, and many other ‘-oses’. Other descriptive nomenclature involves use of a prefix that tells how many carbons the sugar contains. For example, glucose, which contains six carbons, is described as a hexose. The following list shows the prefixes for numbers of carbons in a sugar: • Tri- = 3 • Tetr- = 4 • Pent- = 5 • Hex- =6 • Hept- = 7 • Oct- = 8 Other prefixes identify whether the sugar contains an aldehyde group (aldo-) or a ketone (keto) group. Prefixes may be combined. Glucose, which contains an aldehyde group, can be described as an aldo-hexose. The list that follows gives some common sugars and some descriptors. • Ribose = aldo-pentose • Glucose = aldo-hexose • Galactose = aldo-hexose • Mannose = aldo-hexose • Glyceraldehyde = aldo-triose • Erythrose – aldo-tetrose • Fructose = keto-hexose • Ribulose = keto-pentose • Sedoheptulose = keto-heptose • Dihydroxyacetone = keto-triose Stereoisomer Nomenclature Sugars of a given category (hexoses, for example) differ from each other in the stereoisomeric configuration of their carbons. Two sugars having the same number of carbons (hexoses, for example) and the same chemical form (aldoses, for example), but differing in the stereoisomeric configuration of their carbons are called diastereomers. Biochemists use D and L nomenclature to describe sugars, as explained below. D-sugars predominate in nature, though L-forms of some sugars, such as fucose, do exist. The D and L designation is a bit more complicated than it would appear on the surface. To determine if a sugar is a D-sugar or an L-sugar, one simply examines the configuration of the highest numbered asymmetric carbon. If the hydroxyl is written to the right, it is a D-sugar. If the hydroxyl is on the left, it is an L-sugar. That part is simple. The confusion about D and L arises because L sugars of a given name (glucose, for example) are mirror images of D sugars of the same name. The figure on the previous page shows the structure of D- and L- glucose. Notice that D-glucose is not converted into L-glucose simply by .ipping the configuration of the fifth carbon in the molecule. There is another name for sugars that are mirror images of each other. They are called enantiomers. Thus, L-glucose and D-glucose are enantiomers, but D-Erythrose and D-Threose are diastereomers. Sugars of 5-7 carbons can fairly easily form ring structures (called Haworth structures). For aldoses like glucose, this involves formation of a hemi-acetal. For ketoses like fructose, it involves formation of a hemi-ketal. The bottom line for both is that the oxygen that was part of the aldehyde or the ketone group is the one that becomes a part of the ring. More important than the oxygen, though, is the fact that the carbon attached to it (carbon #1 in aldoses or #2 in ketoses) becomes asymmetric as a byproduct of the cyclization. This new asymmetric carbon is called the anomeric carbon and it has two possible configurations, called alpha and beta. A solution of glucose will contain a mixture of alpha and beta forms. Whether the alpha or the beta arises upon cyclization is partly determined by geometry and partly random. Thus, one can find a bias for one form, but usually not that form exclusively. A given molecule of sugar will flip between alpha and beta over time. A requirement for this is that the hydroxyl on the anomeric carbon is unaltered, thus facilitating flipping back to the straight chain form followed by recyclization. If the hydroxyl becomes chemically altered in any way (for example, replacement of its hydrogen by a methyl group), a glycoside is formed. Glycosides are locked in the same alpha or beta configuration they were in when the modification was made. Glycosides are commonly found in nature. Sucrose, for example , is a di-glycoside – both the glucose and the fructose have had their anomeric hydroxyls altered by being joined together. The last considerations for sugars relative to their structure are their chemical reactivity and modification. The aldehyde group of aldoses is susceptible to oxidation, whereas ketoses are less so. Sugars that are readily oxidized are called ‘ reducing sugars ’ because their oxidation causes other reacting molecules to be reduced. Reducing sugars can easily be identified in a chemical test. Chemical modification of sugars occurs readily in cells. As we will see, phosphorylation of sugars occurs routinely during metabolism. Oxidation of sugars to create carboxyl groups also can occur. Reduction of aldehyde/ketone groups of sugars creates what are called sugar alcohols, and other modifications, such as addition of sulfates and amines also readily occur. Boat/Chair Conformations Independent of stereoisomerization, sugars in ring form of a given type (such as glucose) can “twist" themselves into alternative conformations called boat and chair. Note that this rearrangement does not change the relative positions of hydroxyl groups. All that has changed is the shape of the molecule. As shown for glucose, one can see that the beta-hydroxyl of glucose is closer to the \(CH_2OH\) (carbon #6) in the boat form than it is in the chair form. Steric hindrance can be a factor in favoring one configuration over another. Disaccharides Sugars are readily joined together (and broken apart) in cells. Sucrose (Figure \(7\)), which is common table sugar, is made by joining the anomeric hydroxyl of alpha-D-glucose to the anomeric hydroxyl of beta-D-fructose. Not all disaccharides join the anomeric hydroxyls of both sugars. For example, lactose (milk sugar) is made by linking the anomeric hydroxyl of galactose in the beta configuration to the hydroxyl of carbon #4 of glucose. Oligosaccharides The term ‘oligosaccharide’ is used to describe polymers of sugars of 5-15 units, typically. Oligosaccharides are not commonly found free in cells, but instead are found covalently attached to proteins, which are then said to be glycosylated. Oligosaccharides attached to proteins may be N-linked (through asparagine) or O-linked (though serine or threonine). O-linked sugars are added only in the Golgi apparatus while N-linked sugars are attached starting in the endoplasmic reticulum and then completed in the Golgi. Oligosaccharides often function as identity markers, both of cells and proteins. On the cell surface, glycoproteins with distinctive oligosaccharides attached establish the identity of each cell. The types of oligosaccharides found on the surface of blood cells is a determinant of blood type. The oligosaccharides that are attached to proteins may also determine their cellular destinations. Improper glycosylation or sugar modification patterns can result in the failure of proteins to reach the correct cellular compartment. For example, inclusion cell (I-cell) disease arises from a defective phosphotransferase in the Golgi. This enzyme normally catalyzes the addition of a phosphate to a mannose sugar attached to a protein destined for the lysosome. In the absence of a functioning enzyme, the unphosphorylated glycoprotein never makes it to the lysosome and is instead exported out of the cell where it accumulates in the blood and is excreted in the urine. Individuals with I-cell disease suffer developmental delays, abnormal skeletal development, and restricted joint movement. Polysaccharides Polysaccharides, as their name implies, are made by joining together many sugars. The functions for polysaccharides are varied. They include energy storage, structural strength, and lubrication. Polysaccharides involved in energy storage include the plant polysaccharides, amylose and amylopectin. The polysaccharide involved in energy storage in animals is called glycogen and it is mostly found in the muscles and liver. Amylose/Amylopectin Amylose is the simplest of the polysaccharides, being comprised solely of glucose units joined in an alpha 1-4 linkage. Amylose is broken down by the enzyme alpha-amylase, found in saliva. Amylopectin is related to amylose in being composed only of glucose, but it differs in how the glucose units are joined together. Alpha 1-4 linkages predominate, but every 30-50 residues, a ‘branch’ arises from an alpha 1-6 linkage. Branches make the structure of amylopectin more complex than that of amylose. Glycogen Glycogen is a polysaccharide that is physically related to amylopectin in being built only of glucose and in having a mix of alpha 1-4 and alpha 1-6 bonds. Glycogen, however, has many more alpha 1-6 branches than amylopectin, with such bonds occurring about every 10 residues. One might wonder why such branching occurs more abundantly in animals than in plants. A plausible explanation is based on the method by which these molecules are broken down. The breakdown of these polysaccharides is catalyzed by enzymes, known as phosphorylases, that clip glucose residues from the ends of glycogen chains and attach a phosphate to them in the process, producing glucose-1-phosphate. More highly branched polysaccharides have more ends to clip, and this translates to more glucose-1-phosphates that can be removed simultaneously by numerous phosphorylases. Since glucose is used for energy by muscles, glucose concentrations can be increased faster the more branched the glycogen is. Plants, which are immobile do not have needs for such immediate release of glucose and thus have less need for highly branched polysaccharides. Cellulose Another important polysaccharide containing only glucose is cellulose. It is a polymer of glucose used to give plant cell walls structural integrity and has the individual units joined solely in a beta 1-4 configuration. That simple structural change makes a radical diff erence in its digestibility. Humans are unable to break down cellulose and it passes through the digestive system as roughage. Ruminant animals, such as cattle, however have bacteria in their rumens that contain the enzyme cellulase. It breaks the beta 1-4 links of the glucoses in cellulose to release the sugars for energy. Another polysaccharide used for structural integrity is known as chitin. Chitin makes up the exoskeleton of insects and is a polymer of a modified form of glucose known as N-acetyl-glucosamine. Glycosaminoglycans Yet another category of polysaccharides are the glycosaminoglycans (also called mucopolysaccharides ), some examples of which include keratan sulfate, heparin, hyaluronic acid (right), and chondroitin sulfate. The polysaccharide compounds are linked to proteins, but differ from glycoproteins in having a much larger contingent of sugar residues and, further, the sugars are considerably more chemically modified. Each of them contains a repeating unit of a disaccharide that contains at least one negatively charged residue. The result is a polyanionic substance that, in its interactions with water, makes for a “slimy" feel. Glycosaminoglycans are found in snot, and in synovial fluid, which lubricates joints. Heparin is a glycosaminoglycan that helps to prevent blood from clotting.

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Lipids are a broad class of molecules that all share the characteristic that they have at least a portion of them that is hydrophobic. The class of molecules includes fats, oils (and their substituent fatty acids), steroids, fat-soluble vitamins, prostaglandins, glycerophospholipids, and sphingolipids. Interestingly, each of these can be derived from acetyl-CoA. Fatty Acids Arguably, the most important lipids in our cells are the fatty acids, because they are components of all of the other lipids, except some of the steroids and fat-soluble vitamins. Consisting of a carboxyl group linked to a long aliphatic tail, fatty acids are described as either saturated (no double bonds) or unsaturated (one or more double bonds). Fatty acids with more than one double bond are described as polyunsaturated. Increasing the amount of unsaturated fatty acids (and the amount of unsaturation in a given fatty acid) in a fat decreases its melting temperature. This is also a factor in membrane fluidity. If the melting temperature of a fat is decreased sufficiently so that it is a liquid at room temperature, it is referred to as an oil. It is worth noting that organisms like fish, which live in cool environments, have fats with more unsaturation. This is why fish oil is a rich source of polyunsaturated fatty acids. Biochemically, the double bonds found in fatty acids are predominantly in the cis configuration. So-called trans fats arise as a chemical by-product of partial hydrogenation of vegetable oil (small amounts of trans fats also occur naturally). In humans, consumption of trans fats raises low density lipoprotein (LDL) levels and lowers high density lipoprotein (HDL) levels. Each is thought to contribute to the risk of developing coronary artery disease. The most common fatty acids in our body include palmitate, stearate, oleate, linolenate, linoleate, and arachidonate. Fatty acids are numbered by two completely different schemes. The delta numbering scheme has the carboxyl group as #1, whereas the omega number scheme starts at the other end of the fatty acid with the methyl group as #1. Fatty acids are described as essential if they must be in the diet (can’t be synthesized by the organism). Animals, including humans, cannot synthesize fatty acids with double bonds beyond position delta 9, so linoleic and linolenic acids are considered essential in these organisms. In animal cells, fats are the primary energy storage forms. They are also known as triacylglycerols, since they consist of a glycerol molecule esterified to three fatty acids. Fats are synthesized by replacing the phosphate on phosphatidic acid with a fatty acid. Fats are stored in the body in specialized cells known as adipocytes. Enzymes known as lipases release fatty acids from fats by hydrolysis reactions. Of the various lipases acting on fat, the one that acts first, triacylglycerol lipase, is regulated hormonally. Membrane Lipids The predominant lipids found in membranes are glycerophospholipids (phosphoglycerides) and sphingolipids. The former are related to fats structurally as both are derived from phosphatidic acid. Phosphatidic acid is a simple glycerophospholipid that is usually converted into phosphatidyl compounds. These are made by esterifying various groups, such as ethanolamine, serine, choline, inositol, and others to the phosphate. All of these compounds form lipid bilayers in aqueous solution, due to the amphiphilic nature of their structure. Though structurally similar to glycerophospholipids, sphingolipids are synthesized completely independently of them, starting with palmitic acid and the amino acid serine. The figure on the right shows the structure of several sphingolipids. LIke the glycerophospholipids, sphingolipids are amphiphilic, but unlike them, they may have simple (in cerebrosides) or complex (in gangliosides) carbohydrates attached at one end. Most sphingolipids, except sphingomyelin, do not contain phosphate. Steroids, such as cholesterol are also found in membranes. Cholesterol, in particular, may play an important role in membrane fluidity. Membranes can be thought of a being more “frozen" or more “fluid." Fluidity is important for cellular membranes. When heated, membranes move from a more “frozen" character to that of a more “fluid" one as the temperature rises. The mid-point of this transition, referred to as the Tm, is influenced by the fatty acid composition of the lipid bilayer compounds. Longer and more saturated fatty acids will favor higher Tm values, whereas unsaturation and short fatty acids will favor lower Tm values. Interestingly, cholesterol does not change the Tm value, but instead widens the transition range between frozen and fluid forms of the membrane. Lipid Bilayers The membrane around cells contains many components, including cholesterol, proteins, glycolipids, glycerophospholipids and sphingolipids. The last two of these will, in water, form what is called a lipid bilayer, which serves as a boundary for the cell that is largely impermeable to the movement of most materials across it. With the notable exceptions of water, carbon dioxide, carbon monoxide, and oxygen, most polar/ionic compounds require transport proteins to help them to efficiently navigate across the bilayer. The orderly movement of these compounds is critical for the cell to be able to 1) get food for energy; 2) export materials; 3) maintain osmotic balance; 4) create gradients for secondary transport; 5) provide electromotive force for nerve signaling; and 6) store energy in electrochemical gradients for ATP production (oxidative phosphorylation or photosynthesis). In some cases, energy is required to move the substances (active transport). In other cases, no external energy is required and they move by diffusion through specific cellular channels. The spontaneous ability of these compounds to form lipid bilayers is exploited in the formation of artificial membranous structures called liposomes. Liposomes have some uses in delivering their contents into cells via membrane fusion. Membrane Proteins Other significant components of cellular membranes include proteins. We can put them into several categories. Integral membrane proteins are embedded in the membrane and project through both sides of the lipid bilayer. Peripheral membrane proteins are embedded in or tightly associated with part of the bilayer, but do not project completely through both sides. Associated membrane proteins are found near membranes, but may not be embedded in them. Their association may arise as a result of interaction with other proteins or molecules in the lipid bilayer. Anchored membrane proteins are not themselves embedded in the lipid bilayer, but instead are attached to a molecule (typically a fatty acid) that is embedded in the membrane. The geometry of the lipid bilayer is such that is hydrophobic on its interior and hydrophilic on the exterior. Such properties also dictate the amino acid side chains of proteins that interact with the bilayer. For most membrane proteins, the polar amino acids are found where the protein projects through the bilayer (interacting with aqueous/polar substances) and the non-polar amino acids are embedded within the non-polar portion of the bilayer containing the fatty acid tails. Glycolipids and glycoproteins play important roles in cellular identification. Blood types, for example, differ from each other in the structure of the carbohydrate chains projecting out from the surface of the glycoprotein in their membranes. Cells have hundreds of membrane proteins and the protein composition of a membrane varies with its function and location. Mitochondrial membranes are among the most densely packed with proteins. The plasma membrane has a large number of integral proteins involved in communicating information across the membrane (signaling) or in transporting materials into the cell. Membrane Transport Materials, such as food and waste must be moved across a cell’s lipid bilayer. There are two means of accomplishing this - passive processes and active processes. Passive processes have as their sole driving force the process of diffusion. In these systems, molecules always move from a higher concentration to a lower concentration. These can occur directly across a membrane (water, oxygen, carbon dioxide, and carbon monoxide) or through special transport proteins (glucose transport proteins of red blood cells, for example). In each case, no cellular energy is expended in the movement of the molecules. On the other hand, active processes require energy to accomplish such transport. A common energy source is ATP (see Na+/K+ ATPase), but many other energy sources are employed. For example, the sodium-glucose transporter uses a sodium gradient as a force for actively transporting glucose into a cell. Thus, it is important to know that not all active transport uses ATP energy. Proteins, such as the sodium-glucose transporter that move two molecules in the same direction across the membrane are called symporters (also called synporters). If the action of a protein in moving ions across a membrane results in a change in charge, the protein is described as electrogenic and if there is no change in charge the protein is described as electro-neutral. Sodium-Potassium ATPase Another important integral membrane protein is the Na+/K+ ATPase (Figure 3.5.11), which transports sodium ions out of the cell and potassium ions into the cell. The protein, which is described as an anti-port (molecules moved in opposite directions across the membrane) uses the energy of ATP to create ion gradients that are important both in maintaining cellular osmotic pressure and (in nerve cells) for creating the ion gradients necessary for signal transmission. The transport system moves three atoms of sodium out of the cell and two atoms of potassium into the cell for each ATP hydrolyzed. Bacteriorhodopsin An interesting integral membrane protein is bacteriorhodopsin. The protein has three identical polypeptide chains, each rotated by 120 degrees relative to the others. Each chain has seven transmembrane alpha helices and contains one molecule of retinal (Vitamin A) buried deep within each cavity (shown in purple in lower figure at left). Vitamin A is light sensitive an isomerizes rapidly between a cis and a trans form in the presence of light. The changing conformation of the vitamin A is used to transport protons through the protein and out of the bacterium, creating a proton gradient across the cell membrane, which is used ultimately to make ATP. It is not too difficult to imagine engineering an organism (say a transparent fish) to contain bacteriorhodopsin in its mitochondrial inner membrane. When light is shone upon it, the bacteriorhodopsin could be used to generate a proton gradient (much like electron transport does) and power oxidative phosphorylation. Such a fish would be partly photosynthetic in that it would be deriving energy from light, but would differ from plants in being unable to assimilate carbon dioxide in a series of “dark reactions." Fat Soluble Vitamins Other lipids of note include the fat-soluble vitamins - A, D, E, and K. Vitamin A comes in three primary chemical forms, retinol (storage in liver), retinal (role in vision), and retinoic acid (roles in growth and development). Vitamin D (cholecalciferol) plays important roles in the intestinal absorption of calcium and phosphate and thus in healthy bones. Derived from ultimately from cholesterol, the compound can be synthesized in a reaction catalyzed by ultraviolet light. Vitamin E (tocopherol) is the vitamin about which the least is known. It consists of a group of eight fat-soluble compounds of which the alpha-isomer has the most biological activity. Vitamin K (the name comes from the German for coagulation vitamin) is essential for blood clotting. It is used as a co-factor for the enzyme that modifies prothrombin to increase its affinity for calcium, allowing it to be positioned closer to the site of a wound.

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In living systems, speed is everything. Providing the reaction speeds necessary to support life are the catalysts, mostly in the form of enzymes. • 4.1: Introduction to Catalysis • 4.2: Activation Energy Notice that the reactants start at the same energy level for both conditions and that the products end at the same energy for both as well. Thus, the difference in energy between the energy of the ending compounds and the starting compounds is the same in both cases. • 4.3: General Mechanisms of Action Every chemistry student has had hammered into their heads the fact that a catalyst speeds a reaction without being consumed by it. In other words, the catalyst ends up after a reaction just the way it started so it can catalyze other reactions, as well. Enzymes share this property, but in the middle, during the catalytic action, an enzyme is transiently changed. Such changes may be subtle electronic ones or more significant covalent modifications. • 4.4: Substrate Binding Another important difference between the mechanism of action of an enzyme and a chemical catalyst is that an enzyme has binding sites that not only ‘grab’ the substrate (molecule involved in the reaction being catalyzed), but also place it in a position to be electronically induced to react, either within itself or with another substrate. The enzyme itself may play a role in the electronic induction or the induction may occur as a result of substrates being placed in very close proximity to each • 4.5: Enzyme Flexibility As mentioned earlier, a difference between an enzyme and a chemical catalyst is that an enzyme is flexible. Its slight changes in shape (often arising from the binding of the substrate itself) help to position substrates for reaction after they bind. These changes in shape are explained, in part, by Koshland’s Induced Fit Model of Catalysis, which illustrates that not only do enzymes change substrates, but that substrates also transiently change enzymes. • 4.6: Active Site Reactions in enzymes are catalyzed at a specific location known as the ‘ active site ’. Substrate binding sites are located in close physical proximity to the active site and oriented to provide access for the relevant portion of the molecule to the electronic environment of the enzyme where catalysis is initiated. • 4.7: Chymotrypsin Consider the mechanism of catalysis of the enzyme known as chymotrypsin. Found in our digestive system, chymotrypsin’s catalytic action is cleaving peptide bonds in proteins and it uses the side chain of a serine in its mechanism of catalysis. Many other protein- cutting enzymes employ a very similar mechanism and they are known collectively as serine proteases. As aprotease, it acts fairly specifically, cutting not all peptide bonds, but only those that are adjacent to specific amino acids in t • 4.8: Enzyme Parameters Scientists spend a considerable amount of time characterizing enzymes. To understand how they do this and what the characterizations tell us, we must first understand a few parameters. Imagine I wished to study the reaction catalyzed by an enzyme I have just isolated. I would be interested to understand how fast the enzyme works and how much affinity the enzyme has for its substrate(s). • 4.9: Perfect Enzymes • 4.10: Lineweaver-Burk Plots • 4.11: Enzyme Inhibition • 4.12: Control of Enzymes • 4.13: Ribozymes Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University) 04: Catalysis If there is a magical component to life, an argument can surely be made for it being catalysis. Thanks to catalysis, reactions that could take hundreds of years to complete in the “real world," occur in seconds in the presence of a catalyst. Chemical catalysts, like platinum, speed reactions, but enzymes (which are simply super-catalysts with a twist) put chemical catalysts to shame. To understand enzymatic catalysis, we must first understand energy. In Chapter 2, we noted the tendency for processes to move in the direction of lower energy. Chemical reactions follow this universal trend, but they often have a barrier in place that must be overcome. The secret to catalytic action is reducing the magnitude of that barrier, as we shall see.

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Figure 4.1.1 schematically depicts the energy changes that occur during the progression of a simple reaction. In the figure, the energy differences during the reaction are compared for a catalyzed (plot on the right) and an uncatalyzed reaction (plot on the left). Notice that the reactants start at the same energy level for both conditions and that the products end at the same energy for both as well. Thus, the difference in energy between the energy of the ending compounds and the starting compounds is the same in both cases. This is the first important rule to understand any kind of catalysis – catalysts do not change the overall energy of a reaction. Given enough time, a non-catalyzed reaction will get to the same equilibrium as a catalyzed one. Another feature to note about catalyzed reactions is the reduced energy barrier (also called the activation energy or free energy of activation) to reach the transition state of the catalyzed reaction. This is the second important point about catalyzed reactions – catalysts work by lowering activation energies of reactions and thus molecules more easily reach the energy necessary to get to the point where the reaction occurs. Note that these reactions are reversible. The extent to which they will proceed is a function of the size of the energy difference between the product and reactant states. The lower the energy of the products compared to the reactants, the larger the percentage of molecules that will be present as products at equilibrium. At equilibrium, of course, no change in concentration of reactants and products occurs because at this point, the forward and reverse reaction rates are the same. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University) 4.03: General Mechanisms of Action As noted above, enzymes are orders of magnitude more effective (faster) than chemical catalysts. The secret of their success lies in a fundamental difference in their mechanisms of action. Every chemistry student has had hammered into their heads the fact that a catalyst speeds a reaction without being consumed by it. In other words, the catalyst ends up after a reaction just the way it started so it can catalyze other reactions, as well. Enzymes share this property, but in the middle, during the catalytic action, an enzyme is transiently changed. Such changes may be subtle electronic ones or more significant covalent modifications. It is also important to recognize that enzymes are not fixed, rigid structures, but rather are flexible. Flexibility allows movement and movement facilitates alteration of electronic environments necessary for catalysis. Enzymes are, thus, much more effcient than rigid chemical catalysts as a result of their abilities to facilitate the changes necessary to optimize the catalytic process. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University) 4.04: Substrate Binding Another important difference between the mechanism of action of an enzyme and a chemical catalyst is that an enzyme has binding sites that not only ‘grab’ the substrate (molecule involved in the reaction being catalyzed), but also place it in a position to be electronically induced to react, either within itself or with another substrate. The enzyme itself may play a role in the electronic induction or the induction may occur as a result of substrates being placed in very close proximity to each other. Chemical catalysts have no such ability to bind substrates and are dependent upon them colliding in the right orientation at or near their surfaces. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University) 4.05: Enzyme Flexibility As mentioned earlier, a difference between an enzyme and a chemical catalyst is that an enzyme is flexible. Its slight changes in shape (often arising from the binding of the substrate itself) help to position substrates for reaction after they bind. These changes in shape are explained, in part, by Koshland’s Induced Fit Model of Catalysis, which illustrates that not only do enzymes change substrates, but that substrates also transiently change enzymes. At the end of the catalysis, the enzyme is returned to its original state. Enzyme flexibility also is important for control of enzyme activity. Two distinct structures are typically described– the T (tight) state, which is a lower activity state and the R (relaxed) state, which has greater activity. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University) 4.06: Active Site Reactions in enzymes are catalyzed at a specific location known as the ‘ active site ’. Substrate binding sites are located in close physical proximity to the active site and oriented to provide access for the relevant portion of the molecule to the electronic environment of the enzyme where catalysis is initiated. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University)

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Consider the mechanism of catalysis of the enzyme known as chymotrypsin. Found in our digestive system, chymotrypsin’s catalytic action is cleaving peptide bonds in proteins and it uses the side chain of a serine in its mechanism of catalysis. Many other protein- cutting enzymes employ a very similar mechanism and they are known collectively as serine proteases. As aprotease, it acts fairly specifically, cutting not all peptide bonds, but only those that are adjacent to specific amino acids in the protein. One of the amino acids it cuts adjacent to is phenylalanine. The enzyme’s action occurs in two phases – a fast phase that occurs first and a slower phase that follows. The enzyme has a substrate binding site that includes a region of the enzyme known as the S1 pocket. Let us step through the mechanism by which chymotrypsin cuts adjacent to phenylalanine. The process starts with the binding of the substrate in the S1 pocket. The S1 pocket in chymotrypsin has a hydrophobic hole in which the substrate is bound. Preferred substrates will include amino acid side chains that are hydrophobic, like phenylalanine. If an ionized side chain, like that of glutamic acid binds in the S1 pocket, it will quickly exit, much like water would avoid an oily interior. When the proper substrate binds, it stays and its presence induces an ever so slight shift in the shape of the enzyme. This subtle shape change on the binding of the proper substrate starts the steps of the catalysis and is the reason that the enzyme shows specificity for cutting at specific enzyme positions in the target protein. Only amino acids with the side chains that interact well with the S1 pocket start the catalytic wheels turning. The slight changes in shape of the enzyme upon binding of the proper substrate cause changes in the positioning of three amino acids (aspartic acid, histidine, and serine) in the active site known as the catalytic triad, during the second step of the catalytic action. The shift of the negatively charged aspartic acid towards the electron rich histidine ring favors the abstraction of a proton by the histidine from the hydroxyl group on the side chain of serine, resulting in production of a very reactive alkoxide ion in the active site. Since the active site at this point also contains the polypeptide chain positioned with the phenylalanine side chain embedded in the S1 pocket, the alkoxide ion performs a nucleophilic attack on the peptide bond on the carboxyl side of phenylalanine sitting in the active site. This reaction, which is the third step of catalysis, breaks the bond and causes two things to happen. First, one end of the original polypeptide is freed and exits the active site. The second is that the end containing the phenylalanine is covalently linked to the oxygen of the serine side chain. At this point we have completed the first (fast) phase of the catalysis. The second phase of the catalysis by chymotrypsin is slower. It requires that the covalent bond between phenylalanine and serine’s oxygen be broken so the peptide can be released and the enzyme can return to its original state. The process starts with entry of water into the active site. Water is attacked in a fashion similar to that of the serine side chain in the first phase, creating a reactive hydroxyl group that performs a nucleophilic attack on the phenylalanine-serine bond, releasing it and replacing the proton on serine. The second peptide is released in the process and the reaction is complete with the enzyme back in its original state.

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Scientists spend a considerable amount of time characterizing enzymes. To understand how they do this and what the characterizations tell us, we must first understand a few parameters. Imagine I wished to study the reaction catalyzed by an enzyme I have just isolated. I would be interested to understand how fast the enzyme works and how much affinity the enzyme has for its substrate(s). To perform this analysis, I would perform the following experiment. Into 20 different tubes, I would put enzyme buffer (to keep the enzyme stable), the same amount of enzyme, and then a different amount of substrate in each tube, ranging from tiny amounts in the first tubes to very large amounts in the last tubes. I would let the reaction proceed for a fixed, short amount of time and then I would measure the amount of product contained in each tube. For each reaction, I would determine the velocity of the reaction as the concentration of product found in each tube divided by the time. I would then plot the data on a graph using velocity on the Y-axis and the concentration of substrate on the X-axis. Typically, I would generate a curve like that shown on Figure 4.7.1. Notice how the velocity increase is almost linear in the tubes with the lowest amounts of substrate. This indicates that substrate is limiting and the enzyme converts it into product as soon as it can bind it. As the substrate concentration increases, however, the velocity of the reaction in tubes with higher substrate concentration ceases to increase linearly and instead begins to flatten out, indicating that as the substrate concentration gets higher and higher, the enzyme has a harder time keeping up to convert the substrate to product. What is happening is the enzyme is becoming saturated with substrate at higher concentrations of the latter. Not surprisingly, when the enzyme becomes completely saturated with substrate, it will not have to wait for substrate to diffuse to it and will therefore be operating at maximum velocity. $V_{max}$ & $K_{cat}$ On a plot of Velocity versus Substrate Concentration ( V vs. [S]), the maximum velocity (known as Vmax) is the value on the Y axis that the curve asymptotically approaches. It should be noted that the value of V max depends on the amount of enzyme used in a reaction. Double the amount of enzyme, double the Vmax . If one wanted to compare the velocities of two different enzymes, it would be necessary to use the same amounts of enzyme in the different reactions they catalyze. It is desirable to have a measure of velocity that is independent of enzyme concentration. For this, we define the value Kcat , also known as the turnover number. Mathematically, $\text{Kcat} = \frac{V_{max}}{ [Enzyme]} \tag{4.7.1}$ To determine Kcat, one must obviously know the Vmax at a particular concentration of enzyme, but the beauty of the term is that it is a measure of velocity independent of enzyme concentration, thanks to the term in the denominator. Kcat is thus a constant for an enzyme under given conditions. The units of K cat are $\text{time}^{-1}$. An example would be 35/second. This would mean that each molecule of enzyme is catalyzing the formation of 35 molecules of product every second. While that might seem like a high value, there are enzymes known (carbonic anhydrase, for example) that have Kcat values of $10^6$/second. This astonishing number illustrates clearly why enzymes seem almost magical in their action. $K_M$ Another parameter of an enzyme that is useful is known as KM , the Michaelis constant. What it measures, in simple terms, is the affinity an enzyme has for its substrate. Affinities of enzymes for substrates vary considerably, so knowing KM helps us to understand how well an enzyme is suited to the substrate being used. Measurement of KM depends on the measurement of Vmax. On a V vs. [S] plot, KM is determined as the x value that give Vmax/2. A common mistake students make in describing V max is saying that KM = Vmax/2. This is, of course not true. KM is a substrate concentration and is the amount of substrate it takes for an enzyme to reach Vmax/2. On the other hand Vmax/2 is a velocity and is nothing more than that. The value of KM is inversely related to the affinity of the enzyme for its substrate. High values of KM correspond to low enzyme affinity for substrate (it takes more substrate to get to Vmax ). Low KM values for an enzyme correspond to high affinity for substrate.

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Now, if we think about what an ideal enzyme might be, it would be one that has a very high velocity and a very high affinity for its substrate. That is, it wouldn’t take much substrate to get to \(V_{max}/2\) and the \(K_{cat}\) would be very high. Such enzymes would have values of \(K_{cat} / K_M\) that are maximum. Interestingly, there are several enzymes that have this property and their maximal values are all approximately the same. Such enzymes are referred to as being “perfect" because they have reached the maximum possible value. Why should there be a maximum possible value of \(K_{cat} / K_M\). The answer is that movement of substrate to the enzyme becomes the limiting factor for perfect enzymes. Movement of substrate by diffusion in water has a fixed rate and that limitation ultimately determines how fast the enzyme can work. In a macroscopic world analogy, factories can’t make products faster than suppliers can deliver materials. It is safe to say for a perfect enzyme that the only limit it has is the rate of substrate diffusion in water. Given the “magic" of enzymes alluded to earlier, it might seem that all enzymes should have evolved to be “perfect." There are very good reasons why most of them have not. Speed can be a dangerous thing. The faster a reaction proceeds in catalysis by an enzyme, the harder it is to control. As we all know from learning to drive, speeding causes accident. Just as drivers need to have speed limits for operating automobiles, so too must cells exert some control on the ‘throttle’ of their enzymes. In view of this, one might wonder then why any cells have evolved any enzymes to perfection. There is no single answer to the question, but a common one is illustrated by the perfect enzyme known as triose phosphate isomerase (TPI), which catalyzes a reaction in glycolysis (figure on previous page). The enzyme appears to have been selected for this ability because at lower velocities, there is breakdown of an unstable enediol intermediate that then readily forms methyl glyoxal, a cytotoxic compound. Speeding up the reaction provides less opportunity for the unstable intermediate to accumulate and fewer undesirable byproducts are made. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University) 4.10: Lineweaver-Burk Plots The study of enzyme kinetics is typically the most math intensive component of biochemistry and one of the most daunting aspects of the subject for many students. Although attempts are made to simplify the mathematical considerations, sometimes they only serve to confuse or frustrate students. Such is the case with modified enzyme plots, such a Lineweaver-Burk (Figure 4.9.1). Indeed, when presented by professors as simply another thing to memorize, who can blame students. In reality, both of these plots are aimed at simplifying the determination of parameters, such as \(K_M\) and \(V_{max}\). In making either of these modified plots, it is important to recognize that the same data is used as in making a \(V\) vs. \([S]\) plot. The data are simply manipulated to make the plotting easier. For a Lineweaver-Burk, the manipulation is using the reciprocal of the values of both the velocity and the substrate concentration. The inverted values are then plotted on a graph as \(1/V\) vs. \(1/[S\)]. Because of these inversions, Lineweaver-Burk plots are commonly referred to as ‘double-reciprocal’ plots. As can be seen at left, the value of \(K_M\) on a Lineweaver Burk plot is easily determined as the negative reciprocal of the x-intercept , whereas the \(V_{max}\) is the inverse of the y-intercept. Other related manipulation of kinetic data include Eadie-Hofstee diagrams, which plot V vs V/[S] and give \(V_{max}\) as the Y-axis intercept with the slope of the line being \(-K_M\).

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Inhibition of specific enzymes by drugs can be medically useful. Understanding the mechanisms of enzyme inhibition is therefore of considerable importance. We will discuss four types of enzyme inhibition – competitive, non- competitive, uncompetitive, and suicide. Of these, the first three types are reversible. The last one is not. Competitive Inhibition Probably the easiest type of enzyme inhibition to understand is competitive inhibition and it is the one most commonly exploited pharmaceutically. Molecules that are competitive inhibitors of enzymes resemble one of the normal substrates of an enzyme. An example is methotrexate, which resembles the folate substrate of the enzyme dihydrofolate reductase (DHFR). This enzyme normally catalyzes the reduction of folate, an important reaction in the metabolism of nucleotides. When the drug methotrexate is present, some of the enzyme binds to it instead of to folate and during the time methotrexate is bound, the enzyme is inactive and unable to bind folate. Thus, the enzyme is inhibited. Notably, the binding site on DHFR for methotrexate is the active site, the same place that folate would normally bind. As a result, methotrexate ‘competes’ with folate for binding to the enzyme. The more methotrexate there is, the more effectively it competes with folate for the enzyme’s active site. Conversely, the more folate there is, the less of an effect methotrexate has on the enzyme because folate outcompetes it. No Effect On \(V_{MAX}\) How do we study competitive inhibition. It is typically done as follows. First one performs a set of V vs. [S] reactions without inhibitor (20 or so tubes, with buffer and constant amounts of enzyme, varying amounts of substrate, equal reaction times). V vs. [S] is plotted, as well as 1/V vs. 1/[S], if desired. Next, a second set of reactions is performed in the same manner as before, except that a fixed amount of the methotrexate inhibitor is added to each tube. At low concentrations of substrate, the inhibitor competes for the enzyme effectively, but at high concentrations of substrate, the inhibitor will have a much reduced effect, since the substrate outcompetes it, due to its higher concentration (remember that the inhibitor is at fixed concentration). Graphically, the results of these experiments are shown above. Notice that at high substrate concentrations, the competitive inhibitor has essentially no effect, causing the Vmax for the enzyme to remain unchanged. To reiterate, this is due to the fact that at high substrate concentrations, the inhibitor doesn’t compete well. However, at lower substrate concentrations it does. Increased KM Note that the apparent KM of the enzyme for the substrate increases (-1/KM gets closer to zero - red line above) when the inhibitor is present, thus illustrating the better competition of the inhibitor at lower substrate concentrations. It may not be obvious why we call the changed KM the apparent KM of the enzyme. The reason is that the inhibitor doesn’t actually change the enzyme’s affinity for the folate substrate. It only appears to do so. This is because of the way that competitive inhibition works. When the competitive inhibitor binds the enzyme, it is effectively ‘taken out of action.’ Inactive enzymes have NO affinity for substrate and no activity either. We can’t measure KM for an inactive enzyme. The enzyme molecules that are not bound by methotrexate can, in fact, bind folate and are active. Methotrexate has no effect on them and their KM values are unchanged. Why then, does KM appear higher in the presence of a competitive inhibitor. The reason is that the competitive inhibitor is reducing the amount of active enzyme at lower concentrations of substrate. When the amount of enzyme is reduced, one must have more substrate to supply the reduced amount of enzyme sufficiently to get to Vmax/2. It is worth noting that in competitive inhibition, the percentage of inactive enzyme changes drastically over the range of [S] values used. To start, at low [S] values, the greatest percentage of the enzyme is inhibited. At high [S], no significant percentage of enzyme is inhibited. This is not always the case, as we shall see in non-competitive inhibition. Non-Competitive Inhibition A second type of inhibition employs inhibitors that do not resemble the substrate and bind not to the active site, but rather to a separate site on the enzyme (rectangular site below). The effect of binding a non-competitive inhibitor is significantly different from binding a competitive inhibitor because there is no competition. In the case of competitive inhibition, the effect of the inhibitor could be reduced and eventually overwhelmed with increasing amounts of substrate. This was because increasing substrate made increasing percentages of the enzyme active. With non-competitive inhibition, increasing the amount of substrate has no effect on the percentage of enzyme that is active. Indeed, in non-competitive inhibition, the percentage of enzyme inhibited remains the same through all ranges of [S]. This means, then, that non-competitive inhibition effectively reduces the amount of enzyme by the same fixed amount in a typical experiment at every substrate concentration used The effect of this inhibition is shown above. As you can see, Vmax is reduced in non-competitive inhibition compared to uninhibited reactions. This makes sense if we remember that Vmax is dependent on the amount of enzyme present. Reducing the amount of enzyme present reduces Vmax. In competitive inhibition, this doesn’t occur detectably, because at high substrate concentrations, there is essentially 100% of the enzyme active and the Vmax appears not to change. Additionally, KM for non-competitively inhibited reactions does not change from that of uninhibited reactions. This is because, as noted previously, one can only measure the KM of active enzymes and KM is a constant for a given enzyme. Uncompetitive Inhibition A third type of enzymatic inhibition is that of uncompetitive inhibition, which has the odd property of a reduced Vmax as well as a reduced KM. The explanation for these seemingly odd results is rooted in the fact that the uncompetitive inhibitor binds only to the enzyme-substrate (ES) complex. The inhibitor-bound complex forms mostly under concentrations of high substrate and the ES-I complex cannot release product while the inhibitor is bound, thus explaining the reduced Vmax. The reduced KM is a bit harder to conceptualize. The answer lies in the fact that the inhibitor-bound complex effectively reduces the concentration of the ES complex. By Le Chatelier’s Principle, a shift occurs to form additional ES complex, resulting in less free enzyme and more enzyme in the forms ES and ESI (ES with inhibitor). Decreases in free enzyme correspond to an enzyme with greater affinity for its substrate. Thus, paradoxically, uncompetitive inhibition both decreases Vmax and increases an enzyme’s affinity for its substrate. Suicide Inhibition In contrast to the first three types of inhibition, which involve reversible binding of the inhibitor to the enzyme, suicide inhibition is irreversible because the inhibitor becomes covalently bound to the enzyme during the inhibition and thus cannot be removed. Suicide inhibition rather closely resembles competitive inhibition because the inhibitor generally resembles the substrate and binds to the active site of the enzyme. The primary difference is that the suicide inhibitor is chemically reactive in the active site and makes a bond with it that precludes its removal. Such a mechanism is that employed by penicillin (Figure 4.10.5), which covalently links to the bacterial enzyme, D-D transpeptidase and stops it from functioning. Since the normal function of the enzyme is to make a bond necessary for the peptido-glycan complex of the bacterial cell wall, the cell wall cannot properly form and bacteria cannot reproduce. If one were to measure the kinetics of suicide inhibitors under conditions where there was more enzyme than inhibitor, they would resemble non-competitive inhibition’s kinetics because both involve reducing the amount of active enzyme by a fixed amount in a set of reactions.

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It is appropriate that we talk at this point about mechanisms cells use to control enzymes. There are four general methods that are employed. They include 1. allosterism 2. covalent modification 3. access to substrate 4. control of enzyme synthesis/breakdown Some enzymes are controlled by more than one of these methods. Allosterism The term allosterism refers to the fact that the activity of certain enzymes can be affected by the binding of small molecules to the enzyme. In allostery, the molecules that are binding are non-substrate molecules that bind at a place on the enzyme other than the active site. An excellent example of allosteric control is the regulation of HMG-CoA reductase, which catalyzes an important reaction in the pathway leading to the synthesis of cholesterol. Binding of cholesterol to the enzyme reduces the enzyme’s activity significantly. Cholesterol is not a substrate for the enzyme, but, notably, is the end-product of the pathway that HMG-CoA catalyzes a reaction in. When enzymes are inhibited by an end-product of the pathway in which they participate, they are said to be feedback inhibited. Feedback inhibition always operates by allosterism and further, provides important and efficient control of an entire pathway. By inhibiting an early enzyme in a pathway, the flow of materials for the entire pathway is stopped or reduced, assuming there are not alternate supply methods. In the cholesterol biosynthesis pathway, stopping this one enzyme has the effect of shutting off (or at least slowing down) the entire pathway. Another excellent example is the enzyme aspartate transcarbamoylase (ATCase), which catalyzes an early reaction in the synthesis of pyrimidine nucleotides. This enzyme has two allosteric effectors, ATP and CTP, that are not substrates and that bind at a regulatory site on the enzyme that is apart from the catalytic, active site. CTP, which is the end-product of the pathway, is a feedback inhibitor of the enzyme. ATP, on the other hand, acts to activate the enzyme when it binds to it. Allosterically, regulation of these enzymes works by inducing different physical states (shapes, as it were) that affect their ability to bind to substrate. When an enzyme is inhibited by binding an effector, it is converted to the T (also called tight) state, it has a reduced affinity for substrate and it is through this means that the reaction is slowed. On the other hand, when an enzyme is activated by effector binding, it converts to the R (relaxed) state and binds substrate much more readily. When no effector is present, the enzyme may be in a mixture of T and R state. The V vs. S plot of allosteric enzymes resembles the oxygen binding curve of hemoglobin (see HERE). Even though hemoglobin is not an enzyme and is thus not catalyzing a reaction, the similarity of the plots is not coincidental. In both cases, the binding of an external molecule is being measured – directly by the hemoglobin plot and indirectly by the enzyme plot, since substrate binding is a factor in enzyme reaction velocity. Covalent Control of Enzymes Some enzymes are synthesized in a completely inactive form and their activation requires covalent bonds in them to be cleaved. Such inactive forms of enzymes are called zymogens. Examples include the proteins involved in blood clotting and proteolytic enzymes of the digestive system, such as trypsin, chymotrypsin, and others. The zymogenic forms of these enzymes are known as trypsinogen and chymotrypsinogen, respectively. Synthesizing some enzymes in an inactive form makes very good sense when an enyzme’s activity might be harmful to the tissue where they are being made. For example, the painful condition known as pancreatitis arises when digestive enzymes made in the pancreas are activated too soon and end up attacking the pancreas. Blood clotting involves polymerization of a protein known as fibrin. Since random formation of fibrin is extremely hazardous (heart attack/stroke), the body synthesizes fibrin as a zymogen (fibrinogen) and its activation results from a “cascade" of activations of proteases that arise when a signal is received from a wound. Similarly, removal of fibrin clots is also controlled by a zymogen (plasminogen), since random clot removal would also be hazardous. Another common mechanism for control of enzyme activity by covalent modification is phosphorylation. The phosphorylation of enzymes (on the side chains of serine, threonine or tyrosine residues) is carried out by protein kinases. Enzymes activated by phosphorylation can be regulated by the addition of phosphate groups by kinases or their removal by phosphatases. Other Controls of Enzymes Other means of controlling enzymes relate to access to substrate (substrate-level control) and control of enzyme synthesis. Hexokinase is an enzyme that is largely regulated by availability of its substrate, glucose. When glucose concentration is low, the product of the enzyme’s catalysis, glucose-6-phosphate, accumulates and inhibits the enzyme’s function. Regulation of enzymes by controlling their synthesis is covered later in the book in the discussion relating to control of gene expression. 4.13: Ribozymes Proteins do not have a monopoly on acting as biological catalysts. Certain RNA molecules are also capable of speeding reactions. The most famous of these molecules was discovered by Tom Cech in the early 1980s. Studying excision of an intron in Tetrahymena, Cech was puzzled at his inability to find any proteins catalyzing the process. Ultimately, the catalysis was recognized as coming from the intron itself. It was a self-splicing RNA and since then, many other examples of catalytic RNAs capable of cutting other RNAs have been found. Ribozymes, however, are not rarities of nature. The protein- making ribosomes of cells are essentially giant ribozymes. The 23S rRNA of the prokaryotic ribosome and the 28S rRNA of the eukaryotic ribosome catalyze the formation of peptide bonds. Ribozymes are also important in our understanding of the evolution of life on Earth. They have been shown to be capable via selection to evolve self-replication. Indeed, ribozymes actually answer a chicken/egg dilemma - which came first, enzymes that do the work of the cell or nucleic acids that carry the information required to produce the enzymes. As both carriers of genetic information and catalysts, ribozymes are likely both the chicken and the egg in the origin of life. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University)

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/04%3A_Catalysis/4.12%3A_Control_of_Enzymes.txt

As the cell’s so-called blueprint, DNA must be copied to pass on to new cells and its integrity safeguarded. The information in the DNA must also be accessed and transcribed to make the RNA instructions that direct the synthesis of proteins. • 5.1: DNA Replication The only way to make new cells is by the division of pre-existing cells. This means that all organisms depend on cell division for their continued existence. DNA, as you know, carries the genetic information that each cell needs. Each time a cell divides, all of its DNA must be copied faithfully so that a copy of this information can be passed on to the daughter cell. This process is called DNA replication. • 5.2: DNA Repair All DNA suffers damage over time, from exposure to ultraviolet and other radiation, as well as from various chemicals in the environment. As you already know, even minor changes in DNA sequence, such as point mutations can sometimes have far-reaching consequences. Likewise, unrepaired damage caused by radiation, environmental chemicals or even normal cellular chemistry can interfere with the accurate transmission of information in DNA. • 5.3: Transcription You have learned in introductory biology courses that genes, which are instructions for making proteins, are made of DNA. You also know that information in genes is copied into temporary instructions called messenger RNAs that direct the synthesis of specific proteins. This description of flow of information from DNA to RNA to protein, shown on the previous page, is often called the Central Dogma of molecular biology and is a good starting point for an examination of how cells use the info. • 5.4: Regulation of Transcription The processes described above are required whenever any gene is transcribed. But what determines which genes are transcribed at a given time. What are the molecular switches that turn transcription on or off? The basic mechanism by which transcription is regulated depends on highly specific interactions between transcription regulating proteins and regulatory sequences on DNA. Promoters indicate where transcription begins. • 5.5: RNA Processing So far, we have looked at the mechanism by which the information in genes (DNA) is transcribed into RNA. The newly made RNA, also known as the primary transcript (the product of transcription is known as a transcript) is further processed before it is functional. Both prokaryotes and eukaryotes process their ribosomal and transfer RNAs. • 5.6: Translation Translation is the process by which information in mRNAs is used to direct the synthesis of proteins. As you have learned in introductory biology, in eukaryotic cells, this process is carried out in the cytoplasm of the cell, by large RNA-protein machines called ribosomes. Ribosomes contain ribosomal RNAs (rRNAs) and proteins. The proteins and rRNAs are organized into two subunits, a large and a small. 05: Flow of Genetic Information The only way to make new cells is by the division of pre-existing cells. This means that all organisms depend on cell division for their continued existence. DNA, as you know, carries the genetic information that each cell needs. Each time a cell divides, all of its DNA must be copied faithfully so that a copy of this information can be passed on to the daughter cell. This process is called DNA replication. Before examining the actual process of DNA replication, it is useful to think about what it takes to accomplish this task successfully. Consider the challenges facing a cell in this process: • The sheer number of nucleotides to be copied is enormous: e.g., in human cells, on the order of several billion. • A double-helical parental DNA molecule must be unwound to expose single strands of DNA that can serve as templates for the synthesis of new DNA strands. • This unwinding must be accomplished without introducing significant topological distortion into the molecule. • The unwound single strands of DNA must be kept from coming back together long enough for the new strands to be synthesized. • DNA polymerases cannot begin synthesis of a new DNA strand de novo and require a free 3' OH to which they can add DNA nucleotides. • DNA polymerases can only extend a strand in the 5' to 3' direction. The 5' to 3' extension of both new strands at a single replication fork means that one of the strands is made in pieces. • The use of RNA primers requires that the RNA nucleotides must be removed and replaced with DNA nucleotides and the resulting DNA fragments must be joined. • Ensuring accuracy in the copying of so much information. With this in mind, we can begin to examine how cells deal with each of these challenges. Our understanding of the process of DNA replication is derived from studies using bacteria, yeast, and other systems, such as Xenopus eggs. These investigations have revealed that DNA replication is carried out by the action of a large number of proteins that act together as a complex protein machine called the replisome. Numerous proteins involved in replication have been identified and characterized, including multiple different DNA polymerases in both prokaryotes and eukaryotes. Although the specific proteins involved are different in bacteria and eukaryotes, it is useful to understand the basic considerations that are relevant in all cells, before attempting to address the details of each system. A generalized account of the steps in DNA replication is presented below, focused on the challenges mentioned above. • The sheer number of nucleotides to be copied is enormous. For example, in human cells, the number of nucleotides to be copied is on the order of several billion. Even in bacteria, the number is in the millions. Cells, whether bacterial or eukaryotic, have to replicate all of their DNA before they can divide. In cells like our own, the vast amount of DNA is broken up into many chromosomes, each of which is composed of a linear strand of DNA. In cells like those of E. coli, there is a single circular chromosome. In either situation, DNA replication is initiated at sites called origins of replication. These are regions of the DNA molecule that are recognized by special origin recognition proteins that bind the DNA. The binding of these proteins helps open up a region of single-stranded DNA where the synthesis of new DNA can begin. In the case of E. coli, there is a single origin of replication on its circular chromosome. In eukaryotic cells, there may be many thousands of origins of replication, with each chromosome having hundreds. DNA replication is thus initiated at multiple points along each chromosome in eukaryotes as shown in Figure 5.1.3. Electron micrographs of replicating DNA from eukaryotic cells show many replication “bubbles" on a single chromosome. Figure 5.1.2: Image of a replication bubble Figure 5.1.3: Multiple replication bubbles This makes sense in light of the large amount of DNA that there is to be copied in cells like our own, where beginning at one end of each chromosome and replicating all the way through to the other end from a single origin would simply take too long. This is despite the fact that the DNA polymerases in human cells are capable of building new DNA strands at the very respectable rate of about 50 nucleotides per second! • A double-helical parental molecule must be unwound to expose single strands of DNA that can serve as templates for the synthesis of new DNA strands. Once a small region of the DNA is opened up at each origin of replication, the DNA helix must be unwound to allow replication to proceed. How are the strands of the parental DNA double helix separated? The unwinding of the DNA helix requires the action of an enzyme called helicase. Helicase uses the energy released when ATP is hydrolyzed to unwind the DNA helix. Note that each replication bubble is made up of two replication forks that "move" or open up, in opposite directions. At each replication fork, the parental DNA strands must be unwound to expose new sections of single-stranded template. • This unwinding must be accomplished without introducing topological distortion into the molecule. What is the effect of unwinding one region of the double helix? Unwinding the helix locally causes over-winding or topological distortion of the DNA ahead of the unwound region. The DNA ahead of the unwound helix has to rotate, or it will get twisted on itself. How is this problem solved? Enzymes called topoisomerases can relieve the topological stress caused by local unwinding of the double helix. They do this by cutting the DNA and allowing the strands to swivel around each other to release the tension before rejoining the ends. In E. coli, the topoisomerase that performs this function is called gyrase. • The unwound single strands of DNA must be kept from coming back together long enough for the new strands to be synthesized. Once the two strands of the parental DNA molecule are separated, they must be prevented from going back together to form double-stranded DNA. To ensure that unwound regions of the parental DNA remain single-stranded and available for copying, the separated strands of the parental DNA are bound by many molecules of a protein called single-strand DNA binding protein (SSB). • DNA polymerases cannot begin synthesis of a new DNA strand de novo and require a free 3' OH to which they can add DNA nucleotides. Although single-stranded parental DNA is now available for copying, DNA polymerases cannot begin synthesis of a complementary strand de novo. This is because all DNA polymerases can only add new nucleotides to the 3' end of a pre- existing chain. This means that some enzyme other than a DNA polymerase must first make a small region of nucleic acid, complementary to the parental strand, that can provide a free 3' OH to which DNA polymerase can add a deoxyribonucleotide. This task is accomplished by an enzyme called a primase, which assembles a short stretch of RNA, called the primer, across from the parental DNA template. This provides a short base-paired region with a free 3'OH group to which DNA polymerase can add the first new DNA nucleotide (see figure on previous page). Once a primer provides a free 3'OH for extension, other proteins get into the act. These proteins are involved in loading the DNA polymerase onto the primed template and help to keep it attached to the DNA once it's on. Figure 5.1.4: Addition of a nucleotide to a growing strand The first of these is the clamp loader. As its name suggests, the clamp loader helps to load a protein complex called the sliding clamp onto the DNA at the replication fork. The sliding clamp is then joined by the DNA Polymerase. The function of the sliding clamp is to increase the processivity of the DNA polymerase. This is a fancy way of saying that it keeps the polymerase associated with the replication fork by preventing it from falling off - in fact, the sliding clamp has been described as a seat-belt for the DNA polymerase. The DNA polymerase is now poised to start synthesis of the new DNA strand (in E. coli, the primary replicative polymerase is called DNA polymerase III). As you already know, the synthesis of new DNA is accomplished by the addition of new nucleotides complementary to those on the parental strand. DNA polymerase catalyzes the reaction by which an incoming deoxyribonucleotide is added onto the 3' end of the previous nucleotide, starting with the 3'OH on the end of the RNA primer. The 5' phosphate on each incoming nucleotide is joined by the DNA polymerase to the 3' OH on the end of the growing nucleic acid chain. As we already noted, the new DNA strands are synthesized by the addition of DNA nucleotides to the end of an RNA primer. The new DNA molecule thus has a short piece of RNA at the beginning. • DNA polymerases can only extend a strand in the 5' to 3' direction. The 5' to 3' growth of both new strands means that one of the strands is made in pieces. We have noted that DNA polymerase can only build a new DNA strand in the 5' to 3' direction. We also know that the two parental strands of DNA are antiparallel. This means that at each replication fork, one new strand, called the leading strand can be synthesized continuously in the 5' to 3' direction because it is being made in the same direction that the replication fork is opening up. The synthesis of the other new strand, called the lagging strand, requires that multiple RNA primers must be laid down and the new DNA be made in many short pieces that are later joined.These short nucleic acid pieces, each composed of a small stretch of RNA primer and about 1000-2000 DNA nucleotides, are called Okazaki fragments, for Reiji Okazaki, the scientist who first demonstrated their existence. Figure 5.1.5: Leading and lagging strand replication • The use of RNA primers requires that the RNA nucleotides must be removed and replaced with DNA nucleotides. • We have seen that each newly synthesized piece of DNA starts out with an RNA primer, effectively making a new nucleic acid strand that is part RNA and part DNA. The finished DNA strand cannot be allowed to have pieces of RNA attached. o the RNA nucleotides are removed and the gaps are filled in with DNA nucleotides (by DNA polymerase I in E. coli). The DNA pieces are then joined together by the enzyme DNA ligase. Figure 5.1.6: Proteins at a replication fork The steps outlined above essentially complete the process of DNA replication. Figure 5.1.6 shows a replication fork, complete with the associated proteins that form the replisome. • Ensuring accuracy in the copying of so much information How accurate is the copying of information in the DNA by DNA polymerase? As you are aware, changes in DNA sequence (mutations) can change the amino acid sequence of the encoded proteins and that this is often, though not always, deleterious to the functioning of the organism. When billions of bases in DNA are copied during replication, how do cells ensure that the newly synthesized DNA is a faithful copy of the original information? DNA polymerases, as we have noted earlier, work fast (averaging 50 bases a second in human cells and up to 20 times faster in E. coli). Yet, both human and bacterial cells seem to replicate their DNA quite accurately. This is because of the proof-reading function of DNA polymerases. The proof-reading function of a DNA polymerase enables the polymerase to detect when the wrong base has been inserted across from a template strand, back up and remove the mistakenly inserted base. This is possible because the polymerase is a dual-function enzyme. It can extend a DNA chain by virtue of its 5' to 3' polymerase activity but it can also backtrack and remove the last inserted base because it has a 3' to 5' exonuclease activity (an exonuclease is an enzyme that removes bases, one by one, from the ends of nucleic acids). The exonuclease activity of the DNA polymerase allows it to excise a wrongly inserted base, after which the polymerase activity inserts the correct base and proceeds with extending the strand. In other words, DNA polymerase is monitoring its own accuracy (also termed its fidelity) as it makes new DNA, correcting mistakes immediately before moving on to add the next base. This mechanism, which operates during DNA replication, corrects many errors as they occur, reducing by about 100-fold the mistakes made when DNA is copied.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/05%3A_Flow_of_Genetic_Information/5.01%3A_DNA_Replication.txt

Maintaining the Integrity of the Cell's Information: DNA Repair In the last section we considered the ways in which cells deal with the challenges associated with replicating their DNA, a vital process for all cells. It is evident that if DNA is the master copy of instructions for an organism, then it is important not to make mistakes when copying the DNA to pass on to new cells. Although proofreading by DNA polymerases greatly increases the accuracy of replication, there are additional mechanisms in cells to further ensure that newly replicated DNA is a faithful copy of the original, and also to repair damage to DNA during the normal life of a cell. All DNA suffers damage over time, from exposure to ultraviolet and other radiation, as well as from various chemicals in the environment. Even chemical reactions naturally occurring within cells can give rise to compounds that can damage DNA. As you already know, even minor changes in DNA sequence, such as point mutations can sometimes have far-reaching consequences. Likewise, unrepaired damage caused by radiation, environmental chemicals or even normal cellular chemistry can interfere with the accurate transmission of information in DNA. Maintaining the integrity of the cell's "blueprint" is of vital importance and this is reflected in the numerous mechanisms that exist to repair mistakes and damage in DNA. Post-Replicative Mismatch Repair We earlier discussed proof-reading by DNA polymerases during replication. Does proofreading eliminate all errors made during replication. No. While proof-reading significantly reduces the error rate, not all mistakes are fixed on the fly by DNA polymerases. What mechanisms exist to correct the replication errors that are missed by the proof-reading function of DNA polymerases. Errors that slip by proofreading during replication can be corrected by a mechanism called mismatch repair. While the error rate of DNA replication is about one in \(10^7\) nucleotides in the absence of mismatch repair, this is further reduced a hundred-fold to one in \)10^9\) nucleotides when mismatch repair is functional. What are the tasks that a mismatch repair system faces. It must: • Scan newly made DNA to see if there are any mispaired bases (e.g., a G paired to a T) • Identify and cut out the region of the mismatch. • Correctly fill in the gap created by the excision of the mismatch region. Importantly, the mismatch repair system must have a means to distinguish the newly made DNA strand from the template strand, if replication errors are to be fixed correctly. In other words, when the mismatch repair system encounters an A-G mispair, for example, it must know whether the A should be removed and replaced with a C or if the G should be removed and replaced with a T. Mismatch repair has been well studied in bacteria, and the proteins involved have been identified. Eukaryotes have a mismatch repair system that repairs not only single base mismatches but also insertions and deletions. In bacteria, mismatch repair proteins are encoded by a group of genes collectively known as the mut genes. Some of the most important components of the mismatch repair machinery are the proteins MutS, L and H. MutS acts to recognize the mismatch, while MutL and MutH are recruited to the mismatch site by the binding of Mut S, to help cut out the region containing the mismatch. A DNA polymerase and ligase fill in the gap and join the ends, respectively. But how does the mismatch repair system distinguish between the original and the new strands of DNA? In bacteria, the existence of a system that methylates the DNA at GATC sequences is the solution to this problem. E.coli has an enzyme that adds methyl groups on the to adenines in GATC sequences. Newly replicated DNA lacks thismethylation and thus, can be distinguished from the template strand, which is methylated. In Figure \(2\), the template strand shown in yellow is methylated at GATC sequences. The mismatch repair proteins selectively replace the strand lacking methylation, shown in blue in the figure, thus ensuring that it is mistakes in the newly made strand that are removed and replaced. Because methylation is the criterion that enables the mismatch repair system to choose the strand that is repaired, the bacterial mismatch repair system is described as being methyl-directed. Eukaryotic cells do not use this mechanism to distinguish the new strand from the template, and it is not yet understood how the mismatch repair system in eukaryotes "knows" which strand to repair. Systems to Repair Damage to DNA In the preceding section we discussed mistakes made when DNA is copied, where the wrong base is inserted during synthesis of the new strand. But even DNA that is not being replicated can get damaged or mutated. These sorts of damage are not associated with DNA replication, rather they can occur at any time. What causes damage to DNA? Some major causes of DNA damage are: • Radiation (e.g., UV rays in sunlight, in tanning booths) • Exposure to damaging chemicals (such as benzopyrene in car exhaust and cigarette smoke) • Chemical reactions within the cell (such as the deamination of cytosine to give uracil). This means the DNA in your cells is vulnerable to damage simply from normal sorts of actions, such as walking outdoors, being in traffic, or from the chemical transformations occurring in every cell as part of its everyday activities. (Naturally, the damage is much worse in situations where exposure to radiation or damaging chemicals is greater, such as when people repeatedly use tanning beds or smoke.) What kinds of damage do these agents cause? Radiation can cause different kinds of damage to DNA. Sometimes, as with much of the damage done by UV rays, two adjacent pyrimidine bases in the DNA will be cross-linked to form pyrimidine dimers (note that we are talking about two neighboring pyrimidine bases on the same strand of DNA). This is illustrated in the figure on the previous page where two adjacent thymines on a single DNA strand are cross-linked to form a thymine dimer. Radiation can also cause breaks in the DNA backbone. Chemicals like benzopyrene can attach themselves to bases, forming bulky DNA adducts in which large chemical groups are linked to bases in the DNA. The formation of chemical adducts can physically distort the DNA helix, making it hard for DNA and RNA polymerases to copy those regions of DNA. Chemical reactions occurring within cells can cause cytosines in DNA to be deaminated to uracil, as shown in Figure \(3\). Other sorts of damage in this category include the formation of oxidized bases like 8-oxo-guanine. These do not actually change the physical structure of the DNA helix, but they can cause problems because uracil and 8-oxo-guanine pair with different bases than the original cytosine or guanine, leading to mutations on the next round of replication. How do cells repair such damage? Cells have several ways to remove the sorts of damage described above, with excision repair being a common strategy. Excision repair is a general term for the cutting out and re-synthesis of the damaged region of the DNA. There are a couple of varieties of excision repair: Nucleotide Excision Repair (NER) This system fixes damage by chemicals as well as UV damage. As shown in the figure on the previous page, in nucleotide excision repair, the damage is recognized and a cut is made on either side of the damaged region by an enzyme called an excinuclease (shown in green). A short portion of the DNA strand containing the damage is then removed and a DNA polymerase fills in the gap with the appropriate nucleotides. The newly made DNA is joined to the rest of the DNA backbone by the enzyme DNA ligase. In E. coli, NER is carried out by a group of proteins encoded by the uvrABC genes. As you can see, NER is similar, in principle, to mismatch repair. However, in NER, the distortion of the helix, caused by the DNA damage, clearly indicates which strand of the DNA needs to be removed and replaced. Base Excision Repair (BER) BER deals with situations like the deamination of cytosine to uracil. As noted earlier, cytosines in DNA sometimes undergo deamination to form the base uracil. Because cytosines pair with guanines and uracils pair with adenine, the conversion of cytosine to uracil in the DNA would lead to the insertion of an A in the newly replicated strand instead of the G that should have gone in across from a C. To prevent this from happening, uracils are removed from DNA by base excision repair. In base excision repair, a single base is first removed from the DNA, followed by removal of a region of the DNA surrounding the missing base. The gap is then repaired. The removal of uracil from DNA is accomplished by the enzyme uracil DNA glycosylase, which breaks the bond between the uracil and the sugar in the nucleotide. The removal of the uracil base creates a gap called an apyrimidinic site (AP site). The presence of the AP site triggers the activity of an AP endonuclease that cuts the DNA backbone. A short region of the DNA surrounding the site of the original uracil is then removed and replaced.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/05%3A_Flow_of_Genetic_Information/5.02%3A_DNA_Repair.txt

In the preceding sections, we have discussed the replication of the cell's DNA and the mechanisms by which the integrity of the genetic information is carefully maintained. What do cells do with this information? How does the sequence in DNA control what happens in a cell? If DNA is a giant instruction book containing all of the cell's "knowledge" that is copied and passed down from generation to generation, what are the instructions for? And how do cells use these instructions to make what they need? You have learned in introductory biology courses that genes, which are instructions for making proteins, are made of DNA. You also know that information in genes is copied into temporary instructions called messenger RNAs that direct the synthesis of specific proteins. This description of flow of information from DNA to RNA to protein, shown on the previous page, is often called the Central Dogma of molecular biology and is a good starting point for an examination of how cells use the information in DNA. Consider that all of the cells in a multicellular organism have arisen by division from a single fertilized egg and therefore, all have the same DNA. Division of that original fertilized egg produces, in the case of humans, over a trillion cells, by the time a baby is produced from that egg (that's a lot of DNA replication!). Yet, we also know that a baby is not a giant ball of a trillion identical cells, but has the many different kinds of cells that make up tissues like skin and muscle and bone and nerves. How did cells that have identical DNA turn out so different. The answer lies in gene expression, which is the process by which the information in DNA is used. Although all the cells in a baby have the same DNA, each different cell type uses a different subset of the genes in that DNA to direct the synthesis of a distinctive set of RNAs and proteins. The first step in gene expression is transcription, which we will examine next. What is transcription? Transcription is the process of copying information from DNA sequences into RNA sequences. This process is also known as DNA-dependent RNA synthesis. When a sequence of DNA is transcribed, only one of the two DNA strands is copied into RNA. But, apart from copying one, rather than both strands of DNA, how is transcription different from replication of DNA. DNA replication serves to copy all the genetic material of the cell and occurs before a cell divides, so that a full copy of the cell's genetic information can be passed on to the daughter cell. Transcription, by contrast, copies short stretches of the coding regions of DNA to make RNA. Different genes may be copied into RNA at different times in the cell's lifecycle. RNAs are, so to speak, temporary copies of instructions of the information in DNA and different sets of instructions are copied for use at different times. Cells make several different kinds of RNA: • mRNAs that code for proteins • rRNAS that form part of ribosomes • tRNAs that serve as adaptors between mRNA and amino acids during translation • Micro RNAs that regulate gene expression • Other small RNAs that have a variety of functions. Building an RNA strand is very similar to building a DNA strand. This is not surprising, knowing that DNA and RNA are very similar molecules. What enzyme carries out transcription? Transcription is catalyzed by the enzyme RNA Polymerase. "RNA polymerase" is a general term for an enzyme that makes RNA. There are many different RNA polymerases. Like DNA polymerases, RNA polymerases synthesize new strands only in the 5' to 3' direction, but because they are making RNA, they use ribonucleotides (i.e., RNA nucleotides) rather than deoxyribonucleotides. Ribonucleotides are joined in exactly the same way as deoxyribonucleotides, which is to say that the 3'OH of the last nucleotide on the growing chain is joined to the 5' phosphate on the incoming nucleotide. One important difference between DNA polymerases and RNA polymerases is that the latter do not require a primer to start making RNA. Once RNA polymerases are in the right place to start copying DNA, they just begin making RNA by stringing together RNA nucleotides complementary to the DNA template. This, of course, brings us to an obvious question- how do RNA polymerases "know" where to start copying on the DNA. Unlike the situation in replication, where every nucleotide of the parental DNA must eventually be copied, transcription, as we have already noted, only copies selected genes into RNA at any given time. Consider the challenge here: in a human cell, there are approximately 6 billion basepairs of DNA. Most of this is non- coding DNA, meaning that it won't need to be transcribed. The small percentage of the genome that is made up of coding sequences still amounts to between 20,000 and 30,000 genes in each cell. Of these genes, only a small number will need to be expressed at any given time. What indicates to an RNA polymerase where to start copying DNA to make a transcript? Signals in DNA indicate to RNA polymerase where it should start (and end) transcription. These signals are special sequences in DNA that are recognized by the RNA polymerase or by proteins that help RNA polymerase determine where it should bind the DNA to start transcription. A DNA sequence at which the RNA polymerase binds to start transcription is called a promoter. A promoter is generally situated upstream of the gene that it controls. What this means is that on the DNA strand that the gene is on, the promoter sequence is "before" the gene. Remember that, by convention, DNA sequences are read from 5' to 3'. So the promoter lies 5' to the start point of transcription. Also notice that the promoter is said to "control" the gene it is associated with. This is because expression of the gene is dependent on the binding of RNA polymerase to the promoter sequence to begin transcription. If the RNA polymerase and its helper proteins do not bind the promoter, the gene cannot be transcribed and it will therefore, not be expressed. What is special about a promoter sequence? In an effort to answer this question, scientists looked at many genes and their surrounding sequences. It makes sense that because the same RNA polymerase has to bind to many different promoters, the promoters should have some similarities in their sequences. Sure enough, common sequence patterns were seen to be present in many promoters. We will first take a look at prokaryotic promoters. When prokaryotic genes were examined, the following features commonly emerged (Figure 5.3.5): • A transcription start site (this the base in the DNA across from which the first RNA nucleotide is paired). • A -10 sequence: this is a 6 bp region centered about 10 bp upstream of the start site. The consensus sequence at this position is TATAAT. In other words, if you count back from the transcription start site, which by convention, is called the +1, the sequence found at -10 in the majority of promoters studied is TATAAT). • A -35 sequence: this is a sequence at about 35 basepairs upstream from the start of transcription. The consensus sequence at this position is TTGACA. What is the significance of these sequences? It turns out that the sequences at -10 and -35 are recognized and bound by a subunit of prokaryotic RNA polymerase before transcription can begin. The RNA polymerase of E. coli, for example, has a subunit called the sigma subunit (or sigma factor) in addition to the core polymerase, which is the part of the enzyme that actually makes RNA. Together, the sigma subunit and core polymerase make up what is termed the RNA polymerase holoenzyme. The sigma subunit of the polymerase (shown in brown in Figure 5.3.7) can recognize and bind to the -10 and -35 sequences in the promoter, thus positioning the RNA polymerase (shown in green) at the right place to initiate transcription. Once transcription begins, the core polymerase and the sigma subunit separate, with the core polymerase continuing RNA synthesis and the sigma subunit wandering off to escort another core polymerase molecule to a promoter. The sigma subunit can be thought of as a sort of usher that leads the polymerase to its "seat" on the promoter. As already mentioned, an RNA chain, complementary to the DNA template, is built by the RNA polymerase by the joining of the 5' phosphate of an incoming ribonucleotide to the 3'OH on the last nucleotide of the growing RNA strand. How does the polymerase know where to stop? A sequence of nucleotides called the terminator is the signal to the RNA polymerase to stop transcription and dissociate from the template. Although the process of RNA synthesis is the same in eukaryotes as in prokaryotes, there are some additional issues to keep in mind in eukaryotes. One is that in eukaryotes, the DNA template exists as chromatin, where the DNA is tightly associated with histones and other proteins. The "packaging" of the DNA must therefore be opened up to allow the RNA polymerase access to the template in the region to be transcribed. A second difference is that eukaryotes have multiple RNA polymerases, not one as in bacterial cells. The different polymerases transcribe different genes. For example, RNA polymerase I transcribes the ribosomal RNA genes, while RNA polymerase III copies tRNA genes. The RNA polymerase we will focus on most is RNA polymerase II, which transcribes protein-coding genes to make mRNAs. All three eukaryotic RNA polymerases need additional proteins to help them get transcription started. In prokaryotes, RNA polymerase by itself can initiate transcription (remember that the sigma subunit is a subunit of the prokaryotic RNA polymerase). The additional proteins needed by eukaryotic RNA polymerases are referred to as transcription factors. We will see below that there are various categories of transcription factors. Finally, in eukaryotic cells, transcription is separated in space and time from translation. Transcription happens in the nucleus, and the mRNAs produced are processed further before they are sent into the cytoplasm. Protein synthesis (translation) happens in the cytoplasm. In prokaryotic cells, mRNAs can be translated as they are coming off the DNA template, and because there is no nucleus, transcription and protein synthesis occur in a single cellular compartment. Like genes in prokaryotes, eukaryotic genes also have promoters. Eukaryotic promoters commonly have a TATA box, a sequence about 25 basepairs upstream of the start of transcription that is recognized and bound by proteins that help the RNA polymerase to position itself correctly to begin transcription. (Some eukaryotic promoters lack TATA boxes, and have, instead, other recognition sequences to help the RNA polymerase find the spot on the DNA where it spot on the DNA where it binds and initiates transcription.) We noted earlier that eukaryotic RNA polymerases need additional proteins to bind promoters and start transcription. What are these additional proteins that are needed to start transcription? General transcription factors are proteins that help eukaryotic RNA polymerases find transcription start sites and initiate RNA synthesis. We will focus on the transcription factors that assist RNA polymerase II. These transcription factors are named TFIIA, TFIIB and so on (TF= transcription factor, II=RNA polymerase II, and the letters distinguish individual transcription factors). Transcription in eukaryotes requires the general transcription factors and the RNA polymerase to form a complex at the TATA box called the basal transcription complex or transcription initiation complex. This is the minimum requirement for any gene to be transcribed. The first step in the formation of this complex is the binding of the TATA box by a transcription factor called the TATA Binding Protein or TBP. Binding of the TBP causes the DNA to bend at this spot and take on a structure that is suitable for the binding of additional transcription factors and RNA polymerase. As shown in the figure at left, a number of different general transcription factors, together with RNA polymerase (Pol II) form a complex at the TATA box. The final step in the assembly of the basal transcription complex is the binding of a general transcription factor called TFIIH. TFIIH is a multifunctional protein that has helicase activity (i.e., it is capable of opening up a DNA double helix) as well as kinase activity. The kinase activity of TFIIH adds a phosphate onto the C-terminal domain (CTD) of the RNA polymerase. This phosphorylation appears to be the signal that releases the RNA polymerase from the basal transcription complex and allows it to move forward and begin transcription.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/05%3A_Flow_of_Genetic_Information/5.03%3A_Transcription.txt

The processes described above are required whenever any gene is transcribed. But what determines which genes are transcribed at a given time. What are the molecular switches that turn transcription on or off? Although there are entire books written on this one topic, the basic mechanism by which transcription is regulated depends on highly specific interactions between transcription regulating proteins and regulatory sequences on DNA. We know that promoters indicate where transcription begins, but what determines that a given gene will be transcribed? In addition to the promoter sequences required for transcription initiation, genes have additional regulatory sequences (sequences of DNA on the same DNA molecule as the gene) that control when a gene is transcribed. Regulatory sequences are bound tightly and specifically by transcriptional regulators, proteins that can recognize DNA sequences and bind to them. The binding of such proteins to the DNA can regulate transcription by preventing or increasing transcription from a particular promoter. Regulation in Prokaryotes Let us first consider an example from prokaryotes. In bacteria, genes are often clustered in groups, such that genes that need to be expressed at the same time are next to each other and all of them are controlled as a single unit by the same promoter. The lac operon, shown in Figure 5.4.2, is one such group of genes that encode proteins needed for the uptake and breakdown of the sugar lactose. The three genes of the lac operon, lac z, lac y and lac a are controlled by a single promoter. Bacterial cells generally prefer to use glucose for their energy needs, but if glucose is unavailable, and lactose is present, the bacteria will take up lactose and break it down for energy. Since the proteins for taking up and breaking down lactose are only needed when glucose is absent and lactose is available, the bacterial cells need a way to express the genes of the lac operon only under those conditions. At times when lactose is absent, the cells do not need to express these genes. How do bacteria achieve this? Transcription of the lac cluster of genes is primarily controlled by a repressor protein that binds to a region of the DNA just downstream of the -10 sequence of the lac promoter. Recall that the promoter is where the RNA polymerase must bind to begin transcription. The place where the repressor is bound is called the operator (labeled O in the figure). When the repressor is bound at this position, it physically blocks the RNA polymerase from transcribing the genes, just as a vehicle blocking your driveway would prevent you from pulling out. Obviously, if you want to leave, the vehicle that is blocking your path must be removed. Likewise, in order for transcription to occur, the repressor must be removed from the operator to clear the path for RNA polymerase. How is the repressor removed? When the sugar lactose is present, it binds to the repressor, changing its conformation so that it no longer binds to the operator. When the repressor is no longer bound at the operator, the "road-block" in front of the RNA polymerase is removed, permitting the transcription of the genes of the lac operon. Because the binding of the lactose induces the expression of the genes in the lac operon, lactose is called an inducer. (Technically, the inducer is allolactose, a molecule made from lactose by the cell, but the principle is the same.) What makes this an especially effective control system is that the genes of the lac operon encode proteins that break down lactose. Turning on these genes requires lactose to be present. Once the lactose is broken down, the repressor binds to the operator once more and the lac genes are no longer expressed. This allows the genes to be expressed only when they are needed. But how do glucose levels affect the expression of the lac genes? We noted earlier that if glucose was present, lactose would not be used. A second level of control is exerted by a protein called CAP that binds to a site adjacent to the promoter and recruits RNA polymerase to bind the lac promoter. When glucose is depleted, there is an increase in levels of cAMP which binds to CAP. The CAP cAMP complex then binds the CAP site, as shown in Figure 5.4.3. The combination of CAP binding and the lac repressor dissociating from the operator when lactose levels are high ensures high levels of transcription of the lac operon just when it is most needed. The CAP protein binding may be thought of as a green light for the RNA polymerase, while the removal of repressor is like the lifting of a barricade in front of it. When both conditions are met, the RNA polymerase transcribes the downstream genes. The lac operon we have just described is a set of genes that are expressed only under the specific conditions of glucose depletion and lactose availability. Other genes may be expressed unless a particular condition is met. An example of this is the trp operon in bacterial cells, which encodes enzymes necessary for the synthesis of the amino acid tryptophan. These genes are expressed at all times, except when tryptophan is available from the cell's surroundings. This means that these genes must be prevented from being expressed in the presence of tryptophan. This is achieved by having a repressor protein that will bind to the operator only in the presence of tryptophan. Regulation in Eukaryotes Transcription in eukaryotes is also regulated by the binding of proteins to specific DNA sequences, but with some differences from the simple schemes outlined above. For most eukaryotic genes, general transcription factors and RNA polymerase (i.e., the basal transcription complex) are necessary, but not sufficient, for high levels of transcription. In eukaryotes, additional regulatory sequences called enhancers and the proteins that bind to the enhancers are needed to achieve high levels of transcription. Enhancers are DNA sequences that regulate the transcription of genes. Unlike prokaryotic regulatory sequences, enhancers don't need to be next to the gene they control. Often they are many kilobases away on the DNA. As the name suggests, enhancers can enhance (increase) transcription of a particular gene. How can a DNA sequence far from the gene being transcribed affect the level of its transcription? Enhancers work by binding proteins (transcriptional activators) that can, in turn, interact with the proteins bound at the promoter. The enhancer region of the DNA, with its associated transcriptional activator(s) can come in contact with the basal transcription complex that is bound at a distant TATA box by looping of the DNA (previous page). This allows the protein bound at the enhancer to make contact with the proteins in the basal transcription complex. One way that the transcriptional activator bound to the enhancer increases the transcription from a distant promoter is that it increases the frequency and efficiency with which the basal transcription complex is formed at the promoter. Another mechanism by which proteins bound at the enhancer can affect transcription is by recruiting to the promoter other proteins that can modify the structure of the chromatin in that region. As we noted earlier, in eukaryotes, DNA is packaged with proteins to form chromatin. When the DNA is tightly associated with these proteins, it is difficult to access for transcription. So proteins that can make the DNA more accessible to the transcription machinery can also play a role in the extent to which transcription occurs. In addition to enhancers, there are also negative regulatory sequences called silencers. Such regulatory sequences bind to transcriptional repressor proteins. Transcriptional activators and repressors are modular proteins- they have a part that binds DNA and a part that activates or represses transcription by interacting with the basal transcription complex.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/05%3A_Flow_of_Genetic_Information/5.04%3A_Regulation_of_Transcription.txt

So far, we have looked at the mechanism by which the information in genes (DNA) is transcribed into RNA. The newly made RNA, also known as the primary transcript (the product of transcription is known as a transcript) is further processed before it is functional. Both prokaryotes and eukaryotes process their ribosomal and transfer RNAs. The major difference in RNA processing, however, between prokaryotes and eukaryotes, is in the processing of messenger RNAs. We will focus on the processing of mRNAs in this discussion. You will recall that in bacterial cells, the mRNA is translated directly as it comes off the DNA template. In eukaryotic cells, RNA synthesis, which occurs in the nucleus, is separated from the protein synthesis machinery, which is in the cytoplasm. In addition, eukaryotic genes have introns, noncoding regions that interrupt the gene’s coding sequence. The mRNA copied from genes containing introns will also therefore have regions that interrupt the information in the gene. These regions must be removed before the mRNA is sent out of the nucleus to be used to direct protein synthesis. The process of removing the introns and rejoining the coding sections or exons, of the mRNA, is called splicing. Once the mRNA has been capped, spliced and had a polyA tail added, it is sent from the nucleus into the cytoplasm for translation. The initial product of transcription of a protein coding gene is called the pre-mRNA (or primary transcript). After it has been processed and is ready to be exported from the nucleus, it is called the mature mRNA or processed mRNA. What are the processing steps for messenger RNAs? In eukaryotic cells, pre-mRNAs undergo three main processing steps: • Capping at the 5' end • Addition of a polyA tail at the 3' end. and • Splicing to remove introns In the capping step of mRNA processing, a 7-methyl guanosine (shown at left) is added at the 5' end of the mRNA. The cap protects the 5' end of the mRNA from degradation by nucleases and also helps to position the mRNA correctly on the ribosomes during protein synthesis. The 3' end of a eukaryotic mRNA is first trimmed, then an enzyme called PolyA Polymerase adds a "tail" of about 200 ‘A’ nucleotides to the 3' end. There is evidence that the polyA tail plays a role in efficient translation of the mRNA, as well as in the stability of the mRNA. The cap and the polyA tail on an mRNA are also indications that the mRNA is complete (i.e., not defective). Introns are removed from the pre-mRNA by the activity of a complex called the spliceosome. The spliceosome is made up of proteins and small RNAs that are associated to form protein-RNA enzymes called small nuclear ribonucleoproteins or snRNPs (pronounced SNURPS). The splicing machinery must be able to recognize splice junctions (i.e., the end of each exon and the start of the next) in order to correctly cut out the introns and join the exons to make the mature, spliced mRNA. What signals indicate where an intron starts and ends? The base sequence at the start (5' or left end, also called the donor site) of an intron is GU while the sequence at the 3' or right end (a.k.a. acceptor site) is AG. There is also a third important sequence within the intron, called a branch point, that is important for splicing. There are two main steps in splicing: • In the first step, the pre-mRNA is cut at the 5' splice site (the junction of the 5' exon and the intron). The 5' end of the intron then is joined to the branch point within the intron. This generates the lariat-shaped molecule characteristic of the splicing process • In the second step, the 3' splice site is cut, and the two exons are joined together, and the intron is released. Many pre-mRNAs have a large number of exons that can be spliced together in different combinations to generate different mature mRNAs. This is called alternative splicing, and allows the production of many different proteins using relatively few genes, since a single RNA can, by combining different exons during splicing, create many different protein coding messages. Because of alternative splicing, each gene in our DNA gives rise, on average, to three different proteins. Once protein coding messages have been processed by capping, splicing and addition of a poly A tail, they are transported out of the nucleus to be translated in the cytoplasm.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/05%3A_Flow_of_Genetic_Information/5.05%3A_RNA_Processing.txt

Translation is the process by which information in mRNAs is used to direct the synthesis of proteins. As you have learned in introductory biology, in eukaryotic cells, this process is carried out in the cytoplasm of the cell, by large RNA-protein machines called ribosomes. Ribosomes contain ribosomal RNAs (rRNAs) and proteins. The proteins and rRNAs are organized into two subunits, a large and a small. The large subunit has an enzymatic activity, known as a peptidyl transferase, that makes the peptide bonds that join amino acids to make a polypeptide. The small and large subunits assemble on the mRNA at its 5’end to initiate translation. Ribosomes function by binding to mRNAs and holding them in a way that allows the amino acids encoded by the RNA to be joined sequentially to form a polypeptide. The sequence of an mRNA directly specifies the sequence of amino acids in the protein it encodes. Each amino acid in the protein is specified by a sequence of 3 bases called a codon in the mRNA. For example, the amino acid tryptophan is encoded by the sequence 5’UGG3’ on an mRNA. Given that there are 4 bases in RNA, the number of different 3-base combinations that are possible is \(4^3\), or 64. There are, however, only 20 amino acids that are used in building proteins. This discrepancy in the number of possible codons and the actual number of amino acids they specify is explained by the fact that the same amino acid may be specified by more than one codon. In fact, with the exception of the amino acids methionine and tryptophan, all the other amino acids are encoded by multiple codons. The figure above shows the codons that are used for each of the twenty amino acids. Three of the 64 codons are known as termination or stop codons and as their name suggests, indicate the end of a protein coding sequence. The codon for methionine, AUG, is used as the start, or initiation, codon. This ingenious system is used to direct the assembly of a protein in the same way that you might string together colored beads in a particular order using instructions that used symbols like 111 for a red bead, followed by 222 for a green bead, 333 for yellow, and so on, till you came to 000, indicating that you should stop stringing beads. While the ribosomes are literally the protein factories that join amino acids together using the instructions in mRNAs, another class of RNA molecules, the transfer RNAs (tRNAs) are also needed for translation. Transfer RNAs (see figure, left) are small RNA molecules, about 75-80 nucleotides long, that function to 'interpret' the instructions in the mRNA during protein synthesis. In terms of the bead analogy above, someone, or something, has to be able to bring a red bead in when the instructions indicate 111, and a green bead when the instructions say 222. Unlike a human, who can choose a red bead when 111 is present in the instructions, neither ribosomes nor tRNAs can think. The system, therefore, relies, like so many processes in cells, solely on molecular recognition. A given transfer RNA is specific for a particular amino acid. It is linked covalently to this amino acid at its 3' end by an enzyme called aminoacyl tRNA synthetase. There is an aminoacyl tRNA synthetase specific for each amino acid. A tRNA with an amino acid attached to it is said to be charged. Another region of the tRNA has a sequence of 3 bases, the anticodon, that is complementary to the codon for the amino acid it is carrying. When the tRNA encounters the codon for its amino acid on the messenger RNA, the anticodon will base-pair with the codon, and the amino acid attached to it will be brought in to the ribosome to be added on to the growing protein chain. With an idea of the various components necessary for translation we can now take a look at the process of protein synthesis. The main steps in the process are similar in prokaryotes and eukaryotes. As we already noted, ribosomes bind to mRNAs and facilitate the interaction between the codons in the mRNA and the anticodons on charged tRNAs. In bacterial cells, translation is coupled with transcription and begins even before the mRNA has been completely synthesized. How does the ribosome recognize and bind to the mRNA. Many bacterial mRNAs carry a short purine-rich sequence known as the Shine-Dalgarno site upstream of the AUG start codon, as shown in the figure below. This sequence is recognized and bound by a complementary sequence in the 16S rRNA that is part of the small ribosomal subunit as shown above. Because the Shine-Dalgarno site serves to recruit and bind the ribosome, it is also referred to as the Ribosome Binding Site or RBS. A variation of this process of ribosome assembly operates in eukaryotic cells. We already know that in eukaryotic cells, processed mRNAs are sent from the nucleus to the cytoplasm. The small and large subunits of ribosomes, each composed of characteristic rRNAs and proteins are found in the cytoplasm and assemble on mRNAs to form complete ribosomes that carry out translation. Protein synthesis in eukaryotes starts with the binding of the small subunit of the ribosome to the 5' end of the mRNA. The assembly of the translation machinery begins with the binding of the small ribosomal subunit to the 7-methyl guanosine cap on the 5'end of an mRNA. Meanwhile, the initiator tRNA pairs with the start codon. (Recall that the start codon is AUG, and codes for methionine. The initiator tRNA carries the amino acid methionine). The large subunit of the ribosome then joins the complex, which is now ready to start protein synthesis. Ribosomes have two sites for binding charged tRNAs, each of which is positioned to make two adjacent codons on the mRNA available for binding by tRNAs. The initiation codon occupies the first of these ribosomal sites, the P-site. The anticodon complementary to this is on the initiator tRNA, which brings in the first amino acid of the protein. This initial phase of translation is called initiation and requires the help of protein factors called initiation factors. The second codon of the mRNA is positioned adjacent to the second site on the ribosome, the A site. This is where the tRNA carrying the amino acid specified by the second codon binds. The binding of aminoacyl tRNA to the A-site is mediated by proteins called elongation factors and requires the input of energy. Once the appropriate charged tRNAS have "docked" on the codons by base-pairing between the anticodon on the tRNA and the codon on the mRNA, the ribosome joins the amino acids carried by the two tRNAs by making a peptide bond (see figure at right). Interestingly, the formation of the peptide bond is catalyzed by a catalytic RNA (the 23S rRNA in prokaryotes) rather than by a protein enzyme. This and subsequent steps in the synthesis of the polypeptide are called the elongation phase of translation. Once the first two amino acids are linked , the first tRNA dissociates, and moves out of the P-site and into the E, or Exit site. The second tRNA then moves into the P-site, vacating the A-site for the tRNA corresponding to the next codon. The process repeats till the stop codon is in the A-site. At this point, a release factor binds at the A-site, adds a water molecule to the polypeptide at the P-site, and releases the completed polypeptide from the ribosome, which itself, then dissociates into subunits. As described in Chapter 3, polypeptides made in this way are then folded into their three dimensional shapes, post-translationally modified and delivered to the appropriate cellular compartments to carry out their functions.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/05%3A_Flow_of_Genetic_Information/5.06%3A_Translation.txt

The cost of living is energy and the producers and consumers of energy in the cell are the chemical reactions known collectively as metabolism. Metabolic processes are governed by the same laws of energy as the rest of the universe, so they must be viewed in the light of Gibbs free energy. For the most part, the drivers of changes in Gibbs free energy are changes in concentration of reactants and products but for some reactions, the concentration changes required to run a reaction in the desired direction are not practical. In such cases, cells may use alternative strategies, such as energy coupling reactions (combining an energetically unfavorable reaction with a favorable one, such as the hydrolysis of ATP) to help “drive" the unfavorable reaction. In other cases, cells use alternate pathways around energetically unfavorable reactions. Depending on your mathematical perspective, life is the sum of the product of the biochemical reactions that occur in cells. The collection of these reactions is known as metabolism. We break the subject into two broad areas: 1) oxidative/reductive metabolism and 2) pathways that involve little oxidation/reduction. This chapter deals with the former. • 6.1: Definitions Anabolic processes refer to collections of biochemical reactions that make bigger molecules from smaller ones. Examples include the synthesis of fatty acids from acetyl-CoA, of proteins from amino acids, of complex carbohydrates from simple sugars, and of nucleic acids from nucleotides. Just as any construction project requires energy, so, too, do anabolic processes require input of energy. Anabolic processes tend to be reductive in nature, in contrast to catabolic processes, which are oxidative • 6.2: Perspectives We can view metabolism at several levels. At the highest level, we have nutrients, such as sugars, fatty acids and amino acids entering cells and carbon dioxide and other waste products (such as urea) exiting. Cells use the incoming materials for energy and substance to synthesize sugars, nucleotides, and other amino acids as building blocks for the carbohydrates, nucleic acids, fatty compounds, and proteins necessary for life. • 6.3: Glycolysis Glycolysis, which literally means “breakdown of sugar," is a catabolic process in which six-carbon sugars (hexoses) are oxidized and broken down into pyruvate molecules. The corresponding anabolic pathway by which glucose is synthesized is termed gluconeogenesis. Both glycolysis and gluconeogenesis are not major oxidative/reductive processes by themselves, with one step in each one involving loss/gain of electrons, but the product of glycolysis, pyruvate, can be completely oxidized to CO₂. • 6.4: Gluconeogenesis The anabolic counterpart to glycolysis is gluconeogenesis, which occurs mostly in the cells of the liver and kidney. In seven of the eleven reactions of gluconeogenesis (starting from pyruvate), the same enzymes are used as in glycolysis, but the reaction directions are reversed. Notably, the ΔG values of these reactions in the cell are typically near zero, meaning their direction can be readily controlled by changing substrate and product concentrations. • 6.5: Citric Acid Cycle The primary catabolic pathway in the body is the citric acid cycle because it is here that oxidation to carbon dioxide occurs for breakdown products of the cell’s major building blocks - sugars, fatty acids, amino acids. The pathway is cyclic and thus, doesn’t really have a starting or ending point. All of the reactions occur in the mitochondrion, though one enzyme is embedded in the organelle’s membrane. • 6.6: Glyoxylate Pathway The glyoxylate pathway is related to the Citric Acid Cycle (CAC), which overlaps all of the non-decarboxylation reactions of the CAC does not operate in animals, because they lack two enzymes necessary for the pathway – isocitrate lyase and malate synthase. Isocitrate lyase catalyzes the conversion of isocitrate into succinate and glyoxylate. Because of this, all six carbons of the CAC survive and do not end up as carbon dioxide. • 6.7: Acetyl-CoA Metabolism Acetyl-CoA is one of the most “connected" metabolites in biochemistry, appearing in fatty acid oxidation/reduction, pyruvate oxidation, the citric acid cycle, amino acid anabolism/catabolism, ketone body metabolism, steroid/bile acid synthesis, and (by extension from fatty acid metabolism) prostaglandin synthesis. Most of these pathways will be dealt with separately. Here we will cover the last three. • 6.8: Cholesterol Metabolism The cholesterol biosynthesis pathway is a long one and it requires significant amounts of reductive and ATP energy, which is why it is included here. Cholesterol has important roles in the body in membranes. It as also a precursor of steroid hormones and bile acids and its immediate metabolic precursor, 7-dehydrocholesterol, is a precursor of Vitamin D. The pathway leading to cholesterol is known as the isoprenoid pathway and branches of it lead to other molecules including other fat-soluble vit • 6.9: Ketone Body Synthesis In ketone body synthesis, an acetyl-CoA is split off from HMG-CoA, yielding acetoacetate, a four carbon ketone body that is somewhat unstable, chemically. It will decarboxylate spontaneously to some extent to yield acetone. Ketone bodies are made when the blood levels of glucose fall very low. Ketone bodies can be converted to acetyl-CoA, which can be used for ATP synthesis via the citric acid cycle. • 6.10: Prostaglandin Synthesis The pathway for making prostaglandins is an extension of the fatty acid synthesis pathway. Prostaglandins, molecules associated with localized pain, are synthesized in cells from arachidonic acid (see previous page) which has been cleaved from membrane lipids. The enzyme catalyzing their synthesis is known as prostaglandin synthase, but is more commonly referred to as a cyclooxygenase (or COX) enzyme. • 6.11: Fatty Acid Oxidation Breakdown of fats yields fatty acids and glycerol. Glycerol can be readily converted to DHAP for oxidation in glycolysis or synthesis into glucose in gluconeogenesis. Fatty acids are broken down in two carbon units of acetyl-CoA. To be oxidized, they must be transported through the cytoplasm attached to coenzyme A and moved into mitochondria. The latter step requires removal of the CoA and attachment of the fatty acid to a molecule of carnitine. • 6.12: Fatty Acid Synthesis Synthesis of fatty acids occurs in the cytoplasm and endoplasmic reticulum of the cell and is chemically similar to the beta-oxidation process, but with a couple of key differences. The first of these occur in preparing substrates for the reactions that grow the fatty acid. Transport of acetyl-CoA from the mitochondria occurs when it begins to build up. Two molecules can play roles in moving it to the cytoplasm – citrate and acetylcarnitine • 6.13: Metabolism of Fat Breakdown of fat in adipocytes requires catalytic action of three enzymes, hormone sensitive triacylglycerol lipase (called LIPE) to remove the first fatty acid from the fat, diglyceride lipase to remove the second one, and monoglyceride lipase to remove the third. Only LIPE is regulated and it appears to be the rate limiting reaction. Synthesis of fat starting with glycerol-3-phosphate requires action of acyl transferase enzymes to catalyze addition of fatty acids to the glycerol backbone. • 6.14: Connections to Other Pathways There are several connections between metabolism of fats and fatty acids to other metabolic pathways. As noted, phosphatidic acid is an intermediate in the synthesis of triacylglycerols, as well as of other lipids, including phosphoglycerides. Diacylglycerol (DAG), which is an intermediate in fat synthesis, also acts as a messenger in some signaling systems. Thumbnail: Metabolic Metro Map. (CC BY-SA 4.0; Chakazul).​​​​ 06: Metabolism I - Oxidative Reductive Processes We start by defining a few terms. Anabolic processes refer to collections of biochemical reactions that make bigger molecules from smaller ones. Examples include the synthesis of fatty acids from acetyl-CoA, of proteins from amino acids, of complex carbohydrates from simple sugars, and of nucleic acids from nucleotides. Just as any construction project requires energy, so, too, do anabolic processes require input of energy. Anabolic processes tend to be reductive in nature, in contrast to catabolic processes, which are oxidative. Not all anabolic processes are reductive, though. Protein synthesis and nucleic acid synthesis do not involve reduction, though the synthesis of amino acids and nucleotides does. Catabolic processes are the primary sources of energy for heterotrophic organisms and they ultimately power the anabolic processes. Examples include glycolysis (breakdown of glucose), the citric acid cycle, and fatty acid oxidation. Reductive processes require electron sources, such as NADPH, NADH, or $\text{FADH}_2$. Oxidative processes require electron carriers, such as $\text{NAD}^+$, $\text{NADP}^+$, or FAD. Catabolic processes are ultimately the source of ATP energy in cells, but the vast majority of ATP i heterotrophic organisms is not made in directly in these reactions. Instead, the electrons released by oxidation are collected by electron carriers which donate them, in the mitochondria, to complexes that make ATP (ultimately) by oxidative phosphorylation. In our tour of metabolism, we will tackle in this chapter processes that are the most oxidative/reductive in nature and in the following chapter those pathways that involve less reduction/oxidation. The aim in this coverage is not to go through the step- by-step reactions of the pathway, but rather to focus on control points, interesting enzymes, molecules common between pathways, and how the metabolic pathways meet the organism’s needs.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/06%3A_Metabolism_I_-_Oxidative_Reductive_Processes/6.01%3A_Definitions.txt

We can view metabolism at several levels. At the highest level, we have nutrients, such as sugars, fatty acids and amino acids entering cells and carbon dioxide and other waste products (such as urea) exiting. Cells use the incoming materials for energy and substance to synthesize sugars, nucleotides, and other amino acids as building blocks for the carbohydrates, nucleic acids, fatty compounds, and proteins necessary for life. As we zoom in, we can imagine pathways made up of reactions for breakdown and synthesis of each of these compounds. The figure at left shows such a simple schematic and how the pathways are not isolated from each other – molecular products of one are substrates for another. At a deeper level, we can study individual reactions and discover the enormous complexity and commonality of metabolic reactions. In studying metabolism, we recognize that metabolic pathways are manmade concepts with artificial boundaries. Students commonly think of the molecules in the pathways being tied exclusively to those individual pathways, but with the exception of reactions that have physical barriers (such as those occurring within an organelle), metabolic pathways have many common intermediates used in multiple reactions occurring in the same location at the same time and thus cannot be ascribed to any one pathway. The best we can do is understand general directions of pathways in cells. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University) 6.03: Glycolysis Glycolysis, which literally means “breakdown of sugar," is a catabolic process in which six-carbon sugars (hexoses) are oxidized and broken down into pyruvate molecules. The corresponding anabolic pathway by which glucose is synthesized is termed gluconeogenesis. Both glycolysis and gluconeogenesis are not major oxidative/reductive processes by themselves, with one step in each one involving loss/gain of electrons, but the product of glycolysis, pyruvate, can be completely oxidized to carbon dioxide. Indeed, without production of pyruvate from glucose in glycolysis, a major energy source for the cell is not available. By contrast, gluconeogenesis can synthesize glucose reductively from very simple materials, such as pyruvate and acetyl-CoA/ glyoxylate (at least in plants). For these reasons we include these pathways in the red/ox collection. Glucose is the most abundant hexose in nature and is the one people typically associate with glycolysis, but fructose (in the form of fructose-6-phosphate) is metabolized in the cell and galactose can easily be converted into glucose for catabolism in the pathway as well. The end metabolic products of the pathway are two molecules of ATP, two molecules of NADH and two molecules of pyruvate, which, in turn, can be oxidized further in citric acid cycle. Intermediates Glucose and fructose are the sugar ‘funnels’ serving as entry points to the glycolytic pathway. Other sugars must be converted to either of these forms to be directly metabolized. Some pathways, including the Calvin Cycle and the Pentose Phosphate Pathway (PPP, see below) contain intermediates in common with glycolysis, so in that sense, almost any cellular sugar can be metabolized here. Intermediates of glycolysis that are common to other pathways include glucose-6-phosphate (PPP, glycogen metabolism), F6P (PPP), G3P (Calvin, PPP), DHAP (PPP, glycerol metabolism, Calvin), 3PG (Calvin, PPP), PEP (C4 plant metabolism, Calvin), and pyruvate (fermentation, acetyl-CoA genesis, amino acid metabolism). Reactions The pathway of glycolysis begins with two inputs of energy. First, glucose gets a phosphate from ATP to make glucose-6-phosphate (G6P) and later fructose-6-phosphate (F6P) gets another phosphate from ATP to make fructose-1,6-bisphosphate (F1,6BP). With the pump thus primed, the pathway proceeds first to split the F1,6BP into two 3-carbon intermediates. Later the only oxidation step in the entire pathway occurs. In that reaction, glyceraldehyde-3-phosphate (G3P) is oxidized and a phosphate is added, creating 1,3-bisphosphoglycerate (1,3 BPG). The addition of the phosphate sometimes conceals the oxidation that occurred. G3P was an aldehyde. 1,3 BGP is an acid esterified to a phosphate. The two phosphates in the tiny 1,3BPG molecule repel each other and give the molecule high energy. It uses this energy to phosphorylate ADP to make ATP. Since there are two 1,3 BPGs produced for every glucose, the two ATP produced replenish the two ATPs used to start the cycle. The synthesis of ATP directly from a metabolic reaction is known as substrate level phosphorylation, though it is not a significant source of ATP. Glycolysis has two reactions during which substrate-level phosphorylation occurs. The transfer of phosphate from 1,3BPG to ATP creates 3-phosphoglycerate (3-PG). Conversion of 3-PG to 2-PG occurs by an important mechanism. An intermediate in the reaction (catalyzed by phosphogly cerate mutase) is 2,3 BPG. This intermediate, which is stable, is released with low frequency by the enzyme instead of being converted to 2-PG. 2,3BPG is important because it binds to hemoglobin and stimulates release of oxygen. Thus, cells which are metabolizing glucose rapidly release more 2,3BPG and, as a result, stimulate release of more oxygen, supporting their needs. 2-PG is converted to phosphoenolpyyruvate (PEP) by removal of water, creating a very high energy intermediate. Conversion of PEP to pyruvate is the second substrate level phosphorylation of glycolysis, creating ATP. There is almost enough energy in PEP to stimulate production of a second ATP, but it is not used. Consequently, the energy is lost as heat. If you wonder why you get hot when you exercise, the reaction that converts PEP to pyruvate is a prime culprit. Enzymes/Control Control of glycolysis is unusual for a metabolic pathway, in that regulation occurs at three enzymatic points: $\underbrace{ \ce{Glucose <=> G6P}}_{\text{hexokinase} }$ $\underbrace{ \ce{F6P <=> F1,6BP}}_{\text{phosphofructokinase (PFK)} }$ and $\underbrace{ \ce{PEP <=> pyruvate}}_{\text{pyruvate kinase} }.$ Glycolysis is regulated in a reciprocal fashion compared to its corresponding anabolic pathway, gluconeogenesis. Reciprocal regulation occurs when the same molecule or treatment (phosphorylation, for example) has opposite effects on catabolic and anabolic pathways. Reciprocal regulation is important when anabolic and corresponding catabolic pathways are occurring in the same cellular location. As an example, consider regulation of PFK. It is activated by several molecules, most importantly fructose-2,6- bisphosphate (F2,6BP). This molecule has an inhibitory effect on the corresponding gluconeogenesis enzyme, fructose-1,6-bisphosphatase (F1,6BPase). You might wonder why pyruvate kinase, the last enzyme in the pathway, is regulated. The answer is simple. Pyruvate kinase catalyzes the most energetically rich reaction of glycolysis. The reaction is favored so strongly in the forward direction that cells must do a ‘two-step’ around it in the reverse direction when making glucose. In other words, it takes two enzymes, two reactions, and two triphosphates to go from pyruvate back to PEP in gluconeogenesis. When cells are needing to make glucose, they can’t be sidetracked by having the PEP they have made in gluconeogenesis be converted directly back to pyruvate by pyruvate kinase. Consequently, pyruvate kinase is inhibited during gluconeogenesis, lest a “futile cycle" occur. Another interesting control mechanism called feedforward activation involves pyruvate kinase. Pyruvate kinase is activated allosterically by F1,6BP. This molecule is a product of the PFK reaction and a substrate for the aldolase reaction. It should be noted that the aldolase reaction is energetically unfavorable (high +$\Delta$G°’), thus allowing F1,6BP to accumulate. When this happens, some of the excess F1,6BP activates pyruvate kinase, which jump-starts the conversion of PEP to pyruvate. The resulting drop in PEP levels has the effect of “pulling" on the reactions preceding pyruvate kinase. As a consequence, the concentrations of G3P and DHAP fall, helping to move the aldolase reaction forward. Pyruvate Metabolism As noted, pyruvate produced in glycolysis can be oxidized to acetyl-CoA, which is itself oxidized in the citric acid cycle to carbon dioxide. That is not the only metabolic fate of pyruvate, though. Pyruvate is a “starting" point for gluconeogenesis, being converted to oxaloacetate in the mitochondrion in the first step. Pyruvate in animals can also be reduced to lactate when oxygen is limiting. This reaction, which requires NADH produces $\text{NAD}^+$ and is critical for generating the latter molecule to keep the glyceraldehyde-3-phosphate dehydrogenase reaction of glycolysis going when there is no oxygen. Oxygen is necessary for the electron transport system to operate and this, in turn, is what oxidizes NADH to $\text{NAD}^+$. In the absence of oxygen, thus, an alternative means of making $\text{NAD}^+$ is necessary, or else glycolysis will halt. Bacteria and yeast have NADH requiring reactions that regenerate $\text{NAD}^+$ while producing ethanol from pyruvate under anaerobic conditions, instead of lactic acid. Thus, fermentation of pyruvate is necessary to keep glycolysis operating when oxygen is limiting. It is also for these reasons that brewing of beer (using yeast) involves depletion of oxygen and muscles low in oxygen produce lactic acid (animals). Pyruvate is a precursor of alanine which can be easily synthesized by transfer of a nitrogen from an amine donor, such as glutamic acid. Pyruvate can also be converted into oxaloacetate by carboxylation in the process of gluconeogenesis (see Figure 6.3.8). The enzymes involved in pyruvate metabolism include pyruvate dehydrogenase (makes acetyl-CoA), lactate dehydrogenase (makes lactate), transaminases (make alanine), an pyruvate carboxylase (makes oxaloacetate).

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/06%3A_Metabolism_I_-_Oxidative_Reductive_Processes/6.02%3A_Perspectives.txt

The anabolic counterpart to glycolysis is gluconeogenesis, which occurs mostly in the cells of the liver and kidney. In seven of the eleven reactions of gluconeogenesis (starting from pyruvate), the same enzymes are used as in glycolysis, but the reaction directions are reversed. Notably, the \(\Delta\)G values of these reactions in the cell are typically near zero, meaning their direction can be readily controlled by changing substrate and product concentrations. The three regulated enzymes of glycolysis all catalyze reactions whose \(\Delta\)G values are not close to zero, making manipulation of reaction direction non-trivial. Consequently, cells employ “work-around" reactions catalyzed by four different enzymes to favor gluconeogenesis, when appropriate. Two of the enzymes (pyruvate carboxylase and PEP carboxykinase -PEPCK) catalyze reactions that bypass pyruvate kinase. F1,6BPase bypasses PFK and G6Pase bypasses hexokinase. Notably, pyruvate carboxylase and G6Pase are found in the mitochondria and endoplasmic reticulum, respectively, whereas the other two are found in the cytoplasm along with all of the enzymes of glycolysis. As a result, all of glycolysis and most of gluconeogenesis occurs in the cytoplasm. Controlling these pathways then becomes of critical importance because cells generally need to minimize the extent to which paired anabolic and catabolic pathways are occurring simultaneously, lest they waste energy and make no tangible product except heat. The mechanisms of controlling these pathways work, in some ways, in opposite fashions, called reciprocal regulation (see above). Besides reciprocal regulation, other mechanisms help control gluconeogenesis. First, PEPCK is controlled largely at the level of synthesis. Overexpression of PEPCK (stimulated by glucagon, glucocorticoids, and cAMP and inhibited by insulin) causes symptoms of diabetes. Pyruvate carboxylase is sequestered in the mitochondrion and is sensitive to acetyl-CoA, which is an allosteric activator. Acetyl-CoA concentrations increase as the citric acid cycle activity decreases. Glucose-6-phosphatase is present in low concentrations in many tissues, but is found most abundantly and importantly in the major gluconeogenic organs – the liver and kidney cortex. 6.05: Citric Acid Cycle Cori Cycle With respect to energy, the liver and muscles act complementarily. The liver is the major organ in the body for the synthesis of glucose. Muscles are major users of ATP. Actively exercising muscles generate lactate as a result of running glycolysis faster than the blood can deliver oxygen during periods of heavy exercise. As a consequence, the muscles go anaerobic and produce lactate. This lactate is of no use to muscle cells, so they dump it into the blood. Lactate travels in the blood to the liver, which takes it up and reoxidizes it back to pyruvate, catalyzed by the enzyme lactate dehydrogenase. Pyruvate in the liver is then converted to glucose by gluconeogenesis. The glucose thus made by the liver is dumped into the bloodstream where it is taken up by muscles and used for energy, completing a very important intercellular pathway known as the Cori cycle. Citric Acid Cycle The primary catabolic pathway in the body is the citric acid cycle because it is here that oxidation to carbon dioxide occurs for breakdown products of the cell’s major building blocks - sugars, fatty acids, amino acids. The pathway is cyclic (Figure 6.5.2) and thus, doesn’t really have a starting or ending point. All of the reactions occur in the mitochondrion, though one enzyme is embedded in the organelle’s membrane. As needs change, cells may use a subset of the reactions of the cycle to produce a desired molecule rather than to run the entire cycle (Figure 6.5.2). Focusing on the pathway itself, the traditional point to start discussion is addition of acetyl-CoA to oxaloacetate (OAA) to form citrate. Acetyl-CoA for the pathway can come from a variety of sources. They include pyruvate oxidation (from glycolysis and amino acid metabolism), fatty acid oxidation, and amino acid metabolism. The reaction joining it to OAA is catalyzed by citrate synthase and the $\Delta$G°’ is fairly negative. This, in turn, helps to “pull" the reaction preceding it in the cycle (catalyzed by malate dehydrogenase). In the next reaction, citrate is isomerized to isocitrate by action of the enzyme called aconitase. Isocitrate is a branch point in plants and bacteria for the glyoxylate cycle. Oxidative decarboxylation of isocitrate by isocitrate dehydrogenase produces the first NADH and yields alpha-ketoglutarate. This five carbon intermediate is a branch point for synthesis of glutamate. In addition, glutamate can also be made easily into this citric acid cycle intermediate. Decarboxylation of alpha-ketoglutarate yields succinyl-CoA and is catalyzed by alpha- ketoglutarate dehydrogenase. This enzyme is structurally very similar to pyruvate dehydrogenase and employs the same five coenzymes – NAD, FAD, CoASH, TPP, and lipoic acid. The remainder of the citric acid cycle involves conversion of the four carbon succinyl-CoA into oxaloacetate. Succinyl-CoA is a branch point for the synthesis of heme. Succinyl-CoA is converted to succinate in a reaction catalyzed by succinyl-CoA synthetase (named for the reverse reaction) and a GTP is produced, as well – the only substrate level phosphorylation in the cycle. The energy for the synthesis of the GTP comes from hydrolysis of the high energy thioester bond between succinate and the CoA. Evidence for the high energy of a thioester bond is also evident in the citrate synthase reaction, which is also very energetically favorable. Succinate is also produced by metabolism of odd-chain fatty acids (see below). Oxidation of succinate occurs in the next step, catalyzed by succinate dehydrogenase. This interesting enzyme both catalyzes this reaction and participates in the electron transport system, funneling electrons from the $\text{FADH}_2$ it gains in the reaction to coenzyme Q. The product of the reaction, fumarate gains a water across its trans double bond in the next reaction, catalyzed by fumarase to form malate . Fumarate is also a byproduct of nucleotide metabolism and of the urea cycle . Malate is important also for transporting electrons across membranes in the malate aspartate shuttle and in ferrying carbon dioxide in C4 plants. Conversion of malate to OAA is a rare biological oxidation that has a $\Delta$G°’ with a positive value. The reaction product includes NADH and the reaction is ‘pulled’ by the energetically favorable conversion of OAA to citrate in what was described above as the first reaction of the cycle. OAA intersects other important pathways, including amino acid metabolism (readily converted to aspartic acid), transamination (nitrogen movement) and gluconeogenesis.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/06%3A_Metabolism_I_-_Oxidative_Reductive_Processes/6.04%3A_Gluconeogenesis.txt

A pathway related to the Citric Acid Cycle (CAC) is the glyoxylate pathway (Figure 6.6.1). This pathway, which overlaps all of the non-decarboxylation reactions of the CAC does not operate in animals, because they lack two enzymes necessary for the pathway – isocitrate lyase and malate synthase. Isocitrate lyase catalyzes the conversion of isocitrate into succinate and glyoxylate. Because of this, all six carbons of the CAC survive and do not end up as carbon dioxide. Succinate continues through the remaining reactions of the CAC to produce oxaloacetate. Glyoxylate combines with another acetyl-CoA (one acetyl-CoA was used to start the cycle) to create malate (catalyzed by malate synthase). Malate can, in turn, be oxidized to oxaloacetate. It is at this point that the pathway’s contrast with the CAC is apparent. After one turn of the CAC, a single oxaloacetate is produced and it balances the single one used in the first reaction of the cycle. Thus, in the CAC, no net production of oxaloacetate is realized. By contrast, at the end of a turn of the glyoxylate cycle, two oxaloacetates are produced, starting with one. The extra oxaloacetate can then be used to make other molecules, including glucose in gluconeogenesis. Because animals do not run the glyoxylate cycle, they cannot produce glucose from acetyl-CoA in net amounts, but plants and bacteria can. As a result, these organisms can turn acetyl-CoA from fat into glucose, while animals can’t. Bypassing the decarboxylations (and substrate level phosphorylation) has its costs, however. Each turn of the glyoxylate cycle produces one FADH and one NADH instead of the three NADHs, one $\text{FADH}_2$, and one GTP made in each turn of the CAC. 6.07: Acetyl-CoA Metabolism Acetyl-CoA is one of the most “connected" metabolites in biochemistry, appearing in fatty acid oxidation/reduction, pyruvate oxidation, the citric acid cycle, amino acid anabolism/catabolism, ketone body metabolism, steroid/bile acid synthesis, and (by extension from fatty acid metabolism) prostaglandin synthesis. Most of these pathways will be dealt with separately. Here we will cover the last three. The pathways for ketone body synthesis and cholesterol biosynthesis overlap at the beginning. Each of these starts by combining two acetyl-CoAs together to make acetoacetyl-CoA. Not coincidentally, that is the next to last product of oxidation of fatty acids with even numbers of carbons. In fact, the enzyme that catalyzes the joining is the same as the one that catalyzes its breakage in fatty acid oxidation – thiolase. Thus, these pathways start by reversing the last step of the last round of fatty acid oxidation. Both pathways also include addition of two more carbons from a third acetyl-CoA to form Hydroxy-Methyl-Glutaryl-CoA, or HMG-CoA, as it is more commonly known. It is at this point that the two pathways diverge. 6.08: Cholesterol Metabolism The cholesterol biosynthesis pathway is a long one and it requires significant amounts of reductive and ATP energy, which is why it is included here. Cholesterol has important roles in the body in membranes. It as also a precursor of steroid hormones and bile acids and its immediate metabolic precursor, 7-dehydrocholesterol, is a precursor of Vitamin D. The pathway leading to cholesterol is known as the isoprenoid pathway and branches of it lead to other molecules including other fat-soluble vitamins. From HMG-CoA, the enzyme HMG-CoA reductase catalyzes the formation of mevalonate. The reaction requires NADPH and results in release of coenzyme A and appears to be one of the most important regulatory steps in the synthesis pathway. The enzyme is regulated both by feedback inhibition (cholesterol inhibits it) and by covalent modification (phosphorylation inhibits it). The enzyme’s synthesis is also regulated transcriptionally. When cholesterol levels fall, transcription of the gene increases. Mevalonate gets phosphorylated twice and then decarboxylated to yield the five carbon intermediate known as isopentenyl-pyrophosphate (IPP). IPP is readily converted to dimethylallylpyrophosphate (DMAPP). These two five carbon compounds, also called isoprenes, are the building blocks for the synthesis of cholesterol and related compounds. This pathway is known as the isoprenoid pathway. It proceeds in the direction of cholesterol starting with the joining of IPP and DMAPP to form geranyl-pyrophosphate. Geranyl-pyrophosphate combines with another IPP to make farnesyl-pyrophosphate, a 15-carbon compound. Two farnesyl-pyrophosphates join to create the 30-carbon compound known as squalene. Squalene, in a complicated rearrangement involving reduction and molecular oxygen forms a cyclic intermediate known as lanosterol that resembles cholesterol. Conversion of lanosterol to cholesterol is a lengthy process involving 19 steps that occur in the endoplasmic reticulum. Branching from cholesterol, one can form Vitamin D or the steroid hormones, which include the progestagens, androgens, estrogens, mineralocorticoids, and the glucocorticoids. The branch molecule for all of these is the cholesterol metabolite (and progestagen) known as pregnenalone. The progestagens are precursors of all of the other classes. The estrogens are derived from the androgens in an interesting reaction that required formation of an aromatic ring. The enzyme catalyzing this reaction is known as an aromatase and it is of medical significance. The growth of some tumors is stimulated by estrogens, so aromatase inhibitors are prescribed to prevent the formation of estrogens and slow tumor growth. It is worth noting that synthesis of other fat soluble vitamins and chlorophyll also branches from the isoprenoid synthesis pathway at geranylpyrophosphate. Joining of two geranylgeranylpyrophosphates occurs in plants and bacteria and leads to synthesis of lycopene, which, in turn is a precursor of beta-carotene, the final precursor of Vitamin A. Vitamins E and K, as well as chlorophyll are all also synthesized from geranylgeranylpyrophosphate. Bile Acid Metabolism Another pathway from cholesterol leads to the polar bile acids, which are important for the solubilization of fat during digestion. Converting the very non-polar cholesterol to a bile acid involves oxidation of the terminal carbon on the side chain off the rings. Other alterations to increase the polarity of these compounds include hydroxylation of the rings and linkage to other polar compounds. Common bile acids include cholic acid, chenodeoxycholic acid, glycocholic acid, taurocholic acid, and deoxycholic acid. Another important fact about bile acids is that their synthesis reduces the amount of cholesterol available and promotes uptake of LDLs by the liver. Normally bile acids are recycled efficiently resulting in limited reduction of cholesterol levels. However, inhibitors of the recycling promote reduction of cholesterol levels.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/06%3A_Metabolism_I_-_Oxidative_Reductive_Processes/6.06%3A_Glyoxylate_Pathway.txt

In ketone body synthesis, an acetyl-CoA is split off from HMG-CoA, yielding acetoacetate, a four carbon ketone body that is somewhat unstable, chemically. It will decarboxylate spontaneously to some extent to yield acetone. Ketone bodies are made when the blood levels of glucose fall very low. Ketone bodies can be converted to acetyl-CoA, which can be used for ATP synthesis via the citric acid cycle. People who are very hypoglycemic (including some diabetics) will produce ketone bodies and these are often first detected by the smell of acetone on their breath. Acetone is of virtually no use for energy production since it is not readily converted to acetyl-CoA. Consequently, cells convert acetoacetate into beta-hydroxybutyrate, which is more chemically stable. Though technically not a ketone, beta-hydroxybutyrate is frequently referred to as a ketone body. Upon arrival at a target cell, it can be oxidized back to acetoacetate with conversion to acetyl-CoA. Both acetoacetate and beta-hydroxybutyrate can cross the blood-brain barrier and provide important energy for the brain when glucose is limiting. 6.10: Prostaglandin Synthesis The pathway for making prostaglandins is an extension of the fatty acid synthesis pathway (Figure 6.10.1). Prostaglandins, molecules associated with localized pain, are synthesized in cells from arachidonic acid (see previous page) which has been cleaved from membrane lipids. The enzyme catalyzing their synthesis is known as prostaglandin synthase, but is more commonly referred to as a cyclooxygenase (or COX) enzyme. Inhibition of the action of this enzyme is a strategy of non- steroidal pain relievers (also called NSAIDs), such as aspirin or ibuprofen. Inhibition of the release of arachidonic acid from membranes is the mechanism of action of steroidal anti-inffammatories, which inhibit the phospholipase $\text{A}_2$($\text{PLA}_2$) that catalyzes the cleavage reaction. 6.11: Fatty Acid Oxidation Breakdown of fats yields fatty acids and glycerol. Glycerol can be readily converted to DHAP for oxidation in glycolysis or synthesis into glucose in gluconeogenesis. Fatty acids are broken down in two carbon units of acetyl-CoA. To be oxidized, they must be transported through the cytoplasm attached to coenzyme A and moved into mitochondria. The latter step requires removal of the CoA and attachment of the fatty acid to a molecule of carnitine. The carnitine complex is transported across the inner membrane of the mitochondrion after which the fatty acid is reattached to coenzyme A in the mitochondrial matrix. The process of fatty acid oxidation, called beta oxidation, is fairly simple. The reactions all occur between carbons 2 and 3 (with #1 being the one linked to the CoA) and sequentially include the following: 1. dehydrogenation to create $\text{FADH}_2$ and a fatty acyl group with a double bond in the trans configuration; 2. hydration across the double bond to put a hydroxyl group on carbon 3 in the L configuration; 3. oxidation of the hydroxyl group to make a ketone; and 4. thiolytic cleavage to release acetyl-CoA and a fatty acid two carbons shorter than the starting one. Unsaturated fatty acids complicate the picture a bit (see below), primarily because they have cis bonds, for the most part, if they are of biological origin and these must be converted to the relevant trans intermediate made in step 1. Sometimes the bond must be moved down the chain, as well, in order to be positioned properly. Two enzymes (described below) handle all the necessary isomerizations and moves necessary to oxidize all of the unsaturated fatty acids. Enzymes of Beta Oxidation The reactions of fatty acid oxidation are notable in mirroring the oxidations in the latter half of the citric acid cycle – dehydrogenation of succinate to make a transdouble bond (fumarate), hydration across the double bond to make L-malate and oxidation of the hydroxyl to make a ketone (oxaloacetate). Two of the enzymes of beta-oxidation are notable. The first is acyl-CoA dehydrogenase, which catalyzes the initial dehydrogenation and yields FADH2. It comes in three different forms – ones that work on long, medium, or short chain length fatty acids. The first of these is sequestered in the peroxisome of animals whereas the others are found in the mitochondria. Plants and yeast perform beta oxidation exclusively in the peroxisome. The most interesting of the acyl-CoA dehydrogenases is the one that works on medium length fatty acids. This one, which is the one most commonly deficient in animals, has been linked to sudden infant death syndrome. Reactions two and three in beta oxidation are catalyzed by enoyl-CoA hydratase and 3-hydroxyacyl-CoA dehydrogenase, respectively. The latter reaction yields an NADH. The final enzyme of beta oxidation is thiolase and this enzyme is notable in not only catalyzing the formation of acetyl-CoAs in beta oxidation, but also catalyzing the joining of two acetyl-CoAs (essentially the reversal of the last step of beta oxidation) to form acetoacetyl-CoA– essential for the pathways of ketone body synthesis and cholesterol biosynthesis. Oxidation of Odd-Chain Fatty Acids Though most fatty acids of biological origin have even numbers of carbons, not all of them do. Oxidation of fatty acids with odd numbers of carbons ultimately produces an intermediate with three carbons called propionyl-CoA, which cannot be oxidized further in the beta-oxidation pathway. Metabolism of this intermediate is odd. Sequentially, the following steps occur: 1. carboxylation to make D-methylmalonyl-CoA; 2. isomerization to L-methylmalonyl-CoA; 3. rearrangement to form succinyl-CoA. The last step of the process utilizes the enzyme methylmalonyl-CoA mutase, which uses the $\text{B}_12$ coenzyme in its catalytic cycle. Succinyl-CoA can then be metabolized in the citric acid cycle. Unsaturated Fatty Acid Oxidation As noted above, oxidation of unsaturated fatty acids requires two additional enzymes to the complement of enzymes for beta oxidation. If the beta oxidation of the fatty acid produces an intermediate with a cis bond between carbons three and four, cis-$\Delta$3-Enoyl-CoA Isomerase will convert the bond to a trans bond between carbons two and three and beta oxidation can proceed as normal. On the other hand, if beta oxidation produces an intermediate with a cis double bond between carbons four and five, the first step of beta oxidation (dehydrogenation between carbons two and three) occurs to produce an intermediate with a trans double bond between carbons two and three and a cis double bond between carbons four and five. The enzyme 2,4 dienoyl CoA reductase reduces this intermediate (using NADPH) to one with a single cis bond between carbons three and four. This intermediate is then identical to the one acted on by cis-$\Delta$3-Enoyl-CoA Isomerase above, which converts it into a regular beta oxidation intermediate, as noted above. Alpha Oxidation Yet another consideration for oxidation of fatty acids is alpha oxidation. This pathway is necessary for catabolism of fatty acids that have branches in their chains. For example, breakdown of chlorophyll’s phytol group yields phytanic acid, which undergoes hydroxylation and oxidation on carbon number two (in contrast to carbon three of beta oxidation), followed by decarboxylation and production of a branched intermediate that can be further oxidized by the beta oxidation pathway. Though alpha oxidation is a relatively minor metabolic pathway, the inability to perform the reactions of the pathway leads to Refsum’s disease where accumulation of phytanic acid leads to neurological damage.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/06%3A_Metabolism_I_-_Oxidative_Reductive_Processes/6.09%3A_Ketone_Body_Synthesis.txt

Synthesis of fatty acids occurs in the cytoplasm and endoplasmic reticulum of the cell and is chemically similar to the beta-oxidation process, but with a couple of key differences. The first of these occur in preparing substrates for the reactions that grow the fatty acid. Transport of acetyl-CoA from the mitochondria occurs when it begins to build up. Two molecules can play roles in moving it to the cytoplasm – citrate and acetylcarnitine. Joining of oxaloacetate with acetyl-CoA in the mitochondrion creates citrate which moves across the membrane, followed by action of citrate lyase in the cytoplasm of the cell to release acetyl-CoA and oxaloacetate. Additionally, when free acetyl-CoA accumulates in the mitochondrion, it may combine with carnitine and be transported out to the cytoplasm. Starting with two acetyl-CoA, one is converted to malonyl-CoA by carboxylation catalyzed by the enzyme acetyl-CoA carboxylase (ACC), the only regulatory enzyme of fatty acid synthesis (Figure \(1\)). Next, both molecules have their CoA portions replaced by a carrier protein known as ACP (acyl-carrier protein) to form acetyl-ACP and malonyl-ACP. Joining of a fatty acyl-ACP (in this case, acetyl-ACP) with malonyl-ACP splits out the carboxyl that was added and creates the intermediate at the upper right in the figure at left. Figure \(1\): Fatty Acid Synthesis From this point forward, the chemical reactions resemble those of beta oxidation reversed. First, the ketone is reduced to a hydroxyl using NADPH. In contrast to the hydroxylated intermediate of beta oxidation, the beta intermediate here is in the D-configuration. Next, water is removed from carbons 2 and 3 of the hydroxyl intermediate to produce a trans doubled bonded molecule. Last, the double bond is hydrogenated to yield a saturated intermediate. The process cycles with the addition of another malonyl-ACP to the growing chain until ultimately an intermediate with 16 carbons is produced (palmitoyl-CoA). At this point, the cytoplasmic synthesis ceases. Enzymes of Fatty Acid Synthesis Acetyl-CoA carboxylase, which catalyzes synthesis of malonyl-CoA, is the only regulated enzyme in fatty acid synthesis. Its regulation involves both allosteric control and covalent modification. The enzyme is known to be phosphorylated by both AMP Kinase and Protein Kinase A. Dephosphorylation is stimulated by phosphatases activated by insulin binding. Dephosphorylation activates the enzyme and favors its assembly into a long polymer, while phosphorylation reverses the process.Citrate acts as an allosteric activator and may also favor polymerization. Palmitoyl-CoA allosterically inactivates it. In animals, six different catalytic activities necessary for the remaining catalytic actions to fully make palmitoyl-CoA are contained in a single complex called Fatty Acid Synthase (Figure \(2\)). These include transacylases for swapping CoA with ACP on acetyl-CoA and malonyl-CoA; a synthase to catalyze addition of the two carbon unit from the three carbon malonyl-ACP in the first step of the elongation process; a reductase to reduce the ketone; a dehydrase to catalyze removal of water, and a reductase to reduce the trans double bond. In bacteria, these activities are found on separate enzymes and are not part of a complex. Elongation of Fatty Acids Elongation to make fatty acids longer than 16 carbons occurs in the endoplasmic reticulum and is catalyzed by enzymes described as elongases. Mitochondria also can elongate fatty acids, but their starting materials are generally shorter than 16 carbons long. The mechanisms in both environments are similar to those in the cytoplasm (a malonyl group is used to add two carbons, for example), but CoA is attached to the intermediates, not ACP. Further, whereas cytoplasmic synthesis employs the fatty acid synthase complex (Figure \(2\)), the enzymes in these organelles are separable and not part of a complex. Desaturation of Fatty Acids Fatty acids are synthesized in the saturated form and desaturation occurs later. Enzymes called desaturases catalyze the formation of cis double bonds in mature fatty acids. These enzymes are found in the endoplasmic reticulum. Animals are limited in the desaturated fatty acids they can make, due to an inability to catalyze reactions beyond carbons 9 and 10. Thus, humans can make oleic acid, but cannot synthesis linoleic acid or linolenic acid. Consequently, these two must be provided in the diet and are referred to as essential fatty acids. 6.13: Metabolism of Fat Breakdown of fat in adipocytes requires catalytic action of three enzymes, hormone sensitive triacylglycerol lipase (called LIPE) to remove the first fatty acid from the fat, diglyceride lipase to remove the second one and monoglyceride lipase to remove the third. Of these, only LIPE is regulated and it appears to be the rate limiting reaction. Synthesis of fat starting with glycerol-3-phosphate requires action of acyl transferase enzymes, such as glycerol-3-phosphate acyl transferase, which catalyze addition of fatty acids to the glycerol backbone. Interestingly, there appear to be few controls of the metabolism of fatty acids. The primary control of their oxidation is availability. One way to control that is by control of the breakdown of fat. This process, which can be stimulated by the epinephrine kinase cascade, is controlled through LIPE, found in adipocytes (fat-containing cells). Breakdown of fat in apidocytes requires action of three enzymes, each hydrolyzing one fatty acid from the glycerol backbone. As noted earlier, only HSTL, which catalyzes the first hydrolysis, is regulated. Synthesis of fat requires glycerol-3-phosphate (or DHAP) and three fatty acids. In the first reaction, glycerol-3-phosphate is esterified at position 1 with a fatty acid, followed by a duplicate reaction at position 2 to make phosphatidic acid. This molecule, which is an intermediate in the synthesis of both fats and phosphoglycerides, gets dephosphorylated to form diacylglycerol before the third esterification to make a fat. Glycerophospholipid Metabolism Phosphatidic acid, as noted above, is an important intermediate in the metabolism of glycerophospholipids. These compounds, which are important membrane constituents, can be synthesized in several ways. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University) 6.14: Connections to Other Pathways There are several connections between metabolism of fats and fatty acids to other metabolic pathways. As noted, phosphatidic acid is an intermediate in the synthesis of triacylglycerols, as well as of other lipids, including phosphoglycerides. Diacylglycerol (DAG), which is an intermediate in fat synthesis, also acts as a messenger in some signaling systems. Fatty acids twenty carbons long based on arachidonic acid (also called eicosanoids) are precursors of the classes of molecules known as leukotrienes and prostaglandins. The latter, in turn, are precursors of the class of molecules known as thromboxanes. The ultimate products of beta oxidation are acetyl-CoA molecules and these can be assembled by the enzyme thiolase to make acetoacetyl-CoA, which is a precursor of both ketone bodies and the isoprenoids, a broad category of compounds that include steroid hormones, cholesterol, bile acids, and the fat soluble vitamins, among others.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/06%3A_Metabolism_I_-_Oxidative_Reductive_Processes/6.12%3A_Fatty_Acid_Synthesis.txt

In the last chapter, we focused on metabolic pathways that played important oxidative/reductive roles relative to cellular energy. In this chapter, the pathways that we cover have lesser roles from an energy perspective, but important roles, nonetheless, in catabolism and anabolism of building blocks of proteins and nucleic acids, nitrogen balance, and sugar balance. In a sense, these might be thought of as the “kitchen sink" pathways, but it should be noted that all cellular pathways are important. In this second section of metabolism, we cover metabolic pathways that do not have a strong emphasis on oxidation/reduction. • 7.1: Carbohydrate Storage and Breakdown Carbohydrates are important cellular energy sources. They provide energy quickly through glycolysis and passing of intermediates to pathways, such as the citric acid cycle, amino acid metabolism (indirectly), and the pentose phosphate pathway. It is important, therefore, to understand how these important molecules are made. • 7.2: Pentose Phospate Pathway Portions of the PPP are similar to the Calvin Cycle of plants, also known as the dark reactions of photosynthesis. We discuss these reactions separately in the next section. The primary functions of the PPP are to produce NADPH (for use in anabolic reductions), ribose-5-phosphate (for making nucleotides), and erythrose-4-phosphate (for making aromatic amino acids). Three molecular intermediates of glycolysis can funnel into PPP (or be used as usual in glycolysis). • 7.3: Calvin Cycle The Calvin Cycle occurs exclusively in photosynthetic organisms and is the part of photosynthesis referred to as the “Dark Cycle." It is in this part of the process that carbon dioxide is taken from the atmosphere and ultimately built into glucose (or other sugars). Though reduction of carbon dioxide to glucose ultimately requires electrons from twelve molecules of NADPH (and 18 ATPs). One reduction occurs 12 times (1,3 BPG to G3P) to achieve the reduction necessary to make one glucose. • 7.4: C4 Plants The Calvin Cycle is the means by which plants assimilate carbon dioxide from the atmosphere, ultimately into glucose. Plants use two general strategies for doing so. The first is employed by plants called C3 plants (most plants) and it simply involves the pathway described above. Another class of plants, called C4 plants employ a novel strategy for concentrating the CO2 prior to assimilation. • 7.5: Urea Cycle Yet another cyclic pathway important in cells is the urea cycle (Figure 7.5.1). With reactions spanning the cytoplasm and the mitochondria, the urea cycle occurs mostly in the liver and kidney. The cycle plays an important role in nitrogen balance in cells and is found in organisms that produce urea as a way to excrete excess amines. • 7.6: Nitrogen Fixation The process of nitrogen fixation is important for life on earth, because atmospheric nitrogen is ultimately the source of amines in proteins and DNA. The enzyme playing an important role in this process is called nitrogenase and it is found in certain types of anaerobic bacteria called diazotrophs. Symbiotic relationships between some plants (legumes, for example) and the nitrogen-fixing bacteria provide the plants with access to reduced nitrogen. • 7.7: Amino Acid Metabolism The pathways for the synthesis and degradation of amino acids used in proteins are the most varied among the reactions synthesizing biological building blocks. We start with some terms. First, not all organisms can synthesize all the amino acids they need. Amino acids that an organism cannot synthesize (and therefore must have in their diets) are called essential amino acids. The remaining amino acids that the body can synthesize are called non-essential. • 7.8: Amino Acid Catabolism Breakdown of glutamine by glutaminase is a source of ammonium ion in the cell. The other product is glutamate. Glutamate, of course, can be converted by a transamination reaction to alpha-ketoglutarate, which can be oxidized in the citric acid cycle. • 7.9: Nucleotide Metabolism Synthesis of ribonucleotides by the de novo method occurs in two pathways – one for purines and one for pyrimidines. What is notable about both of these pathways is that nucleotides are built from very simple building blocks. • 7.10: Pyrimidine de novo Biosynthesis Starting materials for pyrimidine biosynthesis include bicarbonate, amine from glutamine, and phosphate from ATP to make carbamoyl-phosphate (similar to the reaction of the urea cycle). Joining of carbamoyl phosphate to aspartic acid (forming carbamoyl aspartate) is catalyzed by the most important regulatory enzyme of the cycle, aspartate transcarbamoylase (also called aspartate carbamoyltransferase or ATCase). • 7.11: Purine de novo Biosynthesis Synthesis of purine nucleotides differs fundamentally from that of pyrimidine nucleotides in that the bases are built on the ribose ring. The starting material is ribose 5-phosphate, which is phosphorylated by PRPP synthetase to PRPP using two phosphates from ATP. PRPP amidotransferase catalyzes the transfer of an amine group to PRPP, replacing the pyrophosphate on carbon 1. Thus begins the synthesis of the purine ring. • 7.12: Deoxyribonucleotide de novo Biosynthesis Synthesis of deoxyribonucleotides de novo requires an interesting enzyme called ribonucleotide reductase (RNR). RNR catalyzes the formation of deoxyribonucleotides from ribonucleotides. The most common form of RNR is the Type I enzyme, whose substrates are ribonucleoside diphosphates (ADP, GDP, CDP, or UDP) and the products are deoxyribonucleoside diphosphates (dADP, dGDP, dCDP, or dUDP). Thymidine nucleotides are synthesized from dUDP. Thumbnail: Metabolic Metro Map. (CC BY-SA 4.0; Chakazul).​​​​​​ 07: Metabolism II Carbohydrates are important cellular energy sources. They provide energy quickly through glycolysis and passing of intermediates to pathways, such as the citric acid cycle, amino acid metabolism (indirectly), and the pentose phosphate pathway. It is important, therefore, to understand how these important molecules are made. Plants are notable in storing glucose for energy in the form of amylose and amylopectin (see and for structural integrity in the form of cellulose. These structures differ in that cellulose contains glucoses solely joined by beta-1,4 bonds, whereas amylose has only alpha1,4 bonds and amylopectin has alpha 1,4 and alpha 1,6 bonds.Animals store glucose primary in liver and muscle in the form of a compound related to amylopectin known as glycogen. The structural differences between glycogen and amylopectin are solely due to the frequency of the alpha 1,6 branches of glucoses. In glycogen they occur about every 10 residues instead of every 30-50, as in amylopectin. Glycogen provides an additional source of glucose besides that produced via gluconeogenesis. Because glycogen contains so many glucoses, it acts like a battery backup for the body, providing a quick source of glucose when needed and providing a place to store excess glucose when glucose concentrations in the blood rise. The branching of glycogen is an important feature of the molecule metabolically as well. Since glycogen is broken down from the "ends" of the molecule, more branches translate to more ends, and more glucose that can be released at once. Breakdown of glycogen involves 1. release of glucose-1- phosphate (G1P), 2. rearranging the remaining glycogen (as necessary) to permit continued breakdown, and 3. conversion of G1P to G6P for further metabolism. G6P can be 1) broken down in glycolysis, 2) converted to glucose by gluconeogenesis, and 3) oxidized in the pentose phosphate pathway. Just as in gluconeogenesis, the cell has a separate mechanism for glycogen synthesis that is distinct from glycogen breakdown. As noted previously, this allows the cell to separately control the reactions, avoiding futile cycles, and enabling a process to occur efficiently (synthesis of glycogen) that would not occur if it were simply the reversal of glycogen breakdown. Synthesis of glycogen starts with G1P, which is converted to an 'activated' intermediate, UDP-glucose. This activated intermediate is what 'adds' the glucose to the growing glycogen chain in a reaction catalyzed by the enzyme known as glycogen synthase. Once the glucose is added to glycogen, the glycogen molecule may need to have branches inserted in it by the enzyme known as branching enzyme. Glycogen Breakdown Glycogen phosphorylase (sometimes simply called phosphorylase) catalyzes breakdown of glycogen into Glucose-1-Phosphate (G1P). The reaction, (see above right) that produces G1P from glycogen is a phosphorolysis, not a hydrolysis reaction. The distinction is that hydrolysis reactions use water to cleave bigger molecules into smaller ones, but phosphorolysis reactions use phosphate instead for the same purpose. Note that the phosphate is just that - it does NOT come from ATP. Since ATP is not used to put phosphate on G1P, the reaction saves the cell energy. Glycogen phosphorylase will only act on non-reducing ends of a glycogen chain that are at least 5 glucoses away from a branch point. A second enzyme, Glycogen Debranching Enzyme (GDE), is therefore needed to convert alpha(1-6) branches to alpha(1-4) branches. GDE acts on glycogen branches that have reached their limit of hydrolysis with glycogen phosphorylase. GDE acts to transfer a trisaccharide from a 1,6 branch onto an adjacent 1,4 branch, leaving a single glucose at the 1,6 branch. Note that the enzyme also catalyzes the hydrolysis of the remaining glucose at the 1,6 branch point. Thus, the breakdown products from glycogen are G1P and glucose (mostly G1P, however). Glucose can, of course, be converted to Glucose-6-Phosphate (G6P) as the first step in glycolysis by either hexokinase or glucokinase. G1P can be converted to G6P by action of an enzyme called phosphoglucomutase. This reaction is readily reversible, allowing G6P and G1P to be interconverted as the concentration of one or the other increases. This is important, because phosphoglucomutase is needed to form G1P for glycogen biosynthesis. Regulation of Glycogen Metabolism Regulation of glycogen metabolism is complex, occurring both allosterically and via hormone-receptor controlled events that result in protein phosphorylation or dephosphorylation. In order to avoid a futile cycle of glycogen synthesis and breakdown simultaneously, cells have evolved an elaborate set of controls that ensure only one pathway is primarily active at a time. Regulation of glycogen metabolism is managed by the enzymes glycogen phosphorylase and glycogen synthase. Glycogen phosphorylase is regulated by both allosteric factors (ATP, G6P, AMP, and glucose) and by covalent modification (phosphorylation/dephosphorylation). Its regulation is consistent with the energy needs of the cell. High energy substrates (ATP, G6P, glucose) allosterically inhibit GP, while low energy substrates (AMP, others) allosterically activate it. GPa/GPb Allosteric Regulation Glycogen phosphorylase exists in two different covalent forms – one form with phosphate (called GPa here) and one form lacking phosphate (GPb here). GPb is converted to GPa by phosphorylation by an enzyme known as phosphorylase kinase. GPa and GPb can each exist in an 'R' state and a 'T' state. For both GPa and GPb, the R state is the more active form of the enzyme. GPa's negative allosteric effector (glucose) is usually not abundant in cells, so GPa does not .ip into the T state often. There is no positive allosteric effector of GPa, so when glucose is absent, GPa automatically flips into the R (more active) state. GPb can convert from the T state to the GPb R state by binding AMP. Unless a cell is low in energy, AMP concentration is low. Thus GPb is not converted to the R state very often. On the other hand, ATP and/or G6P are usually present at high enough concentration in cells that GPb is readily flipped into the T state. GPa/GPb Covalent Conversion Because the relative amounts of GPa and GPb largely govern the overall process of glycogen breakdown, it is important to understand the controls on the enzymes that interconvert GPa and GPb. This is accomplished by the enzyme Phosphorylase Kinase, which transfers phosphates from 2 ATPs to GPb to form GPa. Phosphorylase kinase has two covalent forms – phosphorylated (active) and dephosphorylated (inactive). It is phosphorylated by the enzyme Protein Kinase A (PKA). Another way to activate the enzyme is with calcium. Phosphorylase kinase is dephosphorylated by the same enzyme, phosphoprotein phosphatase, that removes phosphate from GPa. PKA is activated by cAMP, which is, in turn produced by adenylate cyclase after activation by a G-protein. G-proteins are activated ultimately by binding of ligands to specific 7-TM receptors, also known as G-protein coupled receptors. These are discussed in greater detail in Chapter 8. Common ligands for these receptors include epinephrine (binds beta-adrenergic receptor) and glucagon (binds glucagon receptor). Epinephrine exerts it greatest effects on muscle and glucagon works preferentially on the liver. Turning Off Glycogen Breakdown Turning OFF signals is as important, if not more so, than turning them ON. The steps in the glycogen breakdown regulatory pathway can be reversed at several levels. First, the ligand can leave the receptor. Second, the G-proteins have an inherent GTPase activity that serves to turn them off over time. Third, cells have phosphodiesterase (inhibited by caffeine) for breaking down cAMP. Fourth, an enzyme known as phosphoprotein phosphatase can remove phosphates from phosphorylase kinase (inactivating it) AND from GPa, converting it to the much less active GPb. Glycogen Synthesis The anabolic pathway contrasting with glycogen breakdown is that of glycogen synthesis. Just as cells reciprocally regulate glycolysis and gluconeogenesis to prevent a futile cycle, so too do cells use reciprocal schemes to regulate glycogen breakdown and synthesis. Let us first consider the steps in glycogen synthesis. 1) Glycogen synthesis from glucose involves phosphorylation to form G6P, and isomerization to form G1P (using phosphoglucomutase common to glycogen breakdown). G1P is reacted with UTP to form UDP-glucose in a reaction catalyzed by UDP-glucose pyrophosphorylase. Glycogen synthase catalyzes synthesis of glycogen by joining carbon #1 of the UDPG-derived glucose onto the carbon #4 of the non-reducing end of a glycogen chain. to form the familiar alpha(1,4) glycogen links. Another product of the reaction is UDP. It is also worth noting in passing that glycogen synthase will only add glucose units from UDPG onto a preexisting glycogen chain that has at least four glucose residues. Linkage of the first few glucose units to form the minimal "primer" needed for glycogen synthase recognition is catalyzed by a protein called glycogenin, which attaches to the first glucose and catalyzes linkage of the first eight glucoses by alpha(1,4) bonds. 3) The characteristic alpha(1,6) branches of glycogen are the products of an enzyme known as Branching Enzyme. Branching Enzyme breaks alpha(1,4) chains and carries the broken chain to the carbon #6 and forms an alpha(1,6) linkage. Regulation of Glycogen Synthesis The regulation of glycogen biosynthesis is reciprocal to that of glycogen breakdown. It also has a cascading covalent modification system similar to the glycogen breakdown system described above. In fact, part of the system is identical to glycogen breakdown. Epinephrine or glucagon signaling can stimulate adenylate cyclase to make cAMP, which activates PKA, which activates phosphorylase kinase. In glycogen breakdown, phosphorylase kinase phosphorylates GPb to the more active form, GPa. In glycogen synthesis, protein kinase A phosphorylates the active form of glycogen synthase (GSa), and converts it into the usually inactive b form (called GSb). Note the conventions for glycogen synthase and glycogen phosphorylase. For both enzymes, the more active forms are called the 'a' forms (GPa and GSa) and the less active forms are called the 'b' forms (GPb and GSb). The major difference, however, is that GPa has a phosphate, but GSa does not and GPb has no phosphate, but GSb does. Thus phosphorylation and dephosphorylation have opposite effects on the enzymes of glycogen metabolism. This is the hallmark of reciprocal regulation. It is of note that the less active glycogen synthase form, GSb, can be activated by G6P. Recall that G6P had the exactly opposite effect on GPb. Glycogen synthase, glycogen phosphorylase (and phosphorylase kinase) can be dephosphorylated by several enzymes called phosphatases. One of these is called Protein Phosphatase and it is activated when insulin binds to a receptor in the cell membrane. It causes PP to be activated, stimulating dephosphorylation, and thus activating glycogen synthesis and inhibiting glycogen breakdown. Again, there is reciprocal regulation of glycogen synthesis and degradation. Maintaining Blood Glucose Levels After a meal, blood glucose levels rise and insulin is released. It simultaneously stimulates uptake of glucose by cells and incorporation of it into glycogen by activation of glycogen synthase and inactivation of glycogen phosphorylase. When blood glucose levels fall, GPa gets activated (stimulating glycogen breakdown to raise blood glucose) and GSb is formed (stopping glycogen synthesis).

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The Pentose Phosphate Pathway (PPP) is one that many students are confused by. Perhaps the reason for this is that it does not really have a single direction in which it proceeds, as will be apparent below. Portions of the PPP are similar to the Calvin Cycle of plants, also known as the dark reactions of photosynthesis. We discuss these reactions separately in the next section. The primary functions of the PPP are to produce NADPH (for use in anabolic reductions), ribose-5-phosphate (for making nucleotides), and erythrose-4-phosphate (for making aromatic amino acids). Three molecular intermediates of glycolysis can funnel into PPP (or be used as usual in glycolysis). They include G6P, fructose-6-phosphate (in two places), and glyceraldehyde-3-phosphate (also in two places). A starting point for the pathway (though there are other entry points) is the oxidative phase. It includes two reactions generating NADPH. In the first of these, oxidation of glucose-6-phosphate (catalyzed by glucose-6-phosphate dehydrogenase), produces NADPH and 6-phosphogluconolactone. 6-phosphogluconolactone spontaneously gains water and loses a proton to become 6-phosphogluconate. Oxidation of this produces ribulose-5-phosphate and another NADPH and releases $\text{CO}_2$. The remaining steps of the pathway are known as the non-oxidative phase and involve interconversion of sugar phosphates. For example, ribulose-5-phosphate is converted to ribose-5-phosphate (R5P) by the enzyme ribulose-5-phosphate isomerase. Alternatively, ribulose-5-phosphate can be converted to xylulose-5-phosphate (Xu5P). R5P and Xu5P (10 carbons total) can be combined and rearranged by transketolase to produce intermediates with 3 and 7 carbons (glyceraldehyde-3-phosphate and sedoheptulose-7-phosphate, respectively). These last two molecules can, in turn be rearranged by transaldolase into 6 and 4 carbon sugars (fructose-6-phosphate and erythrose-4-phosphate, respectively). Further, the erythrose-4-phosphate can swap parts with Xu5P to create glyceraldehyde-3-phosphate and fructose-6-phosphate. It is important to recognize that the PPP pathway is not a “top-down" pathway, with all the intermediates derived from a starting G6P. All of the reactions are reversible, so that, for example, fructose-6-phosphate and glyceraldehyde-3-phosphate from glycolysis can reverse the last reaction of the previous paragraph to provide a means of synthesizing ribose-5-phosphate non-oxidatively. The pathway also provides a mechanism to cells for metabolizing sugars, such as Xu5P and ribulose-5-phosphate. In the bottom line of the pathway, the direction the pathway goes and the intermediates it produces are determined by the needs of, and intermediates available to, the cell. As noted above, the pathway connects in three places with glycolysis. In non- plant cells, the PPP pathway occurs in the cytoplasm (along with glycolysis), so considerable “intermingling" of intermediates can and does occur. Erythrose-4-phosphate is an important precursor of aromatic amino acids and ribose-5-phosphate is an essential precursor for making nucleotides. 7.03: Calvin Cycle The Calvin Cycle occurs exclusively in photosynthetic organisms and is the part of photosynthesis referred to as the “Dark Cycle." It is in this part of the process that carbon dioxide is taken from the atmosphere and ultimately built into glucose (or other sugars). Though reduction of carbon dioxide to glucose ultimately requires electrons from twelve molecules of NADPH (and 18 ATPs), it is a bit confusing because one reduction occurs 12 times (1,3 BPG to G3P) to achieve the reduction necessary to make one glucose. One of the reasons students find the pathway a bit confusing is because the carbon dioxides are absorbed one at a time into six different molecules of ribulose-1,5-bisphosphate (Ru1,5BP). At no point are the six carbons ever together in the same molecule to make a single glucose. Instead, six molecules of Ru1,5BP (30 carbons) gain six more carbons via carbon dioxide and then split into 12 molecules of 3-phosphoglycerate (36 carbons). The gain of six carbons allows two three carbon molecules to be produced in excess for each turn of the cycle. These two molecules molecules are then converted into glucose using the enzymes of gluconeogenesis. Like the citric acid cycle, the Calvin Cycle doesn’t really have a starting or ending point, but can we think of the first reaction as the fixation of carbon dioxide to Ru1,5BP. This reaction is catalyzed by the enzyme known as ribulose-1,5bisphosphate carboxylase (RUBISCO). The resulting six carbon intermediate is unstable and each Ru1,5BP is rapidly converted to 3-phosphoglycerate. As noted, if one starts with 6 molecules of Ru1,5BP and makes 12 molecules of 3-PG, the extra 6 carbons that are a part of the cycle can be shunted off as two three-carbon molecules of glyceraldehyde-3-phosphate (GA3P) to gluconeogenesis, leaving behind 10 molecules of GA3P to be reconverted into 6 molecules of Ru1,5BP. That part of the pathway requires multiple steps, but only utilizes two enzymes unique to plants - sedoheptulose-1,7bisphosphatase and phosphoribulokinase. RUBISCO is the third enzyme of the pathway that is unique to plants. All of the other enzymes of the pathway are common to plants and animals and include some found in the pentose phosphate pathway and gluconeogenesis.

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The Calvin Cycle is the means by which plants assimilate carbon dioxide from the atmosphere, ultimately into glucose. Plants use two general strategies for doing so. The first is employed by plants called C3 plants (most plants) and it simply involves the pathway described above. Another class of plants, called C4 plants employ a novel strategy for concentrating the \(\ce{CO2}\) prior to assimilation. C4 plants are generally found in hot, dry environments where conditions favor the wasteful photorespiration reactions of RUBISCO, as well as loss of water. In these plants, carbon dioxide is captured in special mesophyll cells first by phosphoenolpyruvate (PEP) to make oxaloacetate. The oxaloacetate is converted to malate and transported into bundle sheath cells where the carbon dioxide is released and it is captured by ribulose-1,5-bisphosphate, as in C3 plants and the Calvin Cycle proceeds from there. The advantage of this scheme is that it allows concentration of carbon dioxide while minimizing loss of water and photorespiration. 7.05: Urea Cycle Yet another cyclic pathway important in cells is the urea cycle (Figure 7.5.1). With reactions spanning the cytoplasm and the mitochondria, the urea cycle occurs mostly in the liver and kidney. The cycle plays an important role in nitrogen balance in cells and is found in organisms that produce urea as a way to excrete excess amines. The cycle scavenges free ammonia (as ammonium ion) which is toxic if it accumulates. The capture reaction also requires ATP, and bicarbonate, and the product is carbamoyl phosphate. This molecule is combined with the non-protein amino acid known as ornithine to make another non-protein amino acid known as citrulline. Addition of aspartate to citrulline creates argninosuccinate, which splits off a fumarate, creating arginine (a source of arginine). If arginine is not needed, it can be hydrolyzed to yield urea (excreted) an ornithine, thus completing the cycle. The first two reactions described here occur in the mitochondrion and the remaining ones occur in the cytoplasm. Molecules of the urea cycle intersecting other pathways include fumarate (citric acid cycle), aspartate (amino acid metabolism), arginine (amino acid metabolism), and ammonia (amino acid metabolism). 7.06: Nitrogen Fixation The process of nitrogen fixation is important for life on earth, because atmospheric nitrogen is ultimately the source of amines in proteins and DNA. The enzyme playing an important role in this process is called nitrogenase and it is found in certain types of anaerobic bacteria called diazotrophs. Symbiotic relationships between some plants (legumes, for example) and the nitrogen-fixing bacteria provide the plants with access to reduced nitrogen. The overall reduction reaction catalyzed by nitrogenase is $\text{N}_2 + 6\text{H}^+ + 6\text{e}^- \rightarrow 2\text{NH}_3$ In these reactions, the hydrolysis of 16 ATP is required. The ammonia can be assimilated into glutamate and other molecules. Enzymes performing nitrogenase catalysis are very susceptible to oxygen and must be kept free of it. It is for this reason that most nitrogen-fixing bacteria are anaerobic. Movement of amines through biological systems occurs largely by the process of transmination, discussed below in amino acid metabolism. 7.07: Amino Acid Metabolism The pathways for the synthesis and degradation of amino acids used in proteins are the most varied among the reactions synthesizing biological building blocks. We start with some terms. First, not all organisms can synthesize all the amino acids they need. Amino acids that an organism cannot synthesize (and therefore must have in their diets) are called essential amino acids. The remaining amino acids that the body can synthesize are called non-essential. Amino acids are also divided according to the pathways involved in their degradation; there are three general categories. Ones that yield intermediates in the glycolysis pathway are called glucogenic and those that yield intermediates of acetyl-CoA or acetoacetate are called ketogenic. Those that involve both are called glucogenic and ketogenic. An important general consideration in amino acid metabolism is that of transamination. In this process, an exchange of amine and oxygen between an amino acid and an alpha-ketoacid occurs (see below) $\text{Alpha-ketoacid}+ \text{amino acid} \leftrightarrow \text{amino acid}+ \text{alpha-ketoacid}$ An example reaction follows $\text{Pyruvate}+ \text{Aspartic acid} \leftrightarrow \text{Alanine}+ \text{Oxaloacetate}$ This reaction is catalyzed by an enzyme known as a transaminase. Amino acids, such as glutamate, can also gain nitrogen directly from ammonium ion, as shown below $\text{Alpha-ketoglutarate} + \text{NH}_4^+ \leftrightarrow \text{Glutamate}$ This reaction can occur, for example, in nitrifying bacteria, and in places where ammonia waste is produced. Many amino acids can be synthesized from citric acid cycle intermediates. For example, synthesis of the non-essential amino acids occurs as follows: aspartic acid can be made by transamination of oxaloacetate. Glutamate comes from transamination of alpha-ketoglutarate. Pyruvate, as noted, is a precursor of alanine (via transamination). Amino acids that can be made from glutamate include glutamine (by addition of an additional ammonium ion), proline, and arginine, Asparagine is made from aspartate by addition of ammonium ion also. Serine is formed from 3-phosphoglycerate and is itself the precursor of both glycine and cysteine. Cysteine and serine are also made from methionine. Tyrosine is made by hydroxylation of phenylalanine.

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Breakdown of glutamine by glutaminase is a source of ammonium ion in the cell. The other product is glutamate. Glutamate, of course, can be converted by a transamination reaction to alpha-ketoglutarate, which can be oxidized in the citric acid cycle. • Asparagine can similarly be broken to ammonium and aspartate by asparaginase and aspartate can be converted by transamination to oxaloacetate for oxidation in the citric acid cycle. • Alanine is converted to pyruvate in a transamination reaction, making it glucogenic. • Arginine is hydrolzyed in the urea cycle to yield urea and ornithine. • Proline is catabolized to glutamate in a reversal of its synthesis pathway. • Serine donates a carbon to form a folate and the other product of the reaction is glycine, which is itself oxidized to carbon dioxide and ammonia. Glycine can also be converted back to serine, which can also be converted back to 3-phosphoglycerate or pyruvate. • Threonine can be broken down in three pathways, though only two are relevant for humans. One pathway leads to acetyl-CoA and glycine. The other leads to pyruvate. • Cysteine can be broken down in several ways. The simplest occurs in the liver, where a desulfurase can act on it to yield hydrogen sulfide and pyruvate. • Methionine can be converted to cysteine for further metabolism. It can be converted to succinyl-CoA for oxidation in the citric acid cycle. It can also be converted to S-Adenosyl-Methionine (SAM), a carbon donor. • Isoleucine and valine can also be converted to succinyl-CoA after conversion first to propionyl-CoA. Since conversion of propionyl-CoA to succinyl-CoA requires vitamin $\text{B}_{12}$, catabolism of these amino acids also requires the vitamin. • Phenylalanine is converted during catabolism to tyrosine, which is degraded ultimately to fumarate and acetoacetate. Thus, both of these amino acids are glucogenic and ketogenic. Tyrosine can also be converted to dopamine, norepinephrine, and epinephrine. • Leucine and lysine can be catabolized to acetoacetate and acetyl-CoA. Lysine is also an important precursor of carnitine. • Histidine can be catabolized by bacteria in intestines to histamine, which causes construction or dilation of various blood vessels when in excess. • Tryptophan’s catabolism is complex, but can proceed through alanine, acetoacetate and acetyl-CoA In summary, the following are metabolized to pyruvate – alanine, cysteine, glycine, serine, and threonine • Oxaloacetate is produced from aspartate and asparagine • Succinyl-CoA is produced from isoleucine, valine, and methionine • Alpha-ketoglutarate is produced from arginine, glutamate, glutamine, histidine and proline. • Phenylalanine and tyrosine are broken down to fumarate and acetoacetate • Leucine and lysine yield acetoacetate and acetyl-CoA. • Tryptophan leads to alanine, acetoacetate and acetyl-CoA. Last, amino acids, besides being incorporated into proteins, serve as precursors of important compounds, including serotonin (from tryptophan), porphyrin heme (from glycine), nitric oxide (from arginine), and nucleotides (from aspartate, glycine, and glutamine). 7.09: Nucleotide Metabolism Arguably, the most interesting metabolic pathways from the perspective of regulation are those leading to the synthesis of nucleotides. We shall consider ribonucleotide synthesis from from scratch ( de novo synthesis). Deoxyribonucleotide synthesis from ribonucleotides will be considered separately. Synthesis of ribonucleotides by the de novo method occurs in two pathways – one for purines and one for pyrimidines. What is notable about both of these pathways is that nucleotides are built from very simple building blocks. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University) 7.10: Pyrimidine de novo Biosynthesis Starting materials for pyrimidine biosynthesis (Figure 7.10.1) include bicarbonate, amine from glutamine, and phosphate from ATP to make carbamoyl-phosphate (similar to the reaction of the urea cycle). Joining of carbamoyl phosphate to aspartic acid (forming carbamoyl aspartate) is catalyzed by the most important regulatory enzyme of the cycle, aspartate transcarbamoylase (also called aspartate carbamoyltransferase or ATCase). ATCase is regulated by three compounds. One of these (aspartate) is a substrate and it activates the enzyme by binding to the catalytic site and favoring the enzyme’s R state. The other two regulators bind to regulatory subunits of the enzyme and either inhibit (CTP) or activate (ATP) the enzyme. The reaction product, carbamoyl aspartate, is transformed in two reactions to orotic acid, which is, in turn combined with phosphoribosylpyrophosphate PRPP). The product of that reaction, orotidyl monophosphate (OMP) is decarboxylated to form the first pyrimidine nucleotide, UMP. Conversion of UMP to UDP is catalyzed by nucleoside monophosphate kinases (NMPs) and UDP is converted to UTP by nucleoside diphosphokinase (NDPK). UDP (like all of the nucleoside diphosphates) is a branch point to deoxyribonucleoside diphosphates, catalyzed by ribonucleotide reductases, which are discussed later. UTP is converted to CTP by CTP synthase. This enzyme, which uses an amino group from glutamine for the reaction, serves to balance the relative amounts of CTP and UTP, thanks to inhibition by excess CTP.

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Synthesis of purine nucleotides differs fundamentally from that of pyrimidine nucleotides in that the bases are built on the ribose ring. The starting material is ribose 5-phosphate, which is phosphorylated by PRPP synthetase to PRPP using two phosphates from ATP. PRPP amidotransferase catalyzes the transfer of an amine group to PRPP, replacing the pyrophosphate on carbon 1. Thus begins the synthesis of the purine ring. PRPP amidotransferase is regulated partly by GMP and partly by AMP. The presence of either of these can reduce the enzyme’s activity. Only when both are present is the enzyme fully inactivated. Subsequent reactions include adding glycine, adding carbon (from N 10-formyltetrahydrofolate), adding amine (from glutamine), closing of the first ring, addition of carboxyl (from $\text{CO}_2$), addition of aspartate, loss of fumarate (a net gain of an amine), addition of another carbon (from $\text{N}_10$-formyltetrahydrofolate), and closing of the second ring to form inosine monophosphate (IMP). IMP is a branch point for the synthesis of the adenine and guanine nucleotides. The pathway leading from IMP to AMP involves addition of amine from asparate and requires energy from GTP. The pathway from IMP to GMP involves an oxidation and addition of an amine from glutamine. It also requires energy from ATP. The pathway leading to GMP is inhibited by its end product and the pathway to AMP is inhibited by its end product. Thus, balance of the purine nucleotides is achieved from the IMP branch point forward. It is at this point that the significance of the unusual regulation of PRPP amidotransferase becomes apparent. If there is an imbalance of AMP or GMP, the enzyme is slowed, but not stopped, thus allowing the reactions leading to IMP to proceed, albeit slowly. At IMP, the nucleotide in excess feedback inhibits its own synthesis, thus allowing the partner purine nucleotide to be made and balance to be achieved. When both nucleotides are in abundance, then PRPP amidotransferase is fully inhibited and the production of purines is stopped, thus preventing them from over-accumulating. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University) 7.12: Deoxyribonucleotide de novo Biosynthesis Synthesis of deoxyribonucleotides de novo requires an interesting enzyme called ribonucleotide reductase (RNR). RNR catalyzes the formation of deoxyribonucleotides from ribonucleotides. The most common form of RNR is the Type I enzyme, whose substrates are ribonucleoside diphosphates (ADP, GDP, CDP, or UDP) and the products are deoxyribonucleoside diphosphates (dADP, dGDP, dCDP, or dUDP). Thymidine nucleotides are synthesized from dUDP. RNR has two pairs of two identical subunits - R1 (large subunit) and R2 (small subunit). R1 has two allosteric binding sites and an active site. R2 forms a tyrosine radical necessary for the reaction mechanism of the enzyme. Because a single enzyme, RNR, is responsible for the synthesis of all four deoxyribonucleotides, it is necessary to have mechanisms to ensure that the enzyme produces the correct amounts of each dNDP. This means that the enzyme must be responsive to the levels of the each deoxynucleotide, selectively making more of those that are in short supply, and preventing synthesis of those that are abundant. These demands are met by having two separate control mechanisms, one that determines which substrate will be acted on, and another that controls the enzyme’s catalytic activity. Ribonucleotide reductase is allosterically regulated via two binding sites - a specificity binding site (binds dNTPs and controls which substrates the enzyme binds and thus, which deoxyribonucleotides are made) and an activity binding site (controls whether or not enzyme is active - ATP activates, dATP inactivates). When a deoxypyrimidine triphosphate, dTTP is abundant, it binds to the specificity site and inhibits binding and reduction of pyrimidine diphosphates (CDP and UDP) but stimulates binding and reduction of GDP by the enzyme. Conversely, binding of the deoxypurine triphosphate, ATP stimulates reduction of pyrimidine diphosphates, CDP and UDP. Students sometimes confuse the active site of RNR with the activity site. The active site is where the reaction is catalyzed, and could also be called the catalytic site, whereas the activity site is the allosteric binding site for ATP or dATP that controls whether the enzyme is active. Synthesis of dTTP by the de novo pathway takes a convoluted pathway from dUDP to dUTP to dUMP to dTMP, then dTDP, and finally dTTP. Conversion of dUMP to dTMP, requires a tetrahydrofolate derivative and the enyzme thymidylate synthase. In the process, dihydrofolate is produced and must be converted back to tetrahyrdolate in order to keep nucleotide synthesis occurring. The enzyme involved in the conversion of dihydrofolate to tetrahydrofolate, dihydrofolate reductase (DHFR), is a target of anticancer drugs like methotrexate or aminopterin, which inhibit the enzyme.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/07%3A_Metabolism_II/7.11%3A_Purine_de_novo_Biosynthesis.txt

Cells must receive and respond to signals from their surroundings. Cellular signals and the pathways through which they are passed on and amplified to produce the desired effects on their targets are the focus of this section. • 8.1: Cell Signaling How do cells receive signals from their environment and how do they communicate among themselves? It is intuitively obvious that even bacterial cells must be able to sense features of their environment, such as the presence of nutrients or toxins, if they are to survive. In addition to being able to receive information from the environment, multicellular organisms must find ways by which their cells can communicate among themselves. • 8.2: Ligand-gated Ion Channel Receptors The simplest and fastest of signal pathways is seen in the case of signals whose receptors are gated ion channels. Gated ion channels are made up of multiple transmembrane proteins that create a pore, or channel, in the cell membrane. Depending upon its type, each ion channel is specific to the passage of a particular ionic species. • 8.3: Nuclear Hormone Receptors Another type of relatively simple, though much slower, signaling is seen in pathways in which the signals are steroid hormones, like estrogen or testosterone. Steroid hormones are related to cholesterol, and as hydrophobic molecules, they are able to cross the cell membrane by themselves. • 8.4: G-protein Coupled Receptors (GPCRs) G-protein coupled receptors are involved in responses of cells to many different kinds of signals, from epinephrine, to odors, to light. In fact, a variety of physiological phenomena including vision, taste, smell and the fight-or-flight response are mediated by GPCRs. • 8.5: Receptor Tyrosine Kinases (RTKs) Receptor tyrosine kinases mediate responses to a large number of signals, including peptide hormones like insulin and growth factors like epidermal growth factor. Like the GPCRs, receptor tyrosine kinases bind a signal, then pass the message on through a series of intracellular molecules, the last of which acts on target proteins to change the state of the cell. 08: Signaling How do cells receive signals from their environment and how do they communicate among themselves? It is intuitively obvious that even bacterial cells must be able to sense features of their environment, such as the presence of nutrients or toxins, if they are to survive. In addition to being able to receive information from the environment, multicellular organisms must find ways by which their cells can communicate among themselves. Since different cells take on specialized functions in a multicellular organism, they must be able to coordinate activities perfectly like the musicians in an orchestra performing a complicated piece of music. Cells grow, divide, or differentiate in response to specific signals. They may change shape or migrate to another location. At the physiological level, cells in a multicellular organism, must respond to everything from a meal just eaten to injury, threat or the availability of a mate. They must know when to repair damage to DNA, when to undergo apoptosis (programmed cell death) and even when to regenerate a lost limb. A variety of mechanisms have arisen to ensure that cell-cell communication is not only possible, but astonishingly swift, accurate and reliable. How are signals sent between cells? Like pretty much everything that happens in cells, signaling is dependent on molecular recognition. The basic principle of cell-cell signaling is simple. A particular kind of molecule, sent by a signaling cell, is recognized and bound by a receptor protein in (or on the surface of) the target cell. The signal molecules are chemically varied- they may be proteins, short peptides, lipids, nucleotides or catecholamines, to name a few. The chemical properties of the signal determine whether its receptors are on the cell surface or intracellular. If the signal is small and hydrophobic it can cross the cell membrane and bind a receptor inside the cell. If, on the other hand, the signal is charged, or very large, it would not be able to diffuse through the plasma membrane. Such signals need receptors on the cell surface, typically transmembrane proteins that have an extracellular portion that binds the signal and an intracellular part that passes on the message within the cell. Receptors are specific for each type of signal, so each cell has many different kinds of receptors that can recognize and bind the many signals it receives. Because different cells have different sets of receptors, they respond to different signals or combinations of signals. The binding of a signal molecule to a receptor sets off a chain of events in the target cell. These events could cause change in various ways, including, but not limited to, alterations in metabolic pathways or gene expression in the target cell. How the binding of a signal to a receptor brings about change in cells is the topic of this section. Although the specific molecular components of the various signal transduction pathways differ, they all have some features in common: • The binding of a signal to its receptor is usually, though not always, followed by the generation of a new signal(s) within the cell. The process by which the original signal is converted to a different form and passed on within the cell to bring about change is called signal transduction. • Most signaling pathways have multiple signal transduction steps by which the signal is relayed through a series of molecular messengers that can amplify and distribute the message to various parts of the cell. • The last of these messengers usually interacts with a target protein(s) and changes its activity, often by phosphorylation. When a signal sets a particular pathway in motion, it is acting like an ON switch. This means that once the desired result has been obtained, the cell must have a mechanism that acts as an OFF switch. Understanding this underlying similarity is helpful, because learning the details of the different pathways becomes merely a matter of identifying which molecular component performs a particular function in each individual case. We will consider several different signal transduction pathways, each mediated by a different kind of receptor. The first two examples we will examine are those with the fewest steps between the binding of the signal by a receptor and a cellular response. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University)

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/08%3A_Signaling/8.01%3A_Cell_Signaling.txt

The simplest and fastest of signal pathways is seen in the case of signals whose receptors are gated ion channels. Gated ion channels are made up of multiple transmembrane proteins that create a pore, or channel, in the cell membrane. Depending upon its type, each ion channel is specific to the passage of a particular ionic species. The term "gated" refers to the fact that the ion channel is controlled by a "gate" which must be opened to allow the ions through. The gates are opened by the binding of an incoming signal (ligand) to the receptor, allowing the almost instantaneous passage of millions of ions from one side of the membrane to the other. Changes in the interior environment of the cell are thus brought about in microseconds and in a single step. This type of swift response is seen, for example, in neuromuscular junctions, where muscle cells respond to a message from the neighboring nerve cell. The nerve cell releases a neurotransmitter signal into the synaptic cleft, which is the space between the nerve cell and the muscle cell it is "talking to". Examples of neurotransmitter signal molecules are acetylcholine and serotonin, shown in Figure 8.2.2. When acetylcholine molecules are released into the synaptic cleft (the space between the pre- and post-synaptic cells) they diffuse rapidly till they reach their receptors on the membrane of the muscle cell. The binding of the acetylcholine to its receptor, an ion channel on the membrane of the muscle cell, causes the gate in the ion channel to open. The resulting ion flow through the channel can immediately change the membrane potential. This, in turn, can trigger other changes in the cell. The speed with which changes are brought about in neurotransmitter signaling is evident when you think about how quickly you remove your hand from a hot surface. Sensory neurons carry information to the brain from your hand on the hot surface and motor neurons signal to your muscles to move the hand, in less time than it took you to read this sentence! 8.03: Nuclear Hormone Receptors Another type of relatively simple, though much slower, signaling is seen in pathways in which the signals are steroid hormones, like estrogen or testosterone, pictured below. Steroid hormones, as you are aware, are related to cholesterol, and as hydrophobic molecules, they are able to cross the cell membrane by themselves. This is unusual, as most signals coming to cells are incapable of crossing the plasma membrane, and thus, must have cell surface receptors. By contrast, steroid hormones have receptors inside the cell (intracellular receptors). Steroid hormone receptors are proteins that belong in a family known as the nuclear receptors. Nuclear hormone receptors are proteins with a double life: they are actually dormant transcription regulators. In the absence of signal, these receptors are in the cytoplasm, complexed with other proteins (HSP in Figure 8.3.2) and inactive. When a steroid hormone enters the cell, the nuclear hormone receptor binds the hormone and dissociates from the HSP. The receptors, then, with the hormone bound, translocate into the nucleus. In the nucleus, Nuclear hormone receptors regulate the transcription of target genes by binding to their regulatory sequences (labeled HRE for hormone- response elements). The binding of the hormone-receptor complex to the regulatory elements of hormone-responsive genes modulates their expression. Because these responses involve gene expression, they are relatively slow. Most other signaling pathways, besides the two we have just discussed, involve multiple steps in which the original signal is passed on and amplified through a number of intermediate steps, before the cell responds to the signal. We will now consider two signaling pathways, each mediated by a major class of cell surface receptor- the G-protein coupled receptors (GPCRs) and the receptor tyrosine kinases (RTKs). While the specific details of the signaling pathways that follow the binding of signals to each of these receptor types are different, it is easier to learn them when you can see what the pathways have in common, namely, interaction of the signal with a receptor, followed by relaying the signal through a variable number of intermediate molecules, with the last of these molecules interacting with target protein(s) to modify their activity in the cell.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/08%3A_Signaling/8.02%3A_Ligand-gated_Ion_Channel_Receptors.txt

G-protein coupled receptors are involved in responses of cells to many different kinds of signals, from epinephrine, to odors, to light. In fact, a variety of physiological phenomena including vision, taste, smell and the fight-or-flight response are mediated by GPCRs. What are G-protein coupled receptors? G-protein coupled receptors are cell surface receptors that pass on the signals that they receive with the help of guanine nucleotide binding proteins (a.k.a. G-proteins). Before thinking any further about the signaling pathways downstream of GPCRs, it is necessary to know a few important facts about these receptors and the G-proteins that assist them. Though there are hundreds of different G-protein coupled receptors, they all have the same basic structure: they all consist of a single polypeptide chain that threads back and forth seven times through the lipid bilayer of the plasma membrane. For this reason, they are sometimes called seven- pass transmembrane (7TM) receptors. One end of the polypeptide forms the extracellular domain that binds the signal while the other end is in the cytosol of the cell. When a ligand (signal) binds the extracellular domain of a GPCR, the receptor undergoes a conformational change that allows it to interact with a G-protein that will then pass the signal on to other intermediates in the signaling pathway. What is a G-protein? As noted above, a G-protein is a guanine nucleotide-binding protein that can interact with a G-protein linked receptor. G-proteins are associated with the cytosolic side of the plasma membrane, where they are ideally situated to interact with the cytosolic tail of the GPCR, when a signal binds to the GPCR. There are many different G-proteins, all of which share a characteristic structure- they are composed of three subunits called alpha, beta and gamma (aß.). Because of this, they are sometimes called heterotrimeric G proteins (hetero=different, trimeric= having three parts). The a subunit of such proteins can bind GDP or GTP and is capable of hydrolyzing a GTP molecule bound to it into GDP. In the unstimulated state of the cell, that is, in the absence of a signal bound to the GPCR, the G-proteins are found in the trimeric form (aß. bound together) and the a subunit has a GDP molecule bound to it. With this background on the structure and general properties of the GPCRs and the G-proteins, we can now look at what happens when a signal arrives at the cell surface and binds to a GPCR. The binding of a signal molecule by the extracellular part of the G-protein linked receptor causes the cytosolic tail of the receptor to interact with, and alter the conformation of, a G-protein. This has two consequences: • First, the alpha subunit of the G- protein loses its GDP and binds a GTP instead. • Second, the G-protein breaks up into the GTP-bound a part and the ß. part. These two parts can diffuse freely along the cytosolic face of the plasma membrane and act upon their targets. What happens when G-proteins interact with their target proteins? That depends on what the target is. G-proteins interact with different kinds of target proteins, of which we will examine two major categories: Ion Channels We have earlier seen that some gated ion channels can be opened or closed by the direct binding of neurotransmitters to a receptor that is an ion-channel protein. In other cases, ion channels are regulated by the binding of G-proteins. That is, instead of the signal directly binding to the ion channel, it binds to a GPCR, which activates a G-protein that then binds and opens the ion channel. The change in the distribution of ions across the plasma membrane causes a change in the membrane potential. Specific Enzymes The interaction of G-proteins with their target enzymes can regulate the activity of the enzyme, either increasing or decreasing its activity. Often the target enzyme will pass the signal on in another form to another part of the cell. As you might imagine, this kind of response takes a little longer than the kind where an ion channel is opened instantaneously. Two well-studied examples of enzymes whose activity is regulated by a G-protein are adenylate cyclase and phospholipase C. When adenylate cyclase is activated, the molecule cAMP is produced in large amounts. When phospholipase C is activated, the molecules inositol trisphosphate (IP3) and diacylglycerol (DAG) are made. cAMP, IP3 and DAG are second messengers, small, diffusible molecules that can "spread the message" brought by the original signal, to other parts of the cell. In these cases, the binding of a signal to the GPCR activated a G- protein, which in turn, activated an enzyme that makes a second messenger that can amplify the message in the cell. We will first trace the effects of activating adenylate cyclase and the resulting increase in cAMP. What is the effect of elevated cAMP levels? cAMP molecules bind to, and activate an enzyme, protein kinase A (PKA). PKA is composed of two catalytic and two regulatory subunits that are bound tightly together. Upon binding of cAMP the catalytic subunits are released from the regulatory subunits, allowing the enzyme to carry out its function, namely phosphorylating other proteins. Thus, cAMP can regulate the activity of PKA, which in turn, by phosphorylating other proteins can change their activity. The targets of PKA may be enzymes that are activated by phosphorylation, or they may be proteins that regulate transcription. The phosphorylation of a transcriptional activator, for example, may cause the activator to bind to a regulatory sequence on DNA and to increase the transcription of the gene it controls. The activation of previously inactive enzymes alters the state of the cell by changing the reactions that are occurring within the cell. For example, the binding of epinephrine to its receptor on the cell surface, activates, through the action of G-proteins, and subsequent activation of PKA, the phosphorylation of glycogen phosphorylase. The resulting activation of glycogen phosphorylase leads to the breakdown of glycogen, releasing glucose (in the form of glucose-1-phosphate) for use by the cell. Changes in gene expression, likewise, lead to changes in the cell by altering the production of particular proteins in response to the signal. Although the steps described above seem complicated, they follow the simple pattern outlined at the beginning of this section: • Binding of signal to receptor • Several steps where the signal is passed on through intermediate molecules (G-proteins, adenylate cyclase, cAMP, and finally, PKA) • Phosphorylation of target proteins by the kinase, leading to changes in the cell. Finally, if the signal binding to the receptor serves as a switch that sets these events in motion, there must be mechanisms to turn the pathway off. The first is at the level of the G-protein. Recall that the alpha subunit of the G-protein is in its free and activated state when it has GTP bound and that it associates with the beta- gamma subunits and has a GDP bound when it is inactive. We also know that the alpha subunit has an activity that enables it to hydrolyze GTP to GDP, as shown in the figure above left. This GTP-hydrolyzing activity makes it possible for the alpha subunit, once it has completed its task, to return to its GDP bound state, re-associate with the beta-gamma part and become inactive again. The second "off switch" is further down the signaling pathway, and controls the level of cAMP. We just noted that cAMP levels increase when adenylate cyclase is activated. When its job is done, cAMP is broken down by an enzyme called phosphodiesterase. When cAMP levels drop, PKA returns to its inactive state, putting a halt to the changes brought about by the activation of adenylate cyclase by an activated G-protein. Let us now examine the events that follow the activation of Phospholipase C (PLC) by a G-protein. As we noted earlier, the activation of PLC results in the production of the second messengers IP3 and DAG. What do these molecules do? The IP3 and DAG produced by activated phospholipase C work together to activate a protein kinase. First, IP3 diffuses to the endoplasmic reticulum membrane where it binds to gated calcium ion channels. This causes calcium channels in the ER membrane to open and release large amounts of calcium into the cytoplasm from the ER lumen, as shown in the figure below. The increase in cytosolic calcium ion concentration has various effects, one of which is to activate a protein kinase called protein kinase C (C for calcium), together with the DAG made in the earlier step. Like PKA, Protein kinase C phosphorylates a variety of proteins in the cell, altering their activity and thus changing the state of the cell. The pathways leading to PKC and PKA activation following the binding of a signal to a GPCR are summarized in Figure 8.4.12.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/08%3A_Signaling/8.04%3A_G-protein_Coupled_Receptors_%28GPCRs%29.txt

Receptor tyrosine kinases mediate responses to a large number of signals, including peptide hormones like insulin and growth factors like epidermal growth factor. Like the GPCRs, receptor tyrosine kinases bind a signal, then pass the message on through a series of intracellular molecules, the last of which acts on target proteins to change the state of the cell. As the name suggests, a receptor tyrosine kinase is a cell surface receptor that also has a tyrosine kinase activity. The signal binding domain of the receptor tyrosine kinase is on the cell surface, while the tyrosine kinase enzymatic activity resides in the cytoplasmic part of the protein (see figure above). A transmembrane alpha helix connects these two regions of the receptor. What happens when signal molecules bind to receptor tyrosine kinases? Binding of signal molecules to the extracellular domains of receptor tyrosine kinase molecules causes two receptor molecules to dimerize (come together and associate). This brings the cytoplasmic tails of the receptors close to each other and causes the tyrosine kinase activity of these tails to be turned on. The activated tails then phosphorylate each other on several tyrosine residues. This is called autophosphorylation. The phosphorylation of tyrosines on the receptor tails triggers the assembly of an intracellular signaling complex on the tails. The newly phosphorylated tyrosines serve as binding sites for signaling proteins that then pass the message on to yet other proteins. An important protein that is subsequently activated by the signaling complexes on the receptor tyrosine kinases is called Ras. The Ras protein is a monomeric guanine nucleotide binding protein that is associated with the cytosolic face of the plasma membrane (in fact, it is a lot like the alpha subunit of trimeric G-proteins). Just like the alpha subunit of a G- protein, Ras is active when GTP is bound to it and inactive when GDP is bound to it.Also, like the alpha subunit, Ras can hydrolyze the GTP to GDP. When a signal arrives at the receptor tyrosine kinase, the receptor monomers come together and phosphorylate each others' tyrosines, triggering the assembly of a complex of proteins on the cytoplasmic tail of the receptor. One of the proteins in this complex interacts with Ras and stimulates the exchange of the GDP bound to the inactive Ras for a GTP. This activates the Ras. Activated Ras triggers a phosphorylation cascade of three protein kinases, which relay and distribute the signal. These protein kinases are members of a group called the MAP kinases (Mitogen Activated Protein Kinases). The final kinase in this cascade phosphorylates various target proteins, including enzymes and transcriptional activators that regulate gene expression. The phosphorylation of various enzymes can alter their activities, and set off new chemical reactions in the cell, while the phosphorylation of transcriptional activators can change which genes are expressed. The combined effect of changes in gene expression and protein activity alter the cell's physiological state. Once again, in following the path of signal transduction mediated by RTKs, it is possible to discern the same basic pattern of events: a signal is bound by the extracellular domains of receptor tyrosine kinases, resulting in receptor dimerization and autophosphorylation of the cytosolic tails, thus conveying the message to the interior of the cell. The message is passed on via a signalling complex to Ras which then stimulates a series of kinases. The terminal kinase in the cascade acts on target proteins and brings about in changes in protein activities and gene expression. The descriptions above provide a very simple sketch of some of the major classes of receptors and deal primarily with the mechanistic details of the steps by which signals received by various types of receptors bring about changes in cells. A major take-home lesson is the essential similarity of the different pathways. Another point to keep in mind is that while we have looked at each individual pathway in isolation, a cell, at any given time receives multiple signals that set off a variety of different responses at once. The pathways described above show a considerable degree of "cross-talk" and the response to any given signal is affected by the other signals that the cell receives simultaneously. The multitude of different receptors, signals and the combinations thereof are the means by which cells are able to respond to an enormous variety of different circumstances.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/08%3A_Signaling/8.05%3A_Receptor_Tyrosine_Kinases_%28RTKs%29.txt

The environment of a cell is very complex, making it very diffcult, if not impossible, to study individual reactions, enzymes, or pathways within it. For this reason, biochemists prefer to isolate molecules (enzymes, DNAs, RNAs, and other molecules of interest) so they can be analyzed without interference from the millions of other processes occurring simultaneously in the cell. Many of the methods used in isolating molecules from cells involve some form of chromatography. To separate compounds from their cellular environments, one must first break open (lyse) the cells. In this section, we describe some of the methods biochemists use to do their work. • 9.1: Cell Disruption There are several ways to break open cells.  Whatever method is employed, the crude lysates obtained contain all of the molecules in the cell, and thus, must be further processed to separate the molecules into smaller subsets, or fractions. • 9.2: Fractionation Fractionation of samples typically starts with centrifugation. Using a centrifuge, one can remove cell debris, and fractionate organelles, and cytoplasm. For example, nuclei, being relatively large, can be spun down at fairly low speeds. Once nuclei have been sedimented, the remaining solution, or supernatant, can be centrifuged at higher speeds to obtain the smaller organelles, like mitochondria. Each of these fractions will contain a subset of the molecules in the cell. • 9.3: Ion Exchange Chromatography In ion exchange chromatography, the support consists of tiny beads to which are attached chemicals possessing a charge. Each charged molecule has a counter-ion. • 9.4: Gel Exclusion Chromatography Gel exclusion chromatography is a low resolution isolation method.  This involves the use of beads that have tiny “tunnels" in them that each have a precise size. The size is referred to as an “exclusion limit," which means that molecules above a certain molecular weight will not fit into the tunnels. Molecules with sizes larger than the exclusion limit do not enter the tunnels and pass through the column relatively quickly by making their way between the beads. • 9.5: Affinity Chromatography Affinity chromatography exploits the binding affinities of target molecules (typically proteins) for substances covalently linked to beads. For example, if one wanted to separate all of the proteins in a sample that bound to ATP from proteins that do not bind ATP, one could covalently link ATP to support beads and then pass the sample through column. All proteins that bind ATP will “stick" to the column, whereas those that do not bind ATP will pass quickly through it. • 9.6: High Performance Liquid Chromatography (HPLC) • 9.7: Histidine Tagging Histidine tagging is a powerful tool for isolating a recombinant protein from a cell lysate.  The protein produced when this gene is expressed has a run of histidine residues fused at either the carboxyl or amino terminus to the amino acids in the remainder of the protein. The histidine side chains of this “tag" have an affinity for nickel or cobalt ions, making separation of histidine tagged proteins from a cell lysate is relatively easy. • 9.8: Electrophoresis DNA molecules are long and loaded with negative charges, thanks to their phosphate backbones. Electrophoretic methods separate large molecules, such as DNA, RNA, and proteins based on their charge and size. For DNA and RNA, the charge of the nucleic acid is proportional to its size (length). For proteins, which do not have a uniform charge, a clever trick is employed to make them mimic nucleic acids. • 9.9: Protein Cleavage • 9.10: Microarrays DNA microarrays, for example, can be used to determine all of the genes that are being expressed in a given tissue, simultaneously. Microarrays employ a grid (or array) made of rows and columns on a glass slide, with each box of the grid containing many copies of a specific molecule, say a single-stranded DNA molecule corresponding to the sequence of a single unique gene. • 9.11: Blotting • 9.12: Making Recombinant DNAs • 9.13: Polymerase Chain Reaction • 9.14: Lac Z Blue-White Screening • 9.15: Reverse Transcription 09: Techniques There are several ways to break open cells. • Lysis methods include lowering the ionic strength of the medium cells are kept in. This can cause cells to swell and burst. Mild surfactants may be used to enhance the efficiency of lysis. Most bacteria, yeast, and plant tissues, which have cell walls, are resistant to such osmotic shocks, however, and stronger disruption techniques are often required. • Enzymes may be useful in helping to degrade the cell walls. Lysozyme, for example, is very useful for breaking down bacterial walls. Other enzymes commonly employed include cellulase (plants), glycanases, proteases, mannases, and others. • Mechanical agitation may be employed in the form of tiny beads that are shaken with a suspension of cells. As the beads bombard the cells at high speed, they break them open. Sonication (20-50 kHz sound waves) provides an alternative method for lysing cells. The method is noisy, however, and generates heat that can be problematic for heat-sensitive compounds. • Another means of disrupting cells involves using a “cell bomb". In this method, cells are placed under very high pressure (up to 25,000 psi). When the pressure is released, the rapid pressure change causes dissolved gases in cells to be released as bubbles which, in turn, break open the cells. • Cryopulverization is often employed for samples having a tough extracellular matrix, such as connective tissue or seeds. In this technique, tissues are .ash-frozen using liquid nitrogen and then ground to a fine powder before extraction of cell contents with a buffer. Whatever method is employed, the crude lysates obtained contain all of the molecules in the cell, and thus, must be further processed to separate the molecules into smaller subsets, or fractions.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/09%3A_Techniques/9.01%3A_Cell_Disruption.txt

Fractionation of samples typically starts with centrifugation. Using a centrifuge, one can remove cell debris, and fractionate organelles, and cytoplasm. For example, nuclei, being relatively large, can be spun down at fairly low speeds. Once nuclei have been sedimented, the remaining solution, or supernatant, can be centrifuged at higher speeds to obtain the smaller organelles, like mitochondria. Each of these fractions will contain a subset of the molecules in the cell. Although every subset contains fewer molecules than does the crude lysate, there are still many hundreds of molecules in each. Separating the molecule of interest from the others is where chromatography comes into play. We will consider several separation techniques. Many chromatographic techniques are performed in “columns." These are tubes containing the material (called the “support") used to perform the separation . Supports are designed to exploit the chemical, or size, differences of the many molecules in a mixture. Columns are “packed" (filled) with the support and a buffer or solvent carries the mixture of compounds to be separated through the support. Molecules in the sample interact differentially with the support and consequently, will travel through it with different speeds. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University) 9.03: Ion Exchange Chromatography In ion exchange chromatography, the support consists of tiny beads to which are attached chemicals possessing a charge. Each charged molecule has a counter-ion. The figure shows the beads (blue) with negatively charged groups (red) attached. In this example, the counter-ion is sodium, which is positively charged. The negatively charged groups are unable to leave the beads, due to their covalent attachment, but the counter- ions can be “exchanged" for molecules of the same charge. Thus, a cation exchange column will have positively charged counter-ions and positively charged compounds present in a mixture passed through the column will exchange with the counter-ions and “stick" to the negatively charged groups on the beads. Molecules in the sample that are neutral or negatively charged will pass quickly through the column. On the other hand, in anion exchange chromatography, the chemical groups attached to the beads are positively charged and the counter-ions are negatively charged. Molecules in the sample that are negatively charged will “stick" and other molecules will pass through quickly. To remove the molecules “stuck" to a column, one simply needs to add a high concentration of the appropriate counter-ions to displace and release them. This method allows the recovery of all components of the mixture that share the same charge. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University) 9.04: Gel Exclusion Chromatography Gel exclusion chromatography (also called molecular exclusion chromatography, size exclusion chromatography, or gel filtration chromatography) is a low resolution isolation method that employs a cool “trick." This involves the use of beads that have tiny “tunnels" in them that each have a precise size. The size is referred to as an “exclusion limit," which means that molecules above a certain molecular weight will not fit into the tunnels. Molecules with sizes larger than the exclusion limit do not enter the tunnels and pass through the column relatively quickly by making their way between the beads. Smaller molecules, which can enter the tunnels, do so, and thus, have a longer path that they take in passing through the column. Because of this, molecules larger than the exclusion limit will leave the column earlier, while those that pass through the beads will elute from the column later. This method allows separation of molecules by their size. 9.05: Affinity Chromatography Affinity chromatography is a very powerful technique that exploits the binding affinities of target molecules (typically proteins) for substances covalently linked to beads. For example, if one wanted to separate all of the proteins in a sample that bound to ATP from proteins that do not bind ATP, one could covalently link ATP to support beads and then pass the sample through column. All proteins that bind ATP will “stick" to the column, whereas those that do not bind ATP will pass quickly through it. The proteins adhering to the column may then released from the column by adding ATP. 9.06: High Performance Liquid Chromatography (HPLC) HPLC (also sometimes called High Pressure Liquid Chromatography) is a powerful tool for separating smaller molecules based on their differential polarities. It employs columns with supports made of very tiny beads that are so tightly packed that .ow of solvents/buffers through the columns requires the application of high pressures (hence the name). The supports used can be polar (normal phase separation) or non-polar (reverse phase separation). In normal phase separations, non-polar molecules elute first followed by the more polar compounds. This order is switched in reverse phase chromatography. Of the two, reverse phase is much more commonly employed to due more reproducible chromatographic profiles (separations) that it typically produces. 9.07: Histidine Tagging Histidine tagging is a powerful tool for isolating a recombinant protein from a cell lysate. It relies on using recombinant DNA techniques to add codons specifying a series of histidines (usually six) to the coding sequence for a protein. The protein produced when this gene is expressed has a run of histidine residues fused at either the carboxyl or amino terminus to the amino acids in the remainder of the protein. The histidine side chains of this “tag" have an affinity for nickel or cobalt ions, making separation of histidine tagged proteins from a cell lysate is relatively easy. Simply passing the sample through a column that has immobilized nickel or cobalt ions allows the histidine- tagged proteins to “stick," while the remaining cell proteins all pass quickly through. The histidine-tagged proteins are then eluted by addition of imidazole (which is chemically identical to the histidine side chain) to the column. Histidine tags can be cleaved off using endopeptidases.

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DNA molecules are long and loaded with negative charges, thanks to their phosphate backbones. Electrophoretic methods separate large molecules, such as DNA, RNA, and proteins based on their charge and size. For DNA and RNA, the charge of the nucleic acid is proportional to its size (length). For proteins, which do not have a uniform charge, a clever trick is employed to make them mimic nucleic acids. Agarose Agarose gel electrophoresis is a method for separating nucleic acids. It is worth noting that nucleic acids are the largest molecules found in cells, in some cases by orders of magnitude. Agarose provides a matrix which encases a buffer. The matrix provides openings for macromolecules to move through and the largest macromolecules have the most difficult time navigating, whereas the smallest macromolecules slip through it the easiest. Unlike column chromatography, electrophoresis uses an electric current as a force to drive the molecules through the matrix. Since the size to charge ratios for DNA and RNA are constant for all sizes of these nucleic acids, the size per force is also constant (since force is directly proportional to charge), so the molecules simply sort on the basis of their size - the smallest move fastest and the largest move slowest. Visualization of the DNA fragments in the gel is made possible by addition of a dye, such as ethidium bromide that fluoresces under ultraviolet light. SDS-PAGE Like DNA and RNA, proteins are large macromolecules. Proteins, however, vary tremendously in their charge. Whereas double-stranded DNA is rod-shaped, most proteins are globular (folded up). Further, proteins are considerably smaller than nucleic acids, so the openings of the matrix of the agarose gel are simply too large to effectively provide separation. Consequently, unlike nucleic acids, proteins cannot be effectively separated by electrophoresis on agarose gels. To separate proteins by electrophoresis, one must make several modifications. First, a matrix made by polymerizing and crosslinking acrylamide units is employed. One can adjust the pore size of the matrix readily by changing the percentage of acrylamide in the gel. Higher percentages of acrylamide create smaller pores and are more effective in separating smaller molecules, whereas lower percentages of acrylamide reverse that. Second, proteins must be physically altered to “present" themselves to the matrix like the negatively charged rods of DNA. This is accomplished by treating the proteins with the detergent called SDS (sodium dodecyl sulfate). SDS denatures the proteins so they assume a rod-like shape and the SDS molecules coat the proteins such that the exterior surface is loaded with negative charges proportional to the mass, just like the backbone of DNA. Third, a “stacking gel" may be employed at the top of the gel to provide a way of compressing the samples into a tight band before they enter the main polyacrylamide gel (called the resolving gel). Just as DNA fragments get sorted on the basis of size (largest move slowest and smallest move fastest), the proteins migrate through the gel matrix at rates inversely related to their size. Upon completion of the electrophoresis, there are several means of staining to visualize the proteins on the gel. They include reagents, such as Coomassie Brilliant Blue or silver nitrate (the latter is much more sensitive than Coomassie Blue staining and can be used when there are very small quantities of protein). Isoelectric Focusing Proteins vary considerably in their charges and, consequently, in their pI values (pH at which their charge is zero). Separating proteins by isoelectric focusing requires establishment of a pH gradient in an acrylamide gel matrix. The matrix’s pores are adjusted to be large to reduce the effect of sieving based on size. Molecules to be focused are applied to the gel with the pH gradient and an electric current is passed through it. Positively charged molecules, for example, move towards the negative electrode, but since they are traveling through a pH gradient, as they pass through it, they reach a region where their charge is zero and, at that point, they stop moving. They are at that point attracted to neither the positive nor the negative electrode and are thus “focused" at their pI. By using isoelectric focusing, it is possible to separate proteins whose pI values differ by as little as 0.01 units. 2-D Gel Electrophoresis Both SDS-PAGE and isoelectric focusing are powerful techniques, but a clever combination of the two is a powerful tool of proteomics - the science of studying all of the proteins of a cell/tissue simultaneously. In 2D gel electrophoresis, an extract containing the proteins is first prepared. One might, for example, be studying the proteins of liver tissue. The liver cells are lysed and all of the proteins are collected into a sample. Next, the sample is subjected to isoelectric focusing as described earlier, to separate the proteins by their pI values. Next, as shown on the previous page, the isoelectric gel containing the separated proteins is rotated through 90º and placed on top of a regular polyacrylamide gel for SDS-PAGE analysis (to separate them based on size). The proteins in the isoelectric gel matrix are electrophoresed into the polyacrylamide gel and separation on the basis of size is performed. The product of this analysis is a 2D gel, in which proteins are sorted by both mass and charge. The power of 2D gel electrophoresis is that virtually every protein in a cell can be separated and appear on the gel as a distinct spot. In the figure, spots in the upper left correspond to large positively charged proteins, whereas those in the lower right are small negatively charged ones. It is possible using high- throughput mass spectrometry analysis to identify every spot on a 2D gel. This is particularly powerful when one compares protein profiles between different tissues or between the samples of the same tissue treated or untreated with a particular drug. Comparison of a 2D separation of a non-cancerous tissue with a cancerous tissue of the same type provides a quick identification of proteins whose level of expression differs between them. Information such as this might be useful in designing treatments or in determining the mechanisms by which the cancer arose.

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Working with intact proteins in analytical techniques, such as mass spectrometry, can be problematic. Consequently, it is often desirable to break a large polypeptide down into smaller, more manageable pieces. There are two primary approaches to accomplishing this - use of chemical reagents or use of proteolytic enzymes. The table on the previous page shows the cutting specificities of various cleavage agents. 9.10: Microarrays 2D gels are one way of surveying a broad spectrum of molecules simultaneously. Other approaches to doing the same thing involve what are called microarrays. DNA microarrays, for example, can be used to determine all of the genes that are being expressed in a given tissue, simultaneously. Microarrays employ a grid (or array) made of rows and columns on a glass slide, with each box of the grid containing many copies of a specific molecule, say a single-stranded DNA molecule corresponding to the sequence of a single unique gene. As an example, consider scanning the human genome for all of the known mRNA sequences and then synthesizing single stranded DNAs complementary to each mRNA. Each complementary DNA sequence would have its own spot on the matrix. The position of each unique gene sequence on the grid is known and the entire grid would represent all possible genes that are expressed. Then for a simple gene expression analysis, one could take a tissue (say liver) and extract the mRNAs from it. These mRNAs represent all the genes that are being expressed in the liver at the time the extract was made. The mRNAs can easily be tagged with a colored dye (say blue). The mixture of tagged mRNAs is then added to the array and base-pairing conditions are created to allow complementary sequences to find each other. When the process is complete, each liver mRNA should have bound to its corresponding gene on the array, creating a blue spot in that box on the grid. Since it is known which genes are in which box, a blue spot in a box indicates that the gene in that box was expressed in the liver. The presence and abundance of each mRNA may then readily determined by measuring the amount of blue dye at each box of the grid. A more powerful analysis could be performed with two sets of mRNAs, each with a different colored tag (say blue and yellow). One set of mRNAs could come from the liver of a vegetarian (tagged blue) and the other from a meat eater (tagged yellow), for example. The mRNAs are mixed and then added to the array and complementary sequences are once again allowed to form duplexes. After unhybridized mRNAs are washed away, the plate is analyzed. Blue spots in grid boxes correspond to mRNAs present in the vegetarian liver, but not in that of the meat eater. Green spots (blue plus yellow) would correspond to mRNAs present in equal abundance in the two livers. The intensity of each spot would also give information about the relative amounts of each mRNA in the tissues. Similar analyses could be done, using cDNAs instead of mRNA. Peptide microarrays have peptides bonded to the glass slide instead of DNA and can be used to study the binding of proteins or other molecules to the peptides.

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Blotting provides a means of identifying specific molecules out of a mixture. It employs three main steps. First, the mixture of molecules is separated by gel electrophoresis. The mixture could be DNA (Southern Blot), RNA (Nothern Blot), or protein (Western Blot) and the gel could be agarose (for DNA/RNA) or polyacrylamide (for protein). Second, after the gel run is complete, the proteins or nucleic acids in the gel are transferred out of the gel onto a membrane/paper that physically binds to the molecules. This “blot", as it is called, has an imprint of the bands of nucleic acid or protein that were in the gel (see figure at left). The transfer can be accomplished by diffusion or by using an electrical current to move the molecules from the gel onto the membrane. The membrane may be treated to covalently link the bands to the surface of the blot. Last, a visualizing agent specific for the molecule of interest in the mixture is added to the membrane. For DNA/RNA, that might be a complementary nucleic acid sequence that is labeled in some fashion (radioactivity or dye). For a protein, it would typically involve an antibody that specifically binds to the protein of interest. The bound antibody can then be targeted by another antibody specific for the first antibody. The secondary antibody is usually linked to an enzyme which, in the presence of the right reagent, catalyzes a reaction that produces a signal (color or light) indicating where the antibody is bound. If the molecule of interest is in the original mixture, it will “light" up and reveal itself. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University) 9.12: Making Recombinant DNAs Molecular biologists often create recombinant DNAs by joining together DNA fragments from different sources. One reason for making recombinant DNA molecules is to enable the production of a specific protein that is of interest. For example, it is possible to engineer a recombinant DNA molecule containing the gene for human growth hormone and introduce it into an organism like a bacterium or yeast, which could make massive quantities of the human growth hormone protein very cheaply. To do this, one needs to set up the proper conditions for the protein to be made in the target cells. For bacteria, this typically involves the use of plasmids. Plasmids are circular, autonomously replicating DNAs found commonly in bacterial cells. Plasmids used in recombinant DNA methods 1. replicate in high numbers in the host cell; 2. carry markers that allow researchers to identify cells carrying them (antibiotic resistance, for example) and 3. contain sequences (such as a promoter and Shine Dalgarno sequence) necessary for expression of the desired protein in the target cell. A plasmid that has all of these features is referred to as an expression vector (see an example in the figure at left). Plasmids may be extracted from the host, and any gene of interest may be inserted into them, before returning them to the host cell. Making such recombinant plasmids is a relatively simple process. It involves 1. cutting the gene of interest with a restriction enzyme (endonucleases which cut at specific DNA sequences); 2. cutting the expression plasmid DNA with restriction enzyme, to generate ends that are compatible with the ends of the gene of interest; 3. joining the gene of interest to the plasmid DNA using DNA ligase; 4. introducing the recombinant plasmit into a bacterial cell; and 5. growing cells that contain the plasmid. The bacterial cells bearing the recombinant plasmid may then be induced to express the inserted gene and produce large quantities of the protein encoded by it. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University) 9.13: Polymerase Chain Reaction PCR allows one to use the power of DNA replication to obtain large amounts of a specific DNA in a short time. As everyone knows, cell division results in doubling the number of cells with each round of division. Each time cells divide, DNA must be replicated, as well, so the amount of DNA is doubling as the cells are doubling. Kary Mullis recognized this fact and came up with the technique of PCR, which mimics DNA replication. In contrast to cellular DNA replication, which amplifies all of a cell’s DNA during a replication cycle, PCR is used to replicate only a specific segment of DNA. This segment of DNA, known as the target sequence, is replicated repeatedly, to obtain millions of copies of the target. Just as in DNA replication, PCR requires a template DNA, 4 dNTPs, primers to initiate DNA synthesis on each strand, and a DNA polymerase to synthesize the new DNA copies. In PCR, the primers bind to sequences .anking the target region that is to be amplified, and are present in large excess over the template. The DNA polymerase used is chosen to be heat stable, for reasons that will be clear shortly. The first step of each PCR cycle involves separating the strands of the template DNA so that it can be replicated. This is accomplished by heating the DNA to near boiling temperatures. In the next step of the cycle, the solution is cooled to a temperature that favors complementary DNA sequences finding each other. Since the primers are present in great excess over the template, they can readily find and base-pair with the complementary sequences in the template on either side of the target sequence. In the third step in the cycle, the DNA polymerase (which has not been denatured during the heat treatment because it is thermostable) extends the primer on each strand, making copies of both DNA strands and doubling the amount of the target sequence. The cycle is then repeated, usually about 30 times. At the end of the process, there is a theoretical yield of \(2^30\) more of the target DNA than there was in the beginning.

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A powerful tool for biotechnologists is the lac Z gene. You may recall from an earlier section on the control of gene expression, that lac Z is part of the lac operon of E. coli and encodes the enzyme ß galactosidase. This enzyme catalyzes the hydrolysis of lactose into glucose and galactose, allowing the bacteria to use lactose as an energy source. ß galactosidase can also break down an artificial substrate called X-gal to produce a compound that is blue in color. X-gal can thus be used to test for the presence of active ß galactosidase. With this background, we can now look at how the lac Z gene can be of help to molecular biologists when they create recombinant plasmids. In the example described earlier, the gene for human growth hormone (hGH) was inserted into a plasmid. As we noted, the plasmid, as well as the hGH gene are cut with restriction endonucleases to create compatible DNA ends that can be ligated. While the ends of the hGH gene are, indeed, capable of being ligated to the ends of the plasmid, the two ends of the plasmid could also readily rejoin. In fact, given that the two ends of the plasmid are are on the same molecule, the chances of their finding each other are much higher than of a plasmid end finding an hGH gene. This would mean that many of the ligated molecules would not be recombinants, but simply recircularized plasmids. Five percent of the plasmids having inserts of the hGH gene would be very good. That would mean that 95% of the bacterial colonies arising from transformation would contain the original plasmid rather than the recombinant. To make the process of screening for the relatively rare recombinants simpler, plasmids have been engineered that carry the lac Z gene, modified to contain, with the coding sequence, restriction enzyme recognition sites. If one of these sites is used to cut open the plasmid and a gene of interest is inserted, this disrupts the lac Z gene. If the plasmid simply recircularizes, the lac Z gene will be intact. To find which bacterial colonies carry the recombinant plasmids, X-Gal is provided in the plates. Bacterial colonies containing plasmids with the lac z sequence disrupted by an inserted gene will not produce functional ßgalactosidase. The X-Gal will not be broken down and there will be no blue color. By contrast, bacterial cells with recircularized plasmids having no inserted hGH gene will make functional ß galactosidase, so in the presence of X-Gal and IPTG these colonies will produce a blue color. This is summarized in the figure on the previous page. 9.15: Reverse Transcription According to the central dogma, DNA codes for mRNA, which codes for protein. An exception to this rule is seen with the retroviruses, RNA-encoded viruses that have a phase in their replication cycle during which their genomic RNA is copied into DNA by a virally-encoded enzyme known as reverse transcriptase. The ability to convert RNA to DNA can be useful in the laboratory. For example, the power of PCR can be brought to RNA by converting RNAs of interest to DNA and then amplifying them by PCR. With reverse transcriptase, this is readily accomplished. First, one creates a DNA oligonucleotide to serve as a primer for reverse transcriptase to use on a target RNA. The primer must, of course, be complementary to a segment (near the 3’ end) of the RNA to be amplified. The RNA template, reverse transcriptase, the primer, and four dNTPs are mixed. With one round of replication, the RNA is converted to a single strand of DNA, which can be separated from the RNA either by heating or by the use of an RNase to digest the RNA. The product of this process is called a complementary DNA (cDNA).

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With this chapter, we tie up a bunch of loose ends and ponder what lies in the future of biochemistry. • 10.1: Looking Back Thousands of enzymes and their substrates have been identified, and hundreds of metabolic pathways traced. The structure of hundreds of proteins is known down to the position of every atom. Following the elucidation of the structure of DNA in 1953, scientists have discovered a dizzying number of facts about how information is stored, used and inherited in cells. Cloned and transgenic animals and gene therapy were a reality in less than 50 years. And the discoveries still keep coming. • 10.2: Looking Forward Toward the end of the twentieth century, new methods began to change the face of biochemistry. The launching of the Human Genome Project and the development of faster and cheaper sequencing technologies provided biochemists with entire genome sequences, not only of humans, but of numerous other organisms. Huge databases were set up to deal with the volume of sequence information generated by the various genome projects. Computer programs cataloged and analyzed these sequences. Dr. Kevin Ahern and Dr. Indira Rajagopal (Oregon State University) 10: Putting It All Together The bounds of biochemistry have expanded enormously since its inception. Wöhler’s demonstration, in 1828, that urea could be synthesized outside of a living cell, showed that there was no “vital force" that distinguished the chemistry of life from that of the non-living world. Chemistry is chemistry, but the term “biochemistry" was coined in 1903 by Carl Neuberg to describe the special subset of chemical reactions that happen in living cells. This specialness derives not from any exceptions to the laws of physics and chemistry, but from the way in which the chemical reactions in cells are organized and regulated, and also from the complexity and size of biological molecules. Faced with far greater complexity than in the inorganic world, the traditional strategy of biochemists has been “divide and conquer." In this approach, individual enzymes and other biological molecules are purified from cells so that their properties can be studied in isolation. The underlying logic of this method, sometimes described as reductionist, is that we can learn about the whole by studying its individual parts. This painstaking approach, used through most of the twentieth century, teased out chemical reactions and molecular interactions that occur within cells, one by one, gradually revealing to scientists much of what we know in biochemistry today. As increasing numbers of biochemical reactions were worked out, biochemists began to see that they were connected together in chains of reactions that we now refer to as metabolic pathways. These metabolic pathways turned out to be remarkably similar between cells across all kingdoms of life. Though there are a few pathways that are unique to certain organisms, many more are the same, or very similar, in organisms as different as bacteria and humans. It also became clear that metabolic pathways interacted with each other via common intermediates or by regulation of one pathway by molecule(s) created by other pathway(s). The similarity of the chemical reactions in all living cells was shown to extend to the common energy currency, ATP, that cells use to power their chemical reactions, as well as the mechanism by which cells make the ATP. Metabolic pathways trace the transformation of molecules in a cell and represent the work of enzymes, which are proteins. The discovery of the structure of DNA led to understanding of how information in genes was used to direct the synthesis of these proteins. The protein-DNA interactions that determine which genes are copied into RNA at any given time were uncovered and helped explain how cells with the same DNA came to express different proteins. The genetic code, as well as the mechanisms of transcription, translation and regulation of gene expression also turned out to be remarkably similar in cells of all kinds, leading Nobel laureate Jacob Monod to joke that what was true for E.coli was also true for E.lephant. The “one component at a time" approach also helped biochemists understand how cells sense changes in their environment and respond to them. The ability to sense conditions outside the environs of cells extends through all groups of organisms. Even the simplest single-celled organism can follow nutrient gradients to move itself closer to food. Cells in multicellular organisms can detect chemical cues in the blood (nutrients, hormones) or impulses from nerve cells and alter their actions. These cues may trigger changes in metabolism, decisions to divide, die, or become senescent, or the performance of specialized functions (e.g., muscle contraction or enzyme secretion). Thus cells are constantly in a state of .ux, adjusting their activities in response to signals from outside themselves as well as their own changing needs. The power of the “take things apart" analytical approach is evident from the astounding pace of discoveries in biochemistry and molecular biology. The first demonstration that an enzyme was a protein was made only in 1926, and it wasn’t till twenty years later that this was sufficient well established that the Nobel Prize was awarded in 1946 for this discovery. Since that time, the methods of biochemistry have uncovered all of the information that you can find in any standard biochemistry textbook, and more. Thousands of enzymes and their substrates have been identified, and hundreds of metabolic pathways traced. The structure of hundreds of proteins is known down to the position of every atom. Following the elucidation of the structure of DNA in 1953, scientists have discovered a dizzying number of facts about how information is stored, used and inherited in cells. Cloned and transgenic animals and gene therapy were a reality in less than 50 years. And the discoveries still keep coming.

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Toward the end of the twentieth century, new methods began to change the face of biochemistry. The launching of the Human Genome Project and the development of faster and cheaper sequencing technologies provided biochemists with entire genome sequences, not only of humans, but of numerous other organisms. Huge databases were set up to deal with the volume of sequence information generated by the various genome projects. Computer programs cataloged and analyzed these sequences, making sense of the enormous quantities of data. Protein coding regions of genomes could be identified and translated “in silico" to deduce the amino acid sequence of the encoded polypeptides. Comparisons could be made between the gene sequences of different organisms. In parallel with the growth of sequence information, more and more protein structures were determined, by using X-ray crystallography and NMR spectroscopy. These structures, too, were deposited in databases to be accessible to all scientists. The accumulation of vast amounts of sequence and structure information went hand in hand with new and ambitious goals for biochemistry. Modern biotechnology techniques have provided tools for studying biochemistry in entirely new ways. The old ways of dividing and conquering to study individual reactions are now being supplemented by approaches that permit researchers to study cellular biochemistry in its entirety. These fields of research, which collectively are often referred to as the ‘-omics’ include genomics (study of all the DNA of a cell), proteomics (study of all the proteins of a cell), transcriptomics (study of all the transciption products of a cell), and metabolomics (study of all the metabolic reactions of a cell), among others. As an example, let us consider proteomics. The field of proteomics is concerned with all of the proteins of a cell. Since proteins are the ‘workhorses’ of cells, knowing which ones are being made at any given time provides us with an overview of everything that is happening in the cells under specific conditions. How is such an analysis performed? First, one extracts all of the proteins from a given cell type (liver, for example). Next, the proteins are separated in a two-step gel method, where the first step resolves proteins based on their charge and the second separates them by mass. The product of this analysis is a single gel (called a 2-D gel) on which all of the proteins have been separated. In the left-right orientation, they differ in their original charge and in the up/down orientation, they differ in their size. By using such a technique, as many as 6000 cellular proteins can be separated and visualized as spots on a single gel. Robotic techniques allow excision of individual spots and analysis on mass spectrometers to identify every protein present in the original extract. Why is this useful? There are several ways in which this information can be illuminating. For example, by comparing the proteins in a normal liver cell with those in a cancerous liver cell, one can quickly determine if there are any proteins that are expressed or missing only in the cancer cells. These differences between normal and cancerous cells may provide clues to the mechanisms by which the cancer arose or suggest ways to treat the cancer. Or, the same sort of analysis could be done on cells to find out about the effects of a hormone or drug treatment. Comparison of the proteins found in untreated and treated cells would give a global view of the protein changes resulting from the treatment. Similar analyses can be performed on the mRNA of cells, employing devices called microarrays. In this case, all the RNAs that are being made at the time that the cell extract is made can be identified by the signals generated when the RNAs hybridize with oligonucleotides complementary to their sequence, that are immobilized in ordered arrays on the surface of a plate. The position and strength of these signals indicates which RNAs are made and in what amounts. The techniques of proteomics and transcriptomics, together with other “global view" approaches of molecules like lipids, carbohydrates, etc., are allowing biochemists to have, for the first time, a “big picture" view of the activities of cells. While these techniques have already provided valuable new insights, they are still incomplete, as a description of what goes on in cells. This is because they provide us with a snapshot that captures what is happening in cells at the moment that they were disrupted to make the extract. But cells are not static entities. At every moment, they are adapting their activities in response to changing combinations of internal and external conditions. Changes in response to any one signal are modified and in.uenced by the every other condition, within and outside the cell, and understand these complex systems as an integrated whole is the new holy grail of biochemistry. The aim, then, is to develop models that depict these dynamic interactions within cells, and to understand how such interactions give rise to the properties and behavior that we observe. This is the goal of the emerging field of systems biology that constructs mathematical models and simulations, based on the large data sets generated by transcriptomic, proteomic and other broad-range techniques. Systems biology is truly an interdisciplinary venture, drawing as it does on mathematics and computer science as much as traditional “bench biochemistry". While the original laboratory techniques of biochemistry are by no means obsolete, they will no longer be the sole tools used to understand what goes on inside of cells. These newer approaches are already leading to applications that are of tremendous value. Understanding the system level differences between normal and diseased cells can lead to major changes in the way diseases are detected, treated or altogether prevented. One recent triumph of systems biology has been in an intriguing discovery about how antibiotic drugs work. System level studies of many classes of antibiotics revealed that, regardless of how we think they work to kill bacteria, all of the drugs appear to have a common effect – that of increasing the level of oxidative damage, leading to cell death. This observation suggested that the potency of antibiotics could be enhanced by blocking bacterial responses that protect against oxidation damage. This idea was tested by screening large numbers of compounds for the ability to inhibit a pathway that bacteria use to repair their oxidation-damaged DNA. This screen yielded several compounds, the best of which was able to increase the effectiveness of the drug gentamicin by about a thousand-fold. Such compounds will be of increasing value in a world where antibiotic resistance is on the rise. Another application of systems biology is in the development of more effective vaccines. Till recently, most vaccines have been developed with little understanding of how exactly they stimulate the immune response. As systems biology approaches give us a better understanding of the changes that vaccines bring about to mediate immunity, it will be possible to identify the patterns that characterize stronger immune responses or adverse reactions to vaccines and even to predict how well particular vaccines may work in specific populations or individuals. Similarly, system level studies can help identify which drugs might be most effective, with the fewest side-effects, for a given patient, leading to a new era of personalized medicine. Related to systems biology, and heavily dependent on it, is synthetic biology, which aims to use the knowledge gained from the former to engineer novel biological systems and pathways. Because the technology now exists to synthesize extremely long pieces of DNA, entire genomes can be made synthetically and used to program cells that they are inserted into. It also allows for the possibility of custom-designing an organism to create particular chemical compounds through artificially assembled pathways. These methods have already been used to produce the drug artemisinin, which is used to treat malaria. The pathway for making a precursor of artemisinin was created by combining a metabolic pathway from yeast with part of another derived from the plant Artemisia annua, the natural source of artemisinin. Similar efforts are underway for anticancer drugs, novel drugs, favoring compounds, etc. One major goal is to create organisms programmed to make biofuels that could potentially replace petroleum. The successes of systems and synthetic biology, even in their infancy, promise great advances both in our understanding of living systems and in the applications that arise out of that knowledge. The next fifty years in biological research may well eclipse even the amazing accomplishments of the last. The practice of medicine will be transformed. Regenerative medicine will improve, as a better knowledge of stem cells allows us to use them more effectively to replace cardiac muscle lost in a heart attack, neurons damaged in Parkinson’s or Alzheimer’s, or even to regrow limbs lost in accidents or war. Treatments for our illnesses can be tailored to be optimal for each individual. Biofuels may bail us out when oil supplies run out and engineered organisms may help clean up a polluted planet. And research on longevity may give us the best gift of all- lives extended long enough to witness these advances and to participate in the creation of a new and better world.

textbooks/bio/Biochemistry/Book%3A_Biochemistry_Free_and_Easy_(Ahern_and_Rajagopal)/10%3A_Putting_It_All_Together/10.02%3A_Looking_Forward.txt

Search Fundamentals of Biochemistry Introduction You have probably studied the cell many times, either in high school or in college biology classes. There are many websites available that review both prokaryotic (bacterial and archaeal cell types) and eukaryotic cells (protist, fungi, plant, and animal cell types). All cells have some similar structural components, including genetic material in the form of chromosomes, a membrane-bound lipid bilayer that separates the inside of the cell from the outside of the cell, and ribosomes that are responsible for protein synthesis. This tutorial is designed specifically from the viewpoint of chemistry. It explores four classes of biomolecules that are also present in all cell types (lipids, proteins, nucleic acids and carbohydrates) and describes in a simplified pictorial manner where they are found, made, and degraded in a typical eukaryotic, animal cell (i.e. their history). This cell review focuses on the organelle structures common in eukaryotic cells. Subsequent chapters will concentrate on the structure and function of specific biomolecules. Let’s think of a cell as a chemical factory that designs, imports, synthesizes, uses, exports, and degrades a variety of chemicals (in the case of the cell, these include lipids, proteins, nucleic acids, and carbohydrates). It also must determine or sense the amount of raw and finished chemicals it has available and respond to its own and external needs by ramping up or shutting off production. Biochemistry is the branch of science dedicated to the study of these chemical processes within a cell. Understanding these processes can also lend insight into disease states and the pharmacological effects of toxins, drugs, and other medicines within the body. The building and breaking down of life-sustaining chemicals within an organism is known as Metabolism. Overall, the three main purposes of metabolism are: (1) the conversion of food to energy to run cellular processes; (2) the conversion of food/fuel to building blocks for the production of primary metabolites, such as proteins, lipids, nucleic acids, and other secondary metabolites; and (3) the elimination of waste products. These enzyme-catalyzed reactions allow organisms to grow and reproduce, maintain their structures, and respond to their environments. Metabolic reactions may be categorized as catabolic– the breaking down of compounds (for example, the breaking down of proteins into amino acids during digestion); or anabolic – the building up (synthesis) of compounds (such as proteins, carbohydrates, lipids, and nucleic acids). Usually, catabolism releases energy, and anabolism consumes energy. The chemical reactions of metabolism are organized into metabolic pathways, in which one chemical is transformed through a series of steps into another chemical, each step often being facilitated by a specific enzyme. Enzymes are crucial to metabolism because enzymes act as catalysts – they allow a reaction to proceed more rapidly. In addition, enzymes can provide a mechanism for cells to regulate the rate of a metabolic reaction in response to changes in the cell’s environment or to signals from other cells, through the activation or inhibition of the enzyme’s activity. Enzymes can also allow organisms to drive desirable reactions that require energy that will not occur by themselves, by coupling them to spontaneous reactions that release energy. Enzyme shape is critical to the function of the enzyme as it determines the specific binding of a reactant. This can occur by a lock and key model where the reactant is the exact shape of the enzyme binding site, or by an induced fit model, where the contact of the reactant with the protein causes the shape of the protein to change in order to bind to the reactant. The catalytic mechanisms, kinetics, and regulatory pathways of enzymes will be studied in detail within this text. Within eukaryotic cells, the metabolic machinery present allows for the construction of membrane-bound organelle structures that help to compartmentalize cellular functions. Therefore, organelles can be thought of as ‘little organs’ within the cell having discrete cellular functions. The figure of the cell below and in the other linked sites based on it was made available with the kind permission of Liliana Torres. Click on the blue hyperlinks for some of the organelles for more detailed information on them. Design – The design for a cell mostly resides in the blueprint for the cell, the genetic code, which is comprised of the DNA in the cell nucleus and a small amount in the mitochondria. Of course, the DNA blueprint must be read out (transcribed) by ribosomes which themselves were encoded by the DNA and contain a combination of RNA and protein subunits. The genetic code has the master plan that determines the sequence of all cellular proteins, which then catalyze almost all other activities in the cell, including catalysis, motility, architectural structure, etc. In contrast to DNA, RNA, and protein polymers, the length and sequence of polysaccharide polymers and lipids are not driven by such a template but rather by the enzymes that catalyze the synthesis. Import/Export: Many of the chemical constituents of the cell arise not from direct synthesis but from the import of both small and large molecules. The imported molecules must pass through the cell membrane and in some cases through additional membranes if they need to reside inside membrane-bound organelles. Molecules can move into the cell by passive diffusion across the membrane but usually, their movement is “facilitated” by a membrane transporter protein. Molecules can also move against a concentration gradient in a process called “active transport”. Given the amphiphilic nature of the bilayer (polar head group exterior, nonpolar interior), you would expect that polar molecules like glucose would have difficulty in moving across the membrane by passive diffusion. Typically, only small nonpolar molecules move across the membrane via passive transport. Membrane-bound transport proteins are involved in the movement of both nonpolar and polar molecules. • transporters, carrier proteins, and permeases: These membrane proteins move specific ligand molecules across a membrane, typically down a concentration gradient. Computer simulations of the facilitated diffusion of lactose across the membrane are shown in the following link. Animation of lactose diffusion through the LacY receptor (The link above and immediately below are from the Theoretical and Computational Biophysics group at the Beckman Institute, the University of Illinois at Urbana-Champaign. These molecular dynamic simulations were made with VMD/NAMD/BioCoRE/JMV/other software support developed by the Group with NIH support.) • ion channels These membrane proteins allow the flow of ions across membranes. Some are permanently open (nongated) while others are gated open or closed depending on the presence of ligands that bind the protein channel and the local environment of the protein in the membrane. The flow of ions through the channel proceeds in a thermodynamically favored direction, which depends on their concentration and voltage gradients across the membrane. • pores: Some membranes (nuclear, mitochondria) assemble proteins (such as porins) to form large, but regulated pores. Porins are found in mitochondrial membranes while nucleoporins are found in the nuclear membrane. Small molecules can generally pass through these membrane pores while large ones are selected based on their tendency to form transient intermolecular attractive forces with the pore proteins. The following link shows the diffusion of water through aquaporin. animation of water diffusion through the aquaporin channel, • endocytosis: Very large particles [for example, Low Density Lipoproteins (LDL) and viruses] can enter a cell through a process called endocytosis. Initially, the LDL or virus binds to a receptor on the surface of the cell. This triggers a series of events that leads to the invagination of the cell membrane at that point. This eventually pinches off to form an endosomal vesicle which is surrounded by a protein called clathrin. “Early” endosomes can pick up new proteins and other constituents as well as shed them as they move and mature through the cell. During this maturation process, protein pumps in the endosome lead to a decrease in the endosomal pH which can lead to conformation changes in protein structure and shedding of proteins. Eventually, the “late” endosome reaches and fuses with the lysosome, an internal organelle that contains degradative enzymes. Undegraded components like viral nucleic acids or cholesterol are delivered to the cell. This transport can also go in the reverse direction (called exocytosis) and recycle receptors to the cell membrane. Likewise, vesicles pinched off from the Golgi complex can fuse with endosomes, with some components surviving the process to reenter the Golgi. Synthesize/Degrade: Cells have to synthesize and degrade small molecules as well as larger polymeric proteins, carbohydrates, lipids, and nucleic acids. The anabolic (synthetic) and catabolic (degradative) pathways are often compartmentalized in time and space within a cell. For example, fatty acid synthesis is carried out in the cytoplasm but fatty acid oxidation is carried out in the mitochondria. Proteins are synthesized in the cytoplasm or completed in the endoplasmic reticulum (for membrane and exported proteins) while they are degraded in the lysosome or more importantly in a large multimolecular structure in the cell called the proteasome. Key Characteristics of a Cell Let’s consider some key characteristics of a cell before we get into the details in later chapters. Cells and their internal compartments have regulated concentrations of ions and hydronium ions. As expected the pH of the cytosol (the aqueous substance surrounding all the organelles within the cell) varies from about 7.0-7.4, depending on the metabolic state of the cell. Some organelles have proton transporters that can significantly alter the pH inside an organelle. For example, the pH inside the lysosome, a degradative organelle, is about 4.8. Furthermore, the creation of a pH gradient across the inner mitochondrial membrane is sufficient to drive the thermodynamically unfavored synthesis of ATP. Compared to the extracellular fluid, the concentration of potassium ions is higher inside the cell, while concentrations of sodium, chloride, and calcium ions are higher on the outside of the cell (see table below). These concentration gradients are maintained by ion transporters and channels and require energy expenditure ultimately in the form of ATP hydrolysis. Changes in these concentrations are integral to the signaling system used by the cell to sense and respond to changes in its external and internal environments. The table below shows approximate ion concentrations in the cell. Table 1.1 Average Cellular and Extracellular Ion Concentrations Ion Inside (mM) Outside (mM) Na+ 140 5 K+ 12 140 Cl- 4 15 Ca2+ 1 uM 2 Cells have an internal framework that provides architectural and internal structural support The “cytoskeletal” architecture of a (with molecular “cables”- and “girder-like” structures) is not dissimilar from a factory. The internal framework of a cell or cytoskeleton, is composed of microfilaments, intermediate filaments, and microtubules. These are comprised of monomeric proteins which self-assemble to form the internal architecture. Parts of the cytoskeleton can be seen in Figure 1.4. Microfilaments of actin monomers (which are stained with a red/orange fluorophore) and microtubules which offer more structural support made of tubulin monomers (stained green) along with the blue-stained nucleus are shown in the image. Organelles are supported and organized by the cytoskeleton (primarily microtubules). Even the cell membrane is supported underneath the inner leaflet by actin (stained orange) and spectrin microfilaments. Motor proteins like myosin (that moves along actin microfilaments) and dynein and kinesin (that move along tubulin microtubules) carry cargo (vesicles, organelles) in a directional fashion. The cell is not a disorganized collection of molecules and organelles. Rather it is highly organized for optimal chemical production, use, and degradation. Cells have a variety of shapes. Some circulating immune cells must slip through the cells that line capillary walls to migrate to sites of infection. The same process occurs when tumor cells metastasize and escape to other sites in the body. In order to do so, the cell must drastically change shape, a response that requires the dissociation of the cytoskeleton polymers into monomers which are available later for repolymerization. The following video shows the mobility and flexibility of a Killer T-Cell as it attacks and kills a cancerous cell. Video 1.1 Killer T Cell Attacking Cancer. Video available on YouTube through creative commons by Cambridge University The cell is an amazingly crowded place In chemistry labs, we typically work with dilute solutions of solute molecules in a solvent. You have probably heard that the body is comprised of 68% water, but the water concentration is obviously dependent on the cellular environment. Solute molecules like protein and carbohydrates are densely packed. Cells are so crowded that the space between larger molecules like proteins is typically smaller than that of a single protein. Studies have shown that the stability of a protein is increased in such conditions, which would help keep the protein in the correctly folded, native state. Another consequence of high intracellular concentrations is that it limits the diffusion of molecules throughout the cell, as would be expected from an equilibrium perspective in dilute solutions. Thus, cytoplasmic cellular functions can be highly localized within specific regions of the cell creating unique microenvironments and higher differentiation potential within a single cell. Hence the study of biomolecules in dilute solutions in the lab may not reveal the actual complexities of interactions and activities of the same molecule in vivo. Recently investigators have added a neutral copolymer of sucrose and epichlorohydrin to cells in vitro. These particles induced the organization of extracellular molecules secreted by the cell, forming an organized extracellular “matrix” which induced the organization of the microfilaments on the inside of the cell as well as inducing changes in cell activity.1 Furthermore, in vitro enzyme activity of a key enzyme in glycolysis dramatically increases under crowded conditions.2 Another result of crowding may be the spatial and temporal association of key enzymes involved in specific metabolic pathways, allowing for the coordinated passage of substrates and products within the colocalized enzyme system. Cell components undergo phase transitions to form substructures within the cell. A perplexing question is how substructures form within a cell. This includes not only the biogenesis of organelles like mitochondria but also smaller particle such as polysaccharide granules, lipid droplets, protein/RNA particles (including the ribosome) as well as the nucleolus of the cell nucleus. It might be easiest to consider this problem using two examples from the lipid world, lipid droplets and membrane rafts. You are very familiar with phase transitions that occur when a sparing soluble nonpolar liquid is added to water. At a high enough concentration, the solubility of the nonpolar liquid is exceeded and a phase transition occurs as evidenced by the appearance of two separate liquid phases. The same process occurs when triglycerides coalesce into lipid droplets with proteins associated on their outside. Another example occurs within a cell membrane when lipids with saturated alkyl chains self-associate with membrane cholesterol (which contains a rigid planar ring system) to form a membrane microdomain called a lipid raft. Lipid rafts are characterized by greater packing efficiency, rigidity, and thickness than other parts of the membrane. These lipid rafts often recruit proteins involved in signaling processes within the cell membranes. This process of phase separation is also called liquid/liquid demixing as two “liquid-like” substances separate. In a similar manner, it appears that proteins that interact with RNA are composed of less diverse amino acid sequences and have more flexible (“more liquid-like) structures allowing their preferential interaction with RNA to form large RNA-protein particles (like the ribosome and other RNA processing structures) in a fashion that mimics liquid/liquid demixing. All of these interactions are just manifestations of the various intermolecular forces that can exist between molecules. These include ionic interactions, ion-dipole interactions, dipole-dipole interactions, and London dispersion forces (A review of intermolecular forces can be found by Kahn Academy on YouTube).

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/01%3A_The_Foundations_of_Biochemistry/1.01%3A_Cellular_Foundations.txt

Search Fundamentals of Biochemistry Organic Molecules On Earth, all carbon-containing molecules have originated from biological, living organisms causing them to be termed organic compounds. The number of known organic compounds is quite large. In fact, there are many times more organic compounds known than all the other (inorganic) compounds discovered so far, about 7 million organic compounds in total. Fortunately, organic chemicals consist of relatively few similar parts, combined in different ways. These structural similarities allow us to predict how a compound we have never seen before may react if we know how other molecules containing the same types of parts are known to react. These parts of organic molecules are called functional groups and are made up of specific bonding patterns with the atoms most commonly found in organic molecules (C, H, O, N, S, and P). The identification of functional groups and the ability to predict reactivity based on functional group properties is one of the cornerstones of organic chemistry. Functional groups are specific atoms, ions, or groups of atoms having consistent properties. A functional group makes up part of a larger molecule. For example, -OH, the hydroxyl group that characterizes alcohols, contains oxygen with attached hydrogen. It could be found on any number of different molecules. Just as elements have distinctive properties, functional groups have characteristic chemistries. An -OH functional group on one molecule will tend to react similarly, although perhaps not identically, to an -OH on another molecule. Organic reactions usually take place at the functional group, so learning about the reactivities of functional groups will prepare you to understand many other aspects about biochemistry. Functional groups are structural units within organic compounds that are defined by specific bonding arrangements between specific atoms. The structure of capsaicin, the fiery compound found in hot peppers, incorporates several functional groups, labeled in the figure below and explained throughout this section. As we progress in our study of biochemistry, it will become extremely important to be able to quickly recognize the most common functional groups, because they are the key structural elements that define how organic molecules react. Below is a brief introduction to the major organic functional groups. Alkanes The ‘default’ in organic chemistry (essentially, the lack of any functional groups) is given the term alkane, characterized by single bonds between carbon and carbon, or between carbon and hydrogen. Methane, CH4, is the natural gas you may burn in your furnace. Octane, C8H18, is a component of gasoline. Alkenes and Alkynes Alkenes (sometimes called olefins) have carbon-carbon double bonds, and alkynes have carbon-carbon triple bonds. Ethene, the simplest alkene example, is a gas that serves as a cellular signal in fruits to stimulate ripening. (If you want bananas to ripen quickly, put them in a paper bag along with an apple – the apple emits ethene gas (also called ethylene), setting off the ripening process in the bananas). Ethyne, commonly called acetylene, is used as a fuel in welding blow torches. Many alkenes can take two geometric forms: cis or trans. The cis and trans forms of a given alkene are different isomers with different physical properties because there is a very high energy barrier to rotation about a double bond. In the example below, the difference between cis and trans alkenes is readily apparent. Biochemists don't usually use the E (entgegen) and Z (zusammen) labels for groups attached to double bonds (using IUPAC priority numbering). Alkanes, alkenes, and alkynes are all classified as hydrocarbons because they are composed solely of carbon and hydrogen atoms. Alkanes are said to be saturated hydrocarbons, because the carbons are bonded to the maximum possible number of hydrogens – in other words, they are ‘saturated’ with hydrogen atoms. The double and triple-bonded carbons in alkenes and alkynes have fewer hydrogen atoms bonded to them – they are thus referred to as unsaturated hydrocarbons. Aromatics The aromatic group is exemplified by benzene (which used to be a commonly used solvent on the organic lab, but which was shown to be carcinogenic), and naphthalene, a compound with a distinctive ‘mothball’ smell. Aromatic groups are planar (flat) ring structures, with conjugated pi bonding with 4n+2 pi electrons. Given the stability of aromatic groups due to delocalization of the pi electrons, these groups are widespread in nature. Alkyl Halides When the carbon of an alkane is bonded to one or more halogens, the group is referred to as an alkyl halide or haloalkane. Chloroform is a useful solvent in the laboratory, and was one of the earlier anesthetic drugs used in surgery. Chlorodifluoromethane was used as a refrigerant and in aerosol sprays until the late twentieth century, but its use was discontinued after it was found to have harmful effects on the ozone layer. Bromoethane is a simple alkyl halide often used in organic synthesis. Alkyl halides groups are quite rare in biomolecules. Alcohols, Phenols, and Thiols In the alcohol functional group, a carbon is single-bonded to an OH group (the OH group, when it is part of a larger molecule, is referred to as a hydroxyl group). Except for methanol, all alcohols can be classified as primary, secondary, or tertiary. In a primary alcohol, the carbon bonded to the OH group is also bonded to only one other carbon. In secondary and tertiary alcohols, the carbon is bonded to two or three other carbons, respectively. When the hydroxyl group is directly attached to an aromatic ring, the resulting group is called a phenol. The sulfur analog of an alcohol is called a thiol (from the Greek thio, for sulfur). Note that the definition of a phenol states that the hydroxyl oxygen must be directly attached to one of the carbons of the aromatic ring. The compound below, therefore, is not a phenol – it is a primary alcohol. The distinction is important because there is a significant difference in the reactivity of alcohols and phenols Ethers and Sulfides In an ether functional group, oxygen is bonded to two carbons. Below is the structure of diethyl ether, a common laboratory solvent and also one of the first compounds to be used as an anesthetic during operations. The sulfur analog of ether is called a thioether or sulfide. Amines Amines are characterized by nitrogen atoms with single bonds to hydrogen and carbon. Just as there are primary, secondary, and tertiary alcohols, there are primary, secondary, and tertiary amines. Ammonia is a special case with no carbon atoms. One of the most important properties of amines is that they are basic, and are readily protonated to form ammonium cations. In the case where nitrogen has four bonds to carbon (which is somewhat unusual in biomolecules), it is called a quaternary ammonium ion. Note: Do not be confused by how the terms ‘primary’, ‘secondary’, and ‘tertiary’ are applied to alcohols and amines – the definitions are different. In alcohols, what matters is how many other carbons the alcohol carbon is bonded to, while in amines, what matters is how many carbons the nitrogen is bonded to. Organic Phosphates Phosphate and its derivative functional groups are ubiquitous in biomolecules. Phosphate linked to a single organic group is called a phosphate ester; when it has two links to organic groups it is called a phosphate diester. A linkage between two phosphates creates a phosphate anhydride. Aldehydes and Ketones There are a number of functional groups that contain a carbon-oxygen double bond, which is commonly referred to as a carbonyl. Ketones and aldehydes are two closely related carbonyl-based functional groups that react in very similar ways. In a ketone, the carbon atom of a carbonyl is bonded to two other carbons. In an aldehyde, the carbonyl carbon is bonded on one side to hydrogen, and on the other side to carbon. The exception to this definition is formaldehyde, in which the carbonyl carbon has bonds to two hydrogens. Carboxylic Acids and Their Derivatives When a carbonyl carbon is bonded on one side to a carbon (or hydrogen) and on the other side to an oxygen, nitrogen, or sulfur, the functional group is considered to be one of the carboxylic acid derivatives, a designation that describes a set of related functional groups. The main member of this family is the carboxylic acid functional group, in which the carbonyl is bonded to a hydroxyl group. The carboxylate ion form has donated the H+ to the solution. Other derivatives are carboxylic esters(usually just called ‘esters’), thioesters, amides, acyl phosphates, acid chlorides, and acid anhydrides. With the exception of acid chlorides and acid anhydrides, carboxylic acid derivatives are very common in biological molecules and/or metabolic pathways and will be discussed in further detail in a later chapter. Practice Recognizing Functional Groups in Molecules A single compound often contains several functional groups, particularly in biological organic chemistry. The six-carbon sugar molecules glucose and fructose, for example, contain aldehyde and ketone groups, respectively, and both contain five alcohol groups. A compound with several alcohol groups is often referred to as a ‘polyol’. The hormone testosterone, the amino acid phenylalanine, and the glycolysis metabolite dihydroxyacetone phosphate all contain multiple functional groups, as labeled below. While not in any way a complete list, this section has covered most of the important functional groups that we will encounter in biochemistry. Table 1.7 provides a summary of all of the groups listed in this section. Table 1.7 Common Organic Functional Groups Exercise \(1\) Identify the functional groups (other than alkanes) in the following organic compounds. State whether alcohols and amines are primary, secondary, or tertiary. Exercise \(2\) Draw one example of each compound fitting the descriptions below, using line structures. Be sure to designate the location of all non-zero formal charges. All atoms should have complete octets (phosphorus may exceed the octet rule). There are many possible correct answers for these, so be sure to check your structures with your instructor or tutor. 1. a compound with molecular formula C6H11NO that includes alkene, secondary amine, and primary alcohol functional groups 2. an ion with molecular formula C3H5O6P2- that includes aldehyde, secondary alcohol, and phosphate functional groups. 3. A compound with molecular formula C6H9NO that has an amide functional group, and does not have an alkene group. Primary metabolites Primary metabolites are components of basic metabolic pathways that are required for life. They are associated with essential cellular functions such as nutrient assimilation, energy production, and growth/development. They have a wide species distribution that spans many phyla and frequently more than one kingdom. Primary metabolites include the building blocks required to make the four major macromolecules within the body: carbohydrates, lipids, proteins, and nucleic acids (DNA and RNA). These are large polymers of the body that are built up from repeating smaller monomer units (Fig. 6.1). The monomer units for building the nucleic acids, DNA and RNA, are the nucleotide bases, whereas the monomers for proteins are amino acids, for carbohydrates are sugar residues, and for lipids are fatty acids or acetyl groups. Reactions forming the Major Macromolecules The major macromolecules are built by putting together repeating monomer subunits through the process of dehydration synthesis. Interestingly, the organic functional units used in the dehydration synthesis processes for each of the major types of macromolecules have similarities with one another. Thus, it is useful to look at the reactions together (Figure 1.29) Primary metabolites that are involved with energy production include numerous enzymes that break down food molecules, such as carbohydrates and lipids, and capture the energy released during the hydrolysis of adenosine triphosphate (ATP). Enzymes are biological catalysts that speed up the rate of chemical reactions. Typically they are proteins, which are composed of amino acid building blocks. The basic structure of cells and of organisms are also composed of primary metabolites. These include cell membranes (e.g. phospholipids), cell walls (e.g. peptidoglycan, chitin), and cytoskeletons (proteins). DNA and RNA which store and transmit genetic information are composed of nucleic acid primary metabolites. Primary metabolites also include molecules involved in cellular signaling, communication, and transport. The structure and function of primary metabolites are key components of this text. These reactions will be detailed in the following chapters.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/01%3A_The_Foundations_of_Biochemistry/1.02%3A_Chemical_Foundations.txt

Search Fundamentals of Biochemistry The types and numbers of chemical reactions that occur in biological cells are staggering. Compared to both physical and chemical reactions that occur in a controlled and closed environment, biological reactions occur in open systems with input and output of both energy and chemical "feedstocks". Yet they are governed by the same physical principles that control all reactions. We can gain insight into biological reactions and how they are controlled by considering the same principles you have used in a myriad of preceding classes, energy changes, equilibria and thermodynamics. Let's review them! Reactions and Energy Changes Why do reactions vary in extent from completely irreversible in the forward reaction to reversible reactions favoring the reactants? It might help to understand a simple physical reaction before we try more complicated chemical reactions. Let's start with a simple ball on a hill. Does a ball at the top of a hill roll downhill spontaneously, or does the opposite happen? No one has ever seen a ball roll spontaneously uphill unless a lot of energy was added to the ball. This physical reaction appears to be irreversible and occurs because the ball has lower potential energy at the bottom of the hill than it does at the top. The gap in the potential energy is related to the "extent" and spontaneity of this reaction. As we have undoubtedly observed before, processes in nature tend to go to a lower energy state. By analogy, we will consider the driving force for a chemical reaction to be the free energy difference, ΔG, between reactants and products. ΔG determines the extent and spontaneity of the reaction. Reversible/Irreversible Reactions, Extent of Reactions, Equilibria Consider a hypothetical reversible reaction in which you start with some reactants, $\ce{A}$ and $\ce{B}$, each at a 1 M concentration (1 mol of each/L solution). but no products, $\ce{P}$ and $\ce{Q}$. For ease assume that the total volume of solution is 1 L, so that we start with 1 mol each of $\ce{A}$ and $\ce{B}$. At time $t=0$, the concentration of products is 0. The reaction can be written as: $\ce{A + B <=> P + Q}. \nonumber$ As time progresses, the amounts or concentrations of $\ce{A}$ and $\ce{B}$ decrease as the amounts or concentrations of products $\ce{P}$ and $\ce{Q}$ increase. At some time, no further changes occur in the amount or concentrations of remaining reactants or products. At this point, the reaction is in equilibrium, a term used often in our common vocabulary to denote a system that is undergoing no net change. Most of the reactions that we will study occur in solution, so we will deal with concentrations (in mol/L or mmol/mL = M). Let's consider how the concentration of reactants and products change as a function of time. Depending on the extent to which a reaction is reversible, 4 different scenarios can be imagined: Scenario 1: Irreversible reaction in which the reverse reaction occurs to a negligible extent. In this reaction, the reverse reaction occurs to such a small extent that we can neglect it. The only reaction that occurs is the conversion of reactants to products. Hence all the reactants are converted to product. At equilibrium [A] = 0. Since 1 mol of A reacted, it must form 1 mol of P and 1 mol Q - i.e. the concentration of products at equilibrium is 1 M. At an earlier time of the reaction, (let's pick a time when [A] = 0.8 M), only part of the reactants have reacted (in this case 0.2 M), producing an equal amount of products, P and Q. Graphs of [A] and [P] as a function of timer are shown below. [A] decreases in a nonlinear fashion to 0 M while [P] increases in a reciprocal fashion to 1 M concentration. This is illustrated in the graph below. Examples of irreversible reactions are reactions of strong acids (nitric, sulfuric, hydrochloric) with bases (OH- and water), or the much more complicated combustion reactions like the burning of sugars (like trees) and hydrocarbons (like octane) to form CO2 and H2O. Scenario 2: Reversible reaction in which the forward reaction is favored. Again [A] decreases and [P] increases, but in this case, some A remains since the reaction is reversible. As [A ]and [B] decrease, [P] and [Q] increase, which increases the chance that they will collide and form the product. Since P and Q can react to form reactants, the [A] at equilibrium is not zero as is shown below. Scenario 3: Reversible Reaction in which forward and reverse reactions are equally favored. Again [A] decreases and [P] increases, but in this case, some A remains since the reaction is reversible. As [A] and [B] decrease, [P] and [Q] increase, which increases the chance that they will collide and form the product. Since P and Q can react to form reactants, the [A] at equilibrium is not zero as is shown below. Because the reactants and products are equally favored, their concentrations will be equal at equilibrium. Scenario 4: Reversible Reaction in which the reverse reaction is favored. Again [A] decreases and [P] increases, but in this case, some A remains since the reaction is reversible. As [A] and [B] decrease, [P] and [Q] increase, which increases the chance that they will collide and form the product. Since P and Q can react to form reactants, the [A] at equilibrium is not zero as is shown below. Because the reaction favors reactants. their concentration will be higher at equilibrium than the products. An example of this kind of reaction, one that favors reactants, is the reaction of acetic acid (a weak acid) with water. $\ce{CH3CO2H(aq) + H2O(l) <=> H3O^{+}(aq) + CH3CO2^{-}(aq)} \nonumber$ Equilibrium Constants Without a lot of experience in chemistry, it is difficult to just look at the reactants and products and determine whether the reaction is irreversible, or reversible, favoring either reactants or products (with the exception of obvious irreversible reactions described above). However, this data can be found in tables of equilibrium constants. The equilibrium constant, as its name implies, is constant, independent of the concentration of the reactants and products. A $K_{eq} > 1$ implies that the products are favored. A $K_{eq} < 1$ implies that reactants are favored. When $K_{eq} = 1$, both reactants and products are equally favored. For the more general reaction, $\ce{aA + bB <=> pP + qQ} \nonumber$ where $a$, $b$, $p$, and $q$ are the stoichiometric coefficients, \mathrm{K}_{\mathrm{eq}}=\frac{[\mathrm{P}]_{\mathrm{eq}}^{\mathrm{p}}[\mathrm{Q}]_{\mathrm{eq}}^{\mathrm{q}}}{[\mathrm{A}]_{\mathrm{eq}}^{\mathrm{a}}[\mathrm{B}]_{\mathrm{eq}}^{\mathrm{b}}} where all the concentrations are those at equilibrium. For a simple reaction where $a$, $b$, $p$, and $q$ are all 1, then $K_{eq} = ([P] [Q])/([A] [B])$. (Note: Equilibrium constants are truly constant only at a given temperature, pressure, and solvent condition. Likewise, they depend on concentration to the extent that their activities change with concentration.) For an irreversible reaction, such as the reaction of a 0.1 M HCl (aq) in water, [HCl]eq = 0, you cannot easily measure a Keq. However, if we assume the reaction goes in reverse to an almost imperceptible degree, [HCl]eq might equal 10-10 M. Hence Keq >> 1. In summary, the extent of reactions can vary from completely irreversible (favoring only the products) to reactions that favor the reactants. Our next goal is to understand what controls the extent of a reaction. That is, of course, the change in the Gibbs free energy. Two different pairs of factors influence the ΔG. One pair is concentration and inherent reactivity of reactants compared to products (as reflected in the Keq). The other pair is enthalpy/entropy changes. We will now consider the first pair. Contributions of Molecule Stability (Keq) and concentration to ΔG Consider the reactions of hydrochloric acid and acetic acid with water. \begin{align*} \ce{HCl (aq) + H2O (l)} & \ce{-> H3O^{+}(aq) + Cl^{-} (aq)} \[4pt] \ce{CH3CO2H (aq) + H2O (l) } & \ce{-> H3O^{+} (aq) + CH3CO2^{}- (aq)} \end{align*} \nonumber Assume that at t = 0, each acid is placed into water at a concentration of 0.1 M. When equilibrium is reached, there is essentially no HCl left in solution, while 99% of the acetic acid remains. Why are they so different? We rationalized that HCl (aq) is a much stronger acid than H3O+(aq) which itself is a much stronger acid than CH3CO2H (aq). Why? All we can say is there is something about the structure of these acids (and the bases) that makes HCl much more intrinsically unstable, much higher in energy, and hence much more reactive than the acid it forms, H3O+(aq). Likewise, H3O+(aq) is much more intrinsically unstable, much higher in energy, and hence more reactive than CH3CO2H (aq). This has nothing to do with concentration since the initial concentration of both HCl (aq) and CH3CO2H (aq) were identical. This observation is reflected in the Keq for these acids (>>1 for HCl and <<1 for acetic acid). This difference in intrinsic stability of reactants compared to products (which is independent of concentration) is one factor that contributes to ΔG. The other factor is concentration. A 0.25 M (0.25 mol/L or 0.25 mmol/ml) solution of acetic acid does not conduct electricity, implying that very few ions of H3O+(aq) + CH3CO2- (aq) exist in the solution. However, if more concentrated acetic acid is added, a dim light becomes evident. Adding more reactants seemed to drive the reaction to form more products, even though the reverse reaction is favored if one considers only the intrinsic stability of reactants and products. Before the concentrated acid was added, the system was at equilibrium. Adding concentrated acid perturbed the equilibrium, which drove the reaction to form additional products. This is an example of Le Chatelier's Principle, which states that if a reaction at equilibrium is perturbed, the reaction will be driven in the direction that will relieve the perturbation. Hence: • if more reactant is added, the reaction shifts to form more products • if more product is added, the reaction shifts to form more reactants • if products are selectively removed (by distillation, crystallization, or further reaction to produce another species), the reaction shifts to form more product. • if reactants are removed (as above), the reaction shifts to form more reactants. • if heat is added to an exothermic reaction, the reaction shifts to get rid of the excess heat by shifting to form more reactants. (opposite for an endothermic reaction). Change in Free Energy G Without doing a complicated derivation, these simple examples suggest that the total $ΔG$ can be expressed as the sum of the two contributions showing the effects of the intrinsic stability ($K_{eq}$) and concentration: \Delta G_{\text {total }}=\Delta \mathrm{G}_{\text {stability }}+\Delta \mathrm{G}_{\text {concentration }} which becomes for the simple reaction $\ce{A + B <=> P + Q}$ (after a rigorous derivation): \begin{aligned} \Delta G &=\Delta G^0+R T \ln \frac{[\mathrm{P}][\mathrm{Q}]}{[\mathrm{A}][\mathrm{B}]} \ &=\Delta G^0+R T \ln \mathrm{Q}_{\mathrm{rx}} \end{aligned} where ΔGo reflects the contribution from the relative intrinsic stability of reactants and products and the second term reflects the contribution from the relative concentrations of reactants and products (which has nothing to do with stability). Qrx is the reaction quotient which for the reaction A + B ↔ P + Q is given by: Q_{r x}=([P][Q]) /([A][B]) at any point in the reaction. Meaning of ΔG Remember that ΔG is the "driving" force for a reaction, analogous to the difference in potential energy for a ball on a hill. Go back to that analogy. if the ball starts at the top of the hill, does it roll downhill? Of course. It goes from high potential energy to low potential energy. The reaction can be written as: Ball top → Ball bottom for which the change in potential energy, ΔPE = PEbottom -PEtop< 0. If the ball starts at the bottom, will it go to the top? Obviously not. For that reaction, Ball bottom → Ball top, ΔPE > 0. If the top of the hill was at the same height at the bottom of the hill (obviously an absurd situation), the ball would not move. It would effectively be at equilibrium, a state of no change. For this reaction, Ball top --> Ball bottom, the ΔPE = 0. As the ball starts rolling down the hill, its potential energy gets closer to the potential it would have at the bottom. Hence the ΔPE changes from negative to more and more positive until it gets to the bottom at which case the ΔPE = 0 and movement ceases. If the ΔPE is not 0, the ball will move until the ΔPE = 0. Likewise, for a chemical reaction that favors products, ΔG < 0. The system is not at equilibrium and the reaction will go in the direction of products. As the reaction proceeds, products buildup, and there is less of a driving force for reactants to go to products (Le Chatelier'sPrinciple), so the ΔG becomes more positive until the ΔG = 0 and the reaction is at equilibrium. A reaction that has a ΔG > 0 is likewise not at equilibrium so it will go in the appropriate direction until equilibrium is reached. Hence for the reaction A + B <==> P + Q, • if ΔG < 0, the reaction goes toward products P and Q • if ΔG = 0, the reaction is at equilibrium and no further change occurs in the concentration of reactants and products. • if ΔG > 0, the reaction goes toward reactants A and B. We can not measure easily the actual free energy G of reactants or products, but we can measure ΔG readily. These points are illustrated in the graph below of ΔG vs time for the hypothetical reaction A + B ↔ P + Q. (Also notice the two insert graphs - in blue and red - which show, in analogy to the ball on the hill graphs, the values of ΔG at the two points where the perturbation to the equilibrium were made.) Notice the ΔG is constantly changing until the system reaches equilibrium. Initially, the equilibrium is perturbed so that the system is not in equilibrium (shown in blue). The perturbation was such that the products are favored. After equilibrium was reached, the system was perturbed again, this time in a fashion to favor the reverse reaction. Notice in this case the ΔG for the reaction as written: A + B ↔ P + Q is positive - i.e. it is not in equilibrium. Therefore the reaction (as written) goes backwards to products. It is important to realize that the reported ΔG is for the reaction as written. Now let's apply ΔG = ΔGo + RTln Q = ΔGo + RTln ([P][Q])/([A][B]) to two reactions we discussed above: • HCl(aq) + H2O(l) ↔ H3O+(aq) + Cl-(aq) • CH3CO2H(aq) + H2O(l) ↔ H3O+(aq) + CH3CO2-(aq) Assume that at time t=0, 0.1 mole of HCl and CH3CO2H were added to two different beakers. At this point the forward reaction is favored, but obviously to different extents. The RTln Q would be identical for both acids since each reactant is present at 0.1 M, but no products yet exist. However, the ΔGo is negative for HCl and positive for acetic acid since HCl is a strong acid. Hence at t=0, ΔG for the HCl reaction is much more negative than for acetic acid. This is summarized in the table below. The direction of the arrow shows if products (-->) or reactants (<---) are favored. The size of the arrow shows very approximately to what extent the ΔG term is favored Reaction at t=0 ΔGo RTln Q ΔG HCl(aq) + H2O(l) ---------------> ---------------> -----------------------------> CH3CO2H(aq) + H2O(l) <------------- ---------------> -> Now when equilibrium is reached, no net change occurs in the concentration of reactants and products, and ΔG = 0. In the case of HCl, there is just an infinitesimal amount of HCl left, and 0.1 M of each product, so concentration favors HCl formation. However, the intrinsic relative stability of reactants and products still favors products. In the case of acetic acid, most of the acetic acid remains (0.099 M) with little product (0.001 M) so concentration favors products. However, the intrinsic relative stability of reactants and products still favors reactants. This is summarized in the table below. Reaction at equlib. ΔGostab RTln Q ΔG HCl(aq) + H2O(l) ---------------> <--------------- favors neither, = 0 CH3CO2H(aq) + H2O(l) <------------- --------------> favors neither, = 0 Compare the two tables above (one at time t= 0 and the other at equilibrium). Notice: • ΔGo does not change in a given set of conditions, since it has nothing to do with concentration. • Only RTln Q changes during the course of a reaction until equilibrium is achieved. Meaning of ΔGo To get a better meaning of the significance of ΔGo, let's consider the following equations under two different conditions: \Delta G=\Delta G^0+R T \ln \frac{[\mathrm{P}][\mathrm{Q}]}{[\mathrm{A}][\mathrm{B}]}=\Delta G^0+R T \ln \mathrm{Q}_{\mathrm{rx}} Condition I: Reaction at equilibrium, ΔG = 0 The equation reduces to: \Delta G^0=-R T \ln \frac{[\mathrm{P}]_{\mathrm{eq}}[\mathrm{Q}]_{\mathrm{eq}}}{[\mathrm{A}]_{\mathrm{eq}}[\mathrm{B}]_{\mathrm{eq}}}=-2.303 \mathrm{R} T \log \mathrm{K}_{\mathrm{eq}} This supports our idea that ΔGo is independent of concentration since Keq should also be independent of concentration. Condition II: Concentration of all reactants and products is 1 M (standard state, assuming solution reaction) The equation reduces to: \begin{aligned} \Delta G=\Delta G^0+R T \ln \frac{[1][1]}{[1][1]}=\Delta G^0+2.303 R T \log 1=\Delta G^0 \ \Delta G &=\Delta G^o+R T \ln \left(\frac{[1][1]}{[1][1]}\right) \ &=\Delta G^o+2.303 R T \log 1 \ &=\Delta G^o \end{aligned} This implies that when all reactants are at this concentration, defined as the standard state (1 M for solutes), the ΔG at that particular moment just happens to be the ΔGo for the reaction. If one of the reactants or products is H3O+, it would make little biological sense to calculate ΔGo for the reaction using the standard state of [H3O+] = 1 M, or a pH of -1. Instead, it is assumed that the pH = 7, [H3O+] = 10-7 M. A new symbol is used for ΔGo under these conditions, ΔGo'. Heat, Enthlapy and Entropy Consider the association reaction of hydrogen atoms into molecular hydrogen $\ce{H + H -> H2}. \nonumber$ Does this reaction occur spontaneously? It does. You should remember that individual $\ce{H}$ atoms are unstable since they don't have a completed outer shell of electrons - in this case, a duet. As they approach, they can interact to form a covalent bond and in the process release energy. The bonded state is a lower energy state than two separated H atoms. This should be clear since energy has to be added to a molecule of $\ce{H2}$ to break the bond. We call this the bond dissociation energy. ` Now consider a more complicated reaction, the burning of octane. $\ce{2C8H18(l) + 25O2(g) → 16CO2(g) + 18H2O(g)} \nonumber$ To carry out this reaction, every C-C, C-H and O-O bond in the reactants must be broken (which requires an input of energy) but lots of energy is released on the formation of C-O and H-O covalent bonds in the products. Is more energy needed to break the bonds in the reactants or is more energy released on the formation of bonds in the product? The answer should be clear. The products must be at a lower energy than the reactants since huge amounts of heat and light energy are released on the combustion of gasoline and other hydrocarbons. These reactions suggest that energy must be released for a reaction to proceed to any extent in a given direction. Now consider, however, the following reaction: $\ce{Ba(OH)2. 8H2O(s) + 2NH4SCN(s) -> 10H2O(l) + 2NH3(g) + Ba(SCN)2(aq)} \nonumber$ When these two solids are added to a beaker and stirred, a reaction clearly takes place, as evidenced by the formation of a liquid (water) and the smell of ammonia. What is surprising is that heat is not released into the surroundings in this reaction. Rather heat was absorbed from the surroundings turning the beaker so cold that it freezes to a piece of wood (with a layer of water added to the wood) on which it was placed. This reaction seems to violate our idea that a reaction proceeds in a direction in which heat is liberated. Reactions, which liberate heat and raise the temperature of the surroundings, are called exothermic reactions. Reactions, which absorb heat from the surroundings and hence lower the temperature of the surroundings, are endothermic reactions. To answer the question we need to consider entropy. A review of thermodynamics You may remember from General Chemistry that the change in the internal energy of a system, $ΔE$, is given by: \begin{aligned} \Delta E_{s y s} &=q+w \ &=q-P_{e x t} \Delta V \end{aligned} where $q$ is the heat (thermal energy) transferred to (+) or from the system (-), $w$ is the work done on (+) or by (-) the system. This is one expression for the 1st Law of Thermodynamics If only pressure/volume (PV) work is done (and not electrical works for example), $w = - P_{ext}ΔV$, where $P_{ext}$ is the external pressure resisting a volume change in the system, $ΔV$. Under these conditions, the heat transfer at constant $P$, $q_P$ is given by: \begin{aligned} \Delta \mathrm{E}_{\mathrm{sys}}-\mathrm{w} &=\Delta \mathrm{E}_{\mathrm{sys}}+\mathrm{P}_{\mathrm{ext}} \Delta \mathrm{V} \ &=\mathrm{q}_{\mathrm{P}} \ &=\Delta \mathrm{H}_{\mathrm{sys}} \end{aligned} $q_p$, which can easily be measured in a coffee cup calorimeter, is equal to the change in enthalpy, $ΔH$, of the system. For exothermic reactions, the reactants have more thermal energy than the products, and the heat energy (measured in kilocalories or kilojoules) released is the difference between the energy of the products and reactants. When heat energy is used to measure the difference in energy, we call the energy enthalpy ($H$) and the heat released as the change in enthalpy ($ΔH$), as illustrated below. For exothermic reactions, $ΔH < 0$. For endothermic reactions, $ΔH > 0$. The equation $\Delta \mathrm{E}_{\mathrm{sys}}=\mathrm{q}+\mathrm{w}=\mathrm{q}-\mathrm{P}_{\mathrm{ext}} \Delta \mathrm{V}$ is one expression of the First Law of Thermodynamics. Another statement of energy conservation, is: \Delta \mathrm{E}_{\text {tot }}=\Delta \mathrm{E}_{\text {universe }}=\Delta \mathrm{E}_{\text {sys }}+\Delta \mathrm{E}_{\text {surrounding }}=0 Clearly, there must be something more that decides whether a reaction goes to a significant extent other tha, if heat is released from the system. That is, the spontaneity of a reaction must depend on more than just ΔHsys. . Another example of a spontaneous natural reaction is the evaporation of water (a physical, not chemical process). $\ce{H2O (l) → H2O (g)} \nonumber$ Heat is absorbed from the surroundings to break the intermolecular forces (H bonds) among the water molecules (the system), allowing the liquid to be turned into a gas. If the surroundings are the skin, evaporation of water in the form of sweat cools the body. What are these reactions spontaneous and essentially irreversible even though they are endothermic? Notice that in both of these endothermic reactions (the reactions of Ba(OH)2.8H2O(s) and 2NH4SCN(s) and the evaporation of water), the products are more disorganized (more disordered) than the products. A solid is more ordered than a liquid or gas, and a liquid is more ordered than a gas. In nature, ordered things become more disordered with time. Entropy (S), the other factor (in addition to enthalpy changes) is often considered to be a measure of the disorder of a system. The greater the entropy, the greater the disorder. For reactions that go from order (low S) to disorder (high S), the change in S, ΔS > 0. For the reaction that goes from low order to high order, ΔS < 0. Caution However, this common description of entropy is quite misleading. Macroscopic examples describing order/disordered states (such as the cleanliness of your room or the shuffling of a deck of cards) are inappropriate since entropy deals with microscopic states. The driving force for spontaneous reactions is the dispersion of energy and matter. Increases in entropy for reactions that involve matter occurs when gases or solutes in solution are dispersed, leading to increases in positional entropy. For reactions involving energy changes, entropy increases when energy is dispersed as random, undirected thermal motion, leading to increases in thermal entropy. In this sense, entropy, $S$ (a measure of ("spreadedness") is a measure of the number of different ways (microstates) that particles or energy can be arranged (W), not a measure of disorder! W is an abbreviation for the German word, Wahrscheinlichkeit, which means probability. It can be shown that for a solute dissolving in a solvent, Wsys = Wsolute x Wsolvent. Note that this is a multiplicative function. Entropy is a logarithmic function of W which allows additivity of solute and solvent W values, a feature found in other thermodynamic state functions like ΔE, ΔH, and ΔS. Hence \ln W \text { sys }=\ln W_{\text {solute }}+\ln W_{\text {solvent }} Boltzmann showed that for molecules, S=k \ln W where $k$ is the Boltzmann constant (1.68 x 10-23 J/K), S units: J/K or S=k N_A \ln W=R \ln W Boltzmann realized the connection between the macroscopic entropy of a system and the microscopic order/disorder of a system through the equation $S = k\ln W$, Increasing S (macroscopic property) occurs with increasing numbers of possible microscopic states for the atoms and molecules of a system. The dissolution of a solute in water and the expansion of a gas into a vacuum, which both proceed spontaneously toward an increase in matter dispersal, are examples of familiar processes characterized by a ΔSsys > 0. We will see in future chapters that entropy changes in the solvent, solutes, and in a protein are critical determinants of protein folding. The spontaneity of exothermic and endothermic processes will depend on the \Delta S_{t o t}=\Delta S_{\text {surr }}+\Delta S_{\text {sys }} ΔSsys often depends on matter dispersal (positional entropy). ΔSsurr depends on energy changes in the surroundings, ΔHsurr = -ΔHsys (thermal entropy). It is more convenient to express thermodynamic properties based on the system which is being studied, not on the surrounding. This can be readily done for the ΔSsurr which depends both on ΔHsys and the temperature. First consider the dependency on ΔHsys. Thermal energy flows into or out of the system, and since ΔHsys = - ΔHsurr, $ΔS_{surr}$ is proportional to -ΔHsys • For an exothermic reaction, ΔSsurr > 0 (since ΔHsys < 0) and the reaction is favored; • For an endothermic reaction, ΔSsurr < 0, (since ΔHsys > 0), and the reaction is disfavored; $ΔS_{surr}$ also depends on the temperature T of the surroundings: $ΔS_{surr}$ is proportional to 1/T If the Tsurr is high, a given heat transfer to or from the surroundings will have a smaller effect on the $ΔS_{surr}$. Conversely, if the Tsurr is low, the effect on ΔSsurr will be greater. (Atkins uses the analogy of the effect of a sneeze in library compared to in a crowded street; An American Chemistry General Chemistry text uses the analogy of giving $5 to a friend with$1000 compared to one who has just \$10.) Hence, \Delta S_{\mathrm{surr}}=\frac{-\Delta \mathrm{H}_{\mathrm{sys}}}{\mathrm{T}} (Note: from a more rigorous thermodynamic approach, entropy can be determined from $dS = dq_{rev}/T$.) Once again, \Delta S_{\text {tot }}=\Delta S_{\text {surr }}+\Delta S_{\text {sys }} $ΔS_{tot}$ depends on both enthalpy changes in the system and entropy changes in the surroundings. Hence, \Delta S_{\text {tot }}=\frac{-\Delta \mathrm{H}_{\mathrm{sys}}}{\mathrm{T}}+\Delta \mathrm{S}_{\mathrm{sys}} Multiplying both sides by $-T$ gives -\mathrm{T} \Delta \mathrm{S}_{\mathrm{tot}}=\Delta \mathrm{H}_{\mathrm{sys}}+\mathrm{T} \Delta \mathrm{S}_{\mathrm{sys}} The thermodynamic function Gibb's Free Energy, $G$, can be defined as: G=H-T S At constant $T$ and $P$, \Delta G=\Delta H-T \Delta S Hence \Delta G_{s y s}=\Delta H_{s y s}-T \Delta S_{s y s}=-T \Delta S_{t o t} Spontaneity is determined by $ΔS_{tot}$ OR $ΔG_{sys}$ since $ΔS_{tot} = -ΔG_{sys}/T$. $ΔG_{sys}$ is widely used in discussing spontaneity since it is a state function, depends only on the enthalpy and entropy changes in the system, and is negative (as is the potential energy change for a falling object) for all spontaneous processes. The second law of thermodynamics can be succinctly stated: For any spontaneous process, the $ΔS_{tot} > 0$. Unlike energy (from the First Law), entropy is not conserved.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/01%3A_The_Foundations_of_Biochemistry/1.03%3A_Physical-Chemical_Foundations.txt

Search Fundamentals of Biochemistry Introduction The development of complex biological organisms on our planet has arisen through the evolutionary mechanism of natural selection. The British naturalist, Charles Darwin proposed the theory of biological evolution by natural selection in his book, ‘On the Origins of Species’ that was published in 1859. Darwin defined evolution as “descent with modification,” the idea that species change over time, give rise to new species, and share a common ancestor. The mechanism that Darwin proposed for evolution is natural selection. Because resources are limited in nature, organisms with heritable traits that favor survival and reproduction will tend to leave more offspring than their peers, causing the traits to increase in frequency within a population over generations. Thus, natural selection causes populations to become adapted, or increasingly well-suited, to their environments over time. Natural selection depends on the environment and requires existing heritable variation in a group. Natural selection acts on an organism’s phenotype, or physical characteristics. Phenotype is determined by an organism’s genetic make-up (genotype) and the environment in which the organism lives. When different organisms in a population possess different versions of a gene for a certain trait, each of these versions is known as an allele. It is primarily this genetic variation that underlies differences in phenotype. Some traits are governed by only a single gene, but most traits are influenced by the interactions of many genes. A variation in one of the many genes that contribute to a trait may have only a small effect on the phenotype; together, these genes can produce a continuum of possible phenotypic values. For example, interactions between different equine coat color genes determine a horse’s coat color. Many colors are possible, but all variations are produced by changes in only a few genes. Extension and agouti are particularly well-known genes with dramatic effects. For example, differences at the agouti gene can help determine whether a horse is bay or black in coloration, and a change to the extension gene can in turn make a horse chestnut-colored instead (Figure 1.30). Yet other gene variants are responsible for the myriad of other coat color possibilities, including palomino, buckskin, and cremello horses. Thus, the primary molecular mechanism that drives natural selection is controlled by the heritability and mutability of genetic traits housed in the major macromolecule, deoxyribonucleic acid (DNA). In chapter 4, you will learn about the structural characteristics of DNA, whereas chapter 9 focuses on the biochemical mechanisms involved with DNA replication and also details the importance of DNA repair process and molecular mechanisms of evolution at the genetic level. Genetic Code Notably, the phenotypic traits determined by the genetic makeup of an organism are not controlled directly by the genetic material, DNA, but by the proteins that are produced from the information housed within the gene. In 1945, geneticist George Beadle proposed the one gene-one enzyme hypothesis suggesting that genes are highly specific when they encode for a protein sequence. However, it would take 16 more years before the biochemical nature of this process was deduced. Efforts to understand how proteins are encoded began after DNA’s structure was discovered in 1953. George Gamow postulated that sets of three bases must be employed to encode the 20 standard amino acids used by living cells to build proteins, which would allow a maximum of 43 = 64 amino acids. The Crick, Brenner, Barnett and Watts-Tobin experiment first demonstrated that codons consist of three DNA bases (Figure 1.31). Marshall Nirenberg and Heinrich J. Matthaei were the first to reveal the nature of a codon in 1961. They used a cell-free system to translate a poly-uracil RNA sequence (i.e., UUUUU…) and discovered that the polypeptide that they had synthesized consisted of only the amino acid phenylalanine. They thereby deduced that the codon UUU specified the amino acid phenylalanine. This was followed by experiments in Severo Ochoa‘s laboratory that demonstrated that the poly-adenine RNA sequence (AAAAA…) coded for the polypeptide poly-lysine and that the poly-cytosine RNA sequence (CCCCC…) coded for the polypeptide poly-proline. Therefore, the codon AAA specified the amino acid lysine, and the codon CCC specified the amino acid proline. Using various copolymers most of the remaining codons were then determined. Subsequent work by Har Gobind Khorana identified the rest of the genetic code. Shortly thereafter, Robert W. Holley determined the structure of transfer RNA (tRNA), the adapter molecule that facilitates the process of translating RNA into protein. This work was based upon Ochoa’s earlier studies, yielding the latter the Nobel Prize in Physiology or Medicine in 1959 for work on the enzymology of RNA synthesis. Extending this work, Nirenberg and Philip Leder revealed the code’s triplet nature and deciphered its codons (Figure 1.32). In these experiments, various combinations of mRNA were passed through a filter that contained ribosomes, the components of cells that translate RNA into protein. Unique triplets promoted the binding of specific tRNAs to the ribosome. Leder and Nirenberg were able to determine the sequences of 54 out of 64 codons in their experiments. Khorana, Holley and Nirenberg received the 1968 Nobel for their work. The three stop codons were named by discoverers Richard Epstein and Charles Steinberg. “Amber” was named after their friend Harris Bernstein, whose last name means “amber” in German. The other two stop codons were named “ochre” and “opal” in order to keep the “color names” theme. Each gene contains a reading frame is defined by the initial triplet of nucleotides from which translation starts. It sets the frame for a run of successive, non-overlapping codons, which is known as an open reading frame (ORF). For example, the string 5′-AAATGAACG-3′, if read from the first position, contains the codons AAA, TGA, and ACG ; if read from the second position, it contains the codons AAT and GAA ; and if read from the third position, it contains the codons ATG and AAC. Every sequence can, thus, be read in its 5′ → 3′ direction in three reading frames, each producing a possibly distinct amino acid sequence: in the given example, Lys (K)-Trp (W)-Thr (T), Asn (N)-Glu (E), or Met (M)-Asn (N), respectively. When DNA is double-stranded, six possible reading frames are defined, three in the forward orientation on one strand and three reverse on the opposite strand. Protein-coding frames are defined by a start codon, usually the first AUG (ATG) codon in the RNA (DNA) sequence. To terminate the translation process, there are three stop codons: UAG is amber, UGA is opal (sometimes also called umber), and UAA is ochre. Stop codons are also called “termination” or “nonsense” codons. They signal the release of the nascent polypeptide from the ribosome. Mutations During the process of DNA replication, errors occasionally occur in the polymerization of the second strand. These errors, called mutations, can affect an organism’s phenotype, especially if they occur within the protein-coding sequence of a gene. Error rates are typically 1 error in every 10–100 million bases—due to the “proofreading” ability of DNA polymerases. Missense mutations and nonsense mutations are examples of point mutations that can cause genetic diseases such as sickle-cell disease and thalassemia respectively. Clinically important missense mutations generally change the properties of the coded amino acid residue among basic, acidic, polar or non-polar states, whereas nonsense mutations result in a stop codon. Mutations that disrupt the reading frame sequence by indels (insertions or deletions) of a non-multiple of 3 nucleotide bases are known as frameshift mutations. These mutations usually result in a completely different translation than from the original RNA, and likely cause a stop codon to be read, which truncates the protein. These mutations may impair the protein’s function and are thus rare in in vivo protein-coding sequences. One reason inheritance of frameshift mutations is rare is that, if the protein being translated is essential for growth under the selective pressures the organism faces, the absence of a functional protein may cause death before the organism becomes viable. Frameshift mutations may result in severe genetic diseases such as Tay–Sachs disease. Although most mutations that change protein sequences are harmful or neutral, some mutations have benefits. These mutations may enable the mutant organism to withstand particular environmental stresses better than wild-type organisms, or reproduce more quickly. In these cases, a mutation will tend to become more common in a population through natural selection. Different sequence variations of the same gene or protein within a single organism, within a population, or between different species are known as sequence polymorphisms. Larger-scale gene duplication events can also lead to evolutionary events. Similar Proteins The evolution of proteins is studied by comparing the sequences and structures of proteins from many organisms representing distinct evolutionary clades. If the sequences/structures of two proteins are similar indicating that the proteins diverged from a common origin, these proteins are called homologous proteins. More specifically, homologous proteins that exist in two distinct species are called as orthologs. In contrast, homologous proteins encoded by the genome of a single species are called paralogs. Unrelated genes that have separate evolutionary origins, but that each encode proteins that have similar functions, are termed analogs (Figure 1.33). DNA sequencing techniques have rapidly improved over the last 15 to 20 years making it possible to sequence the entire genomes of organisms and thus, predict the entire proteome of an organism, based on the translation of the sequenced genome followed by the annotation of predicted ORFs using phylogenetic comparison of similar genes/proteins from other known organisms. This has given rise to the field of Bioinformatics which uses computer science, mathematics, and statistical analysis to analyze the large quantities of biological data created in genome sequencing projects. The phylogenetic relationships, and hence ancestral relationships, of various genes, proteins, and ultimately organisms can be established through the statistical analysis of sequence alignments. Such phylogenetic trees have established that the sequence similarities among proteins reflect closely the evolutionary relationships among organisms. Protein evolution describes the changes over time in protein shape, function, and composition. Through quantitative analysis and experimentation, scientists have strived to understand the rate and causes of protein evolution. Using the amino acid sequences of hemoglobin and cytochrome c from multiple species, scientists were able to derive estimations of protein evolution rates. What they found was that the rates were not the same among proteins. Each protein has its own rate, and that rate is constant across phylogenies (i.e., hemoglobin does not evolve at the same rate as cytochrome c, but hemoglobins from humans, mice, etc. do have comparable rates of evolution.). Not all regions within a protein mutate at the same rate; functionally important areas mutate more slowly and amino acid substitutions involving similar amino acids occurs more often than dissimilar substitutions. Overall, the level of polymorphisms in proteins seems to be fairly constant. Several species (including humans, fruit flies, and mice) have similar levels of protein polymorphism. Gene duplication events followed by mutation can also give rise to paralogs, with unique and different functions within an organism. This can make the annotation of genomes based on sequence alone a difficult task, as homologous protein sequences may not have similar functions in vivo. It is estimated that approximately 10-25% of annotations made on sequence homology are incorrect and require experimental validation. For example, human pancreatic ribonuclease is a digestive enzyme utilized to breakdown nucleic acids. The angiogenin protein is a paralog of pancreatic ribonuclease and shares high sequence homology and 3-D shape (Figure 1.34). However, the functions of these proteins are quite different. Angiogenin induces vascularization by activating transcriptional processes in endothelial cells. However, if the function of only one of these homologs was known, it would be easy to mistakenly hypothesize that the homologous protein would be similar in function. Thus, care must be taken when using bioinformatic tools to not overestimate the predictive ability of sequence alignments. The control of gene expression is critical in all processes of life, allowing for the differentiation of cells to form different body structures and organs, as well as smaller more reversible changes that allow an organism to respond to different environmental situations and stimuli. In chapter 12, you will explore the major biochemical mechanisms used to control gene expression within cells. This will include the discussion of a fairly new and exciting field of study known as epigenetics. In addition to the heritability of traits through the passage of genetic information, it is fast becoming clear that the environmental factors that an organism is exposed to throughout its life can affect gene expression without physically altering the DNA sequence, and that these changes in expression patterns can be long-lasting and can even be inherited in the following generations. The term epigenetics literally means ‘on top of’ or ‘in addition to’ genetics and focuses on the heritable gene expression patterns that are induced by the exposure or experience of an organism within its environment. For example in human populations, stressful events such as starvation can have lasting imprints on children that are born under these conditions. These children have higher risks of obesity and metabolic disorders as adults, including the development of type II diabetes. In fact, these predispositions can be carried not only to the children born during starvation but also to their future children indicating that environmental events can affect gene expression patterns through multiple generations. In more controlled laboratory experiments using rats, it has been demonstrated that the more a mother rat licks and nurtures its offspring, the calmer and more relaxed the offspring will be as an adult. Mother rats that are less nurturing and ignore their young, have offspring that will grow up displaying higher levels of anxiety. These changes are not caused by genetic differences between the offspring, but rather by differences in gene expression patterns. In fact, calm and relaxed mice can be altered to show high anxiety by exposing them to agents that alter gene expression patterns. Mechanisms controlling such heritable alterations in gene expression patterns will be covered in a future chapter. Central Dogma of Biology DNA encodes the genetic material. It must be replicated on cell division. Its information is decoded into an RNA in a process called transcription. That information is decoded to form a protein sequence. Collectively these processes are referred to as the Central Dogma of Biology. A variant occurs when RNA is decoded into DNA, a process called reverse transcription. These processes are described briefly below and in great depth in subsequent chapters. Replication DNA must be duplicated in a process called replication before a cell divides. The replication of DNA allows each daughter cell to contain a full complement of chromosomes. Animation of Replication Transcription and Splicing For a given gene, only one strand of the DNA serves as the template for transcription. An example is shown below. The bottom (blue) strand in this example is the template strand, which is also called the minus (-) strand, or the sense strand. It is this strand that serves as a template for mRNA synthesis. The enzyme RNA polymerase synthesizes an mRNA in the 5' to 3' direction complementary to this template strand. The opposite DNA strand (red) is called the coding strand, the nontemplate strand, the plus (+) strand, or the antisense strand. The easiest way to find the corresponding mRNA sequence (shown in green below) is to read the coding, nontemplate, plus (+), or antisense strand directly in the 5' to 3' direction substituting U for T. ```5' T G A C C T T C G A A C G G G A T G G A A A G G 3' 3' A C T G G A A G C T T G C C C T A C C T T T C C 5'``` `5' U G A C C U U C G A A C G G G A U G G A A A G G 3'` As we've learned more about the structure of DNA, RNA, and proteins, it become clear that transcription and translation differ in eukaryotes and prokaryotes. Specifically, eukaryotes have intervening sequences of DNA (introns) within a given gene that separate coding fragments of DNA (exons). A primary transcript is made from the DNA, and the introns are sliced out and exons joined in a contiguous stretch to form messenger RNA which leaves the nucleus. Translation occurs in the cytoplasm. Remember, prokaryotes do not have a nucleus. Translation Information in a mRNA sequence is decoded to form a protein. In this process, a triplet of nucleotides (a codon) in the RNA has the information of a single amino acid. Translation occurs on a large RNA-protein complex called the ribosome. An intermediary transfer RNA (tRNA) molecule becomes covalently linked to a single amino acid by the enzyme tRNA-acyl synthetase. This "charged" tRNA binds through a complementary anticodon region to the triplet codon in the tRNA. The ribosome/tRNA complex ratchets down the mRNA allowing a new "charged" tRNA complex to bind at an adjacent site. The two adjacent amino acids form a peptide bond in a process driven by ATP cleavage. This process repeats until a "stop" codon appears in the mRNA sequence. The genetic code shows the relationship between the triplet mRNA codon and the amino acid which corresponds to it in the growing peptide chain. As was mentioned in the Protein Chapter (amino acid section) two other amino acids occasionally appear in proteins (excluding amino acids altered through post-translational modification. One is selenocysteine, which is found in Arachea, eubacteria, and animals. The other is just recently found is pyrrolysine, found on Arachea. These new amino acids derive from modifications of Ser-tRNA and probably Lys-tRNA after the tRNA is charged with the normal amino acid, to produce selenocys-tRNA and pyrrolys-tRNA, respectively. The pyrrolysine-tRNA recognizes the mRNA codon UAG which is usually a stop codon, while selenocys-tRNA recognizes UGA, also a stop codon. Clearly, only a small fraction of stop codons in mRNA sequences would be recognized by this usual tRNA complex. What determines that recognition is unclear. Animation of Translation What is a gene? The definition of a gene can differ depending on whom you ask. The world gene has literally become a cultural icon of our day. Can our genes explain what it is to be human? The definition of a gene has changed with time. Eukaryotic genes contain exons (coding regions) and introns (intervening sequences) that are transcribed to produce a primary transcript. In a post-transcriptional process, introns are spliced out by the spliceosome, to produce a messenger RNA, mRNA, which is translated into a protein sequence. (See diagram above). Over the last 100 years, as our understanding of biochemistry has increased, the definition of a gene has evolved from • the basis of inheritable traits • certain regions of chromosomes • a segment of a chromosome that produces one enzyme • a segment of a chromosome that produces one protein • a segment of a chromosome that produces a functional product The last definition was necessary since some gene products that have functions (structural and catalytic) are RNA molecules. The last definition also includes regulatory regions of the chromosome involved in transcriptional control. Snyder and Gerstein have developed five criteria that can be used in gene identification which is important as the complete genomes of organisms are analyzed for genes. 1. identification of an open reading frame (ORF) - this would include a series of codons bounded by start and stop codons. This gets progressively harder to do if the gene has a large number of exons embedded in long introns. 2. specific DNA features within genes - these would include a bias towards certain codons found in genes or splice sites (to remove intron RNA) 3. comparing putative gene sequences for homology with known genes from different organisms, but avoiding sequences that might be conserved regulatory regions. 4. identification of RNA transcripts or expressed protein (which does not require DNA sequence analysis as the top three steps do) - 5. inactivating (chemically or through specific mutagenesis) a gene product (RNA or protein). New findings make it even more complicated to define a gene, especially if the transcripts of a "gene region" are studied. Cheng et al studied all transcripts from 10 different human chromosomes and 8 different cell lines. They found a large number of different transcripts, many of which overlapped. Splicing often occurs between nonadjacent introns. Transcripts were found from both strands and were from regions containing introns and exons. Other studies found up to 5% of transcripts continued through the end of "gene" into other genes. 63% of the entire mouse genome, which is comprised of only 2% exons, is transcribed. The Language of DNA In this short chapter, you will briefly learn how modern molecular biologists manipulate DNA, the blueprint for all of life. The details will be found in subsequence chapters. The four-letter alphabet (A, G, C, and T) that makes up DNA represents a language that when transcribed and translated leads to the myriad of proteins that make us who we are as a species and as individuals. Let's continue with the metaphor that DNA is a language. To master that language, as with any other language, we need to be able to read, write, copy, and edit that language. If you were using a word processor to find one line in a hundred-page document or one article from one book out of the Library of Congress, you would also need a way to search the large print base available. You might want to compare two different copies of files to see if they differ from each other. From the lab and this online discussion and problem set, you will learn how modern scientists read, write, copy, edit, search, and compare the language of the genome. These abilities, acquired over the last twenty years, have revolutionized our understanding of life and have given us the potential to alter, for good or evil, life itself. DNA in human chromosomes exists as one long double-stranded molecule. It is too long to physically study and manipulate in the lab. Using a battery of enzymes, the DNA of chromosomes can be chemically cleaved into smaller fragments, which are more readily manipulable. (Similar techniques are used to sequence proteins, which require overlapping polypeptide fragments to be made.) After the fragments have been made, they must be separated from each other in order to study them. DNA fragments can be separated on the basis of some structural feature that differentiates the fragments from each other. Polarity can not be used since all DNA fragments have negatively charged phosphates in the sugar-phosphate backbone of the molecule. Although each fragment would have a unique sequence, it would be hard to separate all the different fragments, by, for instance, attaching some molecule that binds to a unique sequence in the major groove of a given fragment to a big bead and using that bead to separate out that one unique fragment. You would need a different bead for each unique fragment! The best way to separate the fragments from each other is to base the separation on the actual size of the fragment by using electrophoresis on an agarose or polyacrylamide gel. A carbohydrate extract called agarose is made from algae. Water is added to the extract, which is then heated. The carbohydrate extract dissolves in the water to form a viscous solution. The agarose solution is poured into a mold (like warm jello) and is allowed to solidify. A plastic comb with wide teeth was placed in the agarose when it was still liquid. When the agarose is solid, the comb can be removed, leaving in its place little wells. A solution of DNA fragments can be placed in the wells. The agarose slab with the sample is covered with a buffer solution and electrodes are placed at each end of the slab. The negative electrode is placed near the well-end of the agarose slab while the positive electrode is placed at the other end. If a voltage is applied across the agarose slab, the negatively charged DNA fragments will move through the agarose gel toward the positive electrode. This migration of charged molecules in solution toward an oppositely charged electrode is called electrophoresis. Pretend you are one of the fragments. To you, the gel looks like a tangled cobweb. You sneak your way through the openings in the web as you move straight forward to the positive electrode. The larger the fragment, the slower you move because it is hard to get through the tangled web. Conversely, the shorter the fragment, the faster you move. Using this technique and its many modifications, oligonucleotides differing by just one nucleotide can be separated from each other. In the electrophoresis of DNA fragments, a fluorescent, uncharged dye, ethidium bromide, is added to the buffer solution. This dye literally intercalates -between the base pairs of DNA, which imparts a fluorescent yellow-green color to the DNA when UV light is shown on the agarose gel. Reading DNA We will discuss one method of reading the sequence of DNA. This method, developed by Sanger won him a second Nobel prize. To sequence a single-stranded piece of DNA, the complementary strand is synthesized. Four different reaction mixtures are set up. Each contains all 4 radioactive deoxynucleotides (dATP, dCTP, dGTP, dTTP) required for the reaction and DNA polymerase. In addition, dideoxyATP (ddATP) is added to one reaction tube The dATP and ddATP attach randomly to the growing 3' end of the complementary stranded. If ddATP is added no further nucleotides can be added after since its 3' end has an H and not a OH. That's why they call it dideoxy. The new chain is terminated. If dATP is added, the chain will continue to grow until another A needs to be added. Hence a whole series of discreet fragments of DNA chains will be made, all terminated when ddATP was added. The same scenario occurs for the other 3 tubes, which contain dCTP and ddCTP, dTTP and ddTTP, and dGTP and ddGTP respectively. All the fragments made in each tube will be placed in separate lanes for electrophoresis, where the fragments will separate by size. Didexoynucleotides Figure: Didexoynucleotides PROBLEM: You will pretend to sequence a single-stranded piece of DNA as shown below. The new nucleotides are added by the enzyme DNA polymerase to the primer, GACT, in the 5' to 3' direction. You will set up 4 reaction tubes, Each tube contains all the dXTP's. In addition, add ddATP to tube 1, ddTTP to tube 2, ddCTP to tube 3, and ddGTP to tube 4. For each separate reaction mixture, determine all the possible sequences made by writing the possible sequences on one of the unfinished complementary sequences below. Cut the completed sequences from the page, determine the size of the polynucleotide sequences made, and place them as they would migrate (based on size) in the appropriate lane of an imaginary gel, which you have drawn on a piece of paper. Lane 1 will contain the nucleotides made in tube 1, etc. Then draw lines under the positions of the cutout nucleotides to represent DNA bands in the gel. Read the sequence of the complementary DNA synthesized. Then write the sequence of the ssDNA that was to be sequenced. 5' T C A A C G A T C T G A 3' (STAND TO SEQUENCE) 3' G A C T 5' (primer) 3' G A C T 5' (primer) 3' G A C T 5' (primer) 3' G A C T 5' (primer) 3' G A C T 5' (primer) 3' G A C T 5' (primer) 3' G A C T 5' (primer) 3' G A C T 5' (primer) Since the DNA fragments have no detectable color, they can not be directly visualized in the gel. Alternative methods are used. In the one described above, radiolabeled ddXTP's were used. Once the sequencing gel is run, it can be dried and the bands visualized by radioautography (also called autoradiography). A place of x-ray film is placed over the dried gel in a dark environment. The radiolabeled bands will emit radiation which will expose the x-ray film directly over the bands. The film can be developed to detect the bands. In a newer technique, the primer can be labeled with a fluorescent dye. If a different dye is used for each reaction mixture, all the reaction mixtures can be run in one lane of a gel. (Actually, only one reaction mix containing all the ddXTP's together is performed.) The gel can then be scanned by a laser, which detects fluorescence from the dyes, each at a different wavelength. Figure: DNA sequencing using different fluorescent primers for each ddXTP reaction One recent advance in sequencing allows for real-time determination of a sequence. The four deoxynucleotides are each labeled with a different fluorophore on the 5' phosphate (not the base as above). A tethered DNA polymerase elongates the DNA on a template, releasing the fluorophore into solution (i.e. the fluorophore is not incorporated into the DNA chain). The reaction takes place in a visualization chamber called a zero mode waveguide which is a cylindrical metallic chamber with a width of 70 nm and a volume of 20 zeptoliters (20 x 10-21 L). It sits on a glass support through which laser illumination of the sample is achieved. Given the small volume, non-incorporated fluorescently tagged deoxynucleotides diffuse in and out in the microsecond timescale. When a deoxynucleotide is incorporated into the DNA, its residence time is in the millisecond time scale. This allows for prolonged detection of fluorescence which gives a high signal-to-noise ratio. Newer technology in which sequence is done by moving DNA through pores in membranes could bring sequencing down to \$1000/genome or less. Writing DNA Oligonucleotides can be synthesized on a solid bead. By adding one nucleotide at a time, the sequence and length of the oligonucleotide can be controlled. Copying DNA Several methods exist for copying a sequence of DNA millions of times. Most methods make use of plasmids (which are found in bacteria) and viruses (which can infect any cell). The DNA of the plasmid or virus is engineered to contain a copy of a specific DNA sequence of interest. The plasmid or virus is then reintroduced into the cell where amplification occurs. Initially, a DNA containing a gene or regulatory sequence of interest is cut at specific places with an enzyme called a restriction endonuclease, or restriction enzyme for short. The enzyme doesn't cleave DNA anywhere, but rather at "restricted" places in the sequence, much as an endoprotease cleaves a protein after a given amino acid within a protein chain. Instead of cleaving one strand, as in proteins, the restriction endonuclease must cleave both strands of dsDNA. It can cut the strands cleanly to leave blunt ends, or in a staggered fashion, to leave small tails of ssDNA. Multiple such sites exist at random in the genome. The gene of interest must be flanked on either side by such a sequence. The same enzyme is used to cleave the plasmid or virus DNA. Figure: Cleaving DNA with the Restriction Enzyme EcoR1 The foreign fragment of DNA can then be added to the plasmid or viral DNA as shown to make a recombinant DNA molecule. This technique of DNA cloning is the basis for the entire field of recombinant DNA technology. Figure: Cloning a Restriction Fragment into a Plasmid Animation of Gene Splicing The plasmid can be added to bacteria, which take it up in a process called transformation. The plasmid can be replicated in the bacteria which will copy the DNA fragment of interest. Typically the plasmid carries a gene that can make the bacteria resistant to an antibiotic. Only bacteria that carry the plasmid (and presumably the insert) will grow. To isolate the desired fragment, the plasmids are isolated from bacteria, and cleaved with the same restriction enzyme to remove the desired fragment, after which it can be purified. In addition, the bacteria can be induced to express the protein from the foreign gene. In lab 4, we will transform bacteria with a plasmid containing the gene for human adipoctye acid phosphatase beta, HAAP-B, and induce expression of the gene. A similar method can be used to copy DNA in which the foreign fragment is recombined with the DNA of bacteriophage, a virus, which infects bacteria like E. Coli. The recombinant DNA can be packaged into actual viruses, as shown below. When the virus infects the bacteria, it instructs the cells to make millions of new viruses, hence copying the foreign fragment of interest. Sometimes, "cloning" or copying a fragment of DNA is not what an investigator really wants. If the genomic DNA comes from a human cell, for instance, the gene will contain introns. If you put this DNA into a plasmid or bacteriophage, the introns go with it. Bacteria can replicate this DNA, but often one wants not to just copy (amplify) the DNA but also transcribe it into RNA and then translate it into protein. Bacteria, however, can not splice out the intron RNA, so mature mRNA can not be made. If one could clone into the bacteria's DNA without the introns, this problem would not exist. One such possible method exists in which you start with the actual mRNA for a protein of interest. In this technique, a dsDNA copy is made from a ss-mRNA molecule. Such dsDNA is called cDNA, for complementary or copy DNA. This can then be cloned into a plasmid or bacteriophage vector and amplified as described above. In the mid '80s a new method was developed to copy (amplify) DNA in a test tube. It doesn't require a plasmid or a virus. It just requires a DNA fragment, some primers (small oligonucleotides complementary to sections of DNA on each strand and straddling the section of DNA to be amplified. Just add to this mixture dATP, dCTP, dGTP, dTTP, and a heat-stable DNA polymerase from the organism Thermophilus aquaticus (which lives in hot springs), and off you go. The mixture is first heated to a temperature, which causes the DsDNA strands to separate. The temperature is cooled allowing a large stoichiometric excess of the primers to anneal to the ssDNA. The heat-stable Taq polymerase (from Thermophilus aquaticus) polymerizes DNA from the primers. The temperature is raised again, allowing dsDNA strand separation. On cooling the primers anneal again to the original and newly synthesized DNA from the last cycle and synthesis of DNA occurs again. This cycle is repeated as shown in the diagram. This chain reaction is called the polymerase chain reaction (PCR). The target DNA synthesized is amplified a million times in 20 cycles, or a billion times in 30 cycles, which can be done in a few hours. Editing DNA We will spend much time discussing how specific amino acids could be covalently modified to either identify the presence of a specific amino acid or to modify the activity of the protein. Now it is routine to use recombinant DNA technology, to alter one or more nucleotides, to either change the amino acid or add or delete one or more amino acids. This technique, called site-specific mutagenesis, is used extensively by protein chemists to determine the importance of a given amino acid in the folding, structure, and activity of a protein. The techniques are described in the diagram below; Searching DNA Where on a chromosome is the gene that codes for a given protein? One way to find the gene is to synthesize a small oligonucleotide "probe" which is complementary to part of the actual DNA sequence of the gene (determined from previous experiments). Attach a fluorescent molecule to the DNA probe. Then take a cell preparation in which the chromosomes can be seen under the microscope. Base is added which unwinds the double-stranded DNA helix. A fluorescent probe is added that will bind to the chromosome at the site of the gene to which the DNA is complementary. Hybridization is the process whereby a single-stranded nucleotide sequence (the target) binds through H-bonds to another complementary nucleotide sequence (the probe). What if you don't know the nucleotide sequence of the gene, but you know the amino acid sequence of the protein, as in the example shown below? From the genetic code table, you could predict the possible sequence of all possible RNA molecules that are complementary to the DNA in the gene. Since some of the amino acids have more than one codon, there are many possible sequences of DNA that could code for the protein fragment. The link below shows all possible corresponding mRNA sequences that could code for a short amino acid sequence. The 20 mer sequence of minimal degeneracy in the nucleotide sequence should be used as genomic probes. Comparing DNA The DNA sequence of each individual must be different from every other individual in the world (with the exception of identical twins). The difference must be less than the differences between a human and a chimp, which are 98.5 % identical. Let us say that each of us have DNA sequences that are 99.9 % identical as compared to some "a normal humans". Given that we have about 4 billion base pairs of DNA, that means we are all different in about 0.001 x 4,000,000,000 which is about 4 million base pairs different. This means that on average we have one nucleotide difference for each 1000 base pairs of DNA. Some of these are in genes, but most are probably in between DNA, and many have been shown to be clustered in areas of highly repetitive DNA at the ends of chromosomes (called the telomeres) and in the middle (called the centromeres). Now, remember that there are restriction enzyme sites interspersed randomly along the DNA as well. If some of the differences in the DNA among individuals occur within the sequences where the DNA is cleaved by restriction enzymes, then in some individuals a particular enzyme won't cleave at the usual site, but at a more distal site. Hence, the size of the restriction enzyme fragments should differ for each person. Each person's DNA, when cut by a battery of restriction enzymes, should give rise to a unique set of DNA fragments of sizes unique to that individual. Each person's DNA has a unique Restriction Fragment Length Polymorphism (RFLP). How could you detect such polymorphism? You already know how to cut sample DNA with restriction enzymes, and then separate the fragments on an agarose gel. An additional step is required, however, since thousands of fragments could appear on the gel, which would be observed as one large continuous smear. If however, each fragment could be reacted with a set of small, radioactive DNA probes which are complementary to certain highly polymorphic sections of DNA (like telomeric DNA) and then visualized, only a few sets of discrete bands would be observed in the agarose gel. These discrete bands would be different from the DNA bands seen in another individual's gene treated the same way. This technique is called Southern Blotting and works as shown below. DNA fragments are electrophoresed in an agarose gel. The ds DNA fragments are unwound by heating, and then a piece of nitrocellulose filter paper is placed on top of the gel. The DNA from the gel transfers to the filter paper. Then a small radioactive oligonucleotide probe, complementary to a polymorphic site on the DNA, is added to the paper. It binds only to the fragment containing DNA complementary to the probe. The filter paper is dried, and a piece of x-ray film is placed over the sheet. A set of radioactive fragments (which are not complementary to the probe) are run as well. They serve as a set of markers to ensure that the gel electrophoresis and transfer to the filter paper were correct. This technique is shown on the next page, along with a RFLP analysis from a particular family. When this technique is used in forensic cases or in paternity cases, it is called DNA fingerprinting. With present techniques, investigators can state unequivocally that the odds of a particular pattern not belonging to a suspect are in the range of one million to one. The x-ray film shown below is a copy of real forensic evidence obtained from a rape case. Shown are the Southern blot results from suspect 1, suspect 2, the victim, and the forensic evidence. Analyze the data.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/01%3A_The_Foundations_of_Biochemistry/1.04%3A_Genetic__Foundations.txt

Section 1 Questions Question \(1\) In Figure 1.2, two examples of types of enzyme-substrate binding are shown: the Lock-and-Key model and Induced-Fit. What are some situations in which one style of the enzyme would be favored over the other? Answer Lock and key enzymes are highly specific for their substrate and therefore do not need a transition state to undergo the catalytic reaction. This could be used for substrate channels like Na+/K+ pumps in which a reaction doesn’t need to occur. Induced fit enzymes utilize a transition state, to convert a substrate into a product. The transition state is able to cause a conformational change in the active site of the enzyme and facilitate high-energy reactions such as breaking or forming chemical bonds Question \(2\) Label the following type of import/export mechanisms as passive, active, or facilitated and explain why: endocytosis, ion channels, pores, transporters/permeases. Some may have more than one answer. Answer Endocytosis: Active, facilitated. Endocytosis or “cell eating” is a multi-enzyme mediated process that allows the cell to uptake large particles from its environment. This involves membrane modification, protein receptors, and digestive enzymes and organelles working across gradients. Pores: Passive, facilitated. Once porins establish pores, such as in the nuclear envelope, small molecules like DNA and RNA can passively diffuse in and out of the membrane without the need for carrier proteins. Ion Channels: Active, facilitated OR passive, facilitated. Active ion channels pump small molecules across a gradient and are typically considered to be “gated,” meaning that the enzymes can open and close in a regulated manner to control what is being moved across the membrane. Passive ion channels are permanently open to facilitate transfer and rely on a constantly established concentration gradient to allow for transport to occur. Transporters/Permeases: Active, facilitated. Transporters move larger molecules across a concentration gradient and assist in the movement of soluble proteins and molecules through the hydrophobic membrane Section 2 Questions Label the functional groups present in the chemicals shown below: Answers: Section 3 Questions 1) a. Consider a subset of reactions of glycolysis given below. ΔG'°, substrates, and products are given from colon cancer cells (nmol/g tissue). After examining the conditions of the cell for each enzymatic reaction, predict if the ΔG of the reaction will increase or decrease. (Data from Hirayama A et al. 2009 Cancer Research. The ratio of NAD+/NADH is 10:1 and the concentrations of the cofactors are ATP (110) and ADP (300). Reaction ΔG'° [Substrate] [Product] #1 Hexokinase Glucose → Glucose-6-phosphate -16.6 123 75 #2 Phosphoglucose Isomerase Glucose-6-phosphate → Fructose-6-phosphate 1.67 75 50 #3 Phosphofructokinase Fructose-6-phosphate → Fructose-1,6-bisphosphate -14.2 50 50 #10 Pyruvate Kinase Phosphoenolpyruvate → Pyruvate -31.4 5 25 Lactate Dehydrogenase -25.1 25 25,000 Answer: Reaction #1 - Increase. Reaction #2 - Decrease. Reaction #3 - Increase. Reaction #10 - Increase. Lactate Dehydrogenase - Increase. 2) Consider the reaction below along with the thermodynamic properties: ΔH° = -760 kJ/mol, ΔS° = -0.185 kJ/mol K, and ΔG = -705 kJ/mol Na+(g) + Cl-(g) → Na+(aq) + Cl-(aq) At what temperature would this reaction have an equilibrium constant of 1? Answer: ΔG° = RTln(Keq) Because we want know know the temperature at which Keq = 1, and we know that the ln(1) = 0, ΔG° = 0 when Keq = 1. ΔG° = ΔH° - TΔS° 0 = -760 kJ/mol - T(-0.185 kJ/mol K); Rearrange and solve for T = 4108.1 °K

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/01%3A_The_Foundations_of_Biochemistry/1.05%3A_Chapter_1_Questions.txt

Search Fundamentals of Biochemistry “Nothing in the world is as soft and yielding as it, Yet nothing can better overcome the hard and strong, For they can neither control nor do away with it. The soft overcomes the hard, The yielding overcomes the strong;” These words come from the Tao Te Ching by Lao Zu. Let’s convert this into a chemical riddle and apply it at the nanoscopic level to biochemistry! “What it loses it gains, What it donates it accepts, It is weak yet strong, It strengthens yet destroys;” What is it? The answer (one of many possible) is water! It gains and loses protons, donates and accepts electrons, can be both a weaker or stronger acid/base or oxidizing/reducing agent, and can lead to crystal formation or dissolution, depending on circumstances. Water, at least on our planet, appears necessary for life. We know of no biological life form that exists without it. This molecule has a plethora of properties, which make it unique compared to most other liquids and optimal for the type of life on earth. It has contrasting and oppositional properties. Let’s investigate a few. Water as a solvent Solubility is a property that depends on the nature of both solute and solvent. To a first approximation, We tell students in introductory chemistry and biology courses that for a solute to dissolve in a solvent, and form a solution (an example of a homogenous mixture), the sum of noncovalent interactions (intermolecular forces) between solute and solvent must be greater than those among solute molecules and those among solvent molecules. As students advance in chemistry classes, nuance is added to that general understanding as entropic contributions to solubility must be considered. Entropy is often described as the degree of apparent disorder in the system. Given that description, changes in entropy would appear to favor the soluble state as a solution of the solute in solvent would be more disordered. That simple description must be adjusted to account for the ordered state of solvent (a clathrate) surrounding a solute and of “holes” in the solvent that accommodate larger solute molecules. Enthalpy considerations also must be considered. The description of entropy as a measure of disorder is not precise. Rather it should be described as a measure of the number of microstates of energy or particles available within a system. An entropy increase would arise from an increase in the number of such available microstates, which could correlate with an increase in the disorder of a system. Students might often consider a molecule as either soluble or insoluble in a given solvent. This notion can be reinforced by simple liquid/liquid partitioning experiments in organic chemistry experiments in which two immiscible solvents (for example water and an ether) are used. Yet diethyl ether is partially soluble in water (1 g/100 mL). Nonpolar molecules with no or few bond dipoles are generally considered insoluble. Students would know that acetic acid, a two-carbon molecule, is soluble in water, but how many carbons are necessary for the molecule to become essentially insoluble? Molecules with a single polar group (-OH, CO2H) and a long alkyl/acyl chain are best described as amphiphilic. Amphiphiles like octanol (C8H17OH) and dodecyl sulfate (CH3(CH2)10­CO2H) can form multimolecular aggregates called micelles even as they exist in as a biphasic system, as shown in the following equilibria: $\ce{C8H17OH(liq) ↔ C8H17OH(aq) <=> C8H17OH(micelle)}. \nonumber$ Figure $1$ shows an interactive iCn3D model of a micelle below, which consists of 54 self-associated molecules of dodecylphosphocholine fatty acids. It has almost "complete" separation of polar (on the surface) and nonpolar atoms (buried). Note the grey lines representing the nonpolar tails are buried from the surrounding water molecules, which form H bonds with the polar head groups. Without some limited solubility, the following reaction could not occur: $\ce{nC8H17OH(aq) ↔1-C8H17OH(micelle).} \nonumber$ To solve the general problem of the limited solubility of organic molecules in aqueous-based life, biomolecular structures have evolved to “transport” mostly nonpolar molecules like long-chain carboxylic acids (fatty acids) and cholesterol in circulation. The structure of one such fatty acid and cholesterol-containing particle, nascent high-density lipoprotein (HDL), has been determined by small-angle neutron scattering. Figure $2$ shows an an interactive iCn3D model of it. The gray sticks represent the nonpolar, acyl tails of the long-chain carboxylic (fatty) acids while the polar red (oxygen) and blue (nitrogen) atoms surrounding the surface are polar groups connected to the tails. The long magenta and dark blue "helices" represent a protein that wraps around the particle and stabilizes it. The same ideas can be applied to the solubility of salts. Students will remember general solubility rules (all Gp 1 and Gp 7 salts are soluble) from introductory chemistry. Salts of divalent cations are less soluble as the attractive ion-ion forces within the solid crystal lattice are too strong for the compensatory ion-dipole interactions between the ion and water. Hence salts of Ca2+ and Fe2+ ions such as CaCO3 and FeCO3 are generally insoluble (Ksp values of 1.4 x 10-8 and 3.1 x 10-11, respectively). Insoluble calcium salts (carbonates and silicates) are need for shells of Foraminifera and skeletons of vertebrates. Yet free Ca2+ and Fe2+ ion are found in extracellular and intracellular compartments. Divalent cations like Fe2+ can be toxic at a higher concentration so ways to effectively transport and sequester them have evolved. Figure $3$ shows the structure of human heavy-chain ferritin (4zjk), a protein that forms a hollow shell in which is stored Fe2+ ions (along with counter ions). The model below shows a ferritin with 120 Fe2+ ions (spheres) inside the hollow ferritin sphere. Finally, let’s consider the solubility of gases. The ones that are the most abundant and relevant are O2 and CO2 as they are the reactants and products of oxidative respiration. The gases, although they contain oxygen atoms, are nonpolar and have no net dipole. Hence they are quite insoluble in water. However, they must be soluble enough to allow fish to extract it from water. To solve the solubility problem, evolution has produced proteins like vertebrate hemoglobin that bind oxygen through a transition metal complex containing Fe2+-heme complex (hemoglobin in vertebrates). Some invertebrates use the transition metal Cu ions in hemocyanins for the same purpose. Figure $4$ shows an interactive iCn3D model of dioxygen (red spheres), bound to a planar heme (yellow highlights) which contains an Fe2+ at its center (not shown) at it center in human hemoglobin (6BB5) Water engages in noncovalent interactions with itself and other molecules. Individual noncovalent interactions are weak but if there are many they can lead to very strong interactions. You've studied noncovalent interactions before, which may have been described as “intermolecular forces”. We prefer the term noncovalent interaction. These include ion-ion, ion-dipole, hydrogen bonds, dipole-dipole, induced dipole-induced dipole, and other variants. All of these interactions originate in the electrostatic force between two charged objects. There is only one law that describes the forces of attraction, and that’s Coulomb’s Law: $F=\dfrac{k Q_{1} Q_{2}}{r^{2}} \nonumber$ From this force derives all the electrostatic interactions listed above. The magnitude of the attractions for these electrostatic interactions depends on the way charge is distributed in the attracting species. We will explore these in depth in Chapter 2.4. Water as a reactant: Acids and Bases H2O, with its sharable lone pairs and slightly positive Hs is both a Brønsted–Lowry base and acid. Its acid base chemistry hence is among it’s most important features. Water, acting as a base, can react with both strong and weak acids. Examples of reactions of a strong acid ($\ce{HCl}$) and weak acids (acetic acids and ammonium) with water as a base are shown in Figure $5$. Likewise, water can act as an acid as demonstrated in Figure $6$. In the first example, no net changes occur. In the second, a negatively charged deprotonated amine (a stronger base than water) can accept a proton from water, which acts as an acid. All acid/base reactions go predominantly in the direction of stronger acid/strong base to weaker acid/weaker base. Whether water reacts with a strong acid, such as HCl, or a weak one like acetic acid, the strongest acid that can actually exist in an aqueous system is H3O+(aq). This is an example of the leveling effect. Water as a reactant: nucleophile/electrophile In the reactions above, we characterized water as a Brønsted–Lowry acid or base. More generically, we could have said water is a Lewis acid (electron pair acceptor) or Lewis base (electron pair donor). In many reactions, we can also call water a nucleophile (when it shares it lone pair) or an electrophile (when its slightly positive H atoms react with a nucleophile. Here are some examples. Reaction of water with a transition metal complex. This reaction below is effectively a nucleophilic substitution reaction in which water displaces ammonia as a ligand as shown in Figure $7$ and in the following chemical equation. $\ce{[Cu(NH3)4(H2O)2]^{2+} + 4H2O <=> [Cu(H2O)6]^{2+} + 4NH3 } \nonumber$ Hydration of an alkene The reaction is catalyzed by the addition of a proton from an acid (like H2SO4) which can be called an electrophilic hydration. Once protonated at the carbon which makes the most stable carbocation, water as a nucleophile attacks the positive carbon to produce the alcohol. These steps are illustrated in Figure $8$. Nucleophilic substation at an electrophilic carbonyl This is a very common reaction. When water is the nucleophile, the reaction is also called a hydrolysis reaction. The reactions in Figure $9$ are shown with OH- as the nucleophile instead of water for simplicity. Water as a reactant: Oxidizing/Reducing agent Everyone knows what happens if you throw a piece of solid Na or K into water. An extremely exothermic reaction occurs which releases $\ce{H2}$ gas which can catch fire and lead to an explosion. The reaction of Na is: $\ce{2Na(s) + H2O → 2Na^{+}(aq) + OH^{-} (aq) + H2(g) .} \nonumber$ The oxidation number of elemental sodium is 0, while Na+ is +1, indicating that the sodium metal has been oxidized by the water which acts as an oxidizing agent. This reaction occurs with many pure metals, but some that are less reactive (remember the activity series from introductory chemistry?) required acid, a protonated form of water, as shown in the reaction below: $\ce{Zn(s) + 2H3O^{+}(aq) ⟶ Zn^{2+} (aq) +2H2O(l) +H2(g)}\nonumber$ As in acid/base reactions, in a redox reaction, an oxidizing agent and a reducing agent react to form a new oxidizing and reducing agent. Other reactants can oxidize water to form oxygen. Consider fluorine gas for example: $\ce{3F2 + 2H2O -> O2 + 4HF}\nonumber$ F2 is a strong oxidizing agent (as you would surmise from its electronegativity) than O2 so the reaction proceeds vigorously to the right. Of more biological relevance is the oxidation of water to produce O2 in photosynthesis, a complex series of reactions that is effectively the reverse of combustion: $\ce{6CO2 (g) + 6H2O (l) → C6H12O6(s) + 6O2(g).}\nonumber$ This reaction obviously is endergonic and requires a large input of energy so the reaction proceeds to produce the potent oxidizing agent O2. The special oxygen-evolving complex in photosynthesis is the powerful oxidant that can oxidize H2O to form the weaker oxidizing agent, O2.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/02%3A_Water_and_its_Role_in_Life/2.01%3A_The_multiple_roles_of_water.txt

Search Fundamentals of Biochemistry The previous section described the general acid/base properties of water. There are many functional groups in both small and large biomolecules that act as acids and bases. Common weak acids are carboxylic acids and derivatives of phosphoric acid which become negatively charged on donation of a proton. Common weak bases are amines, which become positively charged on protonation. Such charge acquisition changes the properties of the acid or base. A protonated amine is no longer a nucleophile. A deprotonated carboxylic acid can now engage in an ion-ion IMF. The extent of deprotonation depends on the acidity/basicity of the environment. We have to turn to a bit of mathematics to determine that extent. Reaction of water with self: Autoionization As shown in the previous section, water can react with itself to produce H3O+ and OH- as illustrated in Figure $1$. This autoionization reaction is often represented in a simpler form: $\ce{H2O <=> H^{+} + OH^{-}.}\nonumber$ The equilibrium constant for this simplified reaction can be written as $K_{e q}=\frac{\left[H^{+}\right]\left[O H^{-}\right]}{H_2 O}$ Given the known value of $K_{eq}$ and the concentration of water (55 M), this can be simplistically rewritten as $K_a=55 K_{e q}=\left[H^{+}\right]\left[O H^{-}\right]=10^{-14}$ (see discussion of the pKa of water below. Hence pure, neutral water has equal but small concentrations, 10-7 M of H3O+ and OH-. You remember from introductory chemistry and life in general that the pH of pure water is 7. This derives from the general formulas for both pH and a new quantity, pKa. $\begin{gathered} p H=-\log \left[H_3 O^{+}\right]=-\log \left(10^{-7}\right)=7 \ p K_a=-\log K_a=-\log \left(10^{-14}\right)=14 \end{gathered}$ Note Some texts incorrectly use 15.7 for the pKa of water. Here is a link to an explanation of why 14 is better. The wrong value of 15.7 would make the pKa of water higher than that of methanol (15.3), which simply can't be since the methoxide anion is less stable due to electron release by the methyl group than OH-. All acids of the generic formula HA have pKa. $\ce{HA <=> H^{+} + A^{-}} \nonumber$ The equilibrium constant for this simplified reaction can be written as \begin{aligned} & K_{e q}=\frac{\left[H^{+}\right]\left[A^{-}\right]}{H A} \ K_a= & {[H A] K_{e q}=\left[H^{+}\right]\left[A^{-}\right] } \ & p K_a=-\log K_a \end{aligned} The pKa becomes a simple measure of the strength of an acid. The stronger the acid, the larger the Ka and the smaller the pKa. Here is a table of pKa values for common acids and functional groups. The pKa values change with different substituents on the acids differ. The stronger the acid, the weaker the conjugate base. This should make sense as a weak base is unlikely to reabstract a proton and return to its original acidic form. Likewise, the weakest acids produce the strongest conjugate bases which reprotonate to return to the weak acid state. Group Example weaker acid ≈ pKa Conjugate Base stronger conj. base alkane 50 amine 35 alkyne 25 alcohol 16 water 14 protonated amine 10 phenol 10 thiol 10 imidazole 7 carboxylic acid 5 hydrochloric acid -8 stronger acid   weaker conj. base The Henderson-Hasselbalch Equation We can find the pKa for small acids in solution in pKa tables. However, from a biochemical perspective, we often need to know the charge state of the acid. Since the pH is approximately constant in organisms (more on that later), we know the [H3O+ ]. Hence we can calculate the ratio of $A^- / HA$ using the Henderson-Hasselbalch equation (Equation \ref{HH}), which is derived below. \begin{gathered} K_a=\frac{\left[H^{+}\right]\left[A^{-}\right]}{H A} \ -\log K_a=-\log \left[H^{+}\right]-\log \left(\left[A^{-}\right] /[H A]\right) \ p K_a=p H-\log \left(\left[A^{-}\right] /[H A]\right) \end{gathered} which given the traditional Henderson-Hasselbalch equation below. p H=p K_a+\log \frac{\left[A^{-}\right]}{H A} In your chemistry class, you certainly would have performed titration curve analyses of acids. What is the chemistry that occurs at each step? Let's assume the pH is low and much lower than the pKa of the acid. From the Henderson-Hasselbalch equation, you would surmise that the ratio of A-/HA is very small - that is the acid is essentially fully protonated. That should also make intuitive sense. For a weak acid to be coaxed to give up a proton, a reasonably strong base (like OH-) should be added. So at low pH, the acid exists just as HA. Now consider adding an amount to NaOH to match the concentration of the ionizable proton. At that point in the titration, mass balance would suggest that the acid in its protonated state is gone, and all that remains is A-. What happens if just enough NaOH is added to react with half of the HA. The mass balance would tell us that A-=HA and at that point, the pH = pKa of the acid. The entire titration curve can be calculated from the Henderson-Hasselbalch equation. A graph of it is shown below. The graph simply shifts up as the pKa is increased. The pH starts soaring at the end of the graph after the added hydroxide has reacted with the last ionizable proton. After that, the pH is determined by the concentration of the strong base OH-. The graph is flattest in the middle of the curve at the inflection point of the curve. Note at this pH, pH = pKa. In the middle of the curve, the pH changes least on the addition of small amounts of OH-. This is the basis of buffering which will be covered in the next section. If you know the pH of a solution and the pKa of the ionizable group, you can very quickly estimate the average charge state of the function group. Let's see what the Henderson-Hasselbach equation (Equation \ref{HH}) predicts under three specific pH states: 1. If the pH is 2 units below the pKa (i.e under more acidic conditions when you would expect the group to be protonated), the equation becomes,$-2 = log A/HA, or .01 = A/HA$. This means that the functional group will be about 99% protonated (with either 0 or +1 charge, depending of the functional group). 2. If the pH is 2 units above the pKa, the equation becomes $2 = log A/HA, or 100 = A/HA$. Therefore the functional group will be 99% deprotonated. 3. If the pH = pka, the HH equation becomes $0 = log A/HA, or 1 = A/HA$. Therefore the functional group will be 50% deprotonated. From these simple examples, we have illustrated the +2 rule to determine the charge state. This rule is used to quickly determine protonation, and hence charge state, and is extremely important to know (and easy to derive). Polyprotic Oxyacids Acids that can donate more than one proton are called polyprotic acids. They are typically oxyacids, with the ionizable proton on an oxygen atom, which can form a reasonably stable the oxyanion (negative on the oxygen) as the oxygen is electronegative and stabilize the charge. The negative charge on the conjugate base of oxyacids is further stabilized by resonance delocalization involving the doubly bonded oxygen atom. Two of the most biologically relevant oxyacids are shown in Figure $2$. The pKa for each subsequent ionization is higher since it is more difficult to remove a proton from an increasingly more charged molecular ion. The titration plot of pH vs NaOH is similar to the graph above but has multiple plateaus at pH=pKa, Derivatives of phosphoric acid are found in all major classes of biomolecules. Nucleic acids contain a sugar-phosphate link in their backbone. Many proteins become phosphorylated after they are synthesized. Membrane lipids usually contain a phosphate group. A whole class of phospholipids are found in biomembranes. Charge State of Biomolecules The Henderson-Hasselbalch equation can be used to determine the charge state of ionizable functional groups (carboxylic and phosphoric acids, amines, imidazoles, guanidino groups) even on a large macromolecule such as proteins, which contain carboxylic acid (weak acids) and amines (weak bases). Figure $3$ shows how the weakly acidic aspartic and glutamic acids, two common amino acids found in proteins, contribute negative charge to the protein and how the amine of the amino acid lysine, a weak base, contributes to positive charge. Other amino acids that contain an alcoholic function group can also become phosphorylated to produce a phosphoprotein, which converts a neutral ROH group to a phosphoester with a negative two charge as shown in Figure $4$. pKa and Environment The pKa is really a measure of the equilibrium constant for the reaction. And of course, you remember that ΔGo = -RT ln Keq. Therefore, pKa is independent of the concentration and depends only on the intrinsic stability of reactants with respect to the products. However, this is true only under a given set of conditions such as temperature, pressure and solvent composition. Consider, for example, acetic acid, which in aqueous solution has a pKa of about 4.7. It is a weak acid, which dissociates only slightly to form H+ (in water the hydronium ion, H3O+, is formed) and acetate (Ac-). These ions are moderately stable in water but reassociate readily to form the starting product. The pKa of acetic acid in 80% ethanol is 6.87. This can be accounted for by the decrease in stability of the charged products, which are less shielded from each other by the less polar ethanol. Ethanol has a lower dielectric constant than water. The pKa increases to 10.32 in 100% ethanol, and to a whopping 130 in air! The pKa values of ionizable groups in proteins vary enormously as they depend on the microenvironment of the group. Consider the amino acid aspartic acid (Asp, D), which has a -CH2CO2H R-group or "side chain" similar to acetic acid. In a given protein, a given Asp side chain might be on the surface but another in the same protein might be buried in the protein away from water. You would expect the pKa values for these two different Asp side chains to be different. The average pKa for Asp side chains in 78 different proteins was shown to be 3.5, less than that of acetic acid (4.7) but not dramatically. However, the range of pKa values for Asp in these proteins was huge, with the lowest being 0.5 (a buried Asp in the protein T4 Lysosome) and the highest being 9.2 in the protein thioredoxin from E. Coli. Figure $5$ shows an interactive iCn3D model of the surrounding environment of Asp 70 (D70) in T4 Lysoszyme. Its pKa has been determined experimentally to be 0.5 ,way stronger than acetic acid. The dotted cyan lines show ion-ion interactions between the -CH2CO2- side chain of Asp 70 (D7) and the positively charged imidazolium group of histidine (H31) in the protein. The distance between the two charged groups is about 3.4 A. The next model shows the surroundings of Asp 26 (D26) in E. coli thioredoxin, It has a pKa of 9.2. The dark blue group is surface exposed positively charge lysine side chain which can stabilize a negative charge on the Asp 26. Note, however, that it is much farther away than the imidazolium group in T4 lysozyme that stabilizes the negative change on D70. The rest of the model is colored based on hydrophobicity, which shows that the Asp 26 side chain is essentially surrounded by nonpolar groups. These would destabilize a negative charge on the D26, enhancing the stability of protonated (neutral) Asp, and elevating its pKa to 9.2. Figure $6$: Surrounding environment of Asp 26 (D26) in E. Coli thioredoxin (5HR2). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...i9BNNdbA2bmP5A

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/02%3A_Water_and_its_Role_in_Life/2.02%3A_Weak_Acids_and_Bases_pH_and_pKa.txt

Search Fundamentals of Biochemistry Introduction As one way to ensure homeostasis, the pH is maintained between 7.35 and 7.45 in humans. (Much lower pH values, around 4.5, are found in the lysosome). Lower pH values are associated with metabolic and respiratory acidosis while higher pH values are characteristic of metabolic and respiratory alkalosis. pH is maintained by buffering systems that consist of a weak acid and base. If you understand the Henderson-Hasselbalch equation from the previous section, buffer systems become easy to understand. p H=p K_a+\log \frac{\left[A^{-}\right]}{H A} At the inflection point of the curve, pH = pKa and the system is most resistant to changes in pH on the addition of either acid or base. At this pH, [HA]=[A-]. If a bit of a strong acid is added, it would react with the strongest base in the solution, which would be the conjugate base of the weak acid: HCl + A- --> HA + Cl- The reaction goes from a strong acid, HCl, to the weak acid, HA. Its concentration would increase a bit but since it's a weak acid, it will only ionize to a small extent. The [HA] in the Henderson-Hasselbalch equations increases a bit but not enough to change the pH significantly. If the same amount of HCl were added to pure water, it would react completely to form an equal amount of H3O+ which would significantly alter the pH of pure water (7.0). If a bit of a strong base is added, it would react with the strongest acid in the solution which would be HA: HA + OH- --> H2O + A- The reaction goes from a strong base to the weak acid A-. Its concentration would increase a bit but since it's a weak base, it won't affect the pH significantly. The [A-] in the Henderson-Hasselbalch equations increases a bit but not enough to change the pH significantly. If the same amount of NaOH were added to pure water, it would react to make the solution basic and significantly alter the pH of pure water (7.0). To review, buffer solutions contain a weak acid and its conjugate base. They have maximal buffering capacity at a pH = pKa of the weak acid. In general, a buffered solution is best able to withstand a change in pH only with + 1 pH unit from the pKa. Biological Buffering Agents The most relevant systems for biology are the carbonic acid/carbonate buffering system, which controls blood pH and cells and the phosphate buffering system. Proteins, which have many weak acid and base functional groups, can also act as buffering agents. Carbonic acid/carbonate buffering system: At first glance, the reaction of carbonic acid can be written as follows: H2CO3 (aq) + H2O(l) ßà H3O+(aq) + HCO3-(aq) pKa = 3.6 However, this system is a bit more complex since we must consider CO2 (g) solubility and reactivity as well. The overall chemical reactions look like this, where H2CO3 is the weak oxyacid, carbonic acid and HCO3-(aq) is the weak conjugate based, bicarbonate (or hydrogen carbonate). The [CO2(aq)] >> [H2CO3 (aq)] Rx 1: CO2 (g) ßà CO2(aq) + H2O (l) ßà H2CO3 (aq) + H2O(l) ßà H3O+(aq) + HCO3-(aq) The respiratory system can quickly adjust pH simply by increasing the exhalation of CO2. The kidneys can respond in a slower fashion to remove H3O+ and retain HCO3-. The carbonic acid/bicarbonate buffering system can help us understand how shifting equilibria caused by excessive CO2 released from rapid deep breathing or decreased CO2 release associated with pulmonary disease or shallow rapid breathing can lead to respiratory alkalosis and acidosis, respectively. • Respiratory alkalosis can be caused by “hyperventilation” - breathing rapidly. This would lead to breathing out too much CO2, shifting the above equilibrium to the left, consuming H3O+, and increasing pH, making the blood more alkaline. You could breathe into a bag to increase your CO2 levels. • Respiratory acidosis is caused by increased CO2, which can occur when the lungs aren’t working well, and you can’t get rid of CO2 you produce during respiration Respiratory acidosis can happen with asthma, pneumonia, lung disease or anything that decreases respiration rate. Inhaling CO2 can lead to panic. This makes sense as it would mimic suffocation which is lethal to humans. A suffocation response follows. High CO2 would drive the equilibrium to the right, leading to H3O+ production. An acid-sensing ion channel-1a (ASIC1a) in the amygdala has been discovered which appears to mediate the effect. Panic attacks are sometimes associated with hyperventilation which leads to alkalosis, not acidosis. Less noted is that when some people panic, they take short shallow breaths (in a way almost stopping their breath). This would lead to a buildup of CO2 since it wouldn’t be released in exhalation. The acid channel in the amygdala would be activated and the panic response ensues. A Dilemma? How can carbonic acid with a pKa of 3.6 act as a buffer component at pH 7.5? An astute student might have picked up this conundrum. The solution to this problem involves looking at the full set of reactions for the components of the buffer system. Here is the complete set of reactions again: CO2 (g) ßà CO2(aq) + H2O (l) ßà H2CO3 (aq) + H2O(l) ßà H3O+(aq) + HCO3-(aq) Let's simplify it since there would be no free "gas bubbles" in blood, so CO2 (g) = CO2(aq): CO2(aq) + H2O (l) ßà H2CO3 (aq) + H2O(l) ßà H3O+(aq) + HCO3-(aq) H2CO3 (aq) participates in two different reactions. Rightwards from H2CO3 (aq) : H2CO3 (aq) + H2O(l) ßà H3O+(aq) + HCO3-(aq) Using the simplified equation with H+ gives K_a=\frac{\left[H^{+}\right]\left[\mathrm{HCO}_3^{-}\right]}{\mathrm{H}_2 \mathrm{CO}_3} Hence, \left[\mathrm{H}_2 \mathrm{CO}_3\right]=\frac{\left[\mathrm{H}^{+}\right]\left[\mathrm{HCO}_3^{-}\right]}{K_a} Leftwards from H2CO3 (aq) : H2CO3 (aq) ßàCO2(aq) + H2O (l) K_2=\frac{\left[\mathrm{CO}_2\right]}{\mathrm{H}_2 \mathrm{CO}_3} so \left[\mathrm{H}_2 \mathrm{CO}_3\right]=\frac{\left[\mathrm{CO}_2\right]}{K_a} Setting 2.3.3 and 2.3.5 equal to each other gives: \left[\mathrm{H}_2 \mathrm{CO}_3\right]=\frac{\left[\mathrm{H}^{+}\right]\left[\mathrm{HCO}_3^{-}\right]}{K_a}=\frac{\left[\mathrm{CO}_2\right]}{K_2} Solving for [H+] gives: \left[H^{+}\right]=\frac{\left[\mathrm{CO}_2\right]\left(K_a\right)}{\left[H C O_3^{-}\right]\left(K_2\right)} Now take the -log of each side to produce an equation similar to the Henderson-Hasselbalch equation. \begin{aligned} &-\log \left[H^{+}\right]=-\log \left(\frac{\left[\mathrm{CO}_2\right]}{\left[\mathrm{HCO}_3^{-}\right.}\right)-\log \left(\frac{K_a}{K_2}\right) \ &p H=p K_{a E F F E C T I V E}-\log \left(\frac{\left[\mathrm{CO}_2\right]}{\left[\mathrm{HCO}_3^{-}\right]}\right) \end{aligned} where K_{a E F F E C T I V E}=\frac{K_a}{K_2} This Henderson-Hasselbalch-like equation shows the pH is determined by the ratio $K_a/K_2$ ratio. pKa EFFECTIVE = 6.3. This gives a ratio of $CO_2/HCO_3^{-}$ of 0.08 = 8/100. There is effectively 12-13 x as much HCO3-(aq) as CO2, making the system primed to react with acid produced metabolically. Yet a second conundrum exists. The pH of the blood (7.4) is outside of the optimal range for a buffer system (in this case + 1 pH unit from the pKa which is 6.3 in this case). Again, the system is primed to react with acid as it would move the pH close to the optimal buffering pH of 6.3. Other biological systems also must be involved in maintaining pH. Phosphate buffering system: Phosphates, in the form of dihydrogen (H2PO4-) and monohydrogen phosphate (HPO42-) are also present in the blood. Given the pKa of HPO42-, why is PO43- not present to any significant degree? Since the concentration of phosphates are low in blood, this system is a minor player in blood. Proteins: Proteins are found in all cellular and extracellular fluids and they contain weak acids as buffer components. Proteins contain two amino acids, aspartic acid, and glutamic acid) that contain carboxylic acid side chains. Each comprises about 6% of the proteins. In blood, hemoglobin is the most abundant protein by far. Its role in buffering and in O2 and CO2 will be discussed in a subsequent chapter. Making Buffers in the Lab When studying biomolecules like proteins and nucleic acids in the lab, the pH of the solution is usually maintained under physiological conditions. These macromolecules are either dissolved in or diluted into a buffer solution. Sometimes it's important to study their properties and activities as a function of pH. A wide variety of buffer systems have been developed for the lab study of these molecules. The dihydrogen (H2PO4-)/monohydrogen phosphate (HPO42-) pair are commonly used as the pKa of H2PO4- is 7.21, which makes it physiologically relevant. Care must be taken in selecting buffer systems as some of them might bind calcium ions. The pKa of some weak acids vary significantly with temperature as well. Some common biological buffers are listed below. Buffers pKa (at 25°C) MES 6.10 Bis-Tris 6.50 ACES 6.78 PIPES 6.76 MOPSO 6.90 MOPS 7.20 HEPES 7.48 Tris 8.06 Tricine 8.05 Gly-Gly 8.20 Bicine 8.26 TAPS 8.40 AMPSO 9.00 CAPS 10.40 There are 3 general ways to make a buffered solution: 1. Make us separate equal concentration solutions of both the weak acid (for example Na(H2PO4) and its conjugate base (for example Na2(HPO4). Use the Henderson-Hasselbalch equation to calculate how much of each should be added to give the correct [A-]/[HA] ratio (in the case [HPO42-]/[H2PO4-1]) to give the correct pH. 2. Use a pH meter and monitor the pH when adding both solutions together until the desired pH is reached. 3. Make a solution of one of the components (weak acid or its conjugate base) and bring to near its correct volume for the desired molarity. Monitor the pH as you add a concentrated solution of either HCl or NaOH to get the desired pH. Then bring the solution to the correct volume in a volumetric flask. With this method, you will be adding counter ions (Cl- with HCl and Na+ with NaOH) which you may not want in your buffer solution. Often it is not a problem.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/02%3A_Water_and_its_Role_in_Life/2.03%3A_Buffering_against_pH_Changes_in_Biological_Systems.txt

Search Fundamentals of Biochemistry Introduction In section 2.1, we explored the role of water as a solvent. Using the adage "like dissolves like" that you may have learned in introductory chemistry and biology courses, we can rationalize what substance might dissolve in water. We related this to the types and strengths of attractive interactions that occur between solute and solvent. If in sum they are stronger than self-interactions (solute-solute and solvent-solvent), the solute would dissolve (to a reasonable extent) in the solvent. We also discussed entropic contributions to the dissolution process. For now, we will refocus on the noncovalent interactions. In introductory science courses, noncovalent interactions are often described as intermolecular forces. This term is ambiguous when applied to biochemistry. Take for example hydrogen bonds. They occur between two water molecules, for example, but also within larger molecules (like proteins) if hydrogen bond donors and acceptors within the molecule get close enough to each other in space. The table below summarizes the common noncovalent interactions/“intermolecular forces” that you studied in introductory science classes. It is hard enough for students to recognize and identify these interactions between two small molecules let alone in large molecules like proteins. We will explore these in more detail below, and give examples of noncovalent interactions between small molecules and within large ones such as proteins. We'll also add a few more specific examples of interactions. Noncovalent Interactions - "Intermolecular Forces" Interaction Type Example Distance Dependence Relative Strength Kcal/mol (kJ/mol) Direction Dependence Ion-Ion 1/r 60 (250) nondirectional H-Bond   3-15 (12-63) directional Ion-dipole 1/r2 3-5 (12-21) directional Dipole-dipole 1/r3 0.5-1 (2-4) directional Induced Dipole- Induced Dipole 1/r6 0.5 (2) (depend on size) nondirectional Although there are many different types of noncovalent interactions, one fundamental principle governs all of them. They all originate in the electrostatic force between two charged objects. There is one simple law, Coulomb’s Law, which you would have discussed in introductory science courses, and one simple equation, that describes the electrostatic force: F=\frac{k Q_1 Q_2}{r^2} where F is the force (attractive or repulsive) between two particles of charge Q1 and Q2 with their centers separated by some distance r. Replace the charges with the masses of two objects and you have Newton's Law of Gravitation. Both are inverse squared laws All the interactions described in the table above arise from the electrostatic force. The magnitude of the attractions for the interactions depends on the way charge is distributed in the attracting species. Each has a different dependency on distance. Different words are used to describe noncovalent interactions. This can be distressing to learners who might hear different terms used by chemists and biologists for the same noncovalent interactions. Some use van der Waals forces to describe induced dipole-induced dipole interactions, while others use London dispersion forces or hydrophobic forces/interactions. Others use van der Waals forces to describe all noncovalent interactions except for ion-ion. To avoid ambiguity, let's look at the IUPAC Gold Book Compendium of Chemical Terminology, which offers this definition of van der Waals forces: Definition: van der Waals Forces "The attractive or repulsive forces between molecular entities (or between groups within the same molecular entity) other than those due to bond formation or to the electrostatic interaction of ions or of ionic groups with one another or with neutral molecules. The term includes: dipole–dipole, dipole-induced dipole, and London (instantaneous induced dipole-induced dipole) forces. The term is sometimes used loosely for the totality of nonspecific attractive or repulsive intermolecular forces". IUPAC. Compendium of Chemical Terminology, 2nd ed. (the "Gold Book"). Compiled by A. D. McNaught and A. Wilkinson. Blackwell Scientific Publications, Oxford (1997). Online version (2019-) created by S. J. Chalk. ISBN 0-9678550-9-8. https://doi.org/10.1351/goldbook. Figure $1$ summarizes covalent and noncovalent interactions, using that definition. Using this definition, hydrogen bonds are usually considered a type of dipole-dipole interaction. Historically, several of the noncovalent interactions have alternative names based on the person associated with them. Only the names van der Waal and London are commonly used in introductory chemistry courses Even the word "force" is potentially ambiguous. To a physicist, there are only four known forces: • gravitational, between two objects with mass • electromagnetic (between static charges - the electrostatic force, and moving charges - the magnetic force) • the strong force (holding the nucleus together) • the weak force (also nuclear and involved in radioactive decay). We'll try to use the word interaction throughout this book. Interactions within small molecules, such as covalent bonds, and between molecules, such as induced dipole-induced dipole, vary as some function of r, the distance between the two interacting particles. Only ion-ion interactions vary as 1/r2 (F= k/r2). Attractions lower the overall energy while repulsions raise it. At some optimal distance, the system is in its most favored, lowest energy state. We just switched from discussing forces to discussing energy, another complex term. The relationship between the potential energy for covalent bond formation and for the noncovalent attraction of two atoms as a function of distance is shown in general form in Figure $2$: below. Figure $2$: Potential Energy for Covalent and Noncovlanet interactions The curve in black shows the shape of Epot vs r for the formation of a covalent bond between H atoms. The Morse potential energy function is used to model energy as a function of r for simple diatomic molecules. The red line shows the shape of Epot vs r for the noncovalent attraction of two He atoms through induced dipole-induced dipole interactions. It is modeled using the Lennard-Jones (6-12) potential function (see below). Each has an optimal r0 (the bond length for H2 and two times the van der Waals radius, rW, of each He in 2He). The energy required to break the induced dipole-induced dipole interactions between He atoms is very small, which accounts for the fact that liquid He, in which many He are interacting, only exists at very cold temperatures (boiling point = -269 Celsius). Although the graph for H2 shows the relationship between the potential energy and r0 for the covalent bond, in reality, the sources of stability of any covalent bond are complex and require, in addition, a term for the kinetic energy of the electron. Fundamentally, the strength of a covalent bond is best described using quantum wave functions for the system. The average single covalent bond strength depends on the atoms bonded and varies between 30-120 kcal/mol (125-500 kJ/mol), a factor of 4. Another confusing feature when discussing noncovalent interactions is that while we talk about forces (like the electrostatic force), we often draw graphs of energy E vs r, the distance between two interacting particles. Let's briefly examine the relationship between potential energy (Epot) and force for the electrostatic force given by Coulomb's Law by using a more familiar example, the next gravitational force of a stationary ball placed at various points on a hill, as illustrated in Figure $3$. Assume the ball is motionless at each position in the diagram so only potential energy can be considered. The red arrows (vectors) represent the relative net downward force on the ball at each position. The net downward force at the top and bottom of the hill is zero. As the slope of the hill increases, the net downward force increases. The force is directly proportional to the slope (dE/dr), or simply: F=-\frac{\Delta \mathrm{E}}{\Delta \mathrm{r}}=-\frac{\mathrm{dE}}{\mathrm{dr}} Now let's apply this same relationship to Coulomb's Law for the force. Rearranging gives d E=-\mathrm{Fdr}=-\frac{\mathrm{kq}_1 \mathrm{q}_2}{\mathrm{r}^2} \mathrm{dr} Using calculus and integrating both sides of the equations gives this general relationship between E and r for the electrostatic forces: E=\mathrm{kq}_1 \mathrm{q}_2\left(\frac{1}{\mathrm{r}}\right) A graph of Epot vs r for the electrostatic force is shown in Figure $4$. Note that the curves are hyperbolic (1/r) functions of r. There are attractive OR repulsive components. An equation for Epot vs r for the induced dipole-induced dipole interactions can also be derived. For this interaction, Epot has a different dependency on r and has both an attractive (Epot α -1/r6) AND repulsive term (Epot α +1/r12) which are added together. This potential is called the Lennard-Jones or 6-12 potential. Figure $5$ shows the total and component attractive and repulsive terms Epot vs r. Note how similar these curves are to the graphs for electrostatic energy. Now, let's look at the noncovalent interactions more carefully using examples of small and big molecules. Ion-Ion All introductory chemistry and biology textbooks differentiate ionic and covalent bonding. Ionic bonding occurs between fully charged species. Some ions are monatomic (like Na+ or Cl-), formed from gaining or losing electrons. Others are polyatomic (like ammonium - NH4+ or acetate - CH3COO-, generally formed from molecules gaining or losing protons in Brønsted acid/base reactions. Polyatomic ions are also called molecular ions. An example of the monatomic salt NaCl and the molecular salt ammonium acetate are shown in 2D Lewis structure and molecular modeling representations (spheres and sticks) in Figure $6$: below. Now, an intramolecular ionic bond can form within a larger molecule if a negatively charged group in the molecule comes close enough in 3D space to a positively charged group in the same molecule. In contrast to the examples shown above, the ionic bonds within large molecules like proteins do not occur within a large lattice of ions held together by multitudes of similar ionic bonds. Rather a single ionic bond could exist and persist in a larger molecule held together by a multitude of other noncovalent interactions. An ionic bond between a single monatomic or polyatomic cation and an anion would not exist in an aqueous solution long as the species would dissociate into separate ions solvated by water. Hence the ionic bonds that exist between charged groups within a large molecule like a protein exist in such a different environment than a solid crystal lattice that we give it a different name. It is called a salt bridge, as the ionic bond bridges distal parts of the larger molecule. We also categorize it as an ion-ion noncovalent attraction. Figure $7$ shows a salt bridge/ion-ion interaction (represented as a yellow line) between the side chains of two amino acids, aspartic acid (Asp) 67 (-CH2COO-, similar to acetate) and lysine (Lys) 69 (-RCH2NH3+, similar to NH4+) in a protein, human lysozyme. Figure $8$ shows an interactive iCn3D model of a salt bridge/ion-ion interaction between the carboxylate side chain of Asp 67 and the amine side chain of Lys 69 in human lysozyme (1REX). Figure $8$: Salt bridge (represented as a yellow line) between Asp 67 and Lys 69 in human lysozyme (1REX). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...1qpAtSs3CuvVs8 Most of the protein's atoms have been removed to simplify the structure. We haven't studied proteins yet, but to a first approximation, they are polymers consisting of amino acid monomers. The backbone of the polymer contains a repeating amide group which contains an N-H hydrogen bond donor and a C=O hydrogen bond acceptor. Each amino acid contains an R group side chain oriented away from the backbone. The R groups can be fully charged, polar or nonpolar. This protein, containing 129 amino acids in a large polymer of over 1000 atoms, has just 10 salt bridges/ion-ion interactions within the most stable structure of the protein. The structure files that contain the x,y, and z coordinates of the atoms in a large biomacromolecule like a protein usually don't give coordinates for hydrogen atoms in the structure since they are too small to detect by techniques such as x-ray crystallography or cryoelectron microscopy, which are used to determine the structure of large biomacromolecules. Computer programs can be used to add them so they can be visualized in modeling programs. The left molecule in Figure 8 shows a stick model of just a small part of the protein containing a single salt bridge/ion-ion interaction. The blue represents nitrogen with a +1 formal charge in the side chain of lysine. Hydrogen atoms have been added to the right molecule to illustrate the actual distance between adjacent atoms. Quantum calculations of actual electron density in molecular ions such as H3O+ and NH4+ (and charged amines) show that the electron density in these cations is actually shifted to the electronegative O and N atoms with electron deficiencies over the bonded H atoms (in contrast to the simpler ideal of formal charge), even though we state that the N in a charged amine has a positive formal charge. As the distance r between interacting groups increases past the optimal interaction distance, the attractions decrease. When modeling most noncovalent interactions in large molecules, programs generally use cutoff values of 5-6 Angstroms, beyond which the interactions do not contribute to stabilization. The ion-ion interaction is the strongest interaction of all, given a fixed distance for comparison. Hydrogen Bond (H-bond) The name hydrogen bond is a bit ambiguous, which leads to its misunderstanding by students. It is not a covalent bond between two atoms, X and H, such as C-H and O-H in methane and water, respectively. Rather it is a noncovalent interaction between a slightly positive H on a electronegative atom X and a slightly negative electronegative atom Y on another molecule or part of a large molecule. X and Y are electronegative atoms such as F, O or N with lone pairs The H on X-H (for example O-H or N-H) is slightly positive (δ+) since the X-H bond is polar covalent and electron density in the bond is drawn toward the electronegative atom (for example O or N). Given its small size compared to all other atoms, a slightly positive H, can get very close to a lone pair on a slightly negative (δ-) electronegative atom Y (for example O or N) on another molecule. Since r, the distance between the δ+ H and δ- N or O on two separate molecules is small, Coulomb's Law informs us that the attractive force is significant. This interaction is highly directional and distance-dependent, which accounts for the large range in relative strength (3-15 kcal/mol, 12-63 kJ/mol) for hydrogen bonds within large molecules. Hydrogen bonds occur between hydrogen bond donors and acceptors. This is determined by looking at the slightly negative electronegative atoms in the two interacting molecules. An alcohol (ROH) can be either a hydrogen bond donor or acceptor, while a ketone (R)C=OR) can only be an acceptor since it has no slightly positive H. In a hydrogen bond between an alcohol and a ketone, the O-H on the alcohol is the hydrogen bond donor while a O=C on the ketone is the hydrogen bond acceptor. This is illustrated in Figure $9$ below. Figure $9$: A hydrogen bond between a donor and acceptor In a given hydrogen bond, the donor is the X-H with the slightly positive H. Figure $9$ shows multiple representations of a central water molecule hydrogen-bonded to four other water molecules. The left image shows lone pairs as purple spheres. A common difficulty for students is to identify which of the many hydrogen atoms in any structure can engage in hydrogen bonds. One way is to circle all δ+ Hs in structures (i.e. those covalently attached to N or O) and see if there are any nearby δ-: N or :O atoms close enough to form a hydrogen bond. Figure $10$ shows a molecule of methanol forming two hydrogen bonds to two different water molecules. Only 1 of the 4 Hs on methanol is δ+ (circled in green). The others are bonded to the carbon through nonpolar covalent bonds. Hydrogen bonds are abundant in large molecules like proteins. They occur between backbone atoms, between backbone and side chains atoms, between side-chain atoms, and between protein atoms and water. Their strength depends on the magnitude of δ+ and δ- charges on the H bond donor and acceptor atoms, respectively, the distance r between them, and the angle of the bond Three types of H bonds have been categorized based on their relative strengths, based in large part on the distance between the donor and acceptor: • weak or conventional, 2.4 to 12 kcal/mol (10-50 kJ/mol) • strong or low barrier, 12 to 24 kcal/mol (50-100 kJ/mol), often called short hydrogen bonds (SHB) • very strong or no barrier >24 kcal/mol (100 kJ/mol), (Frey et al). In large proteins of known 3D structures, H bonds are calculated by locating all donors and acceptors with 3 +/- x angstroms from each other. Most structural files do not include H atoms, so the 3 Angstrom distance is from the centers of the electronegative atoms, typically N and O, involved in the hydrogen bond, as shown in Figure $11$ (purple bracket). Conventional H bonds vary between 2.8-3.2 A, which gives a distance range from the actual δ+ hydrogen on the donor to the acceptor δ- N or O (the red line below) of 1.8 to 2.2 A. Short H bonds are < 2.7A which is smaller than the sums of the van der Waals radii of N and O (blue and red circle below), suggesting that the bond has a covalent character (see below). Those between 2.5 - 2.7 are characterized as strong, low barrier, or short hydrogen bonds. Analysis of a large number of PDB structures of proteins shows many short hydrogen bonds characterized by these properties: • the donor and acceptor electronegative atoms A and B are N or O • r, the separation distance, is 2.3 A to 2.7 °A • the A–H–B angle is 1350. Detailed analyses of high-quality protein structures show one short hydrogen bond for every 16 conventional ones. They are found in proteins, protein-ligand complexes and in DNA. They are involved in many aspects of molecular function. It would seem likely that the δ+ H atom, which is covalently attached to a heteroatom like O or N (A), and which is attracted to another heteroatom B, could be exchanged between the two heteroatoms as shown in the chemical equation below, where ---- represents an H bond. A-H ----B ↔ A ----H-B A very strong/no barrier H bond occurs if A and B are very close, have similar δ- charges, and have similar pKa so that the H atom could be equally shared between A and B. It is represented by the representation below. A ||| H ||| B An example is FHF- (F||| H |||F)- in which there is no barrier for the H to move from one heteroatom to another. It thus appears that for strong and very strong H bonds, what we call the hydrogen bond has some covalent bond character. Quantum calculations show an overlap between the unoccupied antibonding σ*molecular orbital of X-H (the hydrogen donor) and the non-bonding lone electron pair molecular orbital of the hydrogen bond acceptor molecule. Even though water is a simple and ubiquitous molecule, scientists still struggle to understand its properties. Lewis structures of water can explain only so much of its physical and chemical properties. However, look at Figure $12$, which shows the electron density around water calculated using quantum theory. Do you see any "rabbit ears" (i.e. lone pairs) emanating from the oxygen atom? Don't think so! Nevertheless, everyone still uses Lewis structures with lone pairs to explain the chemistry of water and other molecules. We present this figure, in advance of a discussion at the end of this section on the halogen bond, which requires an understanding of electron density around bonded atoms. Now let's look at some hydrogen bonds within a single protein molecule. Figure $13$ hydrogen bonds (yellow dotted line) between serine (Ser) 24 (side chain -CH2OH) and asparagine (Asn) 27 (side chain -CH2(C=O)NH2 of hen egg white lysozome (1REX). As in the figures above showing salt bridges, two images are shown, one with polar H atoms added. Find the hydrogen bonds between side chains, side chains and backbone, and between backbone hydrogen bond donors and acceptors. Proteopedia has an excellent review of hydrogen bonds. Dipole-Dipole This interaction involves the alignment of permanent dipoles in molecules such that the geometric center of the δ+ of one permanent dipole on one molecule is close to and aligned with the geometric center of δ- of the permanent dipole on another. Figure $14$ shows two acetone molecules interacting through dipole-dipole interactions. These molecules can't form hydrogen bonds to each other since they both contain just hydrogen bond acceptors. The arrow represents the molecule dipole moment vector (as opposed to individual bond dipole moment for each polar covalent bond in the molecule). Note the difference in Figure $15$. The molecular dipole is the vector sum of the bond dipoles. None of the H atoms bonded to carbon in acetone are δ+ so the molecules contain no H bond donors. Although they contain a δ- oxygen, a hydrogen bond acceptor, two molecules of acetone cannot hydrogen bond to themselves. They can form hydrogen bonds to water. Pure liquid acetone evaporates readily (BP 560 C) due to this lack of strong hydrogen bonds. You can imagine two water molecules forming dipole-dipole interactions as well. However tilting the molecule to align the lone pair on an O with the δ+H on another water molecule and presto, you have a hydrogen bond. H bonds are often viewed as a special case of a dipole-dipole interaction. Modeling programs can determine the charge on each atom of a large molecule like a protein and determine the geometric center and magnitude of overall + and - charge. A line drawn between them represents the permanent "dipole" moment of the entire protein. More simply, the molecular dipole is the vector sum of all of the individual bond dipole moments. Entire proteins have a net dipole moment which probably facilitates the interaction of the protein with other proteins or ligands. Figure $16$ shows the net dipole moment for the protein carboxypeptidase A1 (2v77). This was calculated using the Protein Dipole Moments Server. Proteins, however, do have net charges (not considering any bound counterions) so the molecular dipole for a protein is a bit different conceptually than for a small molecule. Nevertheless, it is a good way to quantitate asymmetric charge distribution in large biomolecules. Asymmetric charge distributions would influence molecular properties. Ion-Dipole Figure $17$ shows interactions between a Na+ ion and the dipoles of multiple water molecules. Figure $18$ shows an interactive iCn3D model of the molecular ion sulfate SO42- bound to a protein through its hydrogen bonding and ion-dipole noncovalent interactions with protein side chain and backbone groups in the sulfate binding protein from Salmonella typhimurium. The SO42- is buried within the protein. The green and yellow dotted line show hydrogen bonds between the sulfate and amide N-Hs on the protein chain surrounding it and the a side chain of the protein. Modeling programs don't show lines depicting dipole-x interactions. The SO42-, through its oxygen, can form hydrogen bonds with nearby donors. Figure $19$ shows an interactive iCn3D model of another example of a protein backbone and side chains ion-dipole interactions, this time with a Na+ ion, a simple non-transition state metal ion, which can not form hydrogen bonds. The protein is tryptophan synthase from Salmonella typhimurium (6dz4). The red spheres represent water oxygen atoms (no hydrogen atoms shown). The ions illustrated in these last two cases are not transition metal ions, whose interactions with ligands can best be considered using ligand field theory and the formation of covalent (dative) bonds between electron pair donors on nucleophilic side chain/main chain atoms and d orbitals on the transition metal. Induced Dipole - Induced Dipole/Hydrophobic Interactions. These noncovalent interactions occur when a temporary dipole, created by random fluctuations in electron density in one molecule, induces a temporary dipole in another molecule nearby. These interactions are weak and can easily be broken by raising the temperature. Induced dipole-induced dipole interactions allow nonpolar gases like He, N2, O2, and CH4 to be liquefied, but it takes higher pressures and/or low temperatures to force the molecules close enough and slow them down enough for sufficient interactions to occur to liquefy the molecules. Although individually weak, the larger the molecules, the greater the extent of induced dipole-induced dipole interactions and the stronger the interactions among molecules. This is reflected in the fact that methane, CH4, is a gas at room temperature, octane, C8H18 is a liquid and C30H62 is a solid. Figure $20$ shows induced dipole interactions between two molecules. Induced dipole-induced dipole interactions are important among large biomolecules as well. Most biologists and probably biochemists prefer to use the words hydrophobic interactions (but not hydrophobic forces) instead of the longer and more formal induced dipole-induced dipole interaction. We will also try to use the more commonly used term within the biochemistry community. Figure $21$ shows an interactive iCn3D model of a hydrophobic cluster around the side chain of a hydrophobic amino acid, valine 143 in human carbonic anhydrase II (4ca2). Val 143 is highlighted in yellow and shown with normal atom (CPK) colors. White to green indicate nonpolar amino acids while dark blue indicates polar ones. You can see that the side chain of Val 143 (highlighted in yellow) is completely surrounded by nonpolar amino acids. If the structure was rendered in spacefill instead of sticks, Val 143 would be closely packed to maximize induced dipole-induced dipole (hydrophobic)interactions. Induced dipole-induced dipole interactions also occur between polar molecules, but they are weaker than the hydrogen bonding and dipole-dipole interactions between them. Pi stacking Aromatic rings stacked over each other can interact through induced-induced dipole (hydrophobic) and dipole-induced dipole interactions. These interactions can depend on the presence of heteroatoms in the aromatic ring. Figure $22$ shows an example with benzene in which a staggered arrangement of the rings is more attractive. For a biological example, everyone is familiar with the structure of B-DNA in which the bases A, G, C and T point inward perpendicular to the double helix axis and are stacked over each other. Figure $23$ shows an interactive iCn3D model of a short stretch of DNA with a sugar-phosphate backbone and bases colored in magenta and cyan. Fives bases on one strand are shown in stick and atomic color to show the pi-stacking interactions of the aromatic ring. Pi stacking also occurs in proteins. Figure $24$ shows an interactive iCn3D model of two sets of pi stacking interactions in the protein arginine kinase (1M15). The aromatic side chains involved in pi stacking are shown in cyan. Cation - Pi Figure $25$ shows an interactive iCn3D model a specific example of an ion-induced dipole interaction (called a cation-pi interaction) between a sodium ion (blue sphere) and the aromatic ring of the side chain tryptophan (cyan) in hen egg white lysozyme (1lpi). Example $1$ For another example of a cation-pi interaction, open up iCn3D with 1REX and view the interaction of lysine (K1) side chain with the nonpolar aromatic ring of phenylalanine (F3). Solution https://structure.ncbi.nlm.nih.gov/i...4d3wXXsSEYhgv7 Here are some more examples. Exercise $1$ Select the link below to answer the following questions. 1. What type of noncovalent interaction best describes the red dotted line in the structure? 2. What type of noncovalent interaction best describes the red dotted line in the structure? Answer 1. cation-pi 2. pi stacking Halogen Bond Lastly, we come to the halogen bond. You might ask if there are halogens found in proteins. The answer is no (until one is found!) but halogenated molecules (drugs, xenobiotics, toxins) bind proteins. Consider the C-X bond where X is a halogen. The electronegativity of C is 2.56 while the halogens have these electronegativity values: F (3.98), Cl (3.16), Br (2.96), and I (2.66). Compare these to oxygen (3.44) and N (3.04). Covalent bonds between two bonded atoms whose electronegativity differences are between 0.4 and 1.8 are considered polar covalent, so C-F, C-Cl, and C-Br are considered polar covalent. The C-I bond is the longest and iodine is the most polarizable of these halogens. An alkyl halide with a C-I bond can undergo SN2 nucleophilic substitution reactions with I- being an excellent leaving group. Hence the C-I bond behaves somewhat as a polar covalent bond. Nevertheless, quantum calculations show that the electron density is not uniformly spread around the X halogen in a C-X bond, but rather is pulled more toward the C, leaving the distal end of the halogen depleted in electron density and slightly positive. This region of relatively depleted electron density is called the σ-hole. Color-coded renderings of the electron density of the halogen involved in a C-X bond show the halogen atom to have bands (like Jupiter) with the more negative electrostatic potential (represented in blue) closest to C and the more positive potential, the σ-hole (represented in red), at the end farthest from the C atom. Calculations show that this effect is greatest for the heavier halogens (Br, I) which have longer C-X bonds. The halogen's slightly positive σ-hole can act analogously to a hydrogen bond donor in its interactions with nearby δ- :O and :N atoms/lone pairs. This might take a while to grasp. You have always heard that in general the halogens are more electronegative than carbon and would hence always be δ- when bonded to it. This case is similar to our chemical intuition about lone pair "rabbit ears" on oxygen, which quantum calculations show not to be an accurate representation of the electron density (see Fig xx). Figure $26$ shows the electrostatic potential on a halogen X atom covalently attached to a carbon in two different molecules, CF3-I and :NC-Br. The red distal end is the σ-hole relatively depleted in electron density and with a higher, more positive electrostatic potential. (This is opposite the usual coloration that biochemists use in which oxygen (δ- or fully -) is colored red and nitrogen (in a protonated amine with a positive charge) is shown in blue.) Figure $27$ shows a molecule with a carbonyl (a hydrogen bond acceptor with a δ- :O) interacting with another molecule through either a hydrogen bond or a halogen bond. Again the red distal end of the halogen X is the σ-hole relatively depleted in electron density. Medicinal chemists use halogen substituents on drug molecules to alter drug binding specificity, membrane diffusion, and t1/2. Increasingly, they are using halogen bonds in rational drug design to increase drug affinity to target proteins. Figure $28$ shows an interactive iCn3D model below shows the interaction of a haloaminopyrimidine inhibitor bound to its binding site on the c-Jun N-Terminal Kinase (JNK) protein (2P33). Note that the sulfur of methionine is forming a halogen bond with the Cl atom. Although the electronegativity of sulfur is 2.58, close to that of carbon (2.55), nevertheless, sulfur is larger and more polarizable so it also develops a slightly positive σ-hole distal to the carbon atom. Analysis of PDB files shows that S--O interactions are common in proteins and most likely impact protein stability. Ultimately all ensembles of molecules/ions reach a low if not the lowest energy state under a given set of conditions. Noncovalent attractions are maximized and repulsions are minimized to achieve this state. Consider for example solid sodium chloride held together by ionic bonds. The ions are closest packed (face-centered cubic) and cannot get closer together (packing density of about 74%) as simple packing considerations and repulsive electrostatic forces and collective van der Waals interactions would prevent it. Each Na+ is surrounded by 6 Cl- ions and vice versa. When large molecules like proteins assume a low energy state, they maximize the attractive noncovalent interactions described in this section while minimizing repulsive ones within a molecule (in given solvent conditions). Packing density reaches similar values as for closest packed spheres (NaCl for example). Figure $29$ shows a slice through a protein and through the crystal lattice of NaCl. The gray circles on the protein show the faces of the sliced atoms. They are superimposed on the surface of the protein shown in colored spheres. If you took a series of cross-sectional slices throughout the protein, you would get a better picture of packing density than a single slice alone. Collective van der Waals interactions are found among all atoms and ions in a protein, which accounts for the closest packing of most atoms, polar and nonpolar, with the packed protein structure. Here is a link to a JSmol tutorial by David Marcy et al, An Introduction to Chemical Bonds and Protein Structure Summary of Noncovalent Interactions in Biomolecules It is not easy to understand noncovalent interactions among small molecules let alone within solvated and densely packed proteins, for example. To help quantitate strong noncovalent interactions involving amino acid side chains, Xie et al have studied amino acids in the gaseous phase using quantum mechanics. Here are some general conclusions (Xie et al. PLoS ONE 10(9): e0137113. https://doi.org/10.1371/journal.pone.0137113. Creative Commons Attribution License) • Ion-Ion (salt bridge) interactions between acidic amino acids side chains (Glu- and Asp-) and alkaline amino acids side chains (Arg+, Lys+ and His+) are the strongest residue-residue interactions. However, this type of interaction may be weakened by solvation effects and broken by lower pH conditions. • Cation- interactions between protonated amino acid side chains (Arg+, Lys+ and His+) and aromatic amino acid side chains (Phe, Tyr, Trp and His) are 2.5 to 5-fold stronger than common hydrogen bond interactions and are less affected by the solvation environment. • Amide bridge interactions, shown in Figure $30$ below, which contain two hydrogen bonds, between the two amide-containing amino acid side chains (in the amino acids Asn and Gln) are three times stronger than hydrogen bond interactions, which are less influenced by the pH of the solution. Figure $30$: An "amide" bridge (Xie et al, ibid) • Ten of the twenty natural amino acids are involved in salt bridge, cation, or amide bridge interactions that often play important roles in protein-protein, protein-peptide, protein-ligand, and protein-DNA interactions. Another computational study was done to categorize the noncovalent interactions between proteins and small molecules (drugs, inhibitors) that bind to them. These small molecules are generically called ligands, a term used in the study of transition metal complexes. They studied 11,016 unique structures found in the Protein Data Bank of small-molecule ligands bound to proteins. A histogram displaying the number of each type of interaction between small ligands and proteins is shown in Figure $31$ below. Figure $31$: Frequency distribution of the most common non-covalent interactions observed in protein–ligands extracted from the PDB. de Freitas and Schapira. Med. Chem. Commun., 2017, 8, 1970-1981. de Freitas and Schapira. DOI: 10.1039/C7MD00381A (Research Article) Two new interactions are shown, the weak hydrogen bond and amide stacking. Amide stacking is readily understandable as an interaction between the slight positive carbonyl carbon of the amide and the electron-dense aromatic ring. You probably find the weak hydrogen bond more troubling. The hydrogen bond donor is a carbon atom attached to a hydrogen, and a hydrogen bond acceptor, the carbonyl oxygen. We have stated that a C-H bond does not engage in a hydrogen bond. We assume that a C-H bond is sufficiently nonpolar so the carbon atom does not have a slight negative charge which leaves hydrogen without a slight positive. Yet it appears that there are many C–H⋯O weak hydrogen bonds between ligands and proteins. C has an electronegativity of 2.5 and H 2.2 with a difference of 0.3, which is a much smaller difference than between N and H (3.04-2.2 =0.8). If the electronic environment around the carbon enhances its slight negative charge, then you could imagine that a C-H could be a hydrogen bond donor. The median distance of the C–H⋯O hydrogen bonding was 3.4 Å, which is 0.4 Å longer than traditional hydrogen bonds (N–H⋯O, N–H⋯N, O–H⋯O), with an angle of around 130°. The Cα–H⋯O=C interactions are about one-half the strength of an NH⋯O=C hydrogen bond. Hence they are weak. In the rest of this book, we will NOT consider a C-H bond as a candidate for a hydrogen bond. Nevertheless, it is important to mention it given their prevalence.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/02%3A_Water_and_its_Role_in_Life/2.04%3A_Solubility_in_an_aqueous_world_-_noncovalent_interactions_in_depth.txt

Search Fundamentals of Biochemistry Introduction Many biomolecules such as triacylglycerols, cholesterol esters, and waxes are nonpolar. Other biomolecules such as proteins and many lipids have both polar and nonpolar parts. We know from experience that oil floats on the surface of water, showing that it is less dense than water and that it doesn't dissolve in water. You have also probably performed liquid-liquid extractions in chemistry labs in which you utilized the solubility properties of nonpolar molecules to extract them from a mixture in water to a more nonpolar phase such as octanol or chloroform. To understand the stability of biomolecules that contain nonpolar parts in aqueous solutions, we need to understand not only noncovalent interactions of the molecules with water (which we explored in Chapter 2.4) but also the thermodynamics of their molecular interactions in aqueous environments. We have been taught and internalized the notion that "like-dissolves like". We anthropomorphize molecules to say nonpolar molecules "like" to be in nonpolar environments. We can rationalize solubility properties by examining the noncovalent attractive and repulsive interactions of a molecule in an aqueous solution but when we do so we are usually focusing on enthalpic contributions to stability. What about entropy? We should consider net changes in noncovalent solute:solute, solute:solvent, and solvent:solvent interactions, as well as their thermodynamic contributions to overall stability. When we consider the thermodynamics of the solubility of molecules in water, we need to determine the ΔG, the free energy change, for all processes involved. The Change in Free Energy (G) and Chemical Potential (μ) ΔG, the free energy change for a reaction, determines the spontaneity and extent of a chemical or physical reaction. The free energy of a system depends on 3 variables, temperature T, pressure P, and n, the number of moles of each substance. For the latter, think of solute X on two different sides of a permeable membrane. If the concentration of X is the same on each side, as shown in Figure $1$, the system is in equilibrium. If the system is composed of two different parts, A and B, the system is at equilibrium (ΔG=0) if TA = TB, PA = PB, and the change in the absolute free energy per mole of A is ΔGA/Δn = ΔGB/Δn. More precisely, using simple calculus, we would discuss incremental changes in absolute free energy/mol, dGA/dn for A, which is the chemical potential of A, μA) and dGB/dn (μB) for B. At equilibrium dGA/dn = dGB/dn. We will use the free energy G here but μ later in this section. G then is the absolute free energy/mol (again chemical potential), where G=Go +RTln[A]. From this the equations you used in introductory chemistry (and that we reviewed in Chapter 1.3), the following equation can be written. \begin{array}{l} \Delta \mathrm{G}=\Delta \mathrm{G}^{0}+\mathrm{RTIn} \mathrm{Qr} \ \Delta \mathrm{G}=\Delta \mathrm{H}-\mathrm{T} \Delta \mathrm{S} \ \Delta \mathrm{G}^{0}=\Delta \mathrm{H}^{0}-\mathrm{T} \Delta \mathrm{S}^{0} \ \Delta \mathrm{G}^{0}=-\mathrm{RTInK} \mathrm{eq} \end{array} Now let's apply this to the chemical equation for the solubility of a given solute in water. If you add either a sparing soluble hydrocarbon (HC) or sodium chloride to water, eventually you reach a point of saturation. For the salt, the water is saturated with dissolved NaCl, and no further increase in NaCl (aq) occurs. For a sparing soluble hydrocarbon, the solution reaches saturation after which phase separation occurs. Let's add a drop of a slightly soluble hydrocarbon liquid (HCL) into water, as pictured in the diagram below. At t=0, the system is not at equilibrium and some of the HC will transfer from the pure liquid to water, so at time t=0, ΔGTOT < 0. This is illustrated in Figure $2$. The following equations can be derived. \begin{array}{c} \Delta \mathrm{G}_{\mathrm{TOT}}=\left(G_{\mathrm{HC}-\mathrm{W}}\right)-\left(G_{\mathrm{HC}-\mathrm{L}}\right)=\mathrm{G}_{\mathrm{HC}-\mathrm{W}}^{0}+R T \ln [\mathrm{HC}]_{\mathrm{W}}-\left(\mathrm{G}_{\mathrm{HC}-\mathrm{L}}^{0}+R T \ln [\mathrm{HC}]_{\mathrm{L}}\right)= \ \Delta \mathrm{G}_{\mathrm{TOT}}=\left(\mathrm{G}_{\mathrm{HC}-\mathrm{W}}^{0}-\mathrm{G}_{\mathrm{HC}-\mathrm{L}}^{0}\right)+R T \ln \left([\mathrm{HC}]_{\mathrm{W}}-\ln [\mathrm{HC}]_{\mathrm{L}}\right)= \ \Delta \mathrm{G}_{\mathrm{TOT}}=\Delta \mathrm{G}^{0}+R T \ln \frac{[\mathrm{HC}]_{\mathrm{W}}}{[\mathrm{HC}]_{\mathrm{L}}} \end{array} Now add a bit more complexity to the last example. Add a hydrocarbon x, to a biphasic system of water and octanol as shown in Figure $3$. Shake it vigorously. At equilibrium, x would have "partitioned" between the two mostly immiscible phases. A simple reaction can be written for this system: X aq ↔ X oct. Clearly, if X is a hydrocarbon, ΔG < 0 for the reaction written above. Also, ΔGo < 0, since this term is independent of concentration and depends only on the intrinsic stability of X in water in comparison to that of octanol. This simple equation holds: \Delta \mathrm{G}_{\mathrm{TOT}}=\left(\mathrm{G}_{\mathrm{X}-\mathrm{oct}}^{0}-\mathrm{G}_{\mathrm{X}-\mathrm{w}}^{0}\right)+R T \ln \frac{[\mathrm{X}]_{\mathrm{oct}}}{[\mathrm{X}]_{\mathrm{w}}}=\Delta \mathrm{G}^{0}+R T \ln \frac{[\mathrm{X}]_{\mathrm{oct}}}{[\mathrm{X}]_{\mathrm{w}}} At equilibrium, ΔG0=0 and the equation can be rewritten as: \Delta \mathrm{G}^{0}=-R T \ln \frac{[\mathrm{X}]_{\mathrm{oct}}}{[\mathrm{X}]_{\mathrm{w}}}=-\mathrm{RTlnK}_{\mathrm{part}} where Kpart is the equilibrium partition coefficient for X in octanol and water. This can readily be determined in the lab. Just shake a separatory flask with a biphasic system of octanol and water after injecting a bit of X. Then separate the layers and determine the concentration of x in each phase. Plug these numbers into the last equation. You should be able to predict the sign and relative magnitude of ΔGo since it does not depend on concentration, but only on the intrinsic stability of the molecules in the different environments. Kpart values are often determined for drugs since they often must diffuse across cell membranes to move into the cytoplasm where they can act. Drugs hence must have a reasonable Kpart to pass through the membrane but not so high that they are insoluble. Introduction to the Hydrophobic Effect Now let's ask this question: What are the enthalpic and entropic contributions to the ΔG for the interaction of a nonpolar molecule HC with water? For this section, we will replace ΔG with Δμ (the change in chemical potential but we will use these terms interchangeably). Likewise, we will use this equation: Δμo = ΔHo - T ΔSo. Also instead of framing the reaction as the dissolution of an organic molecule in water, we will frame is as the transfer of a hydrocarbon X from an aqueous solution to the pure hydrocarbon liquid (HC) or \mathrm{X}(\mathrm{aq}) \leftrightarrow \mathrm{X}(\mathrm{HC}) Figure $4$ shows the standard free energies of transfer of a hydrocarbon X from an aqueous solution to a pure liquid hydrocarbon (HC), X (aq) ↔ X (HC). where \Delta \mu^{\circ}=\mu^{\circ} x(H C)-\mu^{\circ} x(a q) Δμo is less than 0 since transfer back to the pure HC is favored from a stability perspective. In each graph, Δμo is less than 0, and the value of Δμo decreases (gets more negative as you go up the y axis which shows increasingly negative values of Δμo) in a linear fashion with increasing numbers of carbon atoms in the alkyl chain. Notice the lines are unbelievably straight and parallel. Nature is speaking to us in these figures. By determining the surface area of the hydrocarbon molecules and the decrease in Δμo with each added CH2 (methylene group), one can calculate that the Δμo decreases by 25 cal/Å2 (105 J/Å2), per methylene added. We expected that Δμo for the transfer of X to a pure liquid HC would be negative. We could get more information if we would determine both the entropic and enthalpic contributions. Such data is presented in the table below, which shows the transfer of short, single-chain alcohol X (an amphiphile with a polar head and a longer nonpolar "tail") from the pure liquid alcohol (ROH) to water (the opposite of the previous figures.) X(R O H) \leftrightarrow X(W) Thermodynamic Parameters for Transfer of Aliphatic Alcohol X from the Pure Liquid to Water at 25oC (enthalpy determined by calorimetry) alcohol X μw0 ROH0 kcal/mol (kJ/mol) Hw0-H ROH0 kcal/mol (kJ/mol) Sw0-S ROH0 cal/deg mol (J/deg mol) (Cp)w0-(Cp)ROH0 cal/deg mol (J/deg mol) ethanol 0.760 (3.18) -2.43 (-10.2) -10.7 (-44.8) 39 (163) n-propanol 1.58 (6.61) -2.42 (-10.2) -13.4 (-56.1) 56 (234) n-butanol 2.4 (10) -2.25 (-9.41) -15.6 (-65.3) 72 (301) n-pentanol (solubility 22g/L H2O) 3.22 (13.5) -1.87 (-7.82) -17.1 (-71.5) 84 (352) We expect the Δμo to be increasingly positive as the chain length gets longer and their solubilities in water become increasingly disfavored. What is perplexing about this data is not that the transfer of these ROHs to water is disfavored, but that transfer is enthalpically favored (negative ΔH0). This seems to be counterintuitive since it goes against the adage that "like dissolves like" as was discussed earlier. From an enthalpic point of view, the amphiphiles prefer (albeit marginally) to be in water. What makes this reaction disfavored is entropy. The data shows that the nonpolar molecule would prefer not to be in the water because it is disfavored entropically. At first glance, you might guess that the entropy should favor the movement of ROHs into water since they could access a larger volume to access and have greater freedom of motion. Hence here are more possible microstates for the ROH in water. However, this is only part of the process. What we haven't considered is the entropy of the water. To place a hydrocarbon in water, a literal cavity in the water must be created that will accommodate it. Creation of this more ordered cavity must be entropically disfavored (again because the process proceeds to a state with fewer microstates and lower positional entropy). For the reverse process, transferring the hydrocarbon from water to the pure liquid will dissipate the cavity, and lead to more available microstates for the released solvent, bulk water. It is this entropic contribution that favors the movement of a hydrocarbon from water to the pure hydrocarbon lipid. This "hydrophobic effect" is the main thermodynamic drive to move organic molecules out of water. Image this scenario. When you place a hydrocarbon group into water, water seeks (admittedly an anthropomorphic term) to maintain its hydrogen bonding. Hence it is forced into a more ordered structure around the HC to maintain its H-bonding, characterized by fewer microstates. We will explore the hydrophobic effect in greater detail in a future chapter. How can we explain the favorable enthalpic contribution on placing a nonpolar molecule into water? Again, this goes against our adage of "like dissolves like". The negative ΔH suggests interactions among all the participants are more favorable when the nonpolar group is in water. One source of such interactions could be the highly structured water in the "cage" that surrounds the nonpolar molecule. If it were more structured than bulk water, hence more "ice-like" in nature, then the formation of these extra H-bonds would contribute to the negative enthalpy change. When the nonpolar molecule is removed from the water, a process which proceeds with a positive ΔH, the cage of "ice-like" water would "melt", which, like the melting of ice, is not favored enthalpically, as heat must be added. Heat energy must be supplied to break the H-bonds as ice changes state to liquid water. This molecular model to understand the thermodynamic data might yet be a simplistic model, but for time being, let's use it.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/02%3A_Water_and_its_Role_in_Life/2.05%3A_Solubility_in_an_aqueous_world_-_The_Hydrophobic_Effect.txt

Section 1 Questions: Q1) Based on the interactive figure 2.1.1 in the default view, do you hypothesize that water could enter the core of the micelle? Now, let's test your hypothesis! Open the interactive figure, and click Style → Surface Type → Solvent Accessibility. Based on the space-filled solvent accessibility map, do you see any openings for water molecules to enter through? Explain your answer. A1) Hypothesis can range from no water is accessible, or water could freely occupy the "empty" space in the default view of the micelle. However, once the solvent accessibility filter is applied, it is clear that the micelle will exclude water from entering the hydrophobic core. Some areas of the micelle (the polar head groups) are able to for interactions with the water and therefore show up as red/yellow in this view, but it is key to note there are no pores or solvent-accessible spaces on the surface of the micelle. Q2) The structure for Limonene, the compound that gives citrus fruits their classic smell is shown below. Examine the structure and answer the following questions. a) Will Limonene form any associations with water molecules? Explain. b) For our brain to register the citrus aroma, Limonene needs to bind to a surface protein receptor, what type of interactions could be taking place? Explain. c) When Limonene binds to its receptor are the interactions from b) stabilized by enthalpy ΔH or entropy ΔS? A2) a) No, there are no dipoles or polar functional groups on limonene. b) The methyl and ethyl functional groups would likely form hydrophobic bonds with nonpolar amino acids in the protein. Because there are no polar/dipole residues, Limonene cannot make hydrogen or ionic bonds. c) Binding of Limonene to its surface receptor will likely be stabilized by entropy. When the nonpolar Limonene binds to a nonpolar region of the surface receptor, order water will be released, thus creating a +ΔS. Q3) Consider the following reaction of a polar substance and water at room temperature (22°C): Glucose(s) + H2O(l) → Glucose(aq) a) Estimate the enthalpy ΔH and entropy ΔS (+,-,≈0) for the reactants and product. Consider the order/disorder of the over all reaction as well as the net charge of the bond enthalpy. b) Based on the Gibbs free energy equation ΔG = ΔH - TΔS, will this reaction be spontaneous, non-spontaneous, or at equilibrium? A3) a) Glucose(s) + H2O(aq) → Glucose(aq) ΔH: H-bonds H-bonds H-bonds ΔH ≈ 0 ΔS: Solid Liquid Solution ΔS = + b) ΔG = ΔH - TΔS = 0 - (+) = - This dissolution of polar substances into water is spontaneous. Q4) Consider the following reaction of a nonpolar substance and water at room temperature (22°C): Methane (CH4)(g) + H2O(l) → Methane (CH4)(aq) a) Estimate the enthalpy ΔH and entropy ΔS (+,-,≈0) for the reactants and product. Consider the order/disorder of the overall reaction as well as the net charge of the bond enthalpy. b) Based on the Gibbs free energy equation ΔG = ΔH - TΔS, will this reaction be spontaneous, non-spontaneous, or at equilibrium? A4) a) Methane (CH4)(g) + H2O(l) → Methane (CH4)(aq) ΔH: None H-bonds H-bonds ΔH ≈ 0 or slightly - ΔS: Gas Liquid Solution ΔS = - b) ΔG = ΔH - TΔS = 0/- - (-) = + This dissolution of nonpolar substances into water is non-spontaneous. This is primarily due to water forming an ordered cage around the nonpolar methane gas. In terms of enthalpy, the water cage creates a more favorable environment for methane than the pure gas in liquid water, thus the enthalpy can be considered slightly negative. However, the cage structure the water molecules form around the methane gas makes the system ordered. Therefore the overall ΔG for nonpolar substances solubilized with water is positive and therefore non-spontaneous. Need to add in some reactions? (last chunk of section 1) Section 2 Questions Q1) Tyrosine is commonly found in the active sites of enzymes, as the unique structure of its R-group can act as either an acid or base. a) The enzyme DcpS, an mRNA capping enzyme, utilizes a tyrosine residue as an active site acid, and measurements show that the tyrosine is 75% ionized. What must the local pH need to be for this to occur? b) Another way for tyrosine to be used as an acid is to lower its pKa by creating weak interactions with the oxygen of its R-group using neighboring amino acids. What type of interaction(s) could achieve this goal? c) One enzyme utilizing the strategy in b) is Glutathione-S-Transferase. Measurements show that the active site pH is 7.5, and the tyrosine residue is 95% ionized. What must the pKa of tyrosine be for this to occur? A1) a) $pH = pK_a + log \dfrac{[A^{-}]}{[HA]} \nonumber$ $pH = 10.1+ log \dfrac{[75]}{[25]} \nonumber$ pH = 10.1 + (0.477) pH = 10.58 b) Hydrogen bonding can create a change in the net dipole of the oxygen in tyrosine by making it more acidic and decreasing the pKa. c) $pH = pK_a + log \dfrac{[A^{-}]}{[HA]} \nonumber$ $7.5 = pK_a + log \dfrac{[95]}{[5]} \nonumber$ 7.5 = pKa + 1.28 6.22 = pKa Section 3 Questions Q1) Your lab wants to study an enzyme that catalyzes a reaction inside the chloroplast stroma, which has a pH of 8.0 due to the proton gradient that pumps H+ from the stroma to the thylakoid lumen. a) Which of the buffers below would be the best choice to study this enzyme, in vitro? Explain your choice. Are there any other buffers that could work? -Tricine: pKa 8.05 -TAPS: pKa 8.40 -MES: pKa 6.1 -Citrate: pKa 6.40 -HEPES: pKa 7.48 b) How many moles of the conjugate base form of HEPES would there be in 2.5 L of a 175 mM solution at pH 8.0? A1) a) Tricine would be the best choice as buffers work best at a pH closest to their pKa. TAPS or HEPES could work in a pinch if Tricine was not available, but remember, pH and pKa are log scales! So, while the pKa values might not seem that far from the intended pH of 8.0, on the log scale that is quite a difference in H+ concentration. b) $2.5 L × \dfrac{175 mmoles}{L}\ × \dfrac{1 mole}{1000 mmoles}\ = 0.4375 mmoles \nonumber$ Now we want to determine what fraction of those moles are in the conjugate base form: $[A^{-}] + [HA] = 0.4375 \nonumber$ $[A^{-}] = 0.4375 - [HA] \nonumber$ $pH = pK_a + log \dfrac{[A^{-}]}{[HA]} \nonumber$ $8.0 = 7.48 + log \dfrac{[A^{-}]}{[HA]} \nonumber$ $0.52 = log \dfrac{[A^{-}]}{[HA]} \nonumber$ $3.31 = \dfrac{0.4375 - [HA]}{[HA]} \nonumber$ At pH 8.0, [HA] = 0.101 mmoles, which is 23% of the total moles of HEPES in the solution. Q2) As discussed in Section 2.3, when CO2 is inhaled, it reacts with water to form the weak acid carbonic acid, acidifying the blood. The reaction is given below for reference. CO2 + H2O → H+ = HCO3- Currently, the air you breathe contains about 0.04% CO2. This number has risen from 0.03% in the 1960s and is projected to increase to 0.08% by 2100 if fossil fuel consumption remains at its current rate. (PCO2 =0.0003 atm, Keeling, 1960, PCO2 =0.0008 atm MIT System Dynamics Group, 2015). Using the Ideal Gas Laws, there were 0.44 μM CO2 in the 1960s, 0.60 μM CO2 today, and as much as 1.2 μM CO2 in 2100. The pKa of carbonic acid is 6.35 and the pH of your blood is 7.60. a) What is the change in blood pH due to the increase in atmospheric CO2 from the 1960s to today? b) If you were to do the calculations for what the pH of the blood would rise to in 2100, you'd find it to be pH 7.3. Do you think the body would be able to compensate for this? Use the pKa of histidine to explain your answer. A2) a) $pH = pK_a + log \dfrac{[A^{-}]}{[HA]} \nonumber$ $7.60 = 6.35 + log \dfrac{[A^{-}]}{0.6} \nonumber$ $1.25 = log \dfrac{[A^{-}]}{0.6} \nonumber$ $17.8 = \dfrac{[A^{-}]}{0.6} \nonumber$ [A-] = 10.7 μM $pH_1960 = 6.35 + log \dfrac{10.7}{0.44} \nonumber$ pH1960 = 6.35 + 1.39 pH1960 = 7.74 Now knowing the pH of the blood from 1960, the change is 7.74 - 7.6 = 0.14 pH units. b) Yes the body would compensate by increasing the amount of carbonic acid in the blood to buffer the increase in free H+. If the body did not do this, the protonation state of histidine would change and affect every histidine-containing protein in the blood. Section 4 Questions Q1) Categorize the following bonds as ion-ion, ion-dipole, dipole-dipole, H-bond, or hydrophobic, (some maybe be used more than once, or not at all). A1) Q2) You discover a new enzyme, "biochemase" and decide to crystalize the protein to determine if you can determine the role from its structure. You hypothesize the protein forms a homodimer when two identical subunits of a protein come together to form one functional structure. You identify two regions that you believe could be the dimer interface. The first region looks to contain several polar amino acids, whose R-groups are less than 5 Å apart, while the second section contains a large cluster of hydrophobic amino acids. a) You perform some biochemical experiments and discover that when the polar region is removed, the enzyme is not able to self-assemble, but maintains two intact separate structures. What can you conclude about this region and the type of interactions that are stabilizing the dimer interface? b) When you remove the nonpolar region, you notice the protein aggregates or crashes out of the solution due to an inability to fold correctly. What role can you hypothesize the nonpolar amino acids have in keeping the protein properly folded? A2) a) When the polar region is removed, the protein is still able to fold into two stable subunits, but unable to form its homodimer. Therefore we can conclude that the polar region is necessary for the dimer interface, but the protein can still form subunits without it present. b Now, when the nonpolar region is removed the protein cannot form subunits or dimers. So, we can conclude that the nonpolar region is necessary for protein stability, and without this region present, the protein cannot fold properly. Most likely, the hydrophobic region forces the protein to fold in the correct way by forcing the nonpolar amino acids to the core, and the polar to the surface. This would then allow for a proper dimer interface to form. Section 5 Questions Q1) Acetaminophen (Tylenol) and Ibuprofen (Aspirin) are both common pain-relieving/fever-reducing drugs. However, their chemical properties differ, making acetaminophen more suitable for relieving headaches and fever, while ibuprofen can more effectively reduce pain. The pKpart for acetaminophen is 0.91, while the pKpart is 3.97. (DrugBank) The chemical structures for each are given below: a) Using equation 2.5.4, determine the ΔG° for each compound, and hypothesize which is more soluble based on your answer and explain. Assume a normal body temperature of T = 37°C b) With your answer from a) and the information reviews in chapter 1 on functional groups, and types of non-polar interactions in chapter 2, identity the regions of both acetaminophen and ibuprofen that can facilitate interactions. c) Using your ΔG° calculations and functional group analysis, predict which compound is more likely to stay in the blood and bound to red blood cells, and which can rapidly diffuse across the cell membrane. Q2) You are tasked with creating a solution of nonpolar and polar solvents to use for thin layer chromatography (a technique we will cover later), to separate lipids. Consider the following enthalpic and entropic values for these nonpolar and polar solvents. Using the information discussed in section 5, which of the following combinations of solvents will yield a homo

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/02%3A_Water_and_its_Role_in_Life/2.06%3A_Chapter_2_Questions.txt

Search Fundamentals of Biochemistry Introduction Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective; they may serve in transport, storage, or membranes; or they may be toxins or enzymes. Each cell in a living system may contain thousands of different proteins, each with a unique function. Their structures, like their functions, vary greatly. They are all, however, polymers of alpha amino acids, arranged in a linear sequence and connected together by covalent bonds. Alpha Amino Acid Structure The major building blocks of proteins are called alpha (α) amino acids. As their name implies they contain a carboxylic acid functional group and an amine functional group. The alpha designation is used to indicate that these two functional groups are separated from one another by one carbon group. In addition to the amine and the carboxylic acid, the alpha carbon is also attached to hydrogen and one additional group that can vary in size and length. In the diagram below, this group is designated as an R-group. Within living organisms, there are 20 common amino acids used as protein building blocks. They differ from one another only at the R-group position. The fully protonated structure of an amino acid (at low pH) is shown in Figure $1$. The twenty common naturally-occurring amino acids each contain an alpha-carbon, an amino, carboxylic acid, and an R group (or side chain). The R group side chains may be either nonpolar, polar and uncharged, or charged, depending on the functional group, the pH, and the pKa of any ionizable group in the side chain. Two other amino acids occasionally appear in proteins. One is selenocysteine, which is found in Arachea, eubacteria, and animals. Another is pyrrolysine, found in Arachea. Bacteria have been modified to incorporate two new amino acids, O-methyl-tyrosine, and p-aminophenylalanine. The yeast strain Saccharomyces cerevisiae has been engineered to incorporate five new unnatural amino acids (using the TAG nonsense codon and new, modified tRNA and tRNA synthetases) with keto groups that allow chemical modifications to the protein. We will concentrate only on the 20 abundant, naturally-occurring amino acids. Figure $2$ shows the twenty naturally occurring alpha-amino acids as they would appear internally within a protein sequence. The squiggles show that the alpha-amino and carboxyl groups are involved in peptide bonds to adjacent amino acids in the protein sequence. Students often assume that the alpha-amino and carboxyl groups within a protein sequence are free and not part of the peptide bond. This figure should help in resolving that misconception. The three-letter and one-letter abbreviations of each amino acid, as well as their typical pKa values, are also shown. It is important to memorize the three-letter and one-letter codes for the amino acids. Amino acids form polymers through a nucleophilic attack by the amino group of an amino acid at the electrophilic carbonyl carbon of the carboxyl group of another amino acid. The carboxyl group of the amino acid must first be activated to provide a better leaving group than OH-. The resulting link between the amino acids is an amide link which biochemists call a peptide bond. In this reaction, water is released. In a reverse reaction, the peptide bond can be cleaved by water (hydrolysis). This is illustrated in Figure $3$. Proteins are polymers of twenty naturally occurring amino acids. In contrast, nucleic acids are polymers of just 4 different monomeric nucleotides. Both the sequence of a protein and its total length differentiate one protein from another. Just for an octapeptide, there are over 25 billion different possible arrangements of amino acids (820). Compare this to just 65536 different oligonucleotides (4 different monomeric deoxynucleotides) of 8 monomeric units, an 8mer (84). Hence the diversity of possible proteins is enormous. When two amino acids link together to form an amide link, the resulting structure is called a dipeptide. Likewise, we can have tripeptides, tetrapeptides, and other polypeptides. At some point, when the structure is long enough, it is called a protein. The average molecular weight of proteins in yeast is about 50,000 with about 450 amino acids. The large protein might be titin with a molecular weight of about 3 million (about 30,0000 amino acids). A new class of very small proteins (30 or fewer amino acids and perhaps better named as polypeptides) called smORFs (small open reading frames) have recently been discovered to have significant biological activity. These are encoded directly in the genome and are produced by the same processes that produce regular proteins (DNA transcription and RNA translation). They are not the result of selective cleavage of a larger protein into smaller peptide fragments. Figure $4$ shows several ways to represent the structure of a polypeptide or protein, each showing differing amounts of information. Note that the atoms in the side chains are denoted alpha, beta, gamma, delta, epsilon ... Characteristics of Amino Acids The different R-groups have different characteristics based on the nature of atoms incorporated into the functional groups. There are R-groups that predominantly contain carbon and hydrogen and are very nonpolar or hydrophobic. Others contain polar uncharged functional groups such as alcohols, amides, and thiols. A few amino acids are basic (containing amine functional groups) or acidic (containing carboxylic acid functional groups). These amino acids are capable of forming full charges and can have ionic interactions. Each amino acid can be abbreviated using a three-letter and a one-letter code. Figure $5$ shows groupings of the amino acid based on their side chain properties. Click Here for a Downloadable Version of the Amino Acid Chart Nonpolar (Hydrophobic) Amino Acids The nonpolar amino acids can largely be subdivided into two more specific classes, the aliphatic amino acids and the aromatic amino acids. The aliphatic amino acids (glycine, alanine, valine, leucine, isoleucine, and proline) typically contain branched hydrocarbon chains with the simplest being glycine to the more complicated structures of leucine and valine. Proline is also classified as an aliphatic amino acid but contains special properties as the hydrocarbon chain has cyclized with the terminal amine creating a unique 5-membered ring structure. As we will see in the next section covering primary structure, proline can significantly alter the 3-dimensional structure of the due to the structural rigidity of the ring structure when it is incorporated into the polypeptide chain and is commonly found in regions of the protein where folds or turns occur. The aromatic amino acids (phenylalanine, tyrosine, and tryptophan), as their name implies, contain an aromatic functional group within their structure making them largely nonpolar and hydrophobic due to the high carbon/hydrogen content. However, it should be noted that hydrophobicity and hydrophilicity represent a sliding scale and each of the different amino acids can have different physical and chemical properties depending on their structure. For example, the hydroxyl group present in tyrosine increase its reactivity and solubility compared to that of phenylalanine. Methionine, one of the sulfur-containing amino acids is usually classified under the nonpolar, hydrophobic amino acids as the terminal methyl group creates a thioether functional group which generally cannot form a permanent dipole within the molecule and retains low solubility. Polar (Hydrophilic) Amino Acids The polar, hydrophilic amino acids can be subdivided into three major classes, the polar uncharged-, the acidic-, and the basic- functional groups. Within the polar uncharged class, the side chains contain heteroatoms (O, S, or N) that are capable of forming permanent dipoles within the R-group. These include the hydroxyl- and sulfhydryl-containing amino acids, serine, threonine, and cysteine, and the amide-containing amino acids, glutamine and asparagine. Two amino acids, glutamic acid (glutamate), and aspartic acid (aspartate) constitute the acidic amino acids and contain side chains with carboxylic acid functional groups capable of fully ionizing in solution. The basic amino acids, lysine, arginine, and histidine contain amine functional groups that can be protonated to carry a full charge. Many of the amino acids with hydrophilic R-groups can participate within the active site of enzymes. An active site is the part of an enzyme that directly binds to a substrate and carries a reaction. Protein-derived enzymes contain catalytic groups consisting of amino acid R-groups that promote formation and degradation of bonds. The amino acids that play a significant role in the binding specificity of the active site are usually not adjacent to each other in the primary structure but form the active site as a result of folding in creating the tertiary structure, as you will see later in the chapter. Thought Question: Tryptophan contains an amine functional group, why isn't tryptophan basic? Answer: Tryptophan contains an indole ring structure that includes the amine functional group. However, due to the proximity of, and electron-withdrawing nature of the aromatic ring structure, the lone pair of electrons on the nitrogen are unavailable to accept a proton. Instead, they are involved in forming pi-bonds within several of the different resonance structures possible for the indole ring. Figure 2.3A shows four of the possible resonance structures for indole. Conversely, within the imidazole ring structure found in histidine, there are two nitrogen atoms, one of which is involved in the formation of resonance structures (Nitrogen #1 in Figure 2.3B) and cannot accept a proton, and the other (Nitrogen #3) that has a lone pair of electrons that is available to accept a proton. Exercise $1$ Work It Out on Your Own: Given the example above, describe using a chemical diagram, why the amide nitrogen atoms found in asparagine and glutamine are not basic. Answer The lone pair is delocalized into the peptide bond (different resonance structure) so it is unavailable for sharing. Amino Acid Stereochemistry The amino acids are all chiral, with the exception of glycine, whose side chain is H. A chiral molecule is one that is not superimposable with its mirror image. Like left and right hands that have a thumb and fingers in the same order, but are mirror images and not the same, chiral molecules have the same things attached in the same order, but are mirror images and not the same. The mirror image versions of chiral molecules have physical properties that are nearly identical to one another, making it very difficult to tell them apart from one another or to separate them. Because of this nature, they are given a special stereoisomer name called enantiomers and in fact, the compounds themselves are given the same name! These molecules do differ in the way that they rotate plane-polarized light and the way that they react with and interact with biological molecules. Molecules that rotate light in the right-handed direction are called dextrorotary and are given a small "d" letter designation. Molecules that rotate light in the left-handed direction are called levorotary and are given a small "l" letter designation to distinguish one enantiomer from the other. Biochemists also use the older nomenclature of large "L" and "D" to characterize the 3D stereochemistry of the amino acids. All naturally occurring proteins from all living organisms consist of L amino acids, based on their structural similarities to L-glyceraldehyde. Again, the d- and l-designations are specific terms used for the way a molecule rotates plane-polarized light. It does not denote the absolute stereo configuration of a molecule. An absolute configuration refers to the spatial arrangement of the atoms of a chiral molecular entity (or group) and its modern stereochemical description e.g. R or S, referring to Rectus, or Sinister, respectively. Absolute configurations for a chiral molecule (in pure form) are most often obtained by X-ray crystallography. Alternative techniques are optical rotatory dispersion, vibrational circular dichroism, the use of chiral shift reagents in proton NMR and Coulomb explosion imaging. When the absolute configuration is known, the assignment of R or S is based on the Cahn–Ingold–Prelog priority rules. The absolute stereochemistry is related to L-glyceraldehyde, as shown in Figure $6$ below. All naturally occurring amino acids in proteins are L, which corresponds to the S isomer, with the exception of cysteine. As shown in the bottom left of the Figure 6 below, the absolute configuration of the amino acids can be shown with the H pointed to the rear, the COOH groups pointing out to the left, the R group to the right, and the NH3 group upwards. You can remember this with the mnemonic "CORN". Why do Biochemistry still use D and L for sugars and amino acids? This explanation (taken from a website, which may not be available anymore so no reference is available) seems reasonable. "In addition, however, chemists often need to define a configuration unambiguously in the absence of any reference compound, and for this purpose, the alternative (R,S) system is ideal, as it uses priority rules to specify configurations. These rules sometimes lead to absurd results when they are applied to biochemical molecules. For example, as we have seen, all of the common amino acids are L, because they all have exactly the same structure, including the position of the R group if we just write the R group as R. However, they do not all have the same configuration in the (R,S) system: L-cysteine is also (R)-cysteine, but all the other L-amino acids are (S), but this just reflects the human decision to give a sulfur atom a higher priority than a carbon atom and does not reflect a real difference in configuration. Worse problems can sometimes arise in substitution reactions: sometimes inversion of configuration can result in no change in the (R) or (S) prefix, and sometimes retention of configuration can result in a change of prefix. It follows that it is not just conservatism or failure to understand the (R,S) system that causes biochemists to continue with D and L: it is just that the DL system fulfills their needs much better. As mentioned, chemists also use D and L when they are appropriate to their needs. The explanation given above of why the (R,S) system is little used in biochemistry is thus almost the exact opposite of reality. This system is actually the only practical way of unambiguously representing the stereochemistry of complicated molecules with several asymmetric centers, but it is inconvenient with regular series of molecules like amino acids and simple sugars." If you are told to draw the correct stereochemistry of a molecule with 1 chiral C (S isomer for example) and are given the substituents, you could do so easily following the R, S priority rules. However, how would you draw the correct isomer for the L isomer of the amino acid alanine? You couldn't do it without prior knowledge of the absolute configuration of the related molecule, L glyceraldehyde, or unless you remembered the anagram CORN. This disadvantage, however, is more than made up for by the fact that different L amino acids with the same absolute stereochemistry, might be labeled R or S, which makes this nomenclature unappealing to biochemists. Amino Acid Charges Monomeric amino acids have an alpha-amino group and a carboxyl group, both of which may be protonated or deprotonated, and a R group, some of which may be protonated or deprotonated. When protonated, the amino group has a +1 charge, and the carboxyl group a zero charge. When deprotonated the amino group has no charge, while the carboxyl group has a -1 charge. The R groups which can be protonated/deprotonated include Lys, Arg, and His, which have a + 1 charge when protonated, and Glu and Asp (carboxylic acids), Tyr and Ser (alcohols) and Cys (thiol), which have 0 charges when protonated. Of course, when the amino acids are linked by peptide bonds (amide link), the alpha N and the carboxyl C are in an amide link, and are not charged. However, the amino group of the N -terminal amino acid and the carboxyl group of the C-terminal amino acid of a protein may be charged. The Henderson-Hasselbalch equation gives us a way to determine the charge state of any ionizable group knowing the pKa of the group. Write each functional group capable of being deprotonated as an acid, HA, and the deprotonated form as A. The charge of HA and A will be determined by the functional group and the Henderson-Hasselbalch equation from Chapter 2.2. $pH = pK_a + \log \dfrac{[A^{-}]}{HA} \nonumber$ The titration curve for a single ionizable acid with different pKa values is shown below. At the inflection point of the curve, pH = pKa and the system is most resistant to changes in pH on addition of either acid or base. At this pH, [HA]=[A-]. The properties of a protein will be determined partly by whether the side chain functional groups, the N terminal, and the C terminal are charged or not. The HH equation tells us that this will depend on the pH and the pKa of the functional group. • If the pH is 2 units below the pKa, the HH equation becomes -2 = log A/HA, or .01 = A/HA. This means that the functional group will be about 99% protonated (with either 0 or +1 charge, depending of the functional group). • If the pH is 2 units above the pKa, the HH equation becomes 2 = log A/HA, or 100 = A/HA. Therefore the functional group will be 99% deprotonated. • If the pH = pKa, the HH equation becomes 0 = log A/HA, or 1 = A/HA. Therefore the functional group will be 50% deprotonated From these simple examples, we have derived the +2 rule. This rule is used to quickly determine protonation, and hence charge state, and is extremely important to know (and easy to derive). Titration curves for Gly (no ionizable) side chain, Glu (carboxylic acid side chain) and Lys (amine side chain) are shown in Figure $7$. You should be able to associate various sections of these curves with titration of specific ionizable groups in the amino acids. New 5/16/23: Download this Excel spreadsheet for Titration Curves for a Triprotic Acid.  It has adjustable scroll bars to change pKa values. Buffer Review The Henderson-Hasselbalch equation is also useful in calculating the composition of buffer solutions. Remember that buffer solutions are composed of a weak acid and its conjugate base. Consider the equilibrium for a weak acid, like acetic acid, and its conjugate base, acetate: $\ce{CH3CO2H + H2O <=> H3O^{+} + CH3CO2^{-}} \nonumber$ If the buffer solution contains equal concentrations of acetic acid and acetate, the pH of the solution is: or pH = pKa + log [A]/[HA] = 4.7 + log 1 = 4.7 A look at the titration curve for the carboxyl group of Gly (see above) shows that when the pH = pKa, the slope of the curve (i.e. the change in pH on addition of base or acid) is at a minimum. As a general rule of thumb, buffer solutions can be made for a weak acid/base in the range of +/- 1 pH unit from the pKa of the weak acids. At the pH = pKa, the buffer solution best resists the addition of either acid or base, and hence has its greatest buffering ability. The weak acid can react with the added strong base to form the weak conjugate base, and the conjugate base can react with added strong acid to form the weak acid (as shown below) so pH changes on the addition of strong acid and base are minimized. • addition of a strong base produces a weak conjugate base: CH3CO2H + OH- ↔ CH3CO2- + H2O • addition of strong acid produces weak acid: H3O+ + CH3CO2 → CH3CO2H + H2O There are two simple ways to make a buffered solution. Consider an acetic acid/acetate buffer solution. • make equal molar solution of acetic acid and sodium acetate, and mix them, monitoring pH with a pH meter, until the desired pH is reached (+/- 1 unit from the pKa). • take a solution of acetic acid and add NaOH at substoichiometric amounts until the desired pH is reached (+/- 1 unit from the pKa). In this method you are forming the conjugate base,acetate, on the addition of the weak base: CH3CO2H + OH- → CH3CO2- + H2O Isoelectric Point What happens if you have many ionizable groups in a single molecule, as is the case with a polypeptide or protein. Consider a protein. At a pH of 2, all ionizable groups would be protonated, and the overall charge of the protein would be positive. (Remember, when carboxylic acid side chains are protonated, their net charge is 0.) As the pH is increased, the most acidic groups will start to deprotonate and the net charge will become less positive. At high pH, all the ionizable groups will become deprotonated in the strong base, and the overall charge of the protein will be negative. At some pH, then, the net charge will be 0. This pH is called the isoelectric point (pI). The pI can be determined by averaging the pKa values of the two groups which are closest to and straddle the pI. One of the online problems will address this in more detail Remember that pKa is really a measure of the equilibrium constant for the reaction. And of course, you remember that ΔGo = -RT ln Keq. Therefore, pKa is independent of the concentration, and depends only on the intrinsic stability of reactants with respect to the products. This is true only AT A GIVEN SET OF CONDITIONS, SUCH AS T, P, AND SOLVENT CONDITIONS. Consider, for example, acetic acid, which in aqueous solution has a pKa of about 4.7. It is a weak acid, which dissociates only slightly to form H+ (in water the hydronium ion, H3O+, is formed) and acetate (Ac-). These ions are moderately stable in water but reassociate readily to form the starting product. The pKa of acetic acid in 80% ethanol is 6.87. This can be accounted for by the decrease in stability of the charged products which are less shielded from each other by the less polar ethanol. Ethanol has a lower dielectric constant than water. The pKa increases to 10.32 in 100% ethanol, and to a whopping 130 in air! Because amino acids are zwitterions, and several also contain the potential for ionization within their R-groups, their charge state in vivo, and thus, their reactivity can vary depending on the pH, temperature, and solvation status of the local microenvironment in which they are located. Table $1$ shows the standard pKa values for the amino acids and can be used to predict the ionization/charge status of amino acids and their resulting peptides/proteins. Table $1$: Summary of pKas of amino acids However, it should be noted that the solvation status in the microenvironment of an amino acid can alter the relative pKa values of these functional groups and provide unique reactive properties within the active sites of enzymes. A more in-depth discussion of the effects of desolvation will be given in Chapter 6 discussing enzyme reaction mechanisms. Printable Version of pKa Values • A great interactive web site: Amino Acid Acid/Base Titration Curves • pI calculator for any protein sequence • Amino Acid Repository: Properties of Amino Acids Introduction to Amino Acid Reactivity You should be able to identify which side chains contain H bond donors and acceptors. Likewise, some are acids and bases. You should be familiar with the approximate pKa's of the side chains, and the N and C terminal groups. Three of the amino acid side chains (Trp, Tyr, and Phe) contribute significantly to the UV absorption of a protein at 280 nm. This section will deal predominantly with the chemical reactivity of the side chains, which is important in understanding the properties of the proteins. Many of the side chains are nucleophiles. Nucleophilicity is a measure of how rapidly molecules with lone pairs of electrons can react in nucleophilic substitution reactions. It correlates with basicity, which measures the extent to which a molecule with lone pairs can react with an acid (Bronsted or Lewis). The properties of the atom which holds the lone pair are important in determining both nucleophilicity and basicity. In both cases, the atom must be willing to share its unbonded electron pair. If the atoms holding the nonbonded pair is more electronegative, it will be less likely to share electrons, and that molecule will be a poorer nucleophile (nu:) and weaker base. Using these ideas, it should be clear that RNH2 is a better nucleophile than ROH, OH- is better than H2O and RSH is better than H2O. In the latter case, S is bigger and its electron cloud is more polarizable - hence it is more reactive. The important side chain nucleophiles (in order from most to least nucleophilic) are Cys (RSH, pKa 8.5-9.5), His (pKa 6-7), Lys (pKa 10.5) and Ser (ROH, pKa 13). The side chain of serine is generally no more reactive than ethanol. It is a potent nucleophile in a certain class of proteins (proteases, for example) when it is deprotonated. The amino group of lysine is a potent nucleophile only when deprotonated. An understanding of the chemical reactivity of the various R group side chains of the amino acids in a protein is important since chemical reagents that react specifically with a given amino acid side chain can be used to: • identify the presence of the amino acids in unknown proteins or • determine if a given amino acid is critical for the structure or function of the protein. For example, if a reagent that covalently interacts with only Lys is found to inhibit the function of the protein, a lysine might be considered to be important in the catalytic activity of the protein. Figure $8$ shows a summary of nucleophilic addition and substitution at carbonyl carbons. The rest of the section will summarize the chemistry of the side chains of reactive amino acids. Historically the function of a given amino acid in a protein has been studied by reacting them with side chain-specific chemical modifying agents. In addition, some side chains are covalently modified after they are synthesized in vivo (post-translational modification - see below). Reactions of Lysine Figure $9$ the reaction of lysine with anhydrides and ethylacetimidate. • reacts with anhydrides in a nucleophilic substitution reaction (acylation). • reacts reversibly with methylmaleic anhydride (also called citraconic anhydride) in a nucleophilic substitution reaction. • reacts with high specificity and yield toward ethylacetimidate in a nucleophilic substitution reaction (ethylacetimidate is like ethylacetate only with a imido group replacing the carbonyl oxygen). Ethanol leaves as the amidino group forms. (has two N -i.e. din - attached to the C) Figure $10$ a second set of common reactions of lysine, including those used to attach a chromophore or a fluorescent label to the side chain. • reacts with O-methylisourea in a nucleophilic substitution reaction. with the expulsion of methanol to form a guanidino group (has 3 N attached to C, nidi) • reacts with fluorodintirobenzene (FDNB or Sanger's reagent) or trinitrobenzenesulfonate (TNBS, as we saw with the reaction with phosphatidylethanolamine) in a nucleophilic aromatic substitution reaction to form 2,4-DNP-lysine or TNB-lysine. • reacts with Dimethylaminonapthelenesulfonylchloride (Dansyl Chloride) in a nucleophilic substitution reaction. Figure $11$ shows a final common reaction we will encounter: the formation of an imine or Schiff base on the reaction of lysine with an aldehyde or ketone. • reacts with high specificity toward aldehydes to form imines (Schiff bases), which can be reduced with sodium borohydride or cyanoborohydride to form a secondary amine. Reactions of Cysteine Cysteine is a potent nucleophile, which is often linked to another Cys to form a covalent disulfide bond. Figure $12$ shows common reagents used in the lab to label free Cys side chains. These reagents are used to alter Cys side chains to determine if they have functional significance in a protein (such as an active nucleophile in an enzyme-catalyzed reaction. • reacts with iodoacetic acid in an SN2 reaction, adding a carboxymethyl group to the S. • reacts with iodoacetamide in an SN2 reaction, adding a carboxyamidomethyl group to S. • reacts with N-ethylmaleimide in an addition reaction to the double bond Sulfur is directly below oxygen in the periodic table and, in analogy to water, sulfur-containing amino acids are found in different redox states, as illustrated in Figure $13$. Cystine Chemistry Two cysteine side chains can covalently interact in a protein to produce a disulfide (RS-SR) named cystine. Just as HOOH (hydrogen peroxide) is more oxidized than HOH (O in H2O2 has an oxidation number of 1- while the O in H2O has an oxidation number of -2) , RSSR is the oxidized form (S oxidation number -1) and RSH is the reduced form (S oxidation number -2) of thiols. Their oxidation numbers are analogous since O and S are both in Group 6 of the periodic table and both are more electronegative than C. Cystine can react with a free sulfhydryl (RSH) in a thermodynamically non-challenging disulfide exchange reaction, which when conducted with excess free sulfhydryls results in the reduction of cystine in the protein, as shown in Figure $14$. Figure $14$: Disulfide interchange and reduction of protein disulfides This reaction is often used in the lab to quantitate the amount of free cysteine side chains in a protein using Ellman's reagent, as shown in Figure $15$. The 2-nitro-5-thiobennzoic acid anion leaving group absorbs at 412 nm which makes quantitation easy. Only surface and not buried free cysteines will be labeled unless the protein is unfolded to expose all the cysteines. When a protein folds, two Cys side chains might approach each other, and form an intra-chain disulfide bond. Likewise, two Cys side chains on separate proteins might approach each other and form an inter-chain disulfide. For analysis of the protein structure, disulfides are typically cleaved, and the chains are separated before analysis. The disulfides can be cleaved by reducing agents such as beta-mercaptoethanol, dithiothreitol, tris (2-carboxyethyl) phosphine (TCEP), or by oxidizing agents like performic acid which further oxidizes the disulfide to separate cysteic acids. Three common reagents used in disulfide cleavage reactions in the lab are shown in Figure $16$. The reaction for beta-mercaptoethanol (BME) and performic acid are shown in Figure $17$:below. Figure $18$ shows the reaction for dithiothreitol (DTT). Note that it forms a stable cyclohexane-like ring, which makes this reaction very favored thermodynamically. It does not require as much of an excess of DTT as in the reaction with BME. The reaction with tris (2-carboxyethyl) phosphine (TCEP) is not a disulfide interchange reaction as is shown in Figure $19$. Cells maintain a reducing environment by the presence of many "reducing" agents, such as the tripeptide gamma-Glu-Cys-Gly (glutathione). Hence intracellular proteins usually do not contain disulfides, which are abundant in extracellular proteins (such as those found in blood), or in certain organelles such as the endoplasmic reticulum and mitochondrial intermembrane space where disulfides can be introduced. Sulfur redox chemistry is very important biologically. As described above, the sulfur in cysteine is redox-active and hence can exist in a wide variety of states, depending on the local redox environment and the presence of oxidizing and reducing agents. A potent oxidizing agent that can be made in cells is hydrogen peroxide, which can lead to more drastic and irreversible chemical modifications to the Cys side chains. If a reactive Cys is important to protein function, then the function of the protein can be modulated (sometimes reversibly, sometimes irreversibly) with various oxidizing agents, as shown in Figure $20$. Reactions of Histidine Histidine is one of the strongest bases at physiological pH's. His can exist as two tautomers, as shown in Figure $21$. NMR studies show that in model peptides, the proton predominantly is on the ε2, N3, or tele N in the imidazole ring, as it has a pKa 0.6 units higher than δ1, N1,or pro N. The nitrogen atom in a secondary amine might be expected to be a stronger nucleophile than a primary amine through electron release to that N in a secondary amine. Opposing this effect is the steric hindrance by the two attached Cs of the N on attach on an electrophile. However, in His, this steric effect is minimized since the 2Cs are restrained by the ring. With a pKa of about 6.5, this amino acid is one of the strongest available bases at physiological pH (7.0). Hence, it can often cross-react with many of the reagents used to modify Lys side chains. His reacts with reasonably high selectivity with diethylpyrocarbonate. In vivo Post-Translational Modification of Amino Acids Amino acids in naturally occurring proteins are also subjected to chemical modifications within cells. These modifications alter the properties of the amino acid that is modified, which can alter the structure and function of the protein. Most chemical modifications made to proteins within cells occur after the protein is synthesized in a process called translation. The resulting chemical changes are termed post-translational modifications. Several are shown in Figure $23$. Note that simple acid/base reactions are included, but these are not considered examples of post-translational modifications. There are 100s of PTMs and many are part of an elaborate system within a cell to respond to both external (hormones, neurotransmitters, nutrients, metabolites) and internal chemical signals. The PTMs (like phosphorylation, acetylation, etc.) and their removal by enzymes are part of an elaborate system of cell signaling that we will explore in great detail in Chapter 28. However, not all PTMs are benign. Examples include glycation, oxidation, citrullination, and carbonylation of protein side chains. These are often increased during periods of inflammatory stress (both acute and chronic). These latter modified proteins are degraded within the cell to short peptides that retain the chemical modification. Unfortunately, these can be recognized by the immune system as foreign, which leads to an immune response against self and autoimmune disease. One such potentially deleterious PTM is the carboxyethylation of cysteine, catalyzed by the enzyme cystathionine β-synthase as shown in Figure $24$ below. Figure $24$: Carboxyethylation of cysteine The product is very similar to the carboxymethylation of cysteine shown in Figure 12 above. The modifying reagent, 3-hydroxypropionic acids, is a metabolite released by microbes found in the gut. This modification has been shown to produce an autoimmune response in the disease ankylosing spondylitis.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/03%3A_Amino_Acids_Peptides_and_Proteins/3.01%3A_Amino_Acids_and_Peptides.txt

Search Fundamentals of Biochemistry Introduction In the last chapter section, we discussed how to purify a protein (mostly through differential salt precipitation and column chromatography) and follow the purity of a protein (mostly through various types of electrophoresis) during the process. Now we want to continue with the analysis of a "pure" protein, so we can understand it structure and the function conferred by that structure. We need many pieces of information to study protein structure/function relationships. Some are "low resolution" characteristics such as knowing the concentration of a protein. At the highest "resolution" end, we would like to know the 3D structure of a protein with a specific interacting ligand bound to a specific site on the protein. Our goal is then to understand proteins at varying levels of complexity or "resolution", some of which are illustrated in Figure $1$. A variety of chemical and spectra analysis techniques are used to achieve the specified level of structural elucidation. Spectral techniques can give us information on concentration (UV absorbance, fluorescence) and secondary structure (CD spectroscopy). Chemical analyses and mass spectroscopy give information on the amino acid composition and/or sequence. More sophistical techniques (x-ray crystallography, cryoelectron microscopy, NMR spectroscopy) can give us 3D structural information. Each of the analyses shown in the figure above will be summarized below. For some, for which readers might have less experience, more details will be offered. Sequencing the cDNA can, of course, give you some of the information (amino acid composition, N- and C-terminal amino acids, and the primary structure (omitted from the figure above). Even DNA sequencing won't give information on post-translational modification and other covalent processing (limited proteolysis, disulfide bond formation) that some of the methods below would. We will explore some commonly used methods for protein analysis in this section. In previous sections, we learned about the charge and chemical reactivity properties of isolated amino acids and amino acids in proteins. The analysis of a whole protein is complicated since each different amino acid might be represented many times in the sequence. Each protein has an N-terminal and C-terminal amino acid and secondary structure. Some proteins exist biologically as multisubunit proteins, which adds to the complexity of the analyses since now the proteins would have multiple N- and C-terminal ends. In addition, isolated proteins might have chemical modifications (post-translational) which add to the functionalities of the proteins but also add to the complexities of the analyses. Spectral techniques are widely used to give information on concentration (UV absorbance, fluorescence) and secondary structure (CD spectroscopy). Chemical analyses and mass spectroscopy give information on the amino acid composition and/or sequence. More sophistical techniques (x-ray crystallography, cryoelectron microscopy, NMR spectroscopy) can give us 3D structural information. More complete descriptions for two techniques, fluorescence spectroscopy, and mass spectrometry, are presented as their uses in the analyses of biomacromolecules are underrepresented in curriculums as their use in actual laboratories becomes so prevalent. Low-Resolution Analyzes Protein Concentration There are multiple ways to determine protein concentrations in samples. Other components of a protein solution may interfere with the assays so the choice of methods has to be carefully determined. 1. Direct mass determination. A known, accurate amount of a dried protein is added to a solution of specific ionic strength and composition. The absorbance at a specific wavelength (usually 280 nm) is determined, which is used to determine an extinction coefficient at that wavelength (ε1% = absorbance of a 1% protein solution = 1g protein/100 ml solution). If the molecular weight of the protein is known through sequence analysis, then a molar absorptivity can be determined. The concentration of the same protein in an unknown, pure solution of the protein can then be determined. There are several problems with this technique. It requires relatively large amounts of protein to make accurate measurements. An even more difficult problem is that proteins bind not only water but counter-ions. Even a freeze-dried protein (a protein which has been frozen at -600C and placed in a vacuum, which causes sublimation of water and volatile salts in the solution) has probably 10% by weight of bound water (water of hydration). 2. Quantitative amino acid analysis. The protein is hydrolyzed completely to amino acids with 6N HCl. The amino acids are then separated by high-performance liquid chromatography. As amino acids elute from the column, they are reacted with a fluorescent reagent such as ninhydrin, fluorescamine, on orthopthaldehdye (OPA) to produce a fluorescent-amino acid conjugate. The fluorescence intensity of the conjugates is proportional to the concentration of the amino acids in the protein. Before hydrolysis, a known quantity of an amino acid not present in proteins (norleucine, beta-alanine) is added and its recovery is determined at the end of the hydrolysis and fluorescence conjugation to normalize the recovery of the other amino acids. Several problems are encountered using this technique. Incomplete peptide bond hydrolysis, and partial or complete destruction of serine, threonine, tryptophan, and tyrosine occur during the acid hydrolysis. The OH-containing amino acids can be determined by quantitating these amino acids after several different time intervals of hydrolysis, and extrapolating the concentrations back to zero time. Incomplete reactions with the detecting reagent can also occur. The most widely used and perhaps less analytically accurate than the two above are indirect, comparative protein assays based on the chemical properties of amide bonds or the spectrophotometric properties of the side chains Trp, Tyr, Phe. Unknown concentrations can be determined from a standard curve derived from performing the same reactions or spectrophotometric measurements on a series of solutions of known protein concentration. Below is a discussion of each of these techniques. 3. Modified Lowry protein determination. The Lowry Method is actually a modification of the biuret method, whose basis is described below. It is much more sensitive, however. Biuret, as its name implies, derives from the combination of two molecules of urea (bi-ur-et) as shown in the figure (panel A) below. If copper sulfate is added to Biuret in a concentrated hydroxide solution, a violet color results. An illustration of the copper (II) complex with biuret is shown in panel B of the figure. This biuret reaction also arises in the presence of any compound with three or more peptide bonds. Compare in Figure $2$ the structure of Biuret and a polypeptide (Panel A). 4. Dye binding assay (Bradford method). This method is based on the binding of the dye Coomassie Brilliant Blue G-250 to protein, with a resultant change in the absorbance properties of the dye. The structure of the dye is shown in Figure $3$. The magnitude of the difference spectra at 595 nm is directly proportional to the protein concentration. The dye in the unbound, free state, has an absorbance maximum at 465 nm. The method was initially developed by Bradford (Analytic Biochemistry, 72, 1976), and is available commercially. The dye appears to bind to proteins through both hydrophobic interactions and electrostatic ones through a sulfonic acid group on the dye. The predominant advantage of this method is that its cheap, simple, rapid, 3x more sensitive than the modified Lowry method, and subjected to fewer interferences by other compounds. The color fully develops in about 5 minutes but decreases within 10-15 minutes when the proteins start to precipitate. Precipitation occurs more extensively at higher protein concentrations. Hence, the high-concentration standards will be affected more than the low-concentration standards. 5. Bicinchoninic Acid method (BCA). This method is based on the reduction of Cu++ to Cu+ by the peptide bond, and the chelation of the Cu+ by BCA, which is monitored by absorbance at 562 nm. This method, which has been commercialized, is subject to fewer interferences from other compounds than either the modified Lowry or the dye binding assay. Two solutions are required, a BCA solution and a copper sulfate solution. The two are mixed together to form an apple-green working solution. When protein is added, the resulting Cu+ chelates with 2 molecules of BCA as shown in Figure $4$. Figure $4$: BCA analysis for protein quantitation This results in a purple color, which can be monitored spectrophotometrically at 562 nm. An absorbance at 562 nm of 0.012 per microgram of protein added to the working reagent gives this technique high sensitivity. 6. Absorbance A280 or ratios at different wavelengths. This method is based on the fact that the three aromatic acids (Tyr, Phe, Trp) have significant absorbances in the UV. The absorption spectra of the three amino acids as a function of wavelength are shown in Figure $5$. Note the log scale on the y-axis. When separating proteins on chromatography columns, the absorbance at 280 nm of the elute is often measured in isolated fractions or continuously as a measure of the presence and concentrations of the eluting proteins. The absorbance at 280 nm is often used to estimate the total concentration of proteins (an average protein at a concentration of 1 mg/ml has an A280 of about 1). However, only two amino acids absorb significantly at this wavelength, and since proteins have a variable number of these amino acids, this measurement can only be an estimate of the protein concentration of an unknown protein. The Beer-Lambert law shows that the absorbance of a chromophore in solution is given by \mathrm{A}=\epsilon \mathrm{l} \mathrm{c} where A is the absorbance at a given wavelength, ε is the molar absorptivity, l is the path length of the cuvette and c is the concentration (mol/L). Pace et al have shown that based on over a hundred measurements on 61 proteins in aqueous solution, the ε(280), the molar absorptivity at 280 nm, is given by this empirical equation: \epsilon(280)\left(\mathrm{M}^{-1} \mathrm{~cm}^{-1}\right)=(\# \operatorname{Trp})(5,500)+(\# \mathrm{Tyr})(1,490)+(\# \text { cystine })(125) Proteins also absorb strongly at wavelengths less than 240 nm. This part of the absorption spectra arises from the above-mentioned amino acids along with contributions from His, Met, Cys, and the peptide bond. At these wavelengths, the absorbance is less dependent on the actual amino acid composition of the protein, but it becomes increasingly susceptible to interfering substances. Contaminating nucleic acids, which absorb maximally at 260 nm, also contribute to the absorbance at 280 nm. Hence the A280/A260 ratio can be determined and through appropriate calculations, the contribution of nucleic acids can be removed. Optimal reliability is obtained by measuring A280/A205 values, since at 205 nm, a large fraction of the absorbance is derived from the peptide bond. pH changes have little effect on the absorbance of the peptide bond but has a much larger effect on Tyr. At high pH's, the side change hydroxyl is deprotonated (pKa = 10.5), with concomitant changes in the A295. These changes can be used to follow the titration of the Tyr residues in a protein. Molecular Weight Molecular weights can be estimated by hydrodynamic techniques including size exclusion chromatography (under denaturing conditions using standards of known molecule weight), ultracentrifugation, and dynamic light scattering. In addition, they can be determined by polyacrylamide gel electrophoresis, again under denaturing conditions using standards. More precisely they could be determined through protein sequence or more easily through cDNA sequence analyses. The most accurate method for smaller proteins is mass spectrometry, as described in below. Specific Amino Acids Aromatic amino acids can be detected by their characteristic absorbance profiles. Amino acids with specific functional groups can be determined by chemical reactions with specific modifying groups, as shown in a previous section. Amino Acid Composition At a low level of resolution, we can determine the amino acid composition of the protein by hydrolyzing the protein in 6 N HCl, 100oC, under vacuum for various time intervals. After removing the HCl, the hydrolysate is applied to an ion exchange or hydrophobic interaction column, and the amino acids are eluted and quantitated with respect to known standards. A non naturally- occurring amino acid, like norleucine, is added in known amounts as an internal standard to monitor quantitative recovery during the reactions. The separated amino acids are often derivatized with ninhydrin or phenylisothiocyantate to facilitate their detection. The reaction is usually allowed to proceed for 24, 36, and 48 hours since amino acids with OH (like ser) are destroyed. A time course allows the concentration of Ser at time t=0 to be extrapolated. Trp is also destroyed during the process. In addition, the amide links in the side chains of Gln and Asn are hydrolyzed to form Glu and Asp, respectively. N- and C-Terminal Amino Acid Analysis The amino acid composition does not give the sequence of the protein. The N-terminus of the protein can be determined by reacting the protein with fluorodinitrobenzene (FDNB) or dansyl chloride, which reacts with any free amine in the protein, including the epsilon amino group of lysine. The amino group of the protein is linked to the aromatic ring of the dinitrobenzene through an amine and to the dansyl group by a sulfonamide, and are hence stable to hydrolysis. The protein is hydrolyzed in 6 N HCl, and the amino acids are separated by TLC or HPLC. Two spots should result if the protein was a single chain, with some Lys residues. The labeled amino acid other than Lys is the N-terminal amino acid. The C-terminal amino acid can be determined by the addition of carboxypeptidases, enzymes which cleave amino acids from the C-terminal. A time course must be done to see which amino acid is released first. N-terminal analysis can also be done as part of sequencing the entire protein as discussed using Edman degradation. Primary Sequence Protein Sequencing using Edman Degradation Edman degradation, developed by Pehr Edman, is a method of sequencing amino acids in a peptide. In this method, the amino-terminal residue is labeled and cleaved from the peptide without disrupting the peptide bonds between other amino acid residues. The reaction is shown in Figure $6$. Phenyl isothiocyanate is reacted with an uncharged N-terminal amino group, under mildly alkaline conditions, to form a cyclical phenylthiocarbamoyl derivative. Then, under acidic conditions, this derivative of the terminal amino acid is cleaved as a thiazolinone derivative. The thiazolinone amino acid is then selectively extracted into an organic solvent and treated with acid to form the more stable phenylthiohydantoin (PTH)- amino acid derivative that can be identified by using chromatography or electrophoresis. This procedure can then be repeated again to identify the next amino acid. A major drawback to Edman degradation is that the peptides being sequenced in this manner cannot have more than 50 to 60 residues (and in practice, under 30). The peptide length is limited due to the cyclical derivatization not always going to completion. The derivatization problem can be resolved by cleaving large peptides into smaller peptides before proceeding with the reaction. It is able to accurately sequence up to 30 amino acids with modern machines capable of over 99% efficiency per amino acid. An advantage of the Edman degradation is that it only uses 10 - 100 pico-moles of peptide for the sequencing process. The Edman degradation reaction was automated in 1967 by Edman and Beggs to speed up the process and 100 automated devices were in use worldwide by 1973. Because the Edman degradation proceeds from the N-terminus of the protein, it will not work if the N-terminus has been chemically modified (e.g. by acetylation or formation of pyroglutamic acid). Sequencing will stop if a non-α-amino acid is encountered (e.g. isoaspartic acid), since the favored five-membered ring intermediate is unable to be formed. Edman degradation is generally not useful to determine the positions of disulfide bridges. It also requires peptide amounts of 1 picomole or above for discernible results. Secondary Structure The percent and type of secondary structure can be determined using circular dichroism (CD) spectroscopy. In this method, right and left circularly polarized light illuminates a protein, which, since it is made of all L-amino acids, is chiral. (The mirror image would be a protein of the same sequence made of D-amino acids.) Differential absorption of the right and left forms give a CD spectrum Circularly polarized light can be made when plane-polarized light of the same amplitude and wavelength meet out of phase by 900. (If they were out of phase by 1800, they would cancel.) • To see an animation of how circularly polarized light can be created, go to this page and select: 1. Superposition of plane-polarized waves 2. If R and L circularly polarized light of the same wavelength and amplitude are passed through an optically inactive medium, the two waves combine (vectorially) to produce plane-polarized light. • To see an animation of how circularly polarized light can be created, go to this page and select: 1. Superposition of circularly polarized waves Optical activity is observed only when the environment in which a transition occurs is asymmetric. The peptide (amide) bond absorbs UV light in the range of 180 to 230 nm (far-UV range) so this region of the spectra give information about the protein backbone, and more specifically, the secondary structure of the protein. The main electronic energy transitions are n → π* at 220 nm and π → π * at 190 nm for the peptide bond. There is a contribution from aromatic amino acid side chains but it is small, given the large number of peptide bonds. The lone pair on the nitrogen adjacent to the pi bond can be considered to be rehybridized from sp3 to sp2, allowing for conjugation of the p electrons (which lowers the energy of the electrons). The Hückel diagram shown in Figure $7$ below shows 3 molecular (not atomic) orbitals generated from the 3 atomic p orbitals. Figure $7$: Hückel molecular orbitals for the peptide bond The middle one (with 1 node) has energy similar to the separate atomic p orbitals and is considered a nonbonding molecular orbital. This is consistent with the lone nonbonding pair on the nitrogen atom. The peptide bonds in a protein's asymmetric environment will absorb this wavelength range of light (promoting electrons to higher energy levels). In different secondary structures, the peptide bond electrons will absorb right and left circularly polarized light differently (for example, they have different molar absorptivities). Hence α, β and random coil structures all have distinguishable far UV CD spectra. To see an animation of circularly polarized light, go to this page and select 1. Circularly polarized Waves Stated in another way, if plane-polarized light, which is a superposition of right and left circularly polarized light, passes through an asymmetric sample, which absorbs right and left circularly polarized differently (i.e they display circular dichroism), then the light passing through the sample after vector addition of the right and left hand circularly polarized light gives elliptically polarized light. To see an animation of elliptically polarized light, go to this page and select 2. Plane-polarized waves in a medium with circular dichroism If the chiral molecules also have a different index of refraction for R and L circularly polarized light, an added net effect is the rotation of the angle of the elliptically of the polarized light. The far-UV CD spectrum of the protein is sensitive to the main chain conformation. The CD spectra of alpha and beta secondary structures are shown in Figure $8$. Protein side chains also find themselves in such an asymmetric environment. If irradiated with circularly polarized UV light in the range of 250-300 nm (near UV), differential absorption of right and circularly polarized light by the aromatic amino acids (Tyr, Phe, Trp) and disulfide bonds occur and a near UV CD spectra result. If the near UV CD spectra of a protein are taken under two different sets of conditions, and the spectra differ, then it can be surmised that the environment of the side chains is different, and hence the proteins have somewhat different conformations. It will not give information about the secondary structure of the backbone since that requires lower wavelengths for absorption of occur. Rather it can show differences in tertiary structure. Analysis of Proteins Using Fluorescence Spectroscopy Fluorescence spectroscopy is widely used to study many aspects of protein chemistry. This technique is not often used in lower-level undergraduate classes but it has become so important in the study of biomolecules, that a somewhat detailed explanation is necessary. When electrons in a molecule absorb energy, they are promoted to higher electronic energy states. This is the basis of absorption spectroscopy. These excited state electrons can return to the ground state in processes that don't emit photons of light (ie. nonradiative processes) or radiative processes that do emit light. In simple absorption spectroscopy, the excited state electrons relax to the ground state through collision interactions. In radiative deexcitation, light is emitted. This process of light emission is called luminescence, which can be divided into two categories: • fluorescence: If one electron from a ground state electron pair is excited to a higher energy state, the excited electrons can still be spin paired with its ground state counterpart - i.e. they have opposite spins. The excited electron can return to the ground state without reversing its spin. (The excited state is a singlet state with S, the total spin state, given the formula S = 2s +1 where s = 0 (sum of +1/2 and -1/2) and S = 1 for a singlet.) This process, which results in a rapid emission of a photon, is "spin allowed". The rate of photon emission is about 108 s-1, which results in a lifetime (the average time between excitation and emission) of the excited state of about 10 ns. • phosphorescence: If, in contrast to the above case, the spin of the excited electron is flipped, then its transition back to the ground state is "spin forbidden" since the excited state electron and its ground state counterpart have the same spin state. (The excited state is a triplet state with S, the total spin state, given the formula S = 2s +1 where s = 1 (1/2 + 1/2) and S = 3 for triplet). Hence this transition occurs slowly (in the ms - s range). Toys that glow in the dark display even longer phosphorescence lifetimes. (Note: This guide will concentrate on fluorescence.) Competing with the two deexcitation process are nonradiative processes (such as through collisions). Given these competing processes, it might be expected that phosphorescence in liquid solutions at room temperature might not be detectable Molecules which fluoresce are typically aromatic, which absorb readily in the UV and visible light regions. Common fluorophores are quinine, found in tonic water (observe the faint blue glow at the surface when it is placed in direct sunlight), and fluorescein and rhodamine, two fluorophores often added to antifreeze. Atoms are usually nonfluorescent, with the exception of europium and terbium ions from the lanthanide series. These fluoresce when electronic transitions occur between f orbitals, which are shielded from solvent relaxation in these particular ions. Among biological molecules, some, especially macromolecules with aromatic groups, fluoresce. These groups are called intrinsic fluorophores, and include, in the case of proteins, the side chains of tryptophan, tyrosine, and phenylalanine, the aromatic amino acids. The indole side chain of tryptophan is the most fluorescent, and its emission spectra, which is sensitive to solvent conditions, is often blue-shifted when it is buried, and red-shifted when solvent-exposed. Nucleic acids, although they also contain aromatic bases, are poor fluorophores. Many biological molecules can be made fluorescent by covalently modifying them (through nucleophiles on the biological molecule) with exogenously added fluorophores, such as fluorescein isothicyanate, rhodamine isothiocyante, dansyl chloride, etc. These are called extrinsic fluorophores. These include molecules that bind noncovalently to structures such as ds-DNA (ethidium bromide) or lipid membranes (diphenylhexatriene). Some biological fluorophores are substrates for enzyme reactions. An example is the oxidized flavins (FAD, FMN) and the reduced form of NAD (i.e. NADH). Another type of useful fluorophore is indicators, whose fluorescent properties change with a change in a parameter like pH or [Ca ion]. The electronic transitions that occur during fluorescence can be represented by a Jablonski diagram as shown in the two-part Figure $9$ (A and B) below. In panel A, the ground and first excited electronic state are shown. Within each electronic state are multiple vibrational energy levels 0, 1, 2 ..and 0'. 1' 2' .... This simple diagram ignores quenching of fluorescence, resonance energy transfer, etc. The transitions, represented by vertical lines, are considered to be instantaneous. They take about 10-15 s so the nuclei don't move in the process. The ground state electron is considered to be in the 0 vibrational level, So, since thermal energy is insufficient to promote it to the next vibrational level. When light is absorbed, the electron is promoted to a higher vibrational level within a higher electronic level. Usually, the excited electrons relaxes quickly (< 1 ps) to the lowest vibrational level of S1 or possibly S2 through a process called internal conversion. Fluorescence emission then may occur from the lowest vibrational state of S1 to any of the vibrational states of So. Hence the photon emitted is lower in energy (longer in wavelength) than the absorbed photon. Also since both process involve the movement of the electron to different vibrational levels with absorption or emission of a photon, and nonradiative vibrational relaxation within those levels, the emission spectra is often the mirror image of the absorption spectra. (This assumes that the vibrational levels in So and S1 are similarly spaced. Alternatively, electrons in S1 may flip spin and convert to the T1 state, in a process called intersystem crossing, leading to phosphorescence. Panel B above shows blue lines corresponding to individual absorbances and red lins corresponding to emissions. Note that the excitation from 0 to 8' has the highest energy of absorbance (lowest wavelength) but gives little intensity as it would occur with low frequency. If you were to draw a line over the tops of the lines in panel B you would get a simple excitation spectra and emission spectra, which would be mirror images of each other. The emission peak is at a longer wavelength since energy was lost on vibrational, nonradiative relaxation of the excited electron. The difference in peak wavelengths of excitation and emission is called the Stokes Shift. This shift is greatest for fluorophores in polar environments. Inferences can be made considering the disposition of a side chain (buried or surface) if changes in fluorescence properties (intensity, Stoke's shift) are noted on protein denaturation. Also, many probes are weakly fluorescent in aqueous solution, but fluoresce intensely in nonpolar mediums (bound to a hydrophobic pocket in a protein, in a bilayer or lipoprotein, etc.) Emission spectra are usually independent of excitation wavelength (Kasha's rule): This occurs because of the rapid relaxation into the lowest vibrational energy level of the excited state. There are also exceptions to the mirror image rule. Deviations arise from a change in the geometry of nuclei in the excited state molecule. This may occur if the lifetime of the S1 state is long, allowing time for motion before emission. An example of this can be seen with p-terphenyl in cyclohexane, in which the rings become more coplanar in the excited state. Since there is an electron shift in the excited state, a complex between the excited fluorophore and another solution component may arise (charge-transfer complex). Alternatively, some fluorophores form complexes with themselves (pyrene) with increasing concentration. At high concentrations, changes in the emission spectra occur, arising from emission from an excited-state dimer or excimer. Acridine shows two emission spectra at different pH's, arising from changes in the pKa on excitation (5.45 to 10.7). Finally, exciting a fluorophore at different wavelengths (EX 1, EX 2, EX 3) does not change the emission profile but does produce variations in fluorescence emission intensity (EM 1, EM 2, EM 3) that correspond to the amplitude of the excitation spectrum. Fluorophores can be used to chemically modify nucleophilic side chains such as lysines and cysteines. Changes in intrinsic fluorescence in proteins can be used to measure the binding of ligands and conformation changes in the protein that occur on binding interactions, change in solution conditions, and protein denaturation. Let's explore a few fluorescence methods widely used to explore protein structure and function. Fluorescence Quenching Some chemical species (for example iodide and monomeric unpolymerized acrylamide), when added to a protein solution, can decrease the fluorescence from an intrinsic surface accessible fluorophore such as the tryptophan side chain, providing information on the local environment of the intrinsic fluorophore (example tryptophan side chain accessibility). For example, a buried tryptophan or probe will show little change in fluorescence intensity in the presence of a large, polar quencher, while a surface tryptophan or probe will show a significant decrease in fluorescent intensity. It is somewhat amazing that O2 when added to a solution under increasing pressure, can quench the fluorescence of even buried tryptophan side chain, implying that there are minimal diffusional barriers to O2 access. This suggests significant conformational flexibility of the protein. Quenching can be dynamic, occurring on collision of the quench with the intrinsic fluorophore, or static, when the quencher binds to a site near the fluorophore as a prelude to quenching. Collisional quenching is described by the Stern-Volmer equation. \frac{F_{0}}{F}=1+k_{q} \tau_{0}[Q]=1+K_{D}[Q] where Fo and F are the fluorescent intensities in the absence and presence of the quencher, kq is the biomolecular quenching constant, τo is the lifetime of the fluorophore in the absence of the quencher and [Q] is the concentration of the quencher. kqτo = KD is the Stern-Volmer quenching constant. A plot of Fo/F vs [Q] is linear, with a slope of KD. 1/KD is the quencher concentration at which Fo/F = 2, or 50% of the fluorescence intensity is quenched. A linear plot indicates a single class of fluorophores, all equally accessible to the quencher. A nonlinear plot would be found for quenching of tryptophan fluorescence in proteins by charged or polar quenchers for proteins with more than one tryptophan and in which some are buried. Static quenching also results in a linear SV plot. Dynamic and static quenching can be distinguished by different dependencies on temperature and viscosity. Since dynamic quenching depends on diffusion, and higher temperatures result in higher diffusion coefficients, kq should increase with temperature. If static quenching is involved, higher temperatures will probably reduce complex formation. Fluorescence Resonance Energy Transfer (FRET) If an absorbing species is in close proximity to an excited state fluorophore, and if the emission spectra of the fluorophore overlaps the absorption spectra of the second species, coupling of the two dipoles can occur, and energy can be transferred from the excited state of the fluorophore (donor D) to the second absorbing species (acceptor A). This transfer of energy is through dipole coupling and not through the trivial release and absorption of an emitted photon. No photon is produced. This process is called fluorescence resonance energy transfer (FRET). Efficiency, E, of FRET for a single donor/acceptor pair at a fixed distance is given by: \mathrm{E}=\frac{\mathrm{R}_{0}^{6}}{\left(\mathrm{R}_{0}^{6}+\mathrm{r}^{6}\right)}=\frac{1}{1+\left(\frac{\mathrm{r}}{\mathrm{R}_{0}}\right)^{6}} where Ro is the Förster distance (or radius) with a 50% transfer efficiency and r is the distance between the donor and acceptor. Ro is a measure of the spectral overlap of the donor and acceptor (for which most biological macromolecules have a similar value of 30-60 angstroms). This equation shows an efficiency dependent on 1/r 6, making FRET exquisitely sensitive to distance. FRET and its dependency of distance is illustrated in Figure $10$ below. Anisotropy or polarization These measure the extent of rotation of the fluorophore during its fluorescent lifetime. If a small fluorophore binds to a large molecule, its rotation diffusion constant decreases, and its anisotropy increases as illustrated in Figure $11$. Schematic representation of a fluorescence polarization experiment. As a result of rapid tumbling of molecules in solution, when a fluorescently labeled ligand is excited with plane-polarized light, the resulting emitted light is largely depolarized (a). Upon binding another species, a larger proportion of the emitted light remains in the same plane as the excitation energy, because the rotation is slowed as the effective molecular size increases, whether it is an ordered molecular structure (b) or one that is disordered (c)". Since viscosity decreases rates of rotational diffusion, changes in fluorescence (such as inside a bilayer) can be inferred from these measurements. For example, membranes more enriched in saturated fatty acids should show increased anisotropy of a hydrophobic, fluorescent probe, in comparison to the same probe in a bilayer enriched in polyunsaturated fatty acids. Analysis of Protein Using Mass Spectrometry Mass spectrometry is supplanting more traditional methods (see above) as the choice to determine the molecular mass and structure of a protein. Its power comes from its exquisite sensitivity and modern computational methods to determine structure through comparisons of ion fragment data with computer databases of known protein structures. In mass spectrometry, a molecule is first ionized in an ion source. The charged particles are then accelerated by an electric field into a mass analyzer where they are subjected to an external magnetic field. The external magnetic field interacts with the magnetic field arising from the movement of the charged particles, causing them to deflect. The deflection is proportional to the mass to charge ratio, m/z. Ions then enter the detector which is usually a photomultiplier. Sample introduction into the ion source occurs though simple diffusion of gases and volatile liquids from a reservoir, by injection of a liquid sample containing the analyte by spraying a fine mist, or for very large proteins by desorbing a protein from a matrix using a laser. Analysis of complex mixtures is done by coupling HPLC with mass spectrometry in a LCMS. There are many methods to ionize molecules, including atmospheric pressure chemical ionization (APCI), chemical ionization (CI), or electron impact (EI). The most common methods for protein/peptide analyses are electrospray ionization (ESI) and matrix-assisted laser desorption ionization (MALDI). Electrospray ionization (ESI) The analyte, dissolved in a volatile solvent like methanol or acetonitrile, is injected through a fine stainless steel capillary at a slow flow rate into the ion source. A high voltage (3-4 kV) is maintained on the capillary giving it a positive charge with respect to the other oppositely charged electrode. The flowing liquid becomes charged with the same polarity as the polarity of the positively charged capillary. The high field leads to the emergence of the sample as a charged aerosol spray of charged microdrops, which reduces electrostatic repulsions in the liquid. This method essentially uses electrical energy to produce the aerosol instead of mechanical energy to produce a liquid aerosol, as in the case of a perfume atomizer. Surrounding the capillary is a flowing gas (nitrogen) which helps to move the aerosol toward the mass analyzer. The microdrops become smaller in size as the volatile solvent evaporates, increasing the positive charge density on the drops. Eventually, electrostatic repulsions cause the drops to explode in a series of steps, ultimately producing the analyte devoid of solvent. This gentle method of ionization produces analytes that are not cleaved but ready for introduction into the mass analyzer. Proteins emerge from this process with a roughly Gaussian distribution of positive charges on basic side chains. In organic chemistry, you studied mass spectrums of small molecules induced by electron bombardment. This produces ions of +1 charge as an electron is stripped away from the neutral molecule. The highest m/z peak in the spectrum is the parent ion or M+ ion. The highest m/z ratio detectable in the mass spectrum is in the thousands. However, large peptides and proteins with large molecular masses can be detected and resolved since the charge on the ions are great than +1. In 2002, John Fenn was awarded a Noble Prize in Chemistry for the development and use of ESI to study biological molecules. An example of an ESI spectrum of apo-myoglobin is shown in Figure $12$. Note the roughly Gaussian distribution of the peaks, each of which represents the intact protein with charges differing by +1. Proteins have positive charges by virtue of both protonation of amino acid side chains as well as charges induced during the electrospray process itself. Based on the amino acid sequence of myoglobin and the assumption that the pKa of the side chains are the same in the protein as for isolated amino acids, the calculated average net charges of apomyoglobin (apoMb) would be approximately +30 at pH 3.5, +20 at pH 4.5, +9 at pH 6, and 0 at pH 7.8 (the calculated pI). The mass spectrum below was taken by direct injection into the MS of apoMb in 0.1% formic acid (pH 2.8). Charges on the peptide result from those present on the peptide before the electrospray and changes in charges induced during the process. The molecular mass of the protein can be determined by analyzing two adjacent peaks, as shown in Figure $13$. If M is the molecular mass of the analyte protein, and n is the number of positive charges on the protein represented in a given m/z peak, then the following equations gives the molecular mass M of the protein for each peak: \begin{gathered} \mathrm{M}_{\text {peak } 2}=\mathrm{n}(\mathrm{m} / \mathrm{z})_{\text {peak } 2}-\mathrm{n}(1.008) \ \mathrm{M}_{\text {peak } 1}=(\mathrm{n}+1)(\mathrm{m} / \mathrm{z})_{\text {peak } 1}-(\mathrm{n}+1)(1.008) \end{gathered} where 1.008 is the atomic weight of H. Since there is only one value of M, the two equations can be set equal to each other, giving: \mathrm{n}(\mathrm{m} / \mathrm{z})_{\text {peak } 2}-\mathrm{n}(1.008)=(\mathrm{n}+1)(\mathrm{m} / \mathrm{z})_{\text {peak } 1}-(\mathrm{n}+1)(1.008) Solving for n gives: \mathrm{n}=\frac{(\mathrm{m} / \mathrm{z})_{\mathrm{peak} 1}-(1.008)}{(\mathrm{m} / \mathrm{z})_{\mathrm{peak} 2}-(\mathrm{m} / \mathrm{z})_{\mathrm{peak} 1}} Knowing n, the molecular mass M the protein can be calculated for each m/z peak. The best value of M can then be determined by averaging the M values determined from each peak (16,956 from the above figure). For peaks from m/z of 893-1542, the calculated values of n ranged from +18 to +10. Matrix assisted laser desorption ionization (MALDI) In this technique, used for larger biomolecules like proteins and polysaccharides, the analyte is mixed with an absorbing matrix material. Laser excitation is used to excite the matrix, leading to energy transfer that results in ionization and the "launching" of the matrix and analyte in ion form from the solid mixture. Parent ion peaks of (M+H)+ and (M-H)- are formed. Mass Analyzer takes the created ion and separates them based on m/z ratios. Let's consider two, quadrupole ion tap and time of flight. on mass-to-charge ratios. There are several general types of mass analyzers, including magnetic sector, time of flight, quadrupole, ion trap Quadrupole ion trap (used in ESI) - A complex mixture of ions can be contained (or trapped) in this type of mass analyzer. Two common types are linear and 3D quadrupole. If a dipole has two poles (+ and  -) separated by some distance, then a quadrupole has four poles (+, -, +, and -) arranged geometrically such that each + has a - on each side and vice versa. Figure $14$ below shows linear and 3D quadrupoles As dipoles display positive and negative charge separation on a linear axis, quadrupoles have either opposite electrical charges or opposite magnetic fields at the opposing ends of a square or cube. In charge separation, the monopole (sum of the charges) and dipoles cancel to zero, but the quadrupole moment does not. The quadrupole traps ions using a combination of fixed and alternating electric fields. The trap contains helium at 1 mTorr. For the 3D trap, The ring electrode has an oscillating RF voltage which keeps the ions trapped. The end caps also have an AC voltage. Ions oscillate in the trap with a "secular" frequency determined by the frequency of the RF voltage, and of course, the m/z ratio. By increasing the amplitude of the RF field across the ring electron, ion motion in the trap becomes destabilized and leads to ion ejection into the detector. When the secular frequency of ion motion matches the applied AC voltage to the end cap electrodes, resonance occurs and the amplitude of motion of the ions increases, also allowing leakage out of the ion trap into the detector. Tandem Mass Spectrometry (MS/MS): Quadrupole mass analyzers, which can select ions of varying m/z ratios in the ion traps, are commonly used in tandem mass spectrometry (MS/MS). In this technique, the selected ions are further fragmented into smaller ions by a process called collision-induced dissociation (CID). When performed on all of the initial ions present in the ion trap, the sequence of a peptide/protein can be determined. This technique usually requires two mass analyzers with a collision cell in between where selected ions are fragmented by collision with an inert gas. It can also be done in a single mass analyzer using a quadrupole ion trap. Time of Flight (TOF) tube (used in MALDI) - a long tube is used and the time required for ion detection is determined. The small molecular mass ions take the shortest time to reach the detector. Sequence Determination Using Mass Spectrometry In a typical MS/MS experiment to determine a protein sequence, a protein is cleaved into protein fragments with an enzyme such as trypsin, which cleaves on the carboxyl side of positively charge Lys and Arg side chains. The average size of proteins in the human proteome is approximately 50,000. If the average molecular mass of an amino acid in a protein is around 110 (18 subtracted since water is released on amide bond formation), the average number of amino acids in the protein would be around 454. If 10% of the amino acids are Arg and Lys, then on average there would be approximately 50 Lys and Arg, and hence 50 tryptic peptides of average molecular mass 1000. The fragments are introduced in the MS where a peptide fragment fingerprint analysis can be performed. The molecular weights of the fragments can be identified and compared to known peptide digestion fragments from known proteins to identify the analyte protein. Ions with the original N terminus are denoted as a, b, and c, while ions with the original C terminus are denoted as x, y, and z. c and y ions gain an extra proton from the peptide to form positively charged -NH3+ groups. Figure $15$ below shows peaks for a 4-amino acid peptide fragmentation pattern Figure $15$: 4-amino acid peptide fragmentation peaks. https://commons.wikimedia.org/wiki/F...gmentation.gif.   Creative Commons Attribution-Share Alike 3.0 Unported Ions with the original N terminus are denoted as a, b, and c, while ions with the original C terminus are denoted as x, y, and z. c and y ions gain an extra proton from the peptide to form positively charged -NH3+ groups. The actual ions observed depend on many factors including the sequence of the peptide, its original charge, the energy of the collision inducing the fragmentation, etc. Low energy fragmentation of peptides in ion traps usually produces a, b, and y ions, along with peaks resulting from loss of NH3 (a*, b* and y*) or H2O (ao, bo, and yo). No peaks resulting from the fragmentation of side chains are observed. Fragmentation at two sites in the peptide (usually at b and y sites in the backbone) forms an internal fragment. The y1 peak represents the C-terminal Lys or Arg (in this example) of the tryptic peptide. Peak y2 has one additional amino acid compared to y1 and the molecular mass difference identifies the extra amino acid. Peak y3 is likewise one amino acid larger than y2. All three y fragments peaks have a common Lys/Arg C-terminal and the charged fragment contains the C-terminal end of the original peptide. All b fragment peaks for a given peptide contain a common N terminal amino acid with b1 the smallest. Note that the subscript represents the number of amino acids in the fragment. By identifying b and y peaks the actual sequence of small peptides can be determined. Usually, spectra are matched to databases to identify the structure of each peptide and ultimately that of the protein. The actual m values for fragments can be calculated as follows, where (N is the molecular mass of the neutral N terminal group, (C) is the molecular mass of the neutral c terminal group, and (M) is the molecule mass of the neutral amino acids. (For N terminal amino acid, add 1 H. for C terminus add OH) • a: (N)+(M)-CHO • b: (N)+(M) • y: (C)+(M)+H (note in the figure above that the amino terminus of the y peptides has an extra proton in the +1 charged peptides.) m/z values can be calculated from the calculated m values and by adding the one H mass to the overall z if the overall charge is +1, etc. As an example, from these MW values, the sequence of the human Glu1- fibrinopeptide B can be determined from MS/MS spectra shown in an annotated form in Figure $16$. Note that most of the b peaks are b* resulting from the loss of NH3 from the N terminus. Now let's step back and get a broader picture of structure analysis by mass spectrometry. In general, proteins are analyzed either in a "top-down" approach in which proteins are analyzed intact, or a "bottom-up" approach in which proteins are first digested into fragments. An intermediate "middle-down" approach in which larger peptide fragments are analyzed may also sometimes be used. The top-down approach however is mostly limited to low-throughput single-protein studies due to issues involved in handling whole proteins, their heterogeneity, and the complexity of their analyses. In the second approach, referred to as the "bottom-up" MS, proteins are enzymatically digested into smaller peptides using a protease such as trypsin, which cleaves peptide chains mainly at the carboxyl side of the amino acids lysine or arginine, except when either is followed by proline. It is used for numerous biotechnological processes. The process is commonly referred to as trypsin proteolysis or trypsinization, and proteins that have been digested/treated with trypsin are said to have been trypsinized. Subsequently, these peptides are introduced into the mass spectrometer and identified by peptide mass fingerprinting or tandem mass spectrometry. Hence, this approach uses identification at the peptide level to infer the existence of proteins pieced back together with de novo repeat detection. The smaller and more uniform fragments are easier to analyze than intact proteins and can be also determined with high accuracy, this "bottom-up" approach is therefore the preferred method of studies in proteomic studies. A further approach that is beginning to be useful is the intermediate "middle-down" approach in which proteolytic peptides larger than the typical tryptic peptides are analyzed. Proteins of interest are usually part of a complex mixture of multiple proteins and molecules, which co-exist in the biological medium. This presents two significant problems. First, the two ionization techniques used for large molecules only work well when the mixture contains roughly equal amounts of constituents, while in biological samples, different proteins tend to be present in widely differing amounts. If such a mixture is ionized using electrospray or MALDI, the more abundant species have a tendency to "drown" or suppress signals from less abundant ones. Second, the mass spectrum from a complex mixture is very difficult to interpret due to the overwhelming number of mixture components. This is exacerbated by the fact that the enzymatic digestion of a protein gives rise to a large number of peptide products. In light of these problems, the methods of one- and two-dimensional gel electrophoresis and high-performance liquid chromatography are widely used for the separation of proteins. The first method fractionates whole proteins via two-dimensional gel electrophoresis (Figure 3.31). The first dimension of 2D gel electrophoresis is isoelectric focusing (IEF). In this dimension, the protein is separated by its isoelectric point (pI) and the second dimension is SDS-polyacrylamide gel electrophoresis (SDS-PAGE). This dimension separates the protein according to its molecular weight. Once this step is completed in-gel digestion occurs. In some situations, it may be necessary to combine both of these techniques. Gel spots identified on a 2D Gel are usually attributable to one protein. If the identity of the protein is desired, usually the method of in-gel digestion is applied, where the protein spot of interest is excised and digested proteolytically. The peptide masses resulting from the digestion can be determined by mass spectrometry using peptide mass fingerprinting. If this information does not allow unequivocal identification of the protein, its peptides can be subject to tandem mass spectrometry for de novo sequencing. Small changes in mass and charge can be detected with 2D-PAGE. The disadvantages of this technique are its small dynamic range compared to other methods. Some proteins are still difficult to separate due to their acidity, basicity, hydrophobicity, and size (too large or too small). The second method, high-performance liquid chromatography is used to fractionate peptides after enzymatic digestion. Characterization of protein mixtures using HPLC/MS is also called shotgun proteomics and MuDPIT (Multi-Dimensional Protein Identification Technology). A peptide mixture that results from the digestion of a protein mixture is fractionated by one or two steps of liquid chromatography. The eluant from the chromatography stage can be either directly introduced to the mass spectrometer through electrospray ionization, or laid down on a series of small spots for later mass analysis using MALDI. The general schema for analyzing proteins by mass spectrometry is shown in Figure $17$. Protein mixtures are prepared from cell culture or tissue samples and separated by gel electrophoresis. Single proteins are isolated and digested using trypsin to produce a peptide mixture. Peptides are separated by liquid chromatography and analyzed by mass spectrometry 3D Structural Determination There are four main ways that the 3D structure of a protein can be determined. These include X-ray crystallography, multidimensional NMR, cryoelectron microscopy, and computer modeling using artificial intelligence and machine learning. X-Ray Crystallography In this technique, proteins are induced to form solid crystals in which the individual molecules pack in a well-defined crystal lattice. X-rays are aimed at the crystals. The x-rays are scattered off of the crystal and collected by a detector. The scattered x-rays engage in constructive and destructive interference (much as water waves do) and form a diffraction pattern. The diffraction pattern is determined by the spacing and types of atoms in the crystal. Hence from a given 3D structure, a specific diffraction pattern is formed. Using some sophisticated mathematics (Fourier Transformations), the diffraction pattern can be converted back into the 3D structure of the object, in this case, the atoms with the protein. PHET Simulation - Diffraction Constructive and destructive interference and the formation of a "diffraction" pattern can be readily seen performing two PHET simulations. Follow the instructions below. Simulation 1: Slits • Select Slits to open the simulation and select these choices in order: • Type of wave: pick one that looks like a bullet • Frequency (middle green) • Amplitude: max • Check Screen • Choose 2 Slits • Slit width 200 • Slit separation 400 • Click the Green button. You will see light/dark patterns moving toward the screen. The light zones arise from constructive interference and dark from destructive interference of the two waves as they emerge from the slits. Simulation 2: Diffraction Now, look at the diffraction pattern arising from light moving through simple and more complex openings and interacting with an object, using the PHET animation and the step below. • First, refresh the browser window • Select Diffraction to open the simulation and select these choices in order • Pick 450 nm wavelength • Choose in succession the 4 vertical icons (circle, square, circle/square, array of circles, person) • Observe the diffraction patterns as you change the slit size X-ray scattering can also be envisioned as light "reflecting" from a series of planes formed by atoms in the crystal in which the planes are separated by specific distances (in the Angstroms range). The x-rays that are "reflected" from innumerable planes recombine constructively and destructively to form a diffraction pattern. X rays are used since the size of the "slits", and the distance between these "reflective" planes, must be comparable to the wavelength of the incident light, which for x-rays is 0.5 – 2.5 Å. A diffraction pattern is mathematically decoded to form an electron density map since it is the electrons that actually scatter the x-rays. Hydrogen atoms don't appear in x-ray crystal structures since they don't have enough electrons to be effective scattering centers. Computer programs are used to fit the electron density map to a 3D arrangement of atoms separated by characteristic bond distances corresponding to the functions groups and side chains found in protein. The quality/amount of crystals helps determine the quality of the diffraction pattern and the resulting structure. X-ray crystallographers define quality in terms of resolution. A resolution of 5Å - 10Å can reveal the structure of polypeptide chains, 3 Å - 4 Å of groups of atoms, and 1 Å - 1.5 Å of individual atoms. Figure $18$ shows the process from crystal to model. Figure $19$ below shows the electron density map around tyrosine 103 from myoglobin from crystal structures at two different resolutions (left, 1a6m, 1.0 Å resolution and right, 108m, 2.7 Å resolution). Figure $19$:  Electron density map around tyrosine 103 from myoglobin from crystal structures at two different resolutions (left, 1a6m, 1.0 Å resolution and right, 108m, 2.7 Å resolution).  Click on the images to see full iCn3D models showing the electron density map around the Y103. (Choice of Y103 from https://pdb101.rcsb.org/learn/guide-...ata/resolution) This figure shows 2Fo-Fc electron density maps which use the observed diffraction data, Fo, with the diffraction data calculated from the atomic model, Fc. Proteopedia has an excellent description of electron density maps. Not all proteins can be readily crystallized. The process is in many ways an art as much as it is a science. Membrane proteins fall into this category. Nuclear Magnetic Resonance (NMR) Many readers have probably performed 1H-NMR on small molecules introductory and organic chemistry labs. Interpreting spectra of molecules with many hydrogen atoms in straight and branched chains, in rings, and in functional groups is not simple. Imagine doing that to determine the structure of a small protein with 1000s of hydrogen atoms! The spectrum would be essentially indecipherable. Luckily multi-dimension NMR techniques have allowed the solution (not crystal) structure of small proteins to be determined. These methods are outside of the scope of this book. For those interested in more detail, read A brief introduction to NMR spectroscopy of proteins by Poulsen. Let's give a brief introduction to a 2D NMR peak for a simple molecule, ethylacetate. The 1D 1H-NMR spectrum for the molecule is shown in Figure $20$. Now let's show a simulated 2D COSY spectra of the same molecule. The image and explanation below are adapted from Structure & Reactivity in Organic, Biological and Inorganic Chemistry by Chris Schaller, which is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License. Structure and Reactivity In homonuclear correlation spectroscopy (COSY), we can look for hydrogens that are coupled to each other. In ethyl acetate, it's pretty clear where they are. There is a quartet and a triplet; the hydrogens corresponding to those two peaks are probably beside each other in the structure. The COSY spectrum simply takes that 1H spectrum and spreads it out into two dimensions. Instead of being displayed as a row of peaks, the peaks are spread out into a 2D array. Figure $21$ shows an annotated simulated COSY spectrum. The peaks are displayed along one axis and the same peaks are also displayed along the other axis. What does it mean to be coupled? It means that magnetic information is transmitted between the atoms. How can we tell? Essentially, we can send a pulse of electromagnetic radiation into one set of hydrogens and look for a response somewhere else. Of course, if we send a pulse of radio waves at a frequency that will be absorbed by a particular hydrogen, we will see a response in that hydrogen itself. That's why we see the peaks on the diagonal (dotted line). The peaks along the diagonal in the spectra (1.26, 1.26; 2.04, 2.04; 4.12, 4.12) hence don't give any new information since they are the main peaks in the 1D NMR. However, we also see responses from other hydrogens that are magnetically linked to the original one. They give the peaks (shown as purple circles) that do not appear along the diagonal. Those peaks indicate which hydrogens are coupled to which other hydrogens. The hydrogens at 1.26 ppm are coupled to the ones at 4.12 ppm, and that gives a "cross-peak" at (1.26, 4.12). There is also a cross-peak at (4.12, 1.26), because that relationship goes both ways. This coupling should make sense because the protons that give the signals at 4.12 (-O-CH2-CH3) and 1.26 (-CH2-CH3) are on adjacent carbons and split each other's signals as seen in the 1D NMR. This examples shows how we can get information on which protons in a 2D NMR spectra are coupled through 3 bonds (H-C-C-H), critical information for determining protein structures by NMR. There are variants of 2D NMR that are used in protein structure as well. They use NMR-active nuclei in addition to 1H, including 13C (natural abundance 1%) and 15N (natural abundance 0.37%). Given these low abundances, proteins for NMR structure determination are often purified in cells grown in media enriched in 13C and 15N precursors. HMBC (Heteronuclear Multiple Bond Correlation) and HMQC (Heteronuclear Multiple Quantum Coherence): Just as COSY spectra show which protons are coupled to each other, HMBC (and the related HMQC) give information about the relative relationships between protons and carbons in a structure. In an HMQC spectrum, a 13C spectrum is displayed on one axis and a 1H spectrum is displayed on the other axis. Cross-peaks show which proton is attached to which carbon. COSY spectra show 3-bond coupling (from H-C-C-H), whereas HMQC shows a 1-bond coupling (just C-H). Nuclear Overhauser Effect Spectroscopy (NOESY): This technique shows through-space interactions within the molecule, rather than the through-bond interactions seen in COSY and HMBC/HMBQ. This method is especially useful for determining stereochemical relationships in a molecule. In two stereoisomers, the atoms are all connected in exactly the same order, through exactly the same bonds. A COSY or an HMBC spectrum wouldn't be able to distinguish between these isomers. HNCA: This is an example of 3D NMR. It shows a correlation between an amide proton, the amide nitrogen to which it is attached, and the carbons that are attached to the amide nitrogen. HNCA data are viewed in slices, in which you look at one nitrogen at a time. One axis shows the shift of the proton attached to that nitrogen, and the other axis shows the shifts of the carbons attached to the nitrogen. The abbreviation comes from the pathway for transfer of the magnetization (amide H to amide N, and then the attached Cs). NMR structures found in the Protein Data Bank show an ensemble of slightly different structures. This arises from the dynamic behavior of the proteins in water, compared to when the structure is determined from a crystal. Comparative analyses between crystal and NMR structures show that secondary structures are equally accurate, that loops in NMR structures are probably too flexible, and that loops in protein, often on the surface, are too rigid, which makes sense given the packing restraints with a crystal lattice. Cryo-electron Microscopy Cryogenic-electron microscopy (cryo-EM) has recently emerged as a powerful technique in structural biology that is capable of delivering high-resolution density maps of macromolecular structures. A cryo-EM and structure determined from it are shown in Figure $22$. Resolutions approaching 1.5 Å are now possible and maps in the 1–4-Å range inform the construction of atomistic models with a high degree of confidence. This new capacity for investigators to determine macromolecular structures at high resolution and without the need to form crystals has led to an explosion of interest in adopting cryo-EM. Protein suspensions are frozen on 3-mm-diameter transmission-electron microscope (TEM) support grids made from a conductive material (e.g. Cu or Au) that are coated with a carbon film with a regular array of perforations 1–2 μm in diameter. A total of 3–5 μl of sample is loaded onto the grid which is then immediately blotted with filter paper with the aim of creating a film of buffer/protein on the grid that, when frozen, will be thin enough for the electron beam to penetrate. Optimizing the ice thickness is a vital step in sample preparation as thicker layers of ice increase the probability that the incident electron will undergo multiple scattering events and thereby reduce the image quality. In the case of extreme ice thickness, the electron beam does not penetrate at all. After blotting, the grid is rapidly plunged into a bath of liquid ethane—a very effective cryogen that freezes water with sufficient rapidity to prevent the formation of ice crystals. The formation of a vitreous layer of ice is the fundamental step in cryo-EM and preserves the target in a near-native state. The resulting vitreous ice layer with suspended protein molecules must then remain close to liquid nitrogen temperature (− 196 °C) during storage and imaging in the TEM to prevent phase changes to other types of ice that are not amenable to high-quality imaging and preservation of protein structure. Figure $23$ shows a summary of the process and single particle structure determination. Figure $23$:  Principle of cryo-EM and single-particle reconstruction.  Agirrezabala, X., Frank, J., 2010. From DNA to proteins via the ribosome: Structural insights into the workings of the translation machinery. Human Genomics 4, 226.. https://doi.org/10.1186/1479-7364-4-4-226.  CC BY 4.0 The above figure shows cryoEM structure determination process for ribosomes.  When frozen, they are found in random orientations embedded in a thin layer of ice. Exposure to a low-dose electron beam in the transmission electron microscope produces a projection image (i.e the electron micrograph). A typical electron micrograph shows E. coli ribosomes as low-contrast single particles on a noisy background. After the orientations of the particles have been determined, usually by matching them with a reference through computer algorithms, they are used to reconstruct a density map by a back-projection or a similar reconstruction algorithm. This density map is segmented into the different components (subunits, ligands), and the different components are displayed using different colors in a surface representation (bottom panel; small and large subunits are shown in yellow and blue, respectively. A- and P-site tRNAs are colored red pink and green, respectively). Given the increasing popularity of this technique, we present Figure $24$ below which shows another representation with the end-point of a 3D model of higher resolution. Figure $24$: A schematic of the single-particle reconstruction cryoEM pipeline. Hey Tony et al., 2020Machine learning and big scientific data. Phil. Trans. R. Soc. A.3782019005420190054.  http://doi.org/10.1098/rsta.2019.0054.  Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ When the beam strikes the ice lays in which the single structure is found, the ices domes which cause motion. Motion pictures are taken with fast detectors and computers are used to adjust the images for the motion, which would dull and lower the resolution of the structure. In addition, as X-rays damage molecules, so can electron beams. Earlier frames in the movie show less damage. Both the motion and damage effects of the beam can now be corrected to increase the resolution. Here is a YouTube video from PDB 101 that describes the technique. Methods for determining atomic structure.  PDB-101: Educational resources supporting molecular explorations through biology and medicine. Christine Zardecki, Shuchismita Dutta, David S. Goodsell, Robert Lowe, Maria Voigt, Stephen K. Burley. (2022) Protein Science 31: 129-140 https://doi.org/10.1002/pro.4200.   CC By 4.0 license. Homology Modeling and Artificial Intelligence/Machine Learning None of the above techniques would be required (except for validation) if the 3D structure of a protein could be determined from its sequence. Computationally this is an astoundingly large problem, given the astronomically large number of possible conformations for a given protein sequence. However, using the nearly 200,000 structures in the PDB database, modern computer methods utilizing artificial intelligence and machine learning have perhaps solved the folding problem. New protein structure prediction programs such as AlphaFold (using a neural network-based model) and RoseTTAFold have led to the solving of structures for which crystals or homologous proteins are not available. A comparison of protein structures obtained using the program with known 3D structures obtained through x-ray crystallography or other techniques are almost identical. Different metrics can be used to compared predicted structures to the actual one. The root mean squared deviation (RMSD) is a common one. For example, developers of RoseTTAFold used a TM-score, a metric for assessing the topological similarity of protein structures. Compared to RMDS, the TM-score weights smaller distance errors higher than larger distance errors and makes it sensitive to the global fold, not local structural differences. TM values range from 0-100 (100 is a perfect match). Scores below 17 indicate no topology match while those greater than 50 suggest a common fold. The software is described as a "neural network, meaning it simultaneously considers patterns in protein sequences, how a protein’s amino acids interact with one another, and a protein’s possible three-dimensional structure. In this architecture, one-, two-, and three-dimensional information flows back and forth, allowing the network to collectively reason about the relationship between a protein’s chemical parts and its folded structure". Programs of this type might allow the generation of proteins with new therapeutic or commercial potential just based on sequences. These include vaccines, sensors, specific immune system suppressors or activators, and antivirals. AlphaFold has now been used to predict the structure of 214 million proteins from more than one million species — essentially all known protein-coding sequences. We have included many AlphaFold iCn3D models throughout this book. Figure $25$ shows the backbone tube cartoon of the x-ray pdb structure of the small protein (1xww, cyan) and the structure predicted by both RoseTTAFold program and AlphaFold (magenta) just from its primary sequence. Sulfate, a competitive inhibitor, is shown (spacefill) bound in the active site. The alignment is quite spectacular, except for the N-terminal 5 amino acids shown at the very bottom of the figure (6 oclock). This stretch has more disorder even in the x-ray structure as the amino acids have high B-factors, indicating more conformational flexibility. AlphaFold has also been used to predict the structure of protein complexes in which multiples of the same or of a different protein subunit combine to form a larger, quaternary structure. In yet another expansion of the use of machine learning and artifical intelligence, programs can now start with a desired 3D shape (protein backbone, for example) and determine the amino acid sequence necessary to get it. One such program is called ProteinMPNN. It allows protein structure design, not structure prediction. In addition, predicted symmetric protein oligomers (about 30% of all proteins in the Protein Data Bank) can also be generated using "deep network hallucination" which requires as input the number of monomers in the structure and the length of the monomer. The "hallucinated structures" are quite different from typical homooligomeric structures found in the PDB, but the predicted structures match well with cryoEM structures of the designed oligomers. Comparisons of 3D Structure Determination Methods Obtaining a pure, highly concentrated (mM) protein sample is a major bottleneck for both x-ray crystallography and NMR. The high concentration is required because both techniques are insensitive to single molecule analysis, and a large population of a particular protein is required to overcome the signal-to-noise barrier. On a similar note, the sample needs to be very homogenous, so protein purification is necessary at some point. Cryo-EM requires considerably less protein than the other two methods, but still 'a lot' by any standard. Typically, Cryo-EM requires preparations at a concentration of 1 mg/ml in a volume of at least 50 μl, whereas crystal formation might require 500 μl of protein at a concentration of 5 -10 mg/ml. Cryo-Em also requires the protein to be prepared in a low-salt buffer, with minimal additives, to ensure good freezing and image contrast. Recombinant protein production using E. coli is the method of choice when large quantities of protein are required. This process involves taking the gene (often cDNA) of the protein of interest, splicing it into a suitable inducible vector, transforming the vector into an E. coli host, and growing the culture in a rich medium. The bacterial host will multiply during a growth phase, after which it is induced to express the protein of interest. If all goes well, the protein will express solubly and in high numbers. Unfortunately, this process is easier said than done. Many eukaryotic proteins do not express well in prokaryotic hosts, and oftentimes modifications need to be made to optimize the bacterial host, codon usage, media, etc. to obtain a decent yield of recombinant protein. Additionally, proteins often express insolubly as inclusion bodies and require high concentrations (2M to 8M) of denaturants such as urea or guanidine hydrochloride to solubilize them, and then stepwise dialysis into an appropriate buffer to refold them. Alternatively, eukaryotic organisms such as S. cerevisiae (yeast), insect, and mammalian cell lines can be used, especially when post-translation modifications are required, though a decrease in yield and increase in overall cost is common with these organisms. The difficulty of protein production is compounded for NMR by the fact that all proteins need to be 15N and/or 13C labeled, as only these isotopes have nuclei with + ½ and - ½ spin states which enable the energy transitions required for a radiofrequency NMR signal; note that 1H also has ½ spin states but is highly abundant. Protein stability is an issue for both crystallography and NMR. Once a protein has been expressed, purified, and concentrated, it must maintain its structural integrity for the duration of the experiments. For crystallography, this involves the crystallization process, where the protein sample is placed in a variety of solutions (most often involving high concentrations of polyethylene glycol) that induce crystallization. Often referred to as a voodoo technique, crystallization conditions are tested in a high throughput method using 96-well screening plates, and any hits are further optimized using a larger volume of the particular solution. While a crystallization condition may eventually be found, the process can take anywhere from a few days to even a year or two to happen, making the crystallization process the rate-limiting step for protein crystallographers. During this time, the protein must stay in solution and maintain its structure so as to produce a high-quality crystal; a condition that is not often the case. Similarly, a stable, highly concentrated protein sample is required to perform many of the more advanced NMR experiments. This is because many of these experiments require days and even weeks to run, during which the homogeneity of the solution is key to acquiring quality spectra. Should the protein unfold or precipitate out of solution during an experiment, the resulting chemical change would either not produce any signal, or one which could not be used for structure/dynamics determination. For Cryo-EM, working with frozen-hydrated specimens brings a number of challenges both for manipulations and imaging. When handling cryo-EM grids to load them into the microscope, exposure to atmospheric water vapor rapidly leads to frost buildup on the grid. Under the TEM, these ice crystals on the grid surface appear as huge boulders that completely block the electron beam. Thus, grids are kept under liquid nitrogen as much as possible to minimize frost contamination. Problems with ice conditions are common—insufficient rapid freezing leads to the formation of hexagonal ice, while devitrification occurs when samples warm up, leading to the formation of cubic ice. Various degrees of contamination may occur, and frosting at atmospheric pressure causes the above-mentioned ice crystal deposition, while contamination within the column or under low-vacuum conditions gives rise to a more subtle artifact. One of the hallmarks of protein crystallography is that size does not matter. Whether one is working with a 25 kDa monomeric protein, or a 900 kDa multimeric complex, if it can be crystallized and produce a high-resolution diffraction pattern its structure can be determined. This is due to the fact that once in crystal form, a protein is in a more-or-less static conformation which, after passing it through the x-ray beam at different angles, can produce a single structural model. Cryo-EM is similar in this regard. Very large structures, including massive nucleoprotein complexes, such as the ribosome, can be elucidated using Cryo-EM. The same cannot be said for NMR. In NMR, the protein is in a soluble state and therefore in constant movement. The most important movement that governs the spectral quality is that of the molecular tumbling rate. For proteins larger than about 40 kDa, the tumbling rate decreases significantly, in turn increasing the transverse relaxation rate (T2). Essentially, this results in a weaker and rapidly decaying NMR signal, which manifests itself in peak broadening and spectral overlap. One of the major advantages of NMR is its ability to record small and large-scale protein dynamics, a phenomenon that is generally suppressed when a protein is crystallized. Although a crystallized protein may exhibit a certain amount of motion within the lattice, the motions manifest themselves as static or dynamic disorder, the former of which may result in two different conformations of a particular region, and the latter in averaged electron density. In general, crystallization may restrict a protein’s natural flexibility and motions. Cryo-EM suffers from this same limitation as samples are frozen and immobile. However, cryo-EM is capable of capturing a snapshot of the native structure as freezing is instantaneous and does not require the formation of a crystal lattice. Crystallography, however, is not left in the cold when it comes to dynamic structural analysis. Time-resolved crystallography can be used to monitor changes in the protein structure upon the addition of some ligand, or change in the environment. Because all protein crystals are highly hydrated, they are able to serve as crucibles for some biochemical reactions. The crystal is typically soaked in a solution containing the ligand of interest to initiate the biochemical reaction, after which the crystal is quickly placed into the beam line and the diffraction pattern is obtained. This can be performed multiple times if necessary to obtain a variety of structural intermediates. The process though requires many things to go right: the protein cannot become disordered nor should the crystal become cracked during the soaking process, and a high-powered synchrotron is required to collect high-quality diffraction data over short exposure times. In the end, protein X-ray crystallography, cryo-EM, and NMR spectroscopy are not mutually exclusive techniques; one can easily pick up where the other falls short. In analyzing NMR dynamics experiments, for example, one can greatly benefit from existing crystal structure data, or cryo-EM data onto which the NMR structural data can be superimposed. Similarly, NMR structure data can be used to supplement a cryo-EM or crystal structure with more information on the protein's dynamics, binding information, and conformational changes in solution. Recent Updates 8/4/23 Molecular Dynamics Simulations Introduction to Molecular Mechanics and Molecular Dynamics Molecular modeling and computational chemistry are important parts of modern biochemistry.  Modeling is important to display in a meaningful and instructive fashion the large amounts of data produced when X-ray crystallography and NMR are used to determine the structure of large biological molecules and complexes.   Remember, however, that primary X-ray crystal data (in the form of electron density maps)  are just that, and the data must be interpreted like any other type of data.  Structures need to be refined and energy minimized to produce more realistic structures (without van der Waals overlap or missing atoms, for example).  In addition, atoms within any molecule are not static, but move as bonds vibrate, angles bend, etc.  This implies that large biomolecules could adopt many possible conformations of different energies.  For proteins, some of these conformations might center around an average conformation situated at a local or global energy minimum separated from each other by activation energy barriers. In contrast to small molecules whose structure can be minimized using ab initio or semi-empirical quantum mechanics (using programs such as Spartan), large molecular structures (like DNA, RNA, proteins, and their complexes) must be minimized using molecular mechanics, based on Newton's laws.  Atoms are treated as masses, and bonds as springs with appropriate force constants.  A force field, containing all the relevant parameters for a given atom (for example sp3, sp2, sp2 aromatic, and sp C) and bond types is used to solve energy equations that sum all energies over all atoms and bonds in the molecule.  These energies include interactions among bonded atoms (stretching, bending, torsion, wagging) and those among nonbonded atoms (electrostatic and van der Waals).   For minimization calculations, the positions of the atoms within a molecule must be systematically or randomly moved and the energy recalculated with the goal of finding a lower energy and hence more stable molecule.  Minimization calculations can not probe all conformational space and can not easily move a structure from a local minimum to a global minimum if two are separated by a large energy barrier.  Energy minimizations are usually done in the absence of solvent.  Common force fields used for macromolecules are CHARMMAMBER, and GROMOS.  Parameters for specific atom type in a given bond include atomic mass, van der Waals radius, partial charge for atoms (from quantum mechanics) and bond length (from electron diffraction data), angles, and force constants for bonds (modeled as springs, obtained from IR).  These parameters are derived from experiments and theoretical (usually quantum mechanical) calculations on small organic molecules.  A potential energy equation comprised of terms from bond stretching, angle bending, and torsion angle changes (bonded interactions) as well as electrostatic and van der Waals interactions (nonbonded) is then solved (described below). The goal of molecular dynamics is to simulate the actual changes in a molecule as a function of time after an energy input (heat application at a higher temperature) is added to a molecule at equilibrium.  To make the simulation realistic, the structure is placed in a "bath" of thousands of water molecules.  As is described below, if the energies of atoms in a large molecule are known, the forces acting on those atoms can be deduced.  From Newton's Second Law (F=ma), the velocity or change of position of an atom in the structure with time can be determined.  If the dynamic simulation can be run for a long enough period of time, alternate conformations (perhaps those centered around a global minimum as well as those nearby in energy space - a local minimum) may be sampled.  By determining what fraction of the simulated conformations resemble the two alternative conformations, the ΔG for the interconversion of the two states can be calculated.  As you can imagine, these calculations require large amounts of computer time.  They give very important information, however, since protein conformational changes are often, if not always associated with the binding of a biological molecule to a binding partner.  In silico experiments offer important clues and support to results obtained using other methods of study. Molecular mechanics (MM) and molecular dynamics (MD) have become powerful tools in analyzing and predicting the properties of complex biological structures.  The Noble Prize in Chemistry in 2013 was awarded to Martin Karplus, Michael Levitt, and Arieh Warshel "for the development of multiscale models for complex chemical systems".   Karplus in particular developed much of the present basis for MD simulations. Energy (E), Force (F), and Motion To make the energy equations for the individual components more understandable, it is useful to consider the relationship between force and energy.  You have studied two general force equations in introductory chemistry and physics courses.  One is Coulomb’s Law, which describes the electrostatic force of attraction, FC, between two charges, q1 and q2, separated by a distance r. F_C=k \frac{q_1 q_2}{r^2} The other is Hooke’s Law, which describes the restorative force on a mass connected to a spring on stretching or compression of the spring. F=-k x where x is the displacement of the spring from an equilibrium (at rest) position. Our first interest is to understand how these equations might lead us to equations that describe the potential energy of a two-charge system or of a compressed or stretched spring.  We can best understand this by studying the simple example of a ball placed at various locations on a hill.  If placed on a flat surface at the top and bottom of the hill, there is no net force on the ball (Fnet = 0), so it will not move.  If placed at various locations on the down slope, it will experience a net downward force, shown in a qualitative fashion in Figure $26$ below. Figure $26$: Potential Energy vs r for a ball on a hill Astute observers will note that the magnitude of the force vector is proportional to the slope.  From this simplistic approach, we come to the following equation relating F to E: F=-\frac{d E}{d r} The minus sign is required since the force is downward but the energy increases upward. This simplified approach can be extended into three dimensions, to give the following equation (which will have meaning to those with advance calculus background) where F is the negative gradient of the potential energy: F=-\left(\frac{\partial}{\partial x}+\frac{\partial}{\partial y}+\frac{\partial}{\partial z}\right) E=-\nabla E Applying the 1D equation to Hooke’s Law gives \begin{aligned} & d E=-F d r=-k x d x \ & \int d E=-k \int x d x \end{aligned} which gives E=\frac{k x^2}{2} This gives a parabolic graph of E vs displacement. Figure $27$ below shows an interactive PHET simulation of Hooke's Law.  Click Energy and then select energy to see the parabolic plot.  Change the force constant to alter the "steepness" of the resulting parabolic curve. Figure $27$: PHET simulation of Hooke's Law.  https://phet.colorado.edu/en/simulations/hookes-law The same approach can be applied to Coulomb's Law.  Notice that the result equation for E results in increasingly negative values as r get smaller only when q1 and q2 have opposite charges. \begin{aligned} & d E=-F d r=-k \frac{q_1 q_2}{r^2} d r \ & \int d E=-k q_1 q_2 \int r^{-2} d r \end{aligned} gives E=\frac{k q_1 q_2}{r} A graph of E vs r for both attractive and repulsive interactions is shown in Figure $28$ below. Figure $28$: E vs r for both attractive and repulsive interactions Molecular Mechanics Note: The following review is based on an NIH Guide to Molecular Modeling (1996), which to the best of our knowledge was removed from the web. Molecular mechanics uses Newtonian mechanics to calculate energy of atoms in large molecules like proteins.  It assumes that nuclei and electrons are one particle with radii and calculated charges.  Bonds are treated as springs connecting atoms.  Energies are calculated classically (not with quantum mechanics).  Parameters, many based on quantum mechanics calculations on small molecules, are assigned to all bonds, angles, dihedrals, etc.  Interactions are bonded (local) and nonbonded. Bonded interactions involved atoms connected by one bond (bond stretch), two bonds (angle bending) and 3 bonds (dihedral angle change). These three types of bonded interactions are shown with black arrows on the right-hand side of Figure $29$ below. Figure $29$:  Bonded and Nonbonded Interactions in Proteins.  Force field (chemistry). (2023, July 15). In Wikipedia. https://en.wikipedia.org/wiki/Force_field_(chemistry).  CC BY-SA 3.0 Non-bonded atoms (greater than two bonds apart) interact through induced dipole-induced dipole interactions (one example of which is steric repulsions) and electrostatic attraction/repulsion. And example of a nonbonded interaction is shown in the double black arrow labeled Lennard Jones in the above figure. All energy terms from these interactions are summed to give the energy of a given conformation.  The energy should be considered relative to those of other conformations. Here is the basic energy equation for all of these energy terms: \text { Energy }(\mathrm{E})=\mathrm{E}_{\text {Stretch }}+\mathrm{E}_{\text {Bending }}+\mathrm{E}_{\text {Torsion }}+\mathrm{E}_{\text {Non-bonded Interactions }} The "force field" consists of the energy equations and the parameters for each of the energy terms.  There are many different commercially available force fields. Bonded Interaction Energies The mathematical form of the energy terms varies from force-field to force-field. The more common forms will be described. Stretching (Vibrational) Energy \mathrm{E}_{\text {stretch }}=\Sigma_{\text {bonds }} \mathrm{k}_{\mathrm{b}}\left(r-\mathrm{r}_{\mathrm{o}}\right)^2 Figure $30$ illustrates bond stretching or vibration Figure $30$: Bond stretching or vibration The stretching energy equation is based on Hook's law. The kb parameter defines the stiffness of the bond spring.  R0 is the equilibrium distance between the two atoms.   It should make sense that deviations from the equilibrium length would be associated with higher energy.  The E vs r curve is hence a parabola as shown in Figure $3$ below for a system when the lowest energy is at r=6. Figure $31$: Energy vs r (r0=6) for bond stretching (vibration) Obviously, only small changes in r are allowed as too large an r value would lead to bond breaking. Bending Energy \mathrm{E}_{\text {bending }}=\Sigma_{\text {angles }} k_{\Theta}\left(\Theta-\Theta_0\right)^2 Figure $32$ illustrates bond bending Figure $3229$: Bond bending The bending energy equation is also based on Hooke's law. The kΘ parameter controls the stiffness of the angle spring, while the Θ0  is the equilibrium angle. As above, the graph of E vs theta is expected to be a parabola as shown in Figure $33$ below for a system when the lowest energy is at θ=45 Figure $33$: Ebending vs r when lowest energy is at θ=45. Torsion Energy E_{\text {torsion }}=\Sigma_{\text {torsions }} A[1+\cos (\text { ntau }-\Theta)] Figure $34$ illustrates torsion angle rotation Figure $34$: Torsion angle rotation The torsion energy is modeled by a periodic function, much as you have seen with energy plots associated with Newman projections sighting down the central C-C bond in butane, for example.  Two different torsion energy functions are shown in Figure $35$ below. Figure $35$: Torsion energy vs tau for A=1, n=1, phi=0 (blue) and A=1, n=2,phi=90 (orange). Non-Bonded Interaction Energy The non-bonded energy is calculated for all possible pairs of nonbonded atoms, i and j: Enonbonding = Σi Σj [ -Bij/rij6 +A ij/rij12 ] + Σi Σj (qi qj) / rij E_{\text {nonbonding }}=\Sigma_i \Sigma_j\left[-B_{i j} / r_{i j}^6+A_{i j} / r_{i j}^{12}\right]+\Sigma_i \Sigma_j\left(q_i q_j\right) / r_{i j} \mid The first term represents van der Waals interactions while the second term represents Coloumbic electrostatic interactions.  Figure $36$ illustrated nonbonded interactions. Figure $36$: Nonbonded interactions You should remember that induced dipole-induced dipole interactions are short-range and occur among all atoms. The 6-12 energy equation based on the Lennard-Jones' potential, shows a negative (attractive) term proportional to -1/r6 and a repulsive term proportional to +1/r12.  Figure $37$ below shows a graph of the attractive, repulsive, and summation of the energy terms in the Lennard Jones potential. Figure $37$: Lennard Jones potential vs r The A and B parameters control the depth and position (interatomic distance) of the potential energy well for a given pair of non-bonded interacting atoms (e.g. C:C, O:C, etc.). In effect, A determins the degree of stickiness of the van der Waals attraction, and B determines the degree of hardness of the atoms (e.g. marshmallow-like, billiard ball-like, etc.). The B parameter is related to the "stickiness" of the interactions and is related to the polarization of the atoms.  B can be obtained from atomic polarizability measurements, or it can be calculated quantum mechanically. The A parameter is empirically derived to fit nonbonded contacts between atoms in crystal structures. Summary Interactions Some programs assign charges using rules or templates, especially for macromolecules. In some force fields, the torsional potential is calibrated to a particular charge calculation method (rarely made known to the user). The use of a different method can invalidate the force-field consistency.  Sometimes, an additional bonded interaction term, improper dihedrals, is added as illustrated below.  The potential for that is given by the following equation: Eimproper = Σangles kω (ω - ωo)2 E_{\text {improper }}=\Sigma \text { angles } k \omega\left(\omega-\omega_0\right)^2 Molecular Dynamics This section comes directly from the NIH tutorial: "In the broadest sense, MD is concerned with molecular motion. Motion is inherent to all chemical processes. Simple vibrations, like bond stretching, and angle bending, give rise to IR spectra. Chemical reactions, hormone-receptor binding, and other complex processes are associated with many kinds of intra- and intermolecular motions. The driving force for chemical processes is described by thermodynamics. The mechanism by which chemical processes occurs is described by kinetics. Thermodynamics dictates the energetic relationships between different chemical states, whereas the sequence or rate of events that occur as molecules transform between their various possible states is described by kinetics. Conformational transitions and local vibrations are the usual subjects of molecular dynamics studies. MD alters the intermolecular degrees of freedom in a step-wise fashion, analogous to energy minimization. The individual steps in energy minimization are merely directed at establishing a downhill direction to a minimum. The steps in MD, on the other hand, meaningfully represent the changes in atomic position, ri, over time (i.e. velocity). Newton's equation (Fi = miai) is used in the MD formalism to simulate atomic motion. The rate and direction of motion (velocity) are governed by the forces that the atoms of the system exert on each other as described by Newton's equation. In practice, the atoms are assigned initial velocities that conform to the total kinetic energy of the system, which in turn, is dictated by the desired simulation temperature. This is carried out by slowly heating the system (initially at absolute zero) and then allowing the energy to equilibrate among the constituent atoms. The basic ingredients of MD are the calculation of the force on each atom, and from that information, the position of each atom through a s specified period of time (typically on the order of picoseconds = 10-12 seconds). The force on an atom can be calculated from the change in energy between its current position and its position a small distance away. This can be recognized as the derivative of the energy with respect to the change in the atom's position: -dE/dri = Fi. Energies can be calculated using either MM or quantum mechanics methods. MM energies are limited to applications that do not involve drastic changes in electronic structure such as bond-making/breaking. Quantum mechanical energies can be used to study dynamic processes involving chemical changes. The latter technique is extremely novel and of limited availability. Knowledge of the atomic forces and masses can then be used to solve for the positions of each atom along a series of extremely small time steps (on the order of femtoseconds). The resulting series of snapshots of structural changes over time is called a trajectory. The use of this method to compute trajectories can be more easily seen when Newton's equation is expressed in the following form -dE/dri = mia= md2ri/dt2 -d E / d r_i=m_i a=m d^2 r_i / d t^2 In practice, trajectories are not directly obtained from Newton's equation due to the lack of an analytical solution. First, the atomic accelerations are computed from the forces and masses. The velocities are next calculated from the accelerations based on the following relationship: ai = dvi/dt. Lastly, the positions are calculated from the velocities: vi = dri/dt. A trajectory between two states can be subdivided into a series of sub-states separated by a small time step, delta t (e.g. 1 fs). The initial atomic positions at time t are used to predict the atomic positions at time t = delta t. The positions at t = delta t are used to predict the positions at t = 2Δt, and so on. The leapfrog method is a common numerical approach to calculating trajectories based on Newton's equation. The method derives its name from the fact that the velocity and position information successively alternate at ½ time step intervals.  MD has no defined point of termination other than the amount of time that can be practically covered. Here is a molecular dynamics simulation of a small protein module (NTL9) folding. MD simulations can be used to obtain theoretical values for ΔG and Keq values for conformational changes, binding of small ligands, and changes in protonation states for side chains.   This process is based on the idea that the conformations sampled in silico MD simulations reflect those found in vitro (i.e. they are part of the thermodynamically expected and available conformations available to the molecules during normal conformational shifts).  This is called the Ergodic Hypothesis.  Given the short time spans for MD simulations (limited by computer power) this hypothesis can't apply to the dynamic results unless the sample conformations are close in energy without a large activation energy barrier between them.     If it is then the following equation could apply: \begin{aligned} & \Delta G^0=-R T \ln K_{\text {eq }} \ & \Delta G^0=-R T \ln P_2 / P_1=-R T \operatorname{ln} f_2 / f_1 \end{aligned} where Pn is the probability of being in a given state and fn is the fraction in a given state. You will note that none of the potential energy functions use quantum mechanical parameters.  This is due, in part, to the complexity of the systems studied.   This is beginning to change as more effort is devoted to understanding quantum mechanical aspects of complex bonded systems.  New advances in even simple systems can illustrate this point.  Take for example ethane.  The conformational analysis of this simple molecule is discussed in all organic chemistry books.  Plots of energy vs dihedral angle (viewing the molecule down the C-C bond and measuring the angle between C-H bonds on adjacent C atoms) oscillates every 120o.  The E is at a maximum when the dihedral angle is 0, 120, 240 and 360o, each representing the eclipsed conformation.  It reaches minimums at the staggered (gauche) conformations at 60, 180, and 270o.  Why is the eclipsed form higher in energy than the staggered form? All organic books would state that there is greater steric repulsion (of the electron clouds) in the eclipsed forms, which raises their energy compared to the staggered forms?  However,  Pophristic shows that to be incorrect.  For the correct answer, you must turn to quantum mechanics and the phenomena of hyperconjugation.  The staggered conformation is energetically favored not since it is less sterically restricted, but since its is a lower energy form due to resonance like stabilization of the σ CH molecular orbitals.  There is greater correct phase overlap of σ CH and s* CH molecular orbitals on the adjacent Cs when they are in the staggered conformation than in the eclipsed form. Proteome Analysis The proteome is the entire set of proteins that are produced or modified by an organism or system. Proteomics has enabled the identification of ever-increasing numbers of protein. This varies with time and distinct requirements, or stresses, that a cell or organism undergoes. Proteomics is an interdisciplinary domain that has benefited greatly from the genetic information of various genome projects, including the Human Genome Project. It covers the exploration of proteomes from the overall level of protein composition, structure, and activity. It is an important component of functional genomics. After genomics and transcriptomics, proteomics is the next step in the study of biological systems. It is more complicated than genomics because an organism's genome is more or less constant, whereas proteomes differ from cell to cell and from time to time. Distinct genes are expressed in different cell types, which means that even the basic set of proteins that are produced in a cell needs to be identified. In the past, this phenomenon was assessed by RNA analysis, but it was found to lack correlation with protein content. Now it is known that mRNA is not always translated into protein, and the amount of protein produced for a given amount of mRNA depends on the gene it is transcribed from and on the current physiological state of the cell. Proteomics confirms the presence of the protein and provides a direct measure of the quantity present. A cell may make different sets of proteins at different times or under different conditions, for example during development, cellular differentiation, cell cycle, or carcinogenesis. Further increasing proteome complexity, as mentioned, most proteins are able to undergo a wide range of post-translational modifications. Therefore, a proteomics study may become complex very quickly, even if the topic of study is restricted. In more ambitious settings, such as when a biomarker for a specific cancer subtype is sought, the proteomics scientist might elect to study multiple blood serum samples from multiple cancer patients to minimize confounding factors and account for experimental noise. Furthermore, many proteins undergo post-translational modifications such as phosphorylation. Many of these post-translational modifications are critical to the protein's function. Thus, complicated experimental designs are sometimes necessary to account for the dynamic complexity of the proteome.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/03%3A_Amino_Acids_Peptides_and_Proteins/3.03%3A_Proteins_-_Analyses_and_Structural_Predictions_of_Protein_Structure.txt

Search Fundamentals of Biochemistry Introduction Before a protein or other biological macromolecule can be rigorously studied from a structural and functional basis, it must be purified. The problems that can arise during protein purification become clear when one considers that a single protein has to be purified from a mixture of as many 10,000 other cellular or tissue proteins, each of which is made up of the same constituent amino acids. Proteins differ in size (how many amino acids), charge (how many positively and negatively charged amino acids), sequence, and presence of specific binding sites on the proteins. Any technique that could be used to purify protein must be based on these inherent differences. Once the protein is purified, it must be analyzed, typically by a spectral or electrophoretic technique. Protein purification is a series of processes intended to isolate and purify a single protein or complex from cells, tissues, or whole organisms. Protein purification is vital for the characterization of the function, structure, and interactions of the protein of interest. Separation steps usually exploit differences in protein size, physical-chemical properties, binding affinity, and biological activity. Protein purification is either preparative or analytical. Preparative purifications aim to produce a relatively large quantity of purified proteins for subsequent use. Examples include the preparation of commercial products such as enzymes (e.g. lactase), nutritional proteins (e.g. soy protein isolate), and certain biopharmaceuticals (e.g. insulin). Many steps and much quality control is required to remove other host proteins and other biomolecules, which pose a potential threat to the patient's health. Analytical purification produces a relatively small amount of a protein for a variety of research or analytical purposes, including identification, structural characterization, and studies of the protein's structure, post-translational modifications, and function. The choice of a starting material is key to the design of a purification process. In plants or animals, a particular protein usually isn't distributed homogeneously throughout the body; different organs or tissues have higher or lower concentrations of the protein. The use of tissues or organs with the highest concentration decreases the volumes needed to produce a given amount of purified protein. If the protein is present in low abundance, or if it has a high value, scientists may use recombinant DNA technology to develop cells that will produce large quantities of the desired protein. These techniques will be discussed in greater detail in Chapter 5. Sample Processing If the protein of interest is not secreted by the organism into the surrounding solution, the first step of each purification process is the disruption of the cells containing the protein. Depending on how fragile the protein is, one of several techniques could be used including repeated freezing and thawing, sonication, homogenization by high pressure (French press), homogenization by grinding (bead mill), and permeabilization by detergents (e.g. Triton X-100) and/or enzymes (e.g. lysozyme). Finally, the cell debris can be removed by centrifugation so that the proteins and other soluble compounds remain in the supernatant. Also proteases are released during cell lysis, which will start digesting the proteins in the solution. As the protein of interest may be sensitive to proteolysis, it is important to proceed quickly and conduct many steps at low temperatures to reduce unwanted proteolysis. Alternatively, one or more protease inhibitors can be added to the lysis buffer immediately before cell disruption. Sometimes it is also necessary to add DNase in order to reduce the viscosity of the cell lysate caused by a high DNA content. Centrifugation Centrifugation is a process that uses centrifugal force to separate mixtures of particles of varying masses or densities suspended in a liquid. When a vessel (typically a tube or bottle) containing a mixture of proteins or other particulate matter, such as bacterial cells, is rotated at high speeds, the inertia of each particle yields a force in the direction of the particle's velocity that is proportional to its mass. The tendency of a given particle to move through the liquid because of this force is offset by the resistance the liquid exerts on the particle. The net effect of "spinning" the sample in a centrifuge is that massive, small, and dense particles move outward faster than less massive particles or particles with more "drag" in the liquid. When suspensions of particles are "spun" in a centrifuge, a "pellet" may form at the bottom of the vessel that is enriched for the most massive particles with low drag in the liquid. Non-compacted particles remain mostly in the liquid called "supernatant" and can be removed from the vessel thereby separating the supernatant from the pellet. The rate of centrifugation is determined by the angular acceleration applied to the sample, typically measured in comparison to the g. If samples are centrifuged long enough, the particles in the vessel will reach equilibrium wherein the particles accumulate specifically at a point in the vessel where their buoyant density is balanced with centrifugal force. Such an "equilibrium" centrifugation can allow extensive purification of a given particle. In sucrose gradient centrifugation, a linear concentration gradient of sugar (typically sucrose, glycerol, or a silica-based density gradient media, like Percoll) is generated in a tube such that the highest concentration is on the bottom and the lowest on top. Percoll is a trademark owned by GE Healthcare companies. A protein sample is then layered on top of the gradient and spun at high speeds in an ultracentrifuge. This causes heavy macromolecules to migrate toward the bottom of the tube faster than lighter material. During centrifugation in the absence of sucrose, as particles move farther and farther from the center of rotation, they experience greater centrifugal forces (the further they move, the faster they move). However, the useful separation range within the vessel is restricted to a small observable window. A properly designed sucrose gradient will counteract the increasing centrifugal force so the particles move in close proportion to the time they have been in the centrifugal field. After separating the protein/particles, the gradient is then fractionated and collected. These are described in Figure $1$. Precipitation and Differential Solubilization In bulk protein purification, a common first step to isolate proteins is precipitation using a salt such as ammonium sulfate (NH4)2SO4. Ammonium sulfate is often used as it is highly soluble in water, has relative freedom from temperature effects, and typically is not harmful to most proteins. Proteins are precipitated by (NH4)2SO4 in their native state, which is important if you need the protein for structure/function studies. Furthermore, ammonium sulfate can be removed by dialysis as described in Figure $2$. The process of dialysis separates dissolved molecules by their size. The biological sample is placed inside a closed membrane, where the protein of interest is too large to pass through the pores of the membrane, but through which smaller ions can easily pass. As the solution comes to equilibrium, the ions become evenly distributed throughout the entire solution, while the protein remains concentrated in the membrane. This reduces the overall salt concentration of the suspension. The mechanism underlying salt precipitation is actually quite complicated. High concentrations of sodium chloride don't precipitate protein. Other salts like guanidinidum chloride unfold proteins and do not lead to precipitation. Salt ions interact with both the protein and solvent water in somewhat complicated ways (which we will explore later). For now, we will simply be satisfied with the empirical observation that ammonium sulfate is the salt of choice to precipitate and also concentrate proteins from a solution. One advantage of (NH4)2SO4 precipitation of protein from solution is that it can be performed inexpensively with very large volumes, so it is used early in many purification proteins. Different proteins precipitate at different (NH4)2SO4 concentrations, so differential precipitation is often used. (NH4)2SO4 concentrations are increased in a step-wise fashion until the protein of interest is precipitated. Some proteins are not soluble in water. These include transmembrane proteins that span cell membranes and large fibrous proteins. Membrane proteins can be solubilized by the addition of detergents like sodium dodecyl sulfate (SDS), which unfolds the proteins, and octylglucoside or Triton X-100, which keeps the protein structure intact. Chromatography Chromatography is used in almost all protein purification methods and is the key that allows the separation of a given protein from the 1000s of different proteins in cells and tissues. The separation of proteins on a chromatography column depends on the type of column and chemical/physical properties of the molecule. There are four main types used of chromatographies used to separate proteins: • size exclusion chromatography in which proteins can be separated according to their size/shape or molecular weight • ion exchange chromatography in which proteins are separated by their charge/isoelectric point; • hydrophobic interaction chromatography (similar to reverse phase columns for purifying organic molecules) in which they are separated based on their relative hydrophobicity • affinity chromatography in which proteins are separated based on binding to a ligand covalently attached to a column bead. For preparative protein purification, the purification protocol generally contains one or more chromatographic steps. The basic procedure in chromatography is to flow the solution containing the protein through a column packed with a chromatography resin selected to separate proteins based on a specific property of the protein. Different proteins interact differently with the column material, and can thus be separated by the time required to pass the column, or the conditions required to elute the protein from the column. Usually, proteins are detected as they are eluting from the column by measuring the absorbance at 280 nm, at which the aromatic amino acids absorb. Size Exclusion Chromatography (also known as Gel Filtration Chromatography) This method is used to separate proteins based on size and shape. The chromatography beads have tiny openings/pores into which proteins of a size less than the pore diameter, can enter. Large proteins that can't enter the pore flow around the beads and elute faster than small ones that enter the pores. They diffuse out of the pores and enter the rest of the moving solvent before getting "trapped" again for a short time in more pores. Eventually, they work their way through the column and elute at a volume much greater than proteins, which can't enter the pores. Thus, proteins will be separated based on their size as illustrated in Figure $3$. The eluate is collected in sequential test tubes (or fractions). Note that the figure below shows the pores as actual channels that go through the bead. In actuality, the openings in resin beads should be considered to be pores, not channels. Also known as gel filtration chromatography, is a low-resolution isolation method that involves the use of beads that have tiny “tunnels" in them that each have a precise size. The size is referred to as an “exclusion limit," which means that molecules above a certain molecular weight will not fit into the tunnels. Molecules with sizes larger than the exclusion limit do not enter the tunnels and pass through the column relatively quickly by making their way between the beads. Smaller molecules, which can enter the tunnels, do so, and thus, have a longer path that they take in passing through the column. Because of this, molecules larger than the exclusion limit will leave the column earlier, while smaller molecules that pass through the beads will elute from the column later. This method allows the separation of molecules by their size. In any chromatography system, there is a mobile and stationary phase. For size exclusion chromatography, the stationary phase is usually a polymerized agarose or acrylamide bead, which contains pores of various sizes filled with the solvent. Let's pretend that the solvent (typically aqueous buffered solution) inside of the bead is trapped there and doesn't exchange with the solvent moving around the bead, so it would be part of the stationary phase. The mobile phase is the solvent used to elute the column which flows around the bead. The chromatography beads are often supplied in dried form, which must be swollen in the solvent before they are packed in the column. The actual volume of the agarose or acrylamide bead is very small compared to the volume of solution within their hydrated forms. Size and shape effects in size exclusion chromatography Size-size exclusion chromatography is so common, so we will explore it in greater detail Several different column volumes can be defined as shown in Figure $4$, where the packed chromatography beads are shown as circles. Figure $4$: Define volumes in size exclusion chromatography If we consider the mass of the beads to offer a negligible amount to the volume of the bead, the actual volume in the bead is mostly from the trapped solution, which can be considered to be the "stationary" phase. The volume around the bead is called the void volume, Vo. It should be apparent the volume inside the bead is given by V_i=V_t-V_o A solute elutes from the column in a broad peak. If the sample volume applied to the column is very small compared to Vt, the volume at which a solute elutes, $V_e$, is considered to be the center of the elution peak. This is true when $V_{sample} \gg V_e$. If we view this chromatography as a partitioning of solute between the mobile and stationary phases (the basis of all chromatography), we might be interested in what fraction of the stationary phase, Vi, a solute might partition into. Such a ratio would be given by: K=\frac{V_e-V_o}{V_t-V_o} where Vt-Vo (= Vinside) represents 100% of the stationary phase, where $K$ is a distribution coefficient. Consider two cases: 1. A very large solute compared to the pore size of the bead: In this case, Ve-Vo = 0 since Ve would be equal to Vo. (The solute wouldn't "see" any of the Vi.) In this case, K = 0. The solute would elute in the void volume of the column since it is too large to partition into the volume within the beads. All solutes of molecular weight greater than or equal to the smallest solute that can't enter the gel beads will all elute in the void volume. Hence solutes greater than this minimal size will co-elute from the column and not be separated. Vo is usually about 30-40% of the Vt. 2. A very small solute compared to the pore size. In this case Ve-Vo = Vt - Vo, since Ve would be equal to Vt. The solute would "see" all of the solvent within the bead. In this case, K = 1. Similar to above, all solutes of MW equal to or less than the largest solute that can partition into the entire volume within a bead will co-elute at a volume near Vt. Hence $K$ is a partition coefficient, which varies from 0 - 1, and represents that fraction of Vi into which a solute could partition. This K is not exactly a partition coefficient, however, since the actual volume of the gel matrix is assumed to be zero above. The graph in Figure $5$ shows typical Ve as a fraction of Vt for solutes of different sizes (x-axis is Ve/Vt). Large species that cannot enter the pores in the beads flow around it and elute in the void volume (V0) which is about 35-40% of Vt (red bell-shaped curve). Very small species can partition into both V0 and Vi so the elute near Vt (green bell-shaped curve). If a species adsorbs to the column bead through noncovalent interactions (such as hydrogen bonds or ion-ion interactions), it may elute after Vt (purple bell-shaped curve). K depends on the size and shape of the solute. The size and shape of an object determine its flow properties in a fluid. Frictional resistance (itself a force, which acts in the opposite direction to the velocity, another vector quantity), can be shown to be proportional to the velocity. F_f \propto v or F_f=-f v where $f$ is the frictional coefficient, which depends on the shape. Clearly, the bigger the object, the more frictional resistance to movement. For a sphere it can be shown that: f=6 \pi \eta R_s where η is the viscosity (a measure of the resistance to flow of a liquid - water has a low viscosity, real maple syrup a high viscosity), and Rs (Stokes radius) is the radius of the hydrated sphere (the larger Rs, the larger the frictional coefficient, the larger the Ff which resists motion). For an irregularly shaped object, the Stokes radius is the radius of a sphere that would have the same frictional coefficient as the object. Hence the Rs for a protein molecule that was not spherical in shape would be much larger than the Rs for another protein molecule of identical molecular weight that was spherical. Hence the Ve and the K value for a solute on a gel filtration column would best be related to the Stokes radius, since Rs values take into account both size and shape. If you separate two proteins of equal mass but one is highly elongated and the other is spherical, the elongated one, with a large RS, would elute first (assuming that both don't elute together in the void volume, V0. Gel filtration can be used to determine the molecular weight of an unknown, spherical (globular) protein when compared to a standard curve generated from other globular proteins of known molecule weight. To ensure the protein have the same "effective" shape, the proteins are eluted under denaturing conditions to remove shape contributions to the elution order. Separation on the basis of charge - Ion Exchange Chromatography The chromatography resin in this type of chromatography consists of an agarose, acrylamide, or cellulose resin or bead which is derivatized to contain covalently linked positively or negatively charged groups. Proteins in the mobile phase will bind through electrostatic interactions to the charged group on the column. In a mixture of proteins, positively charged proteins will bind to a resin containing negatively charged groups, like the carboxymethyl group, CM (-OCH2COO-) or sulfopropyl, SP, (-OCH2CH2CH2SO3-) while the negatively charged proteins will pass through the column. The positively charged proteins can be eluted from the column with a mobile phase containing either a gradient of increasing salt concentration or a single higher salt concentration (isocratic elution). The most positively charged protein will be eluted last, at the highest salt concentration. Likewise, negatively charged proteins will bind to a resin containing positively charged groups, like the diethylaminoethyl group, DEAE (-OCH2CH2NH(C2H5)2+) or a quaternary ethyl amino group, QAE, and can be separated in an analogous fashion. Ion exchange chromatography separates compounds according to the nature and degree of their ionic charge. The column to be used is selected according to its type and strength of charge. Anion exchange resins have a positive charge and are used to retain and separate negatively charged compounds (anions), while cation exchange resins have a negative charge and are used to separate positively charged molecules (cations). Before the separation begins a buffer is pumped through the column to equilibrate the opposing charged ions. Upon injection of the sample, solute molecules will exchange with the buffer ions as each competes for the binding sites on the resin. The length of retention for each solute depends upon the strength of its charge. The most weakly charged compounds will elute first, followed by those with successively stronger charges. Because of the nature of the separating mechanism, pH, buffer type, buffer concentration, and temperature all play important roles in controlling the separation. Figure $6$ shows a cation exchange column. The beads (brown) contain negatively charged functional groups which can bind positive protein (blue) or concentrated regions of positive charge on a protein. Before loading the column with protein, the negatively-charged beads would interact with positively charged counter cations (often Na+) from the column equilibration buffer. When the protein solution is introduced to the column, the positively charged protein will exchange with the bound Na+ ions (hence the name cation exchanger). Conversely, an anion exchanger consists of positively charged beads, which exchange bound anions. Proteins bounds through ion-ion interactions can be eluted by increasing the Na+ concentration in the eluting solution either in a stepwise or gradient fashion. Ion exchange chromatography is a very powerful tool for use in protein purification and is frequently used in both analytical and preparative separations. Affinity Chromatography In this technique, the chromatography resin is derivatized with a group that binds to a specific site on a given protein of interest. It may be a group that binds to the active site of an enzyme (such as benzamidine-agarose which is used for the purification of trypsin) or an antibody, which recognizes a specific amino acid sequence (an epitope) on a protein. For example, an antibody can be made to a specific peptide from albumin, the antibody covalently linked to agarose, and the antibody-agarose column then used to purify albumin specifically. This is a powerful technique since antibodies can be made that will bind selectively to a single protein. Knowing only the DNA sequence of a protein which has never been previously isolated, the amino acid sequence of the unknown protein can be derived from the DNA sequence. A 10-12 amino acid peptide from that protein can be synthesized in the lab (seethe last section below), and an antibody raised against the peptide. The antibody will most likely bind to the unknown protein as well as to the peptide, and hence could be used to purify the protein. These features of affinity chromatography are illustrated in Figure $7$. In this example in Figure $7$, protein P1 has affinity for ligand Z and will bind to the column while proteins P2 and P3 will pass through the column. Protein P1 can then be eluted from the column using high concentrations of free ligand Z. In vitro peptide synthesis for antibody production When making anti-peptide antibodies that recognized target proteins, or to study an isolated peptide by itself, it is more difficult to isolate and purify a peptide from its original protein than to synthesize it in the lab using solid-phase synthesis. We describe this technique below. Peptides are chemically synthesized by the condensation reaction of the carboxyl group of one amino acid to the amino group of another. Two chemical challenges must be addressed. The formation of an amide bond between the carboxylic acid of one amino acid and the amine of the other is thermodynamically unfavorable, so the carboxyl end must be activated typically by the reaction of the incoming amino acid with a reagent such as dicyclocarbodiimide. Secondly, reactive functional groups on the side chains and the amine of the carboxyl group-activated amino acid must be protected from unwanted reactions. Chemical peptide synthesis most commonly starts at the carboxyl end of the peptide (C-terminus), and proceeds toward the amino-terminus (N-terminus). Protein biosynthesis in living organisms occurs in the opposite direction. Chemical synthesis facilitates the production of peptides, which incorporate unnatural amino acids, peptide/protein backbone modification, and the synthesis of D-amino acids. The established method for the production of synthetic peptides in the lab is known as solid-phase peptide synthesis (SPPS). Pioneered by Robert Bruce Merrifield, SPPS allows the rapid assembly of a peptide chain through successive reactions of amino acid derivatives on an insoluble porous support. The solid support consists of small, polymeric resin beads functionalized with reactive groups (such as amine or hydroxyl groups) that link to the nascent peptide chain. Since the peptide remains covalently attached to the support throughout the synthesis, excess reagents and side products can be removed by washing and filtration. This approach circumvents the comparatively time-consuming isolation of the product peptide from the solution after each reaction step, which would be required when using conventional solution-phase synthesis. Each amino acid to be coupled to the peptide chain N-terminus must be protected on its N-terminus and side chain using appropriate protecting groups such as t-Boc (t-butyloxycarbonyl-, acid-labile) or flourenylmethyloxycarbonyl (Fmoc, base-labile), depending on the side chain and the protection strategy used (see below). The general SPPS procedure involves repeated cycles of alternate N-terminal deprotections and coupling reactions. The resin can be washed between each step to remove side products. The mechanism for the solid phase synthesis of a dipeptide is shown in Figure $8$. A. Deprotection of AA1: The first amino acid is coupled to the resin or purchased pre-coupled. The amine terminus contacting an FMOC group is deprotected with piperidine. The hydrogen abstracted from the FMOC is acidic as its negatively charged conjugated base is aromatic, since the negative charge on that C becomes sp2 hybridized to create the aromatic anion. The weak base piperidine is used to avoid side reactions. B. Activation of AA2: The carboxyl group of AA2 reacts with a carbodiimide, which is attacked by the carboxylate of AA2 leading to the formation of an isourea derivative. This can react with a second nucleophilic catalyst (which is regenerated in step C), hydrobenzotriazole (HBT), to form the activated HBT ester and the very stable urea derivative. C. Coupling Reaction: The activated AA2 now reacts with the amine of solid phase N-terminal deprotected AA1 to form the peptide bond. This cycle repeats until the desired sequence has been synthesized. At the end of the synthesis, the crude peptide is cleaved from the solid support while simultaneously removing all protecting groups using a reagent strong acid like trifluoroacetic acid. The crude peptide can be precipitated from a non-polar solvent like diethyl ether in order to remove organic soluble by-products and then purified using reversed-phase HPLC. The purification process, especially of longer peptides can be challenging, because small amounts of several byproducts, which are very similar to the product, have to be removed. For this reason, so-called continuous chromatography processes such as MCSGP are increasingly being used in commercial settings to maximize the yield without sacrificing purity levels. SPPS is limited by reaction yields, and typically peptides and proteins in the range of 70 amino acids are pushing the limits of synthetic accessibility. Synthetic difficulty also is sequence-dependent; typically aggregation-prone sequences such as amyloids are difficult to make. Longer lengths can be accessed by using ligation approaches such as native chemical ligation, where two shorter fully deprotected synthetic peptides can be joined together in solution. Increasingly, proteins made in cells can be engineered through manipulation of the protein's gene to contain a molecular tag, which can either be a small peptide or a protein, for which antibodies are commercially available. The tag is expressed at either the N- or C-terminal end of the target so as to not interfere with the folding of the expressed target protein. Examples of peptide tags include the His (sequence HHHHHH), FLAG (sequence DYKDDDDK) and HA (YPYDVPDYA) tags. The HA tags derives from the influenza hemagglutinin protein. A small protein such as the green Fluorescent Protein - GFP) can also be used as a tag. The resulting fusion protein of GFP connected to the target protein can also allow the target protein to be localized and followed by confocal fluorescence microscopy within cell. Chromatography resins with covalently attached antibodies to the His, FLAG, HA peptide tags, and GFP are commercially available as affinity chromatography resins as shown in the right-hand side of Figure $9$: below. Affinity reagents other than antibodies can be attached to the beads, as shown in the left-hand side of Figure $9$. Two, in particular, are Ni-Nitrilotriacetic acid (Ni-NTA) and the short peptide glutathione (γ-gluatmylcysteinylglycine). They also bind tagged proteins. The Ni-Nitrilotriacetic binds the His tag by chelation of the nickel ion with the 6 histidine imidazole groups on the His-tagged protein. (Note that His tags can also be bound to anti-His tag antibody beads.) Glutathione binds a protein tag, Glutathione-S-Transferase (GST) linked in a fusion protein to the target. The His tag, which is probably the most widely used, binds strongly to divalent metal ions such as nickel and cobalt. The protein can be passed through a column containing Ni-nitrilotriacetic. All untagged proteins pass through the column. The protein can be eluted with imidazole, which competes with the imidazole side chain on the His tag for binding to the column, or by a decrease in pH (typically to 4.5), which decreases the affinity of the tag for the resin. While this procedure is generally used for the purification of recombinant proteins with an engineered affinity tag (such as a 6xHis tag), it can also be used for natural proteins with an inherent affinity for divalent cations. Hydrophobic Interaction Chromatography (HIC) HIC media is similar to reverse phase chromatography in which a matrix like silica (very polar with exposed OH groups) is derivatized with ester or ether links from the silica surface hydroxyl OHs to nonpolar molecules, usually containing 8 or 18 carbons in the acyl or alkyl chain. Proteins with exposed hydrophobic groups would preferentially bind to the bead. The interactions of the protein with the derivatized beads are increased by adding high concentrations of salt to the aqueous solution, making water effectively more polar. This would shift the equilibrium towards binding of the surface-exposed nonpolar region on the protein to the nonpolar C8 or C18 chains. The ionic strength of the buffer is then reduced to elute proteins in order of increasing hydrophobicity, as shown in Figure $10$. The column matrix, shown in blue has a hydrophobic ligand covalently attached. In high salt conditions, proteins will bind to the matrix with differing affinity, with more hydrophobic proteins (shown in yellow) binding more tightly than more hydrophilic proteins (shown in green) When the salt concentration is decreased, proteins that are more hydrophilic will be released first, followed more hydrophobic proteins. High Performance Liquid Chromatography (HPLC) and Fast Protein Liquid Chromatography (FPLC) High performance liquid chromatography or high pressure liquid chromatography (HPLC) is a form of chromatography applying high pressure to drive the solutes through the column faster than using gravity-forced flow of solvent through the column. The packing beads are small and very closely packed which allows less diffusion and greatly increased resolution. Because of the close packing of the small beads, no flow would occur with an external pump. The most common form of HPLC is "reversed phase" HPLC, where the column packing material is hydrophobic. The proteins are eluted by a gradient of water and increasing amounts of an organic solvent, such as acetonitrile. The proteins elute according to their hydrophobicity. After purification by HPLC, the protein is in a solution that only contains volatile compounds, and can easily be lyophilized (freeze-dried). HPLC purification frequently results in the denaturation of the purified proteins and is thus not applicable to proteins that do not spontaneously refold. Due to the drawbacks of HPLC, an alternative technique using lower pressure was developed and is called Fast protein liquid chromatography (FPLC). In FPLC, the mobile phase is an aqueous solution, or "buffer". The buffer flow rate is controlled by a positive-displacement pump and is normally kept constant, while the composition of the buffer can be varied by drawing fluids in different proportions from two or more external reservoirs. The stationary phase is a resin composed of beads, usually of cross-linked agarose, packed into a cylindrical glass or plastic column. FPLC resins are available in a wide range of bead sizes and surface ligands depending on the application. In the most common FPLC purification systems as shown in Figure $11$, an ion exchange resin is typically chosen. A mixture containing one or more proteins of interest is dissolved in 100% buffer A and pumped into the column. The proteins of interest bind to the resin while other components are carried out in the buffer. The total flow rate of the buffer is kept constant; however, the proportion of Buffer B (the "elution" buffer) is gradually increased from 0% to 100% according to a programmed change in concentration (the "gradient"). Buffer B contains high concentrations of the exchanger ion. Thus as the concentration of Buffer B gradually increases, bound proteins will dissociate depending on their ionic interactions with the column matrix and appear in the eluant. The eluant passes through two detectors which measures salt concentration (by conductivity) and protein concentration (by absorption of ultraviolet light at a wavelength of 280 nm). As each protein is eluted it appears in the eluant as a "peak" in protein concentration and can be collected for further use. Purification Scheme During the protein purification process it is necessary to have a quantitative system to determine, the total amount and concentration of total and target protein at each step, the biological activity of the target protein, and its overall purity. This will help guide and optimize the purification method being developed. Ineffective separation techniques can be disregarded and other techniques that give higher yield or that retain biological activity of the protein can be adopted. Thus, each step in the purification scheme is quantitatively evaluated for the following parameters: total protein, total activity, specific activity, yield, and purification level. Each of these parameters will be defined within the protocol given below. Pretend you are a researcher that wants to isolate a novel, unknown protein from a bacterial culture. You grow 500 ml of the bacteria overnight at 37oC and harvest the bacteria by centrifugation. You remove the culture broth and retain the bacterial pellet. You then lyse the bacteria using freeze/thaw in 10 mL of reaction buffer. You then centrifuge the lysed bacteria to remove the insoluble materials and retain the supernatant that contains the soluble proteins. Your protein of interest has a biological activity that you can measure using a simple assay that causes a color change in the reaction mixture, as illustrated in Figure $12$. You also note that this reaction rate increases with increasing concentrations of your protein supernatant. At this point, you can measure your baseline concentrations for the first purification level (bacterial lysis and removal of insoluble proteins and other cellular debris by centrifugation). Total Protein is calculated by measuring the concentration in a fraction of the sample, and then multiplying that by the total volume of your sample. In this case, you are starting with 10 mL of supernatant. In a typical assay to measure protein concentration, you will use 50 - 200 μL of sample to determine the protein concentration. For example, if you calculate that there is 7.5 μg/μL in your initial assay, you would need to convert that value into mg/mL and then multiply it by 10 mL for a total of 75 mg of protein in 10 mL of supernatant (Table 3.1) Total Activity is measured as the enzyme activity within the assay, multiplied by the total volume of the sample. For example, in the initial sample, you might use 5 to 50 μL of sample in your biological reaction (Figure 3.10). If you calculated the activity in your assay to be 2.5 units/μL, this would be equivalent to 2,500 units/mL or 25,000 units/10 mL of supernatant. Note that, the enzyme unit, or international unit for the enzyme (symbol U, sometimes also IU) described the enzyme's catalytic activity. 1 U (μmol/min) is defined as the amount of the enzyme that catalyzes the conversion of one micromole of substrate per minute under the specified conditions of the assay method. Specific Activity is measured by dividing the Total Activity by the Total Protein. In our example, 25,000 units divided by 75 mg of protein = 333.3 units/mg. Yield is a measure of the biological activity retained in the sample after each purification step. The amount in the first step is set to be 100%. All subsequent yield steps will be evaluated using the first purification step. It is calculated by dividing the total activity of the current step, by the total activity of the first step and then multiplying by 100. Purification level evaluates the purity of the protein of interest by dividing the specific activity calculated after each purification step by the specific activity of the first purification step. Thus, the first step always has a value of 1. A typical purification analysis scheme is shown in Table $1$ below. Table $1$: A typical purification analysis scheme. Note that after each purification step, the Total Protein goes down, as you are separating the target protein away from other proteins in the mixture. Total Activity also goes down with each purification step, as some of your protein of interest is also lost at each purification step, because (1) some protein will stick to the test tubes and glassware, (2) some protein won't bind with 100% efficiency to your column matrix, (3) some protein may bind too tightly to be removed from the column matrix during elution, and (4) some protein may be denatured or degraded during the purification process. The amount of your protein of interest that is retained is represented within the overall percent yield for each purification step. If the percent yield is too low alternative purification methods should be explored. Note that in a good protein purification scheme that the specific activity should go up substantially with each level of purification as the amount of your protein of interest makes up a greater percentage of the total protein within that fraction. If the specific activity only increases modestly within a purification step, or if it decreases during a purification step, this could indicate that (1) your protein of interest is being substantially lost at that step, (2) your protein of interest is being denatured or degraded and is no longer biologically active, or (3) that a required cofactor or binding protein is being reduced at that purification step. Additional experiments may need to be conducted to determine which of the causes predominate, so that steps can be taken to reduce protein inactivation. For example, many proteins are temperature sensitive and will degrade or denature at room temperature. Completing purification steps on ice can often reduce degradation. Overall, the fold increase in purification level should increase exponentially during the purification process. Note that in our example, if after 4 steps of purification our proteins is close to 95% pure, this would indicate that our protein of interest makes up approximately 1.24% of the total protein within the sample. Electrophoresis: Separation and Analysis In column chromatography, flow through the column is driven by hydrostatic pressure causing flow from higher regions of higher pressure at the top of the column reservoir to lower pressure (drops eluting from the bottom of the column). Ultimately the hydrostatic pressure (in columns not driven by mechanical pumps) derives from the gravitational force. However, proteins are also charged particles and can be moved by an external electric field instead of a gravitational field. Electrophoresis is the movement of charged particles in an electric field. As we will show below, the movement of a charged protein within a static matrix in the presence of an external electric field depends on both size and shape. Electrophoresis can be used for both analytical and preparative separations of proteins. The most common uses are for analytical separations. Theory What determines how a protein moves in an electric field? Consider the simple case of a charged particle (+Q) moving in an electric field (E) in a nonconducting medium, such as water. If the particle is moving at a constant velocity toward the cathode (- electrode where cations go), the net force Ftot on the particle is 0 (since F=ma, and the acceleration (a) of the particle is 0 at constant velocity). Two forces are exerted on the particle, one FE, the force exerted on the charged particle by the field, which is in the direction of the motion (toward the cathode), and the other, Ff, the frictional force on the charged particle, which retards its motion toward the cathode, and hence is in the direction opposite to the motion (toward the anode (+) electrode). This is shown in the Figure $13$: Therefore: \mathrm{F}_{\text {tot }}=\mathrm{F}_{\mathrm{E}}+\mathrm{F}_{\mathrm{f}} where FE, the electrical force, is \mathrm{F}_{\mathrm{E}}=\mathrm{QE} and Ff, the frictional force, is \mathrm{F}_{\mathrm{f}}=-\mathrm{fv} In the last equation, v is the velocity of the particle, and f is a constant called the frictional coefficient. This equation shows that the force Ff hindering motion toward the cathode is proportional to the velocity of the particle. This is intuitive since one would expect the higher the velocity, the greater the Ff which would hinder the motion. The frictional coefficient depends on the size and shape of the molecule. The larger the molecule, the larger the frictional coefficient (i.e. more resistance to the motion of the molecule). It can be shown that the frictional coefficient for a spherical particle is given by \mathrm{f}=6 \pi \eta \mathrm{R}_{\mathrm{S}} where η is the viscosity (a measure of the resistance to flow of a liquid - water has a low viscosity, real maple syrup a high viscosity), and Rs (Stokes radius) is the radius of the hydrated sphere (the larger Rs, the larger the frictional coefficient, the larger the Ff which resists motion toward the cathode). This equation should be intuitive from your experiences. From (1), (2), and (3), Fe = Ff , or \mathrm{QE}=\mathrm{fv} Hence v/E = Q/f = U = the electrophoretic mobility, or \mathrm{U}=\frac{\mathrm{V}}{\mathrm{E}}=\frac{\mathrm{Q}}{6 \pi \eta \mathrm{R}_{\mathrm{S}}} Therefore, the electrophoretic mobility U is proportional to the charge density (charge/size, Q/Rs) of the particle, not just the size as is the case for spherical proteins in size exclusion chromatography. Macromolecules of different charge density can thus be separated by electrophoresis. This discussion deals with the simplest case, since in reality there are counter ions in the solution (from salts) which would form a cloud around the charged macromolecule, and partially shield the charged particle from the electric field E. Modern day electrophoresis is conducted in solid gels (such as polyacrylamide), which are formed from liquid acrylamide solutions after the addition of a polymerizing agent. The solid gel is porous to solute and solvent molecules and serves as a medium for electrophoresis while helping to eliminate convection forces in the liquid which interfere with the separation. Electrophoretic experiments have been conducted on the space station in weightless conditions in order to prevent such perturbations. One complication that affects this idealized description of electrophoresis in polyacrylamide gels is that the gels have pores through which the macromolecules move. Think of the protein moving under an electric force through a "spider web-like" matrix. As in gel chromatography, the smaller molecules can pass through the pores more readily than larger molecules, so there is an additional sieving mechanism that contributes to the effective mobility (Also, the gel could alter the local effective electric field). The sieving effect of the gel actually increases the resolving power of this technique. It has been determined that the actual electrophoretic mobility of the protein, U, is a function of the mobility of the protein in a concentrated sucrose solution (Uo) and T, the total concentration of the acrylamide in the polymerized gel. The higher the concentration of acrylamide in the unpolymerized gel solution, the smaller the size of the pores in the polymerized gel. An equation showing the relationship between U, Uo, and T is shown below: \log \mathrm{U}=\log \mathrm{U}_{0}-\mathrm{K}_{\mathrm{r}} \mathrm{T} where Kr is the slope of a plot of log U vs T for a given protein. Since Kr is a function of the radius of the molecule, it is possible to determine the molecular weight of a protein molecule by performing several electrophoretic separations in gels of different acrylamide concentrations (T), and extrapolating results to T = 0, hence eliminating pore size effects. Problems arise, however, if the proteins are not spheroid in shape Is there any way to obtain molecular weight information, in addition to purity determination, on a single gel? What would result if two different proteins, each with the same molecular weight and total net charge, but different shapes, were run on a single acrylamide gel? The one having the more elongated shape (large Stokes radius) would have lower electrophoretic mobility (U = Q/6πηRs). A larger Rs would also cause the protein to enter the pores at a slower rate. Hence both electrophoretic mobility and sieving effects would cause this protein to run anomalously slow and have a higher apparent molecular weight. Also ,imagine two globular proteins of different sizes but with compensatory charge differences which might allow the two proteins to migrate at the same speed in the gel. An astute reader might quickly recognize a problem with the separation of proteins by electrophoresis in a gel. Some proteins are negatively charged (pH > pI), some would be neutral (pH=pI) and the rest would be positive (pH < pI). Only some proteins would enter the gel and move to the electrode at the bottom of the gel. Luckily there is a way to eliminate both charge and shape effects in the electrophoresis of proteins and that is to run the gel under denaturing conditions when all proteins have the same charge density. The denaturant of choice for electrophoresis is usually sodium dodecyl sulfate (SDS), which is an ionic detergent with the structure CH3(CH2)10CH2OSO3- (a single chain amphiphile). This detergent binds to and denatures most proteins, with about 1.4 g SDS binding/g of protein (about 1 SDS/2 amino acids). Since there is 1 negative charge/SDS, the binding of SDS masks any of the charges on the protein, and gives all proteins an overall large negative charge. Additionally, SDS-proteins complexes have been shown to generally have an elongated cylindrical-like shape. Since the amount of SDS bound per unit mass of protein is constant, the overall charge density on all proteins is similar, so the electrophoretic mobility is only determined by sieving effects. SDS also eliminates shape differences in the proteins as a variable, since all proteins have the same general rod-like shape. (The use of SDS is analogous to the use of 8M urea in the gel chromatographic separation of proteins to determine molecular weights). Mobility becomes only a function of the molecular weight of the protein, and not its shape. The molecular weight of an unknown protein can be determined by comparing the protein's position on an SDS polyacrylamide gel with a series of known molecular weight standards from which a linear plot of the ln Mr vs Rf can be used to calculate unknown molecular weights. This is similar to the analysis in gel chromatography, where ln Mr is a linear function of Kavg, the distribution coefficient, when the gel is run under denaturing conditions. However, some proteins run anomalously on such gels (due to incomplete or excess binding SDS), so alternative techniques of molecular weight determination should be used in conjunction with this technique. Proteins are usually heated in SDS to 100oC for 3 minutes, in the presence of a reducing agent such as β-mercaptoethanol (βME), to completely denature the protein to a rod-shaped protein. Apparent molecular weight can be obtained under non-reducing conditions (without βME), but these should be considered just estimates. Running proteins both in the presence and absence of the reducing agent can provide important information on the subunit structure of a protein. A multimeric protein whose subunits are held together by disulfide bonds can be resolved into its individual components when the reducing agent is added. If the subunits are held together by noncovalent intermolecular attractions, the proteins will run identically under the denaturing conditions (SDS), which will eliminate subunit interactions, in the presence or absence of b-ME. To determine the subunit composition of a protein held together by noncovalent interactions, the electrophoresis should be performed in the absence of denaturing agents. Electrolytic vs Galvanic Cells Electrode nomenclature might be confusing to some of you. As mentioned above, cations move towards the cathode (where reduction occurs), so the cathode must be negative. Likewise, anion move towards the anode (where oxidation occurs), so the anode must be positive. This is the opposite of what you might remember from introductory science courses when you discussed primarily galvanic cells. In galvanic cells, an electrical current is generated from a spontaneous set of redox half-reactions. In electrophoresis, electrolytic cells are used, in which reactions such as the electrolysis of water (2H2O(l) → 2H2 (g) + O2(g)) or the productions of Cl2(g) and Mg(s) from the aqueous electrolyte MgCl2(aq) occur. Those who have done electrophoresis will have seen robust bubble production from the electrodes arising from the electrolysis (a redox reaction) of water (2H2O →2H2(g) + O2(g)). In electrolytic cells, a power supply must supply the current to drive the nonspontaneous (unfavored thermodynamically) reactions, such as outlined above. These differences are illustrated in Figure $14$. Figure $14$: Galvanic vs electrolytic cells Polyacrylamide Gel Electrophoresis - PAGE Electrophoresis is performed in a porous, yet solid medium, to eliminate any problems associated with convection currents. Such media are formed from the solidification of a liquid solution of agarose (used mostly for electrophoresis of DNA fragments and very large proteins) or the polymerization of a solution of acrylamide. Polymerization of acrylamide is initiated by the additions of ammonium persulfate in the presence of tetramethylenediamine (TEMED), along with a dimer of acrylamide (N,N'-methylene-bis(acrylamide) connected covalently between the amide nitrogens of the acrylamides by a methylene group. The structures of these compounds are shown in Figure $15$. The free radical polymerization of the acrylamide is initiated by the addition of ammonium persulfate, which on dissolving in water, forms free radicals, as shown above The radical initiates polymerization of the acrylamide, as shown below. The TEMED, through its ability to exist as a free radical, acts as an additional catalyst for polymerization. A rigid gel is only formed, however, when N,N'-methylene-bis(acrylamide is added to the mixture during the polymerization, which cross-links adjacent acrylamide polymers as shown in Figure $16$. The amount of bisacrylamide added during polymerization controls the degree of cross-linking, and hence the pore size of the polymerized gel. The effect of pore size is OPPOSITE to that in gel chromatography. In both cases, large proteins have a difficult time entering the pore. In gel chromatography, large proteins partition preferentially into the mobile liquid phase (the void volume) and are eluted most QUICKLY from the column. In electrophoresis, large proteins, which can not readily enter the pores in the gel, are not as easily transported by the electric field through the gel, and elute most SLOWLY. Pore size can not be controlled as accurately as in the manufacture of gel chromatography resins. How do proteins migrate through the gel? A viscous protein solution is layered on the top of the gel in a small well molded into the gel during the polymerization process. The bottom and top parts of the gel are inserted into reservoirs containing a buffered solution and the appropriate electrode. The electric field is applied and the proteins migrate through the hydrated gel. The nature of the buffer solution in the reservoir and in the polymerized gel is important. The components of the buffer must not bind to the proteins to be separated. Additionally, for native (non-denatured gels), the pH of the medium must be such that the proteins have the appropriate charge, so they will migrate in the expected direction. There are many variations of electrophoresis commonly used. Gels can be polymerized in tubes, or slabs, and in the presence or absence of denaturing agents. Additionally, a given slab might consist of two separate slabs polymerized one on top one other, each with a different acrylamide concentration and pH values. The top part is the stacking gel, te bottom is called the running gel. Other gels have a continuous gradient of acrylamide concentrations (from low at the top to high at the bottom). Most commercially available precast gels use continuous acrylamide concentration gradients. Figure $17$ shows a gel placed in an electrophoresis chamber. Whether the gel has a continuous gradient or is discontinuous, the top part of the gel is a low concentration acrylamide (2-4%), often in a Tris HCl buffer solution (pH 6.5) usually 2 pH units below that used in the running gel. The lower part of the gel 8-15% acrylamide, depending on the choice of gel, which is selected based on the molecular weight of the proteins to be separated. The upper buffer reservoir contains Tris-buffered with a weak acid such as glycine (pKa2 = 9.6) to the same pH as the running gel. Protein electrophorese quickly through the local concentration stacking gel and at the top of continuous gradient gels, and effectively "stack" as they hit the interface between the stacking and running gels, or before they enter too far into the continuous gradient gel. This increases the compactness of the proteins before they enter the "running" section of the gel and increases resolution. For discontinuous gels, how does this stacking process work? When the electrophoresis is started, glycine ions from the upper reservoir (at pH 8.7) enter the stacking gel since at that pH they have an average partial negative charge. The stacking gel buffer ions continue moving in the stacking gel, but when the glycine ions enter the pH 6.5 of the stacking gel, they become zwitterions with a net charge of zero, and hence stop their motion toward the anode. The electrical resistance in the stacking gel then increases since the number of ions moving through the stacking gel decreases. To maintain constant current throughout the circuit, there will be a localized increase in the voltage in the stacking gel (from Ohms Law, V=iR). This will cause the proteins to migrate quickly and all stack in a single, very thin disc right behind the Cl- ions in the stacking gel (which are in front because they have the highest charge density and electrophoretic mobility of any ion in the stacking gel). The proteins will not pass the Cl- ions since if they did, they would immediately slow down since they would no longer be in an area of diminished charged carriers and higher voltage. At the stacking gel/running gel interface, the proteins can not all migrate at the same speed, due to sieving effects of the more concentrated gel, and hence will be separated in the running gel. The glycine eventually enters the running gel, assumes its fully charged state at that pH (8.7), will pass the proteins, and restore the deficiency in charge that occurred in the stacking gel. Detection of proteins in the gel: Most proteins do not absorb at visible wavelengths of light, and hence will not be visible during the course of electrophoresis. To ensure that the proteins are not eluted from the gel into the lower buffer reservoir, a small molecular weight, anionic dye, bromophenol blue is added to the protein before it is placed on the gel. The electrophoresis is halted when the dye reaches the bottom of the gel. The gel assembly is removed from the electrophoresis chamber, the glass plates separated, and the gel washed into a series of solutions with the goal of rendering the banded proteins visible to the eye. • Coomassie Brililant Blue dye: This is the most common stain used in labs. It is dissolved in a methanol/acetic acid solution so it generates significant waste. Proteins bind this dye, with a concomitant spectral shift in the absorbance properties of the bound dye. The methanol and acetic acid in the dye solution also help to "fix" the proteins in the gel, and prevent their diffusion into the solution. After the gel is stained, the background stain in the gel is removed with acetic acid/methanol, leaving the blue-colored protein bands. Some proteins will not be stained with Coomassie blue. • Silver staining: This involves the reduction of Ag(I) to elemental silver and its deposition by protein in the appropriate reaction solutions, much as in a photographic process. (Remember in the BCA assay, peptide bonds reduce Cu(II) to Cu(I), which is chelated to BCA.) A developer and fixer solution is required. This technique is 10-50 X more sensitive than Coomassie Blue staining. Figure $18$ shows gels stained with Coomasie Blue (A)and silver staining (B). • Pre-electrophoresis fluorescent or radioactive modification of the proteins. These allow even greater sensitivity. After the electrophoresis of a radiolabeled protein, the gel can be dried and overlaid with X-ray film for periods as long as months, if necessary, to allow sufficient exposure of the film by a low concentration protein. This visualization technique is called autoradiography. Variations on polyacrylamide gel electrophoresis: Isoelectric focusing: In this technique, a pH gradient is set up within the polyacrylamide gel or strip. This is accomplished by pre-electrophoresing a series of low molecular weight molecules containing amino and carboxyl groups called ampholytes, each with a different isoelectric point. When subjected to an electric field, the most negative of the species will concentrate at the anode, while the most positive will concentrate toward the cathode. The remaining ampholytes will migrate in-between, with the net effect being that the ampholytes migrate to their isoelectric point and set up a linear pH gradient in the gel. Proteins initially in regions with a pH below its isoelectric point are positively charged and migrate toward the cathode, while those that are in media with pH lower than its pI will be negatively charged and migrate towards the anode as shown below in Figure $19$. The migration will lead to a region where the pH coincides with its pI. There the protein will have a zero net charge and stop. Thus amphoteric molecules are located in narrow bands where the pI coincides with the pH. In this technique the point of application is not critical as molecules will always move to their pI region. The stable pH gradient between the electrodes is achieved using a mixture of low molecular weight ampholytes whose pIs covers a preset range of pH. 2D electrophoresis: Two-dimensional gel electrophoresis (2-DE) is based on separating a mixture of proteins according to two molecular properties, one in each dimension. The most used is based on a first dimension separation by isoelectric focusing (IEF) and a second dimension according to molecular weight by SDS-PAGE. A conditioning step is applied to proteins separated by IEF prior to the second-dimension run. This process reduces disulfide bonds and alkylates the resultant sulfhydryl groups of the cysteine residues. Concurrently, proteins are coated with SDS for separation on the basis of molecular weight. After the IEF, the tube or strip is placed across the top of a slab gel and subjected to SDS-polyacrylamide gel electrophoresis in a direction 90o from the initial isoelectric focusing experiment. If the proteins were derived from cells labeled with 35Met, representing unique proteins can be obtained from a given cell population. Figure $20$ shows a 2D electrophoresis gel. In Figure $20$, proteins of Chlamydomonas reinhardtii are resolved by 2-DE from preparative gels stained with MALDI-MS compatible silver reagent for peptide mass fingerprinting analysis. First dimension: isoelectric focusing in a 3-11 pH gradient. Second dimension: SDS-PAGE in a 12% acrylamide (2.6% crosslinking) gel (1.0 mm thick). Numbered spots marked with a circle correspond to proteins compared to be subsequently identified by MALDI-TOF MS. The MALDI-TOF MS analysis of protein sequences is discussed in more detail in section 3.3 below. One of the biggest problems in 2-DE is the analysis and comparison of complex mixtures of proteins. Currently there are databases capable of comparing two-dimensional gel patterns. These systems allow automatic comparison of spots for the precise identification of those needed in the quantitative analysis. Once interesting proteins are identified, the proteins can be excised from gels, destained, and digested to prepare for their identification by mass spectrometry. This technique is known as peptide mass fingerprinting. The ability to precisely determine molecular weight by matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) and to search databases for peptide mass matches has made high-throughput protein identification possible. Proteins not identified by MALDI- TOF can be identified by sequence tagging or de novo sequencing using the Q-TOF electrospray LC-MS-MS. Western blotting: After a standard SDS-slab electrophoresis experiment is run, the gel is overlaid with a piece of nitrocellulose membrane. The sandwich of gel and filter paper is placed back into an electrophoresis chamber, such that the proteins migrate from the gel into the nitrocellulose, where they irreversibly bind. This is illustrated in the figure below. Note however that in the absence of staining, the protein bands in either the PAGE gel or Western blot would not be visible. Standards (lane 5) would be visible if they were labeled with chromophores, as shown in Figure $21$. If a cell lysate was applied to a lane of a PAGE gel, after staining with any technique, the bands would appear as overlapping smears on the stained gel. What makes Western blots so useful is that specific bands can be specifically visualized (stained) on the nitrocellulose membrane by using a detection system linked to an antibody that recognizes just a specific target protein. This is illustrated in Figure $22$. 3D electrophoresis: To detect specific proteins in a 2D electrophoresis experiment, a 3rd dimension of separation, a Western blot, could be performed on the PAGE gel and the nitrocellulose stained with an antibody specific to a target protein. That is illustrated in Figure $23$. Part A, isoelectric focusing, is followed by a PAGE gel (B). The red dots represent proteins that have undergone a post-translational modification in which a phosphate group has been added to tyrosine side chains (for example). Western blotting is performed in panel C and staining in panel D. The left blot in D uses an antibody that recognizes phosphorylated tyrosine side chains on protein. The right blot is D is sometimes called a Far Western blot. If the protein on the nitrocelluose membrane retains some 3D native structure or can be induced to refold, it can be probed on the blot by a protein that binds to the native form of the protein on the blot. In the example shown in panel D above, the p-Tyr-protein target on the nitrocellulose membrane recognizes a fusion protein of PTP-GST. GST is a protein tag for detection. PTP is a protein tyrosine phosphatase, an enzyme that hydrolyzes p-Tyr on specific phosphorylated target proteins.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/03%3A_Amino_Acids_Peptides_and_Proteins/3.04%3A_Protein_Purification.txt

Search Fundamentals of Biochemistry Written by ?? Needs work We will describe three different uses of antibodies for the detection and quantitation of analytes. Enzyme-Linked Immunosorbent Assay (ELISA) (work derived from the Human Atlas Project) Since the very first use of antibodies for the detection of antigens, many different technologies have been developed that make use of the antibodies' capability to bind to other molecules. During the 1950s, Yalow and Berson developed a method where radioactivity is used to determine the amount of an analyte in a solution. This 'radioimmunoassay' (RIA), for which Yarlow received the Nobel prize in 1977, was a very sensitive method for the detection of hormones but using radioactivity for antigen detection is not safe and suitable for general use. Hence, an alternative procedure was developed by linking enzymes to antibodies instead of a radioactive molecule, and by adhering molecules to surfaces. This is the basis of the widely-used "enzyme-linked immunosorbent assay" (ELISA). Many variants of experimental procedures have been developed, and it is common to build assays using more than one antibody to detect a target of interest (see Figure 1). To further enhance the possibilities offered by the immunoassay format, applications based on microarrays have been developed which allow the measurement of more than one analyte in a single reaction chamber (see below). Different detection methods are described in Figure \(1\). Figure \(1\): Different detection methods for ELISA and Other Immunossays. (CC BY-SA 3.0; In ELISA assays, the antibodies may (A) detect an immobilized antigen, (B) capture a labeled antigen, (C) capture an unlabeled antigen and use a second, labeled antibody to detect the captured antigen, or (D) use a third antibody for detection, or even use two antibodies for detection (E). Direct labeling of the antibody or antigen as in (A), (B), and (C) is the simplest and fastest method for detection. Using a secondary antibody as a detection method, as shown in (D) and (E), will further increase the sensitivity and selectivity of the analysis. The method used in (D) also allows greater flexibility, whereas method (E) further increases the specificity, as three antibodies must bind the antigen in order to produce a reporter molecule. Out of the presented assays, the most commonly used concepts are shown in (C) and (D). We will describe how ELISA data is used to determine concentrations of analytes in Chapter 5. A new era in immunoassays started with the development of a technology called microarrays. The term microarray most commonly describes the ordered organization of small-volume droplets that have dried on a small surface area. The reaction dimensions are miniaturized so that many assays can be performed in multiple samples in parallel. Glass slides can be used and robotic pipettors can deposit very small drops of liquid (1 nL = 10-9 L on the glass surface in an ordered fashion, with the spots having sizes of around 0.15 mm. Another common technique for multiplexing is to use even smaller and color-coded particles (diameter of 0.005 mm). These particles can be coated with antibodies to fish out the analyte from the solution. Microarray assays are used for parallel analysis of DNA and RNA molecules. In addition, multiplexed techniques are used to determine many proteins simultaneously, and to study post-translation modifications such as phosphorylation. Another example is the analysis of antibodies circulating in the blood of patients. Protein microarrays can reveal the interactions of ligands with the whole protein or larger protein fragments, while peptide microarrays are used to detect small peptides (epitopes) of the proteins that bind antibodies. A typical epitope mapping result is shown in Figure \(2\) (Edfors et al., 2014). Figure \(2\): Epitope Mapping of Polyclonal Antibodies. Polyclonal antibodies binding to a peptide array where the result displays four distinct linear epitopes and the consecutive overlapping peptides which are bound. X-axis: peptides, Y-axis: mean fluorescence intensity (MFI). (Edfors et al., 2014) Image from The Human Atlas Project Synthesizing millions of overlapping peptides with only one amino acid residue shift on such arrays enables the mapping of antibody binding regions at high resolution. This gives a very detailed analysis of the linear (continuous) epitopes recognized by an antibody. Immunohistochemistry - Detecting Proteins in Vivo (work derived from the Human Atlas Project) Immunohistochemistry (IHC) is a powerful microscopy-based technique for visualizing cellular components, for instance, proteins or other macromolecules in tissue samples. The strength of IHC is the intuitive visual output that reveals the existence and localization of the target protein in the context of different cell types, biological states, and/or subcellular localization within complex tissues. The IHC technique was invented during the 1940s (Coons, Creech, & Jones, 1941) and is routinely used as an important tool in health care and pathology for e.g. diagnostic purposes or to stratify patients for optimized treatment regimes. IHC is also widely used in research where molecules of interest are analyzed to study their roles in both healthy and diseased cells and tissues on the molecular, cellular or tissue level. There are many different ways to perform visualization of targets in tissues using IHC or IHC-based methods, and numerous protocols exist for different applications and assays. Even though IHC is generally a robust and established method, new assays often need careful optimization depending on the tissue or on the properties of the target protein, binder-molecule and/or reporter system. The classical IHC assay is illustrated in Figure \(3\) and involves the detection of epitopes expressed by a single protein target within a tissue sample using a "primary antibody" capable of binding those epitopes with high specificity. After the epitope-antibody binding event, a "secondary antibody" capable of binding the primary antibody with high specificity is added. The secondary antibody is coupled to a reporter molecule and after the antibody-antibody binding event, a chemical substrate is added which reacts with the reporter molecule to produce a colored precipitate at the site of the whole epitope-antibody complex. Figure \(3\): The Basic Principle of Immunohistochemistry. mage from The Human Atlas Project In the schematic illustration, a formalin-fixed paraffin-embedded tissue section is stained using a primary antibody directed toward a specific protein target. A solution containing the primary antibody is added to the tissue section and the antibodies are allowed some time to find and bind to their target. After this step, unbound and surplus antibodies are washed away and the secondary antibody is added. The secondary antibody, which carries a linker molecule with horseradish peroxidase (HRP) enzymes, is also allowed some time to bind to the primary antibody, followed by another washing step. After this, 3,3' Diaminobenzidine (DAB) is added. The HRP enzyme transforms the DAB substrate into a brownish precipitate that is deposited in the tissue at the site of the reaction, thus producing a visual representation of where the primary antibody first bound its target. Tissue preparation The tissue plays a central role in the experiment and it is important that it is processed so that epitopes and proper morphology is preserved. The most common processing for IHC is to prepare formalin-fixed paraffin-embedded (FFPE) tissue blocks. The purpose of formalin fixation is to produce chemical cross-linking of proteins within the tissue. This terminates all cellular processes and freezes the cellular components at the place and in the conformation at the time of fixation and also to prevent degradation. After adequate fixation, the tissue is further processed and ultimately embedded in paraffin blocks, which are then sectioned into thin slices (usually 4-10µm) using a microtome. The sections are transferred to glass slides and allowed to adhere prior to further processing. Other methods for fixation besides formalin are sometimes used. These include other types of aldehydes or using different alcohol solutions. The best choice of fixative is very much dependent on the assay. A common alternative to FFPE is to prepare frozen tissue samples. In this case, the tissue is embedded in a cryoprotective medium and frozen, and fixation is performed post-sectioning. Frozen tissues are sectioned in cryostats and have the advantage of short processing times and of better preservation of sensitive epitopes, but can often be inferior to FFPE tissues in terms of preserving histological morphology. Antigen (epitope) retrieval A concern associated with cross-linking fixatives like formalin or the length of time spent in the fixative medium is the masking of epitopes, which can obstruct the primary antibody from binding to its target. Especially with FFPE samples, there is often a need to revert some of the chemical crosslinking and "retrieve" the epitopes before proceeding to the actual IHC. There are several antigen retrieval protocols available and the main strategies include treating the tissue slide with heat, digestive enzymes, detergents, or combinations thereof. The most common method for antigen retrieval in FFPE samples is to pressure-boil the tissue slides in an acidic citrate buffer for around 15-20 minutes. Antibody binding The quality and specificity of the binding molecule are crucial for any IHC based technique, and the choice of binder can directly affect the outcome, reliability, and possibly also the interpretation of the assay. Antibodies are by far the most common type of binding-molecule used for IHC, and although most antibodies are able to adequately detect the correct molecule of interest, they may also vary greatly in their specificity for their intended target. Antibodies with high specificity are therefore more reliable when interpreting "on-target" binding, since they produce little or no "off-target" binding or "background". Antibodies that are less specific can produce more off-target binding, and the resulting background will possibly interfere with the correct interpretation of the true on-target signals. There are two main types of antibodies; polyclonal antibodies which are a heterogeneous mix of antibodies that bind different epitopes on the target and monoclonal antibodies which bind the same epitope. Polyclonal antibodies are often very potent due to their ability to detect and bind multiple epitopes on the same target. However, the epitopes they bind are often poorly defined, and with multiple and varying epitope-specificities comes the increased likelihood of off-target binding events and background noise. However, the potency of polyclonal antibodies can be advantageous since the concentration of binding events around the on-target molecule usually outweighs potential background noise. A drawback is that polyclonal antibodies are usually limited resources since they are derived from animal sera. Monoclonal antibodies, by contrast, have more continuity since they can be produced in hybridoma cell lines. Monoclonal antibodies are also often well-defined in terms of epitope binding, but can still generate results that are hard to interpret if the specificity is low or if the target epitope is present in low abundance. Careful optimization and titration of antibody concentration for each assay are needed, since the result is dependent not only on the antibody's specificity and affinity for the target, but also on the concentration and availability of on-target and potential off-target epitopes present in the sample. Adding too many antibodies to the sample will increase the number of possible low-affinity off-target binding events once the on-target epitope(s) are saturated with binders. By lowering the antibody concentration, off-target binding events become rarer as they usually have lower affinity than on-target binding events. The risk when attempting to reduce background while using a low-affinity antibody is that the on-target signals are concomitantly weakened to the point of providing a false negative result. Other types of binder molecules sometimes used in IHC-based techniques include affibodies, peptides, antibody fragments or other small molecules. Detection systems The whole purpose of performing IHC is to obtain a visual representation of where the target can be found within the experimental tissue and preferably and also gain information about the target's expression pattern among heterogeneous cell populations and/or subcellular sites. This is exemplified in Figure \(4\), which illustrates how different antibodies are used to visualize different cellular or tissue compartments within a complex tissue. To visualize the target-antibody interaction, some kind of detection system that produces an observable stain or signal is needed. The most common method for introducing a detection system to the experiment is to use a secondary antibody that carries a pre-bound reporter molecule, i.e. enzyme or fluorophore. Secondary antibodies are usually targeted specifically towards antibody molecules from different animal species. For example, if the primary antibody is raised in a rabbit, then the secondary antibody must be raised in another animal and targeted specifically towards rabbit antibodies. The right column shows a magnification of the corresponding images in the left column. In the IHC image, consecutive sections of a human esophagus stained using four different antibodies allow for direct comparison of different protein expression patterns within the tissue and within subcellular compartments. The top images are only counterstained for hematoxylin for comparison. The p63 antibody stains cell nuclei in a population of cells that reside in the basal part of the esophageal epithelium. The EGFR (Epidermal growth factor receptor) antibody appears to stain the same cell population as p63, but stains cellular membranes instead of nuclei. The G6PD (Glucose-6-phosphate dehydrogenase) antibody stains the cytoplasm of a wider repertoire of esophageal epithelial cells and also cells residing in the connective tissue. The Laminin (LAMB2) antibody stains only cells and structures in the connective tissue underlying the esophagus. Image from The Human Atlas Project For FFPE tissue samples the most common detection method is to use enzymatic reactions to generate a colored precipitate at the site of antibody binding. Secondary antibodies with an attached enzyme, e.g. horseradish peroxidase (HRP) or alkaline phosphatase (AP), are capable of converting chromogens like 3,3' Diaminobenzidine (DAB) or 5-bromo-4-chloro-3-indolyl phosphate/ p-nitroblue tetrazolium chloride (BCIP/NBT) into brown or bluish precipitates that are deposited in the tissue at the site of the reaction. Chromogenic stains are observable using light microscopy and are usually very stable over long periods of time, which is beneficial if the experiment needs to be archived or reviewed at a later time point. For frozen tissue sections, it is more common to use fluorophore-linked secondary antibodies that emit a specific color (usually green, red, or blue) when excited by the correct wavelengths of light. Moreover, fluorophores are usually not stable for long periods of time. However, the benefit of using fluorophores is that they provide an easy method for performing double-labeling experiments where several antibodies directed toward multiple targets are assayed in the same sample. The secondary antibodies need to be targeted towards different primary antibodies and also to be coupled to different fluorophores. The different secondary antibodies are then observed separately by exciting them sequentially with different wavelengths of light. These different excitation results are saved as separate images (or color channels) and may later be overlaid to infer protein co-localizations etc. Using reporter-carrying secondary antibodies for detection is in itself an amplification step since several secondary antibodies are able to bind a single primary antibody, but sometimes further amplification steps are desired to increase the signal and sensitivity of the experiment. In such cases, the secondary antibody may instead carry "linker molecules", for instance biotin polymers, which are able to recruit a larger number of reporter molecules in subsequent steps. This strategy for amplifying signals is useful for both enzymatic and fluorescent detection methods. Counterstaining Immunohistochemical staining using chromogens offers benefits from having a counterstain applied that enhances the contrast and facilitates the observation of histological features. The most common type of counterstain used for FFPE samples is hematoxylin, which stains cellular cytoplasm with a pale bluish color, and stain cell nuclei in a darker bluish nuance. Fluorescent stainings are usually not counterstained with hematoxylin, since the detection method is not based on light microscopy. Instead, the most common way to obtain counterstaining for fluorescence is to label cell nuclei by adding fluorescent dyes that bind nucleic acids.. After the actual immunohistochemical reaction, the only remaining steps are to use a coverslip to seal and protect the sample and for long-term storage. The most common way is to "glue" the coverslip to the sample using commercially available purpose-made resins. Image from NIH ImageJ-Programmpaket Specific examples IHC is widely used in both research and clinical practice. The Human Protein Atlas (HPA) project is a prime example of how high-throughput IHC is used to achieve large-scale mapping of the human proteome in a multitude of tissues, cancers, and cells. In the HPA project, a streamlined in-house large-scale antibody production chain facilitates the generation of specific antibodies, which after passing basic characterization and validation regimes, are used to systematically stain tissue microarrays containing hundreds of tissue cores within a single experiment. The system for IHC employed by HPA relies heavily on the standardization of protocols and automatization using machines, but the evaluation of the optimal titration for each antibody is performed manually before the antibody is approved for staining on the full set of tissues. Each stained tissue core is annotated with respect to immunohistochemical staining in tissues and cell types, and thereafter published as a high-resolution image on the web portal to be freely viewed by anyone. In clinical practice, IHC is mainly used within pathology to aid physicians to evaluate tissue specimens with respect to healthy and or diseased states, to set diagnoses, and to define the molecular subtype of different types of cancer. A specific example where IHC is used diagnostically is when pathologists are presented with a metastatic tumor sample and the tissue origin of the primary tumor is unknown. In these cases, pathologists use a panel of different antibodies that target tissue-specific proteins, such as prostate-specific antigen for prostate cancer, and estrogen receptor for gynecological cancers, or cytokeratin 20 for gastrointestinal cancers . Once a broad classification is made, additional tissue-specific antibodies are used to further pinpoint the origin of the primary tumor. This information is useful for choosing the best or most appropriate strategy for drug therapy and/or to locate the primary tumor for radiation therapy and/or surgery.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/03%3A_Amino_Acids_Peptides_and_Proteins/3.05%3A_Extension_-_Antibodies_in_Quantitation_and_In_Vivo_Detection.txt

Search Fundamentals of Biochemistry Peptide Bond Formation and Primary Protein Structure Proteins are polymers of amino acids that fold into shapes that confer function on the proteins. In biological systems, the amino acids are linked together by a large ribonucleic acid/protein nanoparticle called the ribosome. Thus, as the amino acids are linked together to form a specific protein, they are placed within a very specific order that is dictated by the genetic information contained within a specific type of RNA called messenger RNA (mRNA). The mRNA sequences are encoded in the genomic DNA sequence. The specific ordering of amino acids is known as the protein's primary sequence. The translation mechanism used by the ribosome to synthesize proteins will be discussed in detail in Chapter 26. The amino acids are linked together using dehydration synthesis (loss of water) reaction that connects the carboxylic acid of the upstream amino acid with the amine functional group of the downstream amino acid to form an amide linkage (Figure 2.10). You will remember from other chemistry courses that the formation of an amide from a carboxylic acid (thermodynamically stable) and an amine requires activation of the carboxylic acid end to form a derivative with a better leaving group. This carbonyl of the modified end serves as an electrophile in the attack of the amine nitrogen, a nucleophile) in a nucleophilic substitution reaction. The activation reaction, which we will discuss in subsequent chapters involves the transfer of a phosphate from a phosphoanhydride, ATP, to the carboxylic acid group to form a mixed anhydride with the phosphate serving as a leaving group. Note that the reverse reaction is hydrolysis and requires the incorporation of a water molecule to separate two amino acids and break the amide bond. Notably, the ribosome serves as the enzyme that mediates the dehydration synthesis reactions required to build protein molecules, whereas a class of enzymes called proteases is required for protein hydrolysis. Within protein structures, the amide linkage between amino acids is known as the peptide bond. Subsequent amino acids will be added onto the carboxylic acid terminal of the growing structure. Proteins are always synthesized in a directional manner starting with the amine and ending with the carboxylic acid tail. New amino acids are always added onto the carboxylic acid tail, never onto the amine of the first amino acid in the chain. The directionality of protein synthesis is dictated by the ribosome. Figure \(1\) below shows an overly simplistic version of the reaction that produces the amide bond. Please note two features of the reaction as shown in the diagram: 1. The activation step (phosphorylation of the carboxylic acid end of the amino acid by ATP) is not shown. 2. The reaction is shown with an unlikely protonation state. If the carboxyl group is protonated, which would occur at a low pH, the amine would also be protonated and should correctly be shown as RNH3+. The protonation state in the above figure was chosen to emphasize the loss of H2O (dehydration) in the reaction. Many textbooks that aren't rigorously based in chemistry show unlikely protonation states for this reaction. By discussing this now, we hope to highlight common mistakes and misconceptions found in many resources. Figure \(2\) shows a generic structure from a longer peptide or protein. Proteins range in size from around 50 amino acids in length to the largest known protein, titin (aka called connectin), a muscle protein. The human version has over 34,000 amino acids and a molecular weight > 3.9 million! Some consider structures with fewer than 50 amino acids to be peptides (Figure 2.13). Others suggest that structures with 40-50 amino acids should be considered small proteins. One way to differentiate them is by how they are synthesized in vivo. Storz et al consider polypeptides to be small proteins if they are encoded in the genome by a continuous stretch of DNA base in an "open reading frame" (doi: 10.1146/annurev-biochem-070611-102400). Peptides, on the other hand, could be structures that are: • "intrinsically disordered" with no definite fold, • derived from proteins by proteolysis, and/or • not synthesized by ribosomes As genomes were sequences and annotated, some arbitrary parameters were set. For the yeast genome, annotated proteins were defined as those made from an open reading frame leading to a polypeptide of 100 amino acids (which on average has a molecular weight of 11,000). If no cutoff was used, the number of proteins encoded by the genome would be huge. Submissions of DNA sequences to the NIH GenBank must encode proteins no smaller than about 66 amino acids (MW about 7250). Even this ignores small proteins that have been isolated and characterized from cells. So the cutoff of 50 amino acids (MW about 5500) derived from open reading frames seems like the best arbitrary cutoff going from peptides to proteins. Due to the large pool of amino acids that can be incorporated at each position within the protein, there are billions of different possible protein combinations that can be used to create novel protein structures! For example, think about a tripeptide made from this amino acid pool. At each position, there are 20 different options that can be incorporated. Thus, the total number of resulting tripeptides possible would be 20 X 20 X 20 or 203, or 8,000 different tripeptide sequences! Now think about how many options there would be for a small peptide containing 40 amino acids. There would be 2040 options, or a mind-boggling 1.09 X 1052 potential sequence options! Each of these options would vary in the overall protein shape, as the nature of the amino acid side chains helps to determine the interaction of the protein with the other residues in the protein itself and with its surrounding environment. Nearly 200,000 3D structures of biomacromolecules are known and over a million have been determined using artificial intelligence computer programs. How can we simplify our understanding of the diversity of protein structures? Is each structure completely unique? What do they have in common? To simplify and inform our understanding of the diversity of biological organisms, we place them into groups (from domains and kingdoms to genuses and species), based on common characteristics. Likewise, proteins are divided into a hierarchy of structures with increasing information content. This overview describes the four basic levels of protein structure, primary (10), secondary (20), tertiary (30) and quaternary (40). Each will be probed in greater deal in the next chapter. These classes of structures will be illustrated below with a protein named hydroxynitrile lyase (5Y02). (This protein has been simplified to illustrate key features of structure as will be described at the end). Primary (10) Structure: the amino acid sequence of a protein. The primary (10) structure of a protein is simply the amino acid sequence of a protein written from N- to C-terminal. It does not require visualization to describe it. Consider two different short continuous sequences from the hydroxynitrile lyase: • Gln-Lys-Gln-Ile-Asp-Gln-Ile or in single letter code QKQIDQI. This is the sequence for amino acids 20-26 in the protein. This stretch of 10 structure has multiple repeated amino acids. • Asp-Leu-Gly-Pro-Ala-Val or in single letter code DLGPAV. This is the sequence for amino acids 48-53 in the protein. This stretch of 10 structure does not contain repetitive amino acids. A 2-D line drawing of the sequence offers more information about the sequence but does not provide information about the actual conformation of these sections of 10 structure within a given protein. These can be shown in Figure \(3\) in which the overall structure of the protein is shown in grey sticks with short stretches of primary structure shown in colored spacefill and 2D line drawings. Figure \(3\): Alternative renderings of a "primary" sequence within a protein Secondary (20) and Tertiary (30) Structures Secondary (20) structures are repetitive structures within a protein held together by hydrogen bonds between amide Hs and carbonyl Os in the backbone main chain atoms. It's most easily examined through the specific rendering of the overall tertiary (30) or 3-D structure of the protein. Five different renderings showing the 3D (the tertiary structure of the protein are shown in Figure \(4\). Representation A shows a stick drawing of the protein with red indicating bonds to oxygen and blue bonds to nitrogen. No bonds to hydrogen are shown as these are too small to be detected using common techniques used to determine structures of such large molecules. It looks like a complicated mess of bonds so understanding unique features with the 30 structure of the protein are difficult. Representation B shows just the backbone of the protein. The outline of how the protein twists and turns in space becomes more evident. The N- and C-terminal ends are more clearly seen. Representation C shows just the bonds connecting the alpha C atoms of each amino acid. The protein's overall topology is now clearly evident. If you follow the chain from the N- to C-terminal ends, it should be evident that there are regularities in the conformations of the protein chain. The individual yellow zig-zags are called beta strands. These strands appear elongated and aligned with other beta strands to form a larger beta-sheet. The sheet is held together through hydrogen bonds between backbone amide Hs and carbonyl Os on adjacent strands. Beta strands are a type of secondary structure. The red zig-zag lines represent another type of secondary structure called the alpha-helix. The helix is again held together by hydrogen bonds between amide Hs and carbonyl Os within a single continuous strand. The backbone of the alpha helix appears less elongated than in a beta-strand as it is wound into a coil (the alpha helix) along a central axis. If you took tweezers (using atomic force microscopy) and pulled on the helix, it could stretch and become more elongated like the beta strands. The rest of the protein alpha carbon chain shown in blue is less regular. However, it is still ordered as it propagates through space in what is termed a random coil. In the protein, it adopts mostly a fixed conformation but it has more conformational flexibility than alpha helices and beta sheets. The alpha helices and beta strands (sheets) are examples of secondary structures. Representations D and E are cartoon drawings clearly showing the alpha helices (red) and beta stands and sheets (yellow). It would be extremely difficult to discern alpha or beta secondary structures with stick representations showing all the bonds in a protein. Some of the atoms must be visually (not literally) removed to see the repetitive propagation of the protein backbone through the overall structure. If your goal was to understand the disposition of side chains in a small part of a protein, a cartoon view by itself would not be useful. Modeling programs allow mixed rending of a protein to include both cartoon and stick representations together. Secondary structures, held together by hydrogen bonds between backbone atoms are characterized by repetitive changes in the angle of propagation of the chain between connected amino acids in an alpha helix and strand. In a given beta-strand, the relative change in the angle of propagation is nearly 00 compared to a much large angular change required to bend the amino acid backbone into an alpha helix. Here is the IUPAC definition of secondary structure. We added the word "repetitive" to clearly show that random coils are not an example of secondary structures. Definition: Secondary Structure (from the IUPAC Gold Book) The [repetitive] conformational arrangement (α-helix, β-pleated sheet, etc.) of the backbone segments of a macromolecule such as a polypeptide chain of a protein without regard to the conformation of the side chains or the relationship to other segments. Quaternary Structure Separate protein chains often interact through noncovalent interactions and sometimes through disulfide bond formation between free cysteine side chains on different chains to form dimers, trimers, tetramers, octamers, etc. Dimers can be homodimers (if the two chains are identical) or heterodimers (if they are different). The example we used in the section, hydroxynitrile lyase, forms a homodimer, as shown in Figure \(5\). The left images shows a cartoon version, with one monomer in orange and the other identical monomer in green. The images to the right shows a translucent surface representation of the dimer, with the car The mixed-rendered image on the right shows a translucent surface image of each monomer and underneath the cartoon image • primary structure: the linear amino acid sequence of a protein • secondary structure: regular repeating structures arising when hydrogen bonds between the peptide backbone amide hydrogens and carbonyl oxygens occur at regular intervals within a given linear sequence (strand) of a protein or between two adjacent strands Disulfide bonds within individual chains and between them stabilize both tertiary and quaternary structures of both peptides and proteins. These are illustrated in Figure \(6\). Protein Shape and Function The primary structure of each protein leads to the unique folding pattern that is characteristic of that specific protein. In summary, the primary sequence is the linear order of the amino acids as they are linked together in the protein chain as shown in Figure \(7\). In the next section, we will discuss protein folding that gives rise to secondary, tertiary, and sometimes quaternary protein structures.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/03%3A_Amino_Acids_Peptides_and_Proteins/3.2%3A_The_Structure_of_Proteins-_An_Overview.txt

Section 1 Questions: Q1) A small protein has the amino acid sequence below: C1NVC2KYAPITALYC3AEEC4QQH There are four cysteine residues in the sequence and are designated by the subscripts. The protein is digested with chymotrypsin, and the resulting slurry is followed by an anionic exchange column. a) Where are the disulfide bonds in this structure? Are they disrupted by any of the above treatments? b) Identify the peptide fragments created by the chymotrypsin digest. Which fragment will elute first from the anion exchange column? A1) a) The two disulfides are occurring between C1/C2 and C3/C4. Due to the proline in the center of this structure, we know there is likely not a folding pattern that would let another combination of disulfides to occur. No, the disulfides are not disrupted by the chymotrypsin treatment. b) Since chymotrypsin cleaves aromatic amino acids at the C-terminus, the fragment following digestion would be: 1) C1NVC2KY 2) APITALY 3) C3AEEC4QQH When determining which will elute off an anionic column (a positively charged bead that binds to negative residues), we need to determine the overall charge of the peptide fragments. The lysine (K) in fragment 1 will give an overall positive charge. Fragment 2 contains no charged amino acids, and the two glutamic acid (E) residues in fragment 3 will yield an overall negative charge. Since we know the column will bind most tightly to negative residues and repel positives, Fragment 1 should be eluted first, followed by fragment 2, and lastly fragment 3. Q2) In most proteases, there is a Ser or Cys residue in the active site. Site-directed mutagenesis experiments have shown that active site Ser can be replaced with a Cys, and vice versa, with the protease, still remaining catalytically active. a) Based on the structure of Cys and Ser, suggest an explanation as to why this could be. b) There are other amino acids with R-groups that have similar to Ser and Cys. Hypothesize if a site-directed mutagenesis experiment changing a Ser/Cys to Thr, Tyr or Met would still retain catalytic function. Explain your reasoning. A2) a)For both amino acids, their R-groups contain elements with unbonded lone pairs of electrons. These lone pairs on Ser (:OH) and Cys (:SH) can each act as nucleophiles and allow the protease to engage in SN-2 type reactions for proteolytic cleavage. b) When considering the likelihood of Thr, Tyr or Met to maintain activity of the active site, the most likely candidate would be threonine. Threonine is structurally the most similar to serine and cysteine and therefore has the highest chance of not causing any steric hindrances in the active site. Threonine is however larger, so one could potentially expect a decrease in activity. Tyrosine while still containing a hydroxyl group, has a large aromatic group that would likely disrupt the interactions of the binding pocket and substrate recognition. Similarly, methionine does contain a sulfur group, but the length of the side chain most likely will cause an inhibition of substrate binding/recognition. Q3) What pH would you use for an ion-exchange chromatography column to separate the following small peptides? Explain your answer. Peptide 1) RGAG Peptide 2) RGAE Peptide 3) HGAE Peptide 4) EGAE A3) First, assign the formal charge on each peptide: P1) +1 P2) Neutral P3) -1* Note the histidine here! P4) -2 Next to ensure that each peptide is able to maintain a unique charge, we need to assess if any pH changes could affect the net charge of the peptides and the pKas of the relevant amino acids that can contribute to the charge. R=12.48, E=4.25, H=6.00. Now, to ensure arginine is positive, glutamic acid is negative, and histidine is neutral, the pH of the column would need to be higher than 6 but less than 12.48. Q4) Match the three letter code to the one letter code for the following amino acids. Tyr W Ala P Asp A Asn Y Pro T Trp D Thr N A4) Tyr = Y; Ala = A; Asp = D; Asn = N; Pro = P; Trp = W; Thr = T Q5) Vasopressin is a small peptide hormone with its C-terminus converted to an amide. It is produced in the hypothamus, but ultimately becomes functionally active in the pituitary gland. One in the bioactive form, vasopressin aids in kidney function by increasing water retention, as well as increases blood pressure in the arteries. (Möller and Mari, Biochem J 2007; doi:10.1042/BJ20061480) The peptide sequence for the mammalian protein is given below: C1YFGNC2PRG-NH2 a) Write the sequence of vasopressin in the 3 letter code b) Draw out the structure of the first 5 amino acids of vasopressin c) What is the overall charge of vasopressin at pH 5? Assume the carboxylate group has a pKa of 4.0 and the amino group has a pKa of 10. d) What is the isoelectric point of vasopressin? A5) a) Cys-Tyr-Phe-Gly-Asn-Cys-Pro-Arg-Gly b) c) When we are looking at the amino acids whose R-groups can affect the overall charge of the peptide, for vasopressin that would be Tyr, and Arg. At a pH of 6 there is not enough hydroxide present to deprotonate the -OH of Tyr, so there is no charge as its pKa is 10.46. However, the pKa of Arginine is 12.48, and therefor at a pH of 6, the amine of the R-group remains protonated. Additionally, the N-terminal amino group at pH 6 contains a +1 charge and without a carboxylate group on the C-terminus to balance the charges, an additional +1 is added to the peptide. Therefore, the overall charge of the peptide is +2. Reminder: Cys in disulfide! d) When considering which amino acid's pKa to choose for pI calculation, you first need to determine which in the peptide can contribute to a charge on the molecule. For vasopressin, that would be Cys (pKa =8.37), Tyr (pKa = 10.46), Arg (pKa = 12.48), and the NH3+ (pKa = 10) at the N-terminus. Remember! The C-terminus of vasopressin is not a free carboxyl! Rather, it is aminated resulting in no net charge. So, we want to determine which to pKa values are the closest and straddle the pH at which the molecule has a net neutral charge. With the pH < 8.37, the peptide has a net charge of +2. However when the pH is 10 < pH > 8.37, the cysteine residues are now deprotonated resulting in an S-. When we factor in both cysteine residue's negative charge and the +1 from the N-terminus and the +1 from arginine, the peptide is now at a net neutral! So, to calculate the pI, we need to add the two pKa values that straddle the net neutral pH (8.37 and 10) and find the average between them. This results in a pI of 9.18!| Section 2 Questions: Q1) When a growing peptide chain is being synthesized by the ribosome, what terminus (amino or carboxy) is added on to? Also, which amino acid is always the "starting" amino acid for a polypeptide? A1) Much like how DNA is always synthesized from 5' → 3', proteins are always synthesized from the amino terminus to the carboxy terminus, beginning with the start codon Methionine. Q2) Match the type of protein structure to its definition: a) Primary Structure (1°) 1) This structure can also be called a homodimer or heterodimer, results because of two proteins forming interactions b) Secondary Structure (2°) 2) A growing polypeptide synthesized directly out of the ribosome c) Tertiary Structure (3°) 3) The 3D structure of a protein, fully synthesized and correctly folded d) Quaternary Structure (4°) 4) The protein structure formed using the R-groups of the polypeptide to create α-helices and β-sheets. A2) a = 2 b = 4 c = 3 d = 1 Section 3 Questions Q1) You are considering choosing between traditional centrifugation and density centrifugation for the following scenarios. Explain which method would result in the best result for your experiment and why. a) You want a crude cell pellet free of all supernatant. b) From a plant cell, you want to separate the nucleus from the chloroplast. c) You want to separate the nuclear envelope from the nucleus (hint: think about the structure of the nucleus!) A2) a) Traditional centrifugation. All that is needed for this experiment is the cell material to be pelleted away from the supernatant, so traditional centrifugation is sufficient. b) Density centrifugation. To separate on an organellar level, density centrifugation should be used to increase efficiency. Bonus fun fact! Choosing an osmotically inert material such as Percoll can improve separation by not inducing hyper- or hypotonic lysis! c) Density centrifugation. To fractionate on a suborganellar level, density centrifugation is a must. Proceeded by the appropriate experiments, an osmotic material such as sucrose can be used to fraction the nuclear envelope away from the nucleus. Q2) You are creating a cell-free extract of Arabidopsis proteins that you want to keep for extended storage at -80°C. However, when thawed you still want the protein to remain functional for future assays and you know just adding glycerol will cause the protein to denature. a) What is a method you can use to add glycerol to your protein extract while keeping it stable in solution? Explain why. A2) To increase the concentration of glycerol in the buffer without adding directly, dialysis should be used. This allows for an exchange of buffer components, ultimately bringing both solutions to equilibrium. So, if you have a higher concentration of glycerol in the dialysis solution than in the cell-free extract, dialysis will cause the concentration of glycerol in the cell-free extract to increase slowly over time, preventing protein precipiation. Q3) The components of a cell-free extract contain: 25 mM HEPES, 100mM KCl, 5 mM MgCl2 250 mM sucrose 10% glycerol and 1 mM dithiothreitol. You dialyze with a buffer containing 25 mM HEPES, 100 mM KCl, 12 mM MgCl2, 17% glycerol, and 2 mM dithiothreitol. Hypothesize if the components of the cell-free extract will increase, decrease, or stay the same following overnight dialysis. (Li et al., 2002 doi: 10.1105/tpc.010258) A3) The HEPES and KCl concentrations will stay the same, as both the cell extract and dialysis solution contain equal concentrations. The concentration of sucrose will decrease. The concentrations of the MgCl2, glycerol, and dithiothreitol will all increase. Q4) Match the type of chromatography to its definition. a) Cation-exchange 1) Antibodies are bound to beads and bind to tagged proteins b) Affinity 2) A bead or gel matrix is created resulting in low molecular weight proteins exiting the column first, and larger last c) Anion-exchange 3) A negatively charged bead binds to net positively charged proteins causing net negative and neutral proteins to elute faster d) Size-exclusion 4) A positively charged bead binds to net negatively charged proteins causing net positive and neutral proteins to elute faster e) Hydrophobic Interacting 5) A high salt concentration causes all proteins to bind to the column matrix. Decreasing the salt solution causes hydrophilic proteins to elute first, followed by hydrophobic A4) a = 3; b = 1; c = 4; d = 2; e = 5 Q5) You want to separate 4 proteins with the following molecular weights: 120 kDa, 100 kDa, 150kDa, and 70 kDa. a) What percentage of acrylamide gel would you use to resolve these proteins, 7%, 12%, or 15%? Explain. b) After seeing your data from a), you decide a 25 kDa protein is also of interest. Can you use the same acrylamide % as in a)? Why or why not? And would you be able to resolve all 5 proteins on your gel? A5) a) A 7% gel would be the best option here. The lower percentage of acrylamide used for the gel, the larger the pores allowing for easier movement of high weight molecular weights. b) No, a 7% acrylamide gel would not be able to resolve a 25 kDa protein. You would need a higher percentage gel, at least 12% to resolve a protein with a 25 kDa MW. With a 12% gel, it might be hard to distinguish between 100 and 120 kDa. You might consider running a gradient gel (4-15%) to see all 5 proteins. Q6) You are studying a protein that undergoes the posttranslational modification of phosphorylation to become active, the addition of negatively charged phosphates to the outer surface of the protein. While you know the protein is phosphorylated to become active, you want to determine how many phosphates are added. When the protein is inactive (not-phosphorylated) the pI is 8.0. What experiment could you plan to determine the number of phosphates added to the active form of your protein? A6) You could plan to do isoelectric focusing of your protein in the inactive and active form to visualize the shift in pI. Knowing how much negative charge each phosphate adds to the pI, you can determine the number of phosphates added by how your protein moves on the isoelectric gel. Section 5 Questions Q1) Given the data below from a Bradford Absorbance experiment, determine the concentration of a protein extract: Q2) The following data is from a circular dichroism experiment, based on the absorbance pattern, what is the predominant secondary structure of the protein ? Q3) FRET Question Q4) Question about MS with poor sequence coverage that doesn't contain a lot of basic AAs; use a new enzyme to digest. Q5) NMR Question (use data?) Q6) Question about Cryo-EM?

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/03%3A_Amino_Acids_Peptides_and_Proteins/3.6%3A_Chapter_3_Questions.txt

Search Fundamentals of Biochemistry Introduction In Chapter section 3.2, we discussed primary, secondary, tertiary, and quaternary structures of proteins. We used as an example the protein hydroxynitrile lyase (5Y02). An interactive iCn3D model of hydroxynitrile lyase is shown in Figure \(1\). Figure \(1\): hydroxynitrile lyase (5Y02). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/icn3d/share.html?gGY8QBkCP8dPmfoH6 (Copyright; author via source) Blue indicates random coil, red indicates alpha helices and yellow shows beta strands. As we discussed in Chapter 3.2, the beta strand backbones are quite extended compared to the alpha helices which are more compressed. Atomic force microscopy can be used to pull the helix into alternative extended shapes. Let's explore main chain conformations in greater detail. If we wish to understand protein structure, it is important to first understand the possible conformations of the backbone chain before we start considering the effect of side chains and the overall structure of the protein. Where do we start? It's a very complicated topic but we can simplify it by considering the structures of short hydrocarbons and other organic groups and use ideas you learned in earlier chemistry courses. So let's start with butane, a 4C hydrocarbon. You will remember from previous chemistry courses that different rotational conformations (conformers) of butane have different energies. These conformers are often visualized using Newman projections sighting down the internal C2-C3 carbon bond in butane. The figure below shows ball and stick representations as well as Newman projects for three different conformers formed by rotation around the C2-C3 bond. You are familiar with bond angles, which is the angle between two bonds connecting three connected atoms. For this discussion, we will consider the dihedral angle which describes the angle of rotation around the middle bond of a 4-atom, 3-bond connected system. It's easiest to use Newman projections to understand dihedrals, which represents the angle between groups bonded to the front and back (circle) atoms sighted down the front-to-back atoms. Figure \(2\) shows multiple conformers of butane, represented in ball and stick form or as Newman projections. The syn and eclipsed conformer with a dihedral angle of 0° has the highest energy since the two eclipsed bonds have high torsional energy and the two closest approaching H atoms on the methyl C atoms have steric (1,6) interactions. Rotating the front C 60° gives a gauche conformation of lower energy. Rotating to 120° leads to higher energy given the torsional strain between the eclipsed bonds. Rotating fully to 180° gives the conformer of lowest energy and torsional (bond-bond) strain. Steric strain between the two closest H atoms on the methyl groups also raises the energy. A red line has been drawn between the 4 carbon atoms connected by three bonds in the anti or trans-conformer. Notice that the red line is a zig-zag as shown in the rotated red line drawing beneath the ball and stick conformer shown. All the conformers are available but the anti or trans-confomer is most stable and abundant. Now, let's try this approach on a more complicated molecule, a 12-carbon atom carboxylic acid, dodecanoic acid. Figure \(3\) shows the molecule in the all- trans conformation (where all the carbon atoms in the chain are arranged in an anti, zig-zag manner), and a second gauche conformation for the bond highlighted by a red line. Note that rotation around that bond to produce the gauche conformer, a higher energy form, produces a kink in the chain. We will see this again when we discuss lipid structure. Now let's compare the all-trans dodecanoic acid structure to one largely extended conformer of a tetrapeptide, Gly-Gly-Ala-Gly (GGAG) as shown in Figure \(4\). Notice the similarities in these two structures. The atoms in the main chain of the peptide, N, Ca, C (the carbonyl C) are arranged in the familiar zig-zag fashion, characteristic of the lowest energy conformer. The dihedral angles along the backbone would be, to a first approximation, around + 180° depending on whether the rotation is clockwise or counterclockwise from the syn conformer. However, the actual conformation of the GGAG peptide, if found as part of the primary structure of a protein, could adopt a whole range of other conformers with potentially different dihedral angles for each bond in the main chain. Look back at the iCn3D model for hydroxynitrile lyase above and it should be clear that if the stretch of amino acids Gly-Gly-Ala-Gly is in a beta sheet, its dihedral angles for the main chain would be closer to +1800 than if they were in an alpha helix, which would dihedral angles much less than 180°, perhaps closer to + 600 It might seem like all possible dihedral angles are possible for the main chain atoms of a protein, which would make understanding protein structure too complicated for our minds to comprehend. However, that are two major things that simplify conformational analyzes of proteins. Trans and Cis Peptide Bonds Note in the extended peptide example in the above figure, the alpha Cs of adjacent amino acids are on opposite sides of the peptide bond between the carbonyl carbon atom and the amide nitrogen atom. They are trans to that bond. A rectangle was drawn on the above figure centered on the C-N peptide bond for two adjacent amino acids to clearly show the trans orientation of the alpha Cs. Rotation around single bonds can occur, so an isomerization of the trans peptide bond to the cis isomer can occur. Both isomeric forms of the dipeptide Ala-Ala are shown in the top part of Figure \(5\). This trans arrangement of the alpha Cs in Ala-Ala and most internal X-Y amino acids pairs in a protein is sterically favored by a factor of 1000/1 over the cis form, which clearly shows torsional between the aligned (red) bonds and steric strain between amino acid side chain atoms. The case is different for X-Y amino acid pairs when Y is proline, which is shown in the bottom part of the above figure. Proline is a cyclic amino acid and as such would be expected to be more sterically restricted in a protein sequence. In X-Pro peptide bonds in proteins, the trans/cis ratio is about 4/1. Clearly the torsional (between bonds) and steric strain (between atoms) are similar in both isomers.. You would expect to find both trans and cis X-Pro peptide bonds in proteins given the constraints placed on protein conformation in the tertiary structure of the protein. Figure \(6\) is an interactive iCn3D model showing two pairs of X-Y peptide bonds in the protein carboxypeptidase. Answer the questions below about the two pairs of peptide bonds, which are shown in stick form and CPK colors. Figure \(6\): 2 peptide bonds in carboxypeptidase. Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...3ARF97MgN4427A (Copyright; author via source) Exercise \(1\) Two X-Y amino acids are shown in stick form in the protein carboxypeptidase A 1. What is the sequence of each pair shown from N to C terminus 2. Are the X-Y peptides bonds cis or pro? Answer Add texts here. Do not delete this text first. Backbone dihedral angles and Ramachandran Plots Now let's focus on possible dihedral angles for the peptide bond in protein chains. Just as saturated fatty acid chains have preferred conformations (all anti or trans), peptide chains also have preferred conformations. The complexity is much greater, however. For our example using dodecanoic acid, we dealt only with torsion or dihedral angles around the methylene (-CH2-) carbons. For proteins, we must consider the covalent links which attach the amino acids together, as well as the rotations possible in 20 different amino acids. The peptide bond connects the carbonyl C of the ith amino acid to the alpha amine N of the i th+1 amino acid. The resulting bond is an amide link. X-ray analysis shows that the C-N bond has double bond character. This can be accounted for by delocalizing the nonbonding electron pair of the N to the carbonyl C forming a double bond, with the pi-bonded electrons of the carbonyl C-O bond moving to the O. These resonance structures lead to a planar arrangement of the peptide carbonyl C and amide N and the two other atoms connected to each, since the hybridization of the C and N has sp2 character, with 120o bond angles, as illustrated in Figure \(7\). Figure \(7\): Resonance forms of peptide bond This greatly simplifies the number of conformations that a protein can adopt since these 6 atoms can be considered to reside and move in a plane. The alpha C serves as the corner attachment point of two different planes, each of which can rotate independently of the other plane. The two planes can twist around the alpha carbon. The rotation angles for the two planes are called phi (φ) and psi (ψ) are analogous to the torsion angles in the acyl chain of dodecanoic acid. They can vary from -180 to +180o. The R group substituent attached to the alpha C can also rotate around the alpha C and the beta C of the side chain. This angle is defined as chi. Other rotations also occur within the side chain. We will concentrate on phi (φ) and psi(ψ) angles. Figure \(8\) shows the peptide Gly-Gly-Ala-Gly (at high pH to give the protonation state shown) with the six atoms around the C-N peptide bonds shown in rectangles. Phi (φ) and psi (ψ) angles are also shown. Using these ideas, we can consider a protein backbone as a series of linked sequences of rigid, planar peptide units which can rotate around phi/psi angles. When the chain is fully extended (as shown in the links above), φ/ψ are + 180o, with the syn eclipse form defined as having 00 φ/ψ angles. When φ and ψ equal 0o, the two peptide bonds flanking the alpha Cs are in the same plane. This conformation is prohibited since the O of the C=O on one plane and the H of the H-N on the other are overlapping - i.e. they approach closer than their van der Waals radii. The Proteopedia site below gives a phenomenal explanation and visual representation of φ/ψ angles in proteins. Ramachandran was the first to calculate which φ/ψi angles are allowed. He modeled the angles permitted to a tripeptide, assuming the atoms were hard spheres. The angles allowed depended in part on the limiting distance chosen for interatomic contacts. (i.e. the usual H -- H distance is 2.0 angstroms, and 3.0 for C--C bonds.) The plot below shows the allowed regions in red. Only 3 small regions of conformational space are available. If you allow a closer approach by 0.1 angstroms, more conformation space is available, but only one new area is available, shown in yellow in Figure \(10\). Right-hand alpha helices fall at -570,-470 while left hand alpha helices fall at +570,+470. (Notice these are not mirror images of each other. The mirror image of a right-handed alpha helix would be a left-handed helix made of D-amino acids.) Parallel beta sheets are at -1190, -1130, while antiparallel sheets falls at -1390, +1350. Note that beta sheets have φ/ψ angles closer to the fully extended + 1800 of the all anti (trans) conformation of dodecanoic acid than does the alpha helix which is more compact. Other types of helices also are found. The 310 helix, a sharper helix with 3 amino acids/turn, falls at -490,-260. All of these examples of secondary structure fall in allowed regions. Modern Ramachandran plots do not model the atoms as hard spheres but instead consider the potential energy of the atoms using the Lennard-Jones potential (6-12 potential) for induced dipole-induced dipole interactions. We discussed this potential function Chapter 2.4. A Ramachandran plot of Ala-Ala-Ala is nearly identical to the plot for Phe-Phe-Phe (which is unbranched at the beta carbon, the first methylene C in the side chain). The plot for Thr-Thr-Thr, which has a branch at the beta C (with OH and CH3 attached) shows somewhat less room than the other plots. Pro-Pro-Pro is most restricted for obvious reasons. For a longer chain than a tripeptide, there are more restrictions than for (Ala)3, since the chain can't assume a conformation when it passes through itself. The plots for actual proteins have many points which do fall in forbidden regions. However, these points would be allowed if the peptide bonds twist a few degrees. Gly bonds also fall outside the allowed regions. This is understandable, since the side chain of Gly is H, and it is used in proteins where sharp turns of the chain are necessary. Figure \(11\) shows Ramachandran plots for Gly, Ala, Tyr, and Pro in actual proteins (made years OK but can't find reference) Figure \(12\), taken from the Proteopedia page above, shows the Ramachandran plots for 100,000 proteins from the Protein Data Bank. Now it's time to explore in more detail secondary, tertiary and quaternary structures in more detail. There are many ways to organize the structural organization of the protein world. For the rest of this chapter, we will adopt the approach used by Carl Branden and John Tooze in their seminal book, "Introduction to Protein Structure".

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/04%3A_The_Three-Dimensional_Structure_of_Proteins/4.01%3A_Main_Chain_Conformations.txt

Search Fundamentals of Biochemistry Secondary structures are those repetitive structures involving H bond between amide Hs and carbonyl Os in the protein backbone. These include • helices (alpha - α , 310 and pi - π), in which the hydrogen bonds are within a short continuous stretch of amino acids (a strand), • beta strands (sheets) in which the hydrogen bonds are between backbone atoms (again amide Hs and carbonyl Os) on noncontinuous stretches of the protein, and • reverse turns, which occur within a very short continuous stretch of amino acids. Helices A schematic showing idealized geometries of helices, with amino acids shown as dots for simplicity, is shown in Figure \(1\). The pitch (p) represents the spacing between the chain on one side of the helix, the number of amino acids per turn (n), and handedness (+ = right-handed or – l= left- handed) are shown in the figure. There are three major types of helices in proteins, the alpha helix (n = 3.6), 310 helix (n = 3) and the pi helix (n = 4.4). Note that n is most commonly not an integer. Alpha The alpha helix is the most common type of helix. They are formed when the carbonyl O of the ith amino acid forms hydrogen bonds to the amide H of the ith+4 aa (4 amino acids away). Figure \(2\) show a short section of an alpha helix running from N-terminal (bottom) to C-terminal (top) with the sequence DTASDAA. The amino acids i, i+1, ... i+4 are labeled at their alpha carbons. The red oval highlights the intrastrand H bond between the C=O of the ith amino acid (Asp) and the amide H of the ith+4 aa (Ser). Figure \(3\) shows a cartoon image showing a more extended helix and a schematic showing hydrogen bonding partners. The phi/psi angles for amino acids in the alpha helix are approximately - 57,-47, which emphasizes the regular repeating nature of the structure. It can also be characterized by n (the number of amino acid units/turn = 3.6) and the pitch (the helix rise/turn = 5.4 angstroms = 0.54 nm). Since there are 3.6 amino acids per turn, and a full circle or turn is 3600, each amino acid is staggered at 1000 increments looking down on the helix axis. To refresh your mind, the phi/psi diagram for a fully extended polypeptide chain (phi 1800, psi 1800) is shown below in Figure \(4\). Figure \(5\) shows side and end-on view of a helix from the antifreeze protein (1wfa) from winter flounder. The green coil (often shown in red when displaying alpha helices in full proteins) shows the repetitive nature of the backbone. Note that side chains are pointing away from the helix axis. H-bonds are shown as yellow dotted lines within the backbone (one is also shown between two side chains on the top). The spacefill rendering is shown in colors optimal for those who are colorblind. The end-on view shows that the center of the helix is fully packed with atoms from the helix and is NOT open (a common misconception among students). Some facts: • the alpha helix is more compact than the fully extended polypeptide chain with phi/psi angles of 180o • in proteins, the average number of amino acids in a helix is 11, which gives 3 turns. • the left-handed alpha helix, although allowed from inspections of a Ramachandran plot, is never observed, since the side chains are too close to the backbone. • the core of the helix is packed tightly. No central cavities or or pores are present in the helix. • All the R-groups extend backward and away from the helix axis. • Some amino acids are more commonly found in alpha helices. Amino acids can be divided into two kinds, those with branches at the beta C and those with none. Consider first those that aren't branched. Gly is too conformationally flexible to be found with high frequency in alpha helices, while Pro is too rigid. The amino acids with side chains that can H-bond (Ser, Asp, and Asn) and aren't too long appear to act as competitors of main chain H bond donors and acceptors, and destabilize alpha helices. The rest with no branches at the beta C can form helices. Those with branches at the beta carbon (Val, Ile) destabilize the alpha helix due to steric interactions of the bulky side chains with the helix backbone. (Remember left-handed alpha helices are not found in nature for similar reasons.) • alpha keratins, the major component of hair, skin, fur, beaks, and fingernails, are almost all alpha helix. Figure \(6\) shows an interactive iCn3D model of an alpha helix from bacteriophage T4 lysozyme (1DYG). Side chains, which are not involved in helix-stabilizing hydrogen bonds are shown in cyan. H-bonds are shown as green dotted lines. The amino acid side chain R-groups can be hydrophilic or hydrophobic, and can be localized in specific positions on the helix forming amphipathic regions on the protein or fully hydrophobic helices may also extend through the plasma membrane as shown in Figure \(7\). In amphipathic helices, hydrophilic residues are positioned on one side of the helix and hydrophobic on the other as shown in the side view (A) or top-down views (B & C). R-groups may also be fully hydrophobic within alpha helices that span the plasma membrane as shown in (D). Helical wheel projections can be made showing the polarity of the faces of the helix looking down the axis. Here are two such helical wheel projections: For the sequence MLQSMVSLLQSLVSLIIQ, Figure \(8\) shows that the helix is amphiphilic. A helical wheel for the membrane-crossing section of the human receptor-type tyrosine-protein phosphatase C protein, ALIAFLAFLIIVTSIALLVVL, is shown in Figure \(9\). The amide bond in the peptide has a significant permanent dipole moment. Since the dipoles of individual amino acids are oriented in the same direction in an alpha helix, the whole helix has a significant dipole moment. This is illustrated in Figure \(10\). recreate this image: In the alpha helix all the amides in the backbone point in one direction from the C-terminus to N-terminus. This leads effectively to one long dipole of magnitude n(3.5) Debyes, where n is the number of amino acids in the helix. A really long helix can then produce a significant electric field and affect protein binding properties. 310 helices The 310 helix is stabilized by hydrogen bonds between the carbonyl O of the ith amino acid and the amide H of the ith+3 aa (3 amino acids away). It has 3 residues/turn, and a pitch (rise per turn) of 6 angstroms, with a rise of 1.3-2 angstroms/residue. Typical phi/psi angles are -500,-26°. As with the alternative description of the alpha helix, the 310 helix has 3 amino acids per turn and 10 atoms in the main chain/turn (counting Cα-N-C-Cα atoms). It is longer (for the same number of amino acids) and thinner. The amino acid side chains are staggered at 1200 increments as you look around the helix axis. Although not very prevalent (about 3 percent of protein amino acids are in 310 helix with an average 3.3 amino acids in the helix), they presumably serve some function. 310 helices as long as 11 residues have been found. Within a protein, a helix will be stable if the packing around it will allow it. Many more alpha-helix side chain interactions with surrounding protein are likely, given a 1000 staggering of side chains compared to a 1200 staggering in a 310 helix, which lineup in 3 ridges looking down the helical axis. Molecular dynamics studies suggest that parts of a 310 helix might reversibly interconvert to an alpha helix, allowing conformational and binding flexibility. The S4 helix in some voltage-sensitive potassium ion channels with a canonical R1xxR2xxR3xxR4xxK5xxR6 (where R and K are Arg and Lys) have been shown to adopt a 310 helical conformation. Figure \(11\) shows an interactive iCn3D model of a 9 amino acids (150-158) 310helix from dienelactone hydrolase (1DIN) Figure \(11\): A 310 helix from from dienelactone hydrolase (1DIN). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...Kufi7gmpSBdjYA (Copyright; author via source) π (pi) helices This helix has 4.4 residues/turn, and a helix pitch (rise) of about 4.1 nm angstroms. It has hydrogen bonds between the carbonyl O of the ith amino acid and the amide H of the ith+5 amino acids (5 amino acids away). It rise is about 1.2 angstroms/residue and has approximate phi/psi angles of -550, -700. An alternative designation for the pi helix is 4.416 with 16 main chain atoms in 1 full turn (see above for the alpha helix). Some reasons for its low abundance include minimal contact between the main chain atoms given the larger radius, and phi-psi angles close to disallowed values. It might also not form kinetically as fast as the other helices since nucleation of it would be more difficult. At the same time, molecular dynamic simulation simulations show that alpha helices can interconvert reversibly with pi helices. They are often found between two alpha helices, again suggesting dynamic interconversions between the two forms are likely. About 55% of characterized pi helices contain 5 amino acids. Each side chain is staggered by 850, with a rise of about 1.3 Angstroms. Figure 12 below shows an interactive iCn3D model of a short pi helix (aa 265-276) from barley beta-D-glucan glucohydrolase (1x38). Helices in Proteins: Comparison of alpha, 310 and pi helices Beta Structure Beta Structure: Parallel and antiparallel beta strands are much more extended than alpha helices (phi/psi of -57,-47) but not as extended as a fully extended polypeptide chain (with phi/psi angles of +/- 180) as shown in the figure below. Parallel beta strands have phi/psi angles of -119, +113, while the antiparallel angles are -139, +135. Figure \(13\) Each single strand of the beta-sheet can be pictured as a twofold helix, i.e. a helix with 2 residues/turn. The arrangement of each successive peptide plane is pleated due to the tetrahedral nature of the alpha C. Hydrogen bonds are inter-strand, not intra-strand as in the alpha helix. The figure below shows how the "pleats" in a sheet containing parallel beta strands can be envisioned as rippled sheets. Figure \(14\) They can be visualized by folding a sheet of paper into narrow folds or pleated strips side by side to make a "pleated sheet" of paper. Each strip of paper can be pictured as a single peptide strand in which the peptide backbone makes a zig-zag along the strip, with the alpha carbons lying at the folds of the pleats. The R groups are attached to the carbons and extend above and below the folds of the pleat in the trans conformation. Consider a strand as a continuous and contiguous polypeptide backbone propagating in one direction. Hence, using this definition, a helix consists of a single strand, and all the H-bonds are within the strand (or intrastrand). A beta sheet would then consist of multiple strands, since each "strand" is separated from other "strands" by an intervening contiguous stretch of amino acid which bends within the protein in a way that allows the next section of the peptide backbone, the next "strand", to H-bond with the first "strand". But remember, even in this case, all the H-bonds holding the alpha and beta structures together are intramolecular. In a parallel beta sheet structure, the optimal H bond pattern leads to a less extended structure (phi/psi of -119, +113) than the optimal arrangement of the H bonds in the antiparallel structure (phi/psi of -139, +135). Also, the H bonds in the parallel sheet are bent significantly. (i.e. the carbonyl O on one strand is not exactly opposite the amide H on the adjacent strand, as it is in the antiparallel sheet.) Hence antiparallel beta strands are presumably more stable, even though both are abundantly found in nature. Short parallel beta sheets of 4 strands or less are not common, which might reflect their lower stability. The side chains in the beta sheet are normal to the plane of the sheet, extending out from the plane on alternating sides. Parallel sheets characteristically distribute hydrophobic side chains on both sides of the sheet, while antiparallel sheets are usually arranged with all the hydrophobic residues on one side. This requires an alternation of hydrophilic and hydrophobic side chains in the primary sequence. Antiparallel sheets are found in silk with the sheets running parallel to the silk fibers. The following repeat is found in the primary sequence: (Ser-Gly-Ala-Gly)n), with Gly pointing out from one face, and Ser or Ala from the other. Unfortunately, there is no PDB structure of the silk "amyloid" protein showing this repetitive structure. The monomer and aggregates of this protein are quite insoluble so few x-ray structures for proteins like this are available. Figure \(15\) shows an interactive iCn3D model of the N-terminal part (domain) of the Bombyx mori fibroin silk protein (pdb = 3UA0), which does give an excellent example of antiparallel beta sheets. Notice that two chains align to form a face of the curved antiparallel beta sheet. Figure \(16\) shows a more detailed static image of the antiparallel beta sheets in 3UA0. Note the yellow sticks between the strands representing the H-bonds. Beta strands have a tendency to twist in the righthand direction. This leads to important consequences in how the beta strands are connected. Parallel strands can form twisted sheets or saddles as well as beta barrels. Figure \(17\) shows an interactive iCn3D model of the parallel beta sheet structure from the arabinose binding protein (1ABE). Figure \(18\) shows a static image of the parallel beta sheets in 1ABE. Note the yellow sticks between the strands representing the H-bonds. Figure \(19\) shows an interactive iCn3D model of a parallel beta barrel from the triose phosphate isomerase (1WYI). Figure \(20\) shows a static image of the parallel beta sheets in triose phosphate isomerase. Note the yellow sticks between the strands representing the H-bonds. Also, note that the barrel is not hollow but is filled with side chains. Some facts on parallel beta structure: • in parallel strands, right-handed connectivity is common. • in a protein with parallel strands in register, and given the inherent twist in the stands, the strands arrange in a way to have the H bonds stretched equally at the ends of the chains, giving rise to a twisted saddle shape (top structure above). • in a protein with parallel strands out of register, and given the inherent twist in the stands, the strands arrange in a way to have the H bonds stretched equally at the ends of the chains, giving rise to a beta barrel (bottom structure above). Connectors, Loops, Linkers and Bends About 50% of the amino acids in a globular (spherical) protein are in regular secondary structure (alpha or beta). The amino acids in helices and beta strands are connected by stretches of amino acids which still have order, but that order is less regular than those found in helices and beta strands, which are characterized by stretches of amino acids with the same phi/psi angles. Some bear the hallmarks of secondary structure - they are held together by intrachain hydrogen bonds. We will consider a few here. Turns, Reverse Turns and Hairpins One example of a connector involving secondary structure (i.e. hydrogen bonds between amide Hs and carbonyl Os of the backbone), is a reverse turn called the beta bend or beta turn. These turns often connect successive antiparallel beta strands and are then called beta hairpins. A hairpin is a special case of a turn, in which the direction of the protein backbone reverses and the flanking secondary structure elements interact. For example, the beta hairpin connects two hydrogen-bonded, antiparallel β-strands. The word beta can be confusing. It does not mean that the structure has hydrogen-bonded amino acids that have the same phi-psi angles as beta strands. It's easy to remember the name beta as the beta bend connects two antiparallel beta strands. The term beta really comes from the fact that it is a member of a class of turns named with Greek letters, including alpha-, gamma-, delta-, pi- and beta- turns. They are almost always on the surface, and usually consist of 4 amino acids. However, there are several types of beta turns and different ways to classify them. One involves the number of residues (n) between the two residues that are hydrogen bonded. n=2: These contain four amino acids. Amino acids 1 and 4 form hydrogen bonds with n=2 amino acids in between. Another way to describe them is that the hydrogen bond between residues 1 and 4 is between the backbone carbonyl O of the ith amino acid and the amide H of the ith+3 aa (three amino acids away) so the structure contains 4 amino acids (ith, ith+1, ith+2, and ith+3). There are two common types: • Type I: phi 2 = -60, psi 2 =-30; phi 3 = -90, psi 3 = 0; The first amino acid in the actual turn (ith + 1) is actually in a left-handed alpha helix conformation. Glycine, asparagine or aspartate are stable at this position since glycine is small and the side chains of Asp and Asn can form hydrogen bonds to the main chain. Glycine is usually found in the second position of the actual turn (ith + 2). • Type II: phi 2 = -60, psi 2=120; phi 3 = 90, psi 3 = 0; The first residue of the actual turn is typically Gly while the second often is a polar amino acid such as Ser and Thr Here are 2D line drawings showing Type I and Type II beta turns. Figure \(21\) The figure below shows Type I (left) and Type 2 (right) from human egg white lysozyme. Figure \(22\) Figure \(23\) shows an interactive iCn3D model showing two reverse turns in hen egg white lysozyme (1dpx). Notice the tightness of the reverse turn and the presence of proline and glycine. n=3: These contain five amino acids. Amino acids 1 and 5 form hydrogen bonds with n=3 amino acids in between. Another way to describe them is that the hydrogen bond between residues 1 and 4 is between the backbone carbonyl O of the ith amino acid and the amide H of the ith+4 aa (four amino acids away) . Shifting back to the Greek letter naming system, the gamma turn, the second most common turn, has just three total residues, (ith, ith+1, and ith+2) with the hydrogen bond between the backbone carbonyl of the ith amino acid and the backbone amide H of the ith+2 amino acid. An ω-loop is a catch-all term for a longer, extended or irregular loop without fixed internal hydrogen bonding. Turns are sometimes found within flexible linkers or loops connecting protein domains. Linker sequences vary in length and are typically rich in polar uncharged amino acids. Flexible linkers allow connecting domains to freely twist and rotate to recruit their binding partners via protein domain dynamics. Mostly modeling programs display the linear sequence of a protein and in addition a linear cartoon rendering showing alpha structure as squiggles or helices, beta structure as yellow arrows. and connecting amino acids between secondary structures as lines. The figure below shows a 1D connectivity diagram for part of the protein alpha-lactalbumin (1a4v). Figure \(24\) Amino Acid Propensities for Secondary Structures Finally, what types of amino acids are most likely to be found in different types of secondary structures? Some rationales for the "propensity" for secondary structure are shown in the figure below. Figure \(25\) OpenStax, Proteins. OpenStax CNX. Sep 30, 2016 http://cnx.org/contents/bf17f4df-605c-4388-88c2-25b0f000b0ed@2. File:Chirality with hands.jpg. (2017, September 16). Wikimedia Commons, the free media repository. Retrieved 17:34, July 10, 2019 from commons.wikimedia.org/w/index.php?title=File:Chirality_with_hands.jpg&oldid=258750003. Wikipedia contributors. (2019, July 6). Zwitterion. In Wikipedia, The Free Encyclopedia. Retrieved 21:48, July 10, 2019, from en.Wikipedia.org/w/index.php?title=Zwitterion&oldid=905089721 Wikipedia contributors. (2019, July 8). Absolute configuration. In Wikipedia, The Free Encyclopedia. Retrieved 15:28, July 14, 2019, from en.Wikipedia.org/w/index.php?title=Absolute_configuration&oldid=905412423 Structural Biochemistry/Enzyme/Active Site. (2019, July 1). Wikibooks, The Free Textbook Project. Retrieved 16:55, July 16, 2019 from en.wikibooks.org/w/index.php?title=Structural_Biochemistry/Enzyme/Active_Site&oldid=3555410. Structural Biochemistry/Proteins. (2019, March 24). Wikibooks, The Free Textbook Project. Retrieved 19:16, July 18, 2019 from en.wikibooks.org/w/index.php?title=Structural_Biochemistry/Proteins&oldid=3529061. Fujiwara, K., Toda, H., and Ikeguchi, M. (2012) Dependence of a α-helical and β-sheet amino acid propensities on teh overall protein fold type. BMC Structural Biology 12:18. Available at: Wikipedia contributors. (2019, July 16). Keratin. In Wikipedia, The Free Encyclopedia. Retrieved 17:50, July 19, 2019, from en.Wikipedia.org/w/index.php?title=Keratin&oldid=906578340 Wikipedia contributors. (2019, July 13). Alpha-keratin. In Wikipedia, The Free Encyclopedia. Retrieved 18:17, July 19, 2019, from en.Wikipedia.org/w/index.php?title=Alpha-keratin&oldid=906117410 Open Learning Initiative. (2019) Integumentary Levels of Organization. Carnegie Mellon University. In Anatomy & Physiology. Available at: Wikipedia contributors. (2019, July 16). Collagen. In Wikipedia, The Free Encyclopedia. Retrieved 03:42, July 20, 2019, from en.Wikipedia.org/w/index.php?title=Collagen&oldid=906509954 Wikipedia contributors. (2019, July 2). Rossmann fold. In Wikipedia, The Free Encyclopedia. Retrieved 16:01, July 20, 2019, from en.Wikipedia.org/w/index.php?title=Rossmann_fold&oldid=904468788 Wikipedia contributors. (2019, May 30). TIM barrel. In Wikipedia, The Free Encyclopedia. Retrieved 16:46, July 20, 2019, from en.Wikipedia.org/w/index.php?title=TIM_barrel&oldid=899459569 Wikipedia contributors. (2019, July 16). Protein folding. In Wikipedia, The Free Encyclopedia. Retrieved 18:30, July 20, 2019, from en.Wikipedia.org/w/index.php?title=Protein_folding&oldid=906604145 Wikipedia contributors. (2019, June 11). Globular protein. In Wikipedia, The Free Encyclopedia. Retrieved 18:49, July 20, 2019, from en.Wikipedia.org/w/index.php?title=Globular_protein&oldid=901360467 Wikipedia contributors. (2019, July 11). Intrinsically disordered proteins. In Wikipedia, The Free Encyclopedia. Retrieved 19:52, July 20, 2019, from en.Wikipedia.org/w/index.php?title=Intrinsically_disordered_proteins&oldid=905782287

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/04%3A_The_Three-Dimensional_Structure_of_Proteins/4.02%3A_Secondary_Structure_and_Loops.txt

Search Fundamentals of Biochemistry Tertiary Structure The tertiary structure of a single chain protein is the overall 3D structure of the protein. A protein of a given primary structure folds to form a 3D structure with embedded secondary structures, super secondary structures and domains. Folded proteins can have a variety of shapes from a roughly spherical or "globular" to a more extended "fibrillar" form. Let's consider the more globular one first. How a protein folds will be discussed in greater detail later, but a more descriptive and simpler view will help us understand the structural features of the folded protein in its tertiary structure. Start with an unfolded protein. It has a polar backbone with dangling polar charged, polar uncharged, and nonpolar sides attached to the alpha carbon of each amino acid in the primary sequence. On folding, where do these side chains of varying polarity end up? To a first approximation, you may think that a globular protein might fold such that all the hydrophobic amino acid side chains are buried in the interior of the protein, surrounded by other hydrophobic side chains. In a similar fashion, you might expect the polar and charged side chains could be on the surface, exposed to water. Such a model would be analogous to a micelle, which has an almost "perfect" separation of polar (on the surface) and nonpolar atoms (buried). Figure $1$ shows an interactive iCn3D model of a micelle below, which consists of 54 self-associated molecules of dodecylphosphocholine fatty acids. If protein folding was only that simple! Topologically, it is impossible for a protein to fold in an intramolecular fashion in a strict analogy to the intermolecular aggregation of single chain amphiphiles into a micelle. Consider also that the entire backbone is polar! To a first approximation we would expect the bulk of nonpolar groups would be buried, surrounded by other nonpolar groups. Likewise, we would expect the bulk of polar and charged groups would be on the surface. Figure $2$ shows an interactive iCn3D model of part of human low molecular weight protein tyrosyl phosphatase (1xww). It shows one buried nonpolar side chain (Phe 10) surrounded by essentially all nonpolar side chains of other amino acids. Figure $2$: A buried phenylalanine in low molecular weight protein tyrosyl phosphatase (1xww) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...LC53qmf4FyszT7 The completely nonpolar Phe 10 side chain is shown in cyan. The atoms with 5 Angstroms are color-coded using the Wimley–White whole residue hydrophobicity scale, which uses not only side chain but peptide bond contributions that are determined experimentally by determining free energy of transfers of groups to nonpolar environments. The color scale runs from cyan (nonpolar) to red (polar/charged. Like a micelle, the protein is roughly spherical. Noncovalent interactions between atoms within a protein chain help drive protein folding. The noncovalent interactions (also termed intermolecular forces in traditional introductory chemistry classes, include ion-ion, ion-dipole, hydrogen bonds, dipole-dipole, induced dipole - induced dipole (often called London Dispersion Forces) and variants including ion-induced dipole, etc. We generally use the same terms when describing these interactions within and between proteins. Ion-Ion interactions are usually called salt bridges, and induced dipole-induced dipole are often called hydrophobic interactions. Let's look at some specific interactions with a given protein chain (the light chain of the mouse immunoglobulin G, PDB 1D = 4hdi). Manipulate the iCn3D model in the exercises below and answer the following questions. Use your mouse to hover over amino acids to help in identification. Exercise $1$ Name the types of interactions between the following side chains in the iCn3D image above. A large images can see by using this link: https://structure.ncbi.nlm.nih.gov/icn3d/share.html?DrdnVubujRaihTcr9 a. Leu 52 and Ile 53, Val 63, Phe 67 and Leu 78 b. Arg 24 and Asp 75 c. Asp 170 and Lys 108 d. Tyr 178 and Lys 147 e. Glu 190 and Arg 160 f. Tyr 191 and Phe 214 Answer Add texts here. Do not delete this text first. a a. hydrophobic interactions, induced dipole-induced dipole b. salt bridge, ion-ion c. salt bridge, ion-ion d. pi-cation e. salt bridge, ion-ion f. Aromatic-aromatic, induced dipole-induced dipole You can analyze the noncovalent interactions within and between a protein using PIC- protein interactions calculator In the next exercise, identify the likely hydrogen donors and acceptors in the pairs shown Exercise $1$ Are these hydrogen bonds • side chain to side chain? • main chain to main chain? • side chain to main chain? a. Ile 111: Gln 171 b. Gln 6: Thr 107 c. Ile 53: Trp 40 d. Try 37:Thr 97 Answer a. Ile 111: Gln 171 - side chain to side chain b. Gln 6: Thr 107 - side chain to side chain c Ile 53: Trp 40 - main chain to main chain d. Try 37:Thr 97 - main chain to main chain A more realistic understanding of noncovalent interactions But are all the nonpolar side chains buried? How about the polar uncharged and polar charged side chains? What are the preferred dispositions of side chains in proteins as derived from the crystal structure of thousands of proteins? Here are some conclusions from a paper by Pace (Biochemistry. 40, pg 310 (2001). • On average, about 50% of the amino acids are in secondary structures. On average, there is about 27% alpha helix, and 23% beta structure. Of course, some proteins are almost all alpha-helical, and some are almost all beta structure, but most are a mixture. • The side chain location varies with polarity. Nonpolar side chains, such as Val, Leu, Ile, Met, and Phe are predominately (83%) in the interior of the protein. • Charged polar side chains are almost equally partitioned between being buried or exposed on the surface. (54% - Asp, Glu, His, Arg, Lys are buried away from water, a bit startling!) • Uncharged polar groups such as Asn, Gln, Ser, Thr, Tyr are mostly (63%) buried, and not on the surface (a bit startling). • Globular proteins are quite compact, with water excluded. The packing density (Volvdw/Voltot) is about 0.75, which is like the NaCl crystal and equals the closest packing density of 0.74. This compares to organic liquids, whose density is about 0.6-0.7. Tertiary structure and pKa Values If a charged side chain is buried in a protein, you would expect that it would be surrounded, in general, by either oppositely charged side chains, to which it could form an internal salt bridge (ion-ion interaction), or a polar uncharged group with which it could interact through dipole-dipole or, more specifically, H bond interactions. You would also expect that if it were not near an oppositely charged side chain, that it would exist, if buried, in an uncharged state. Hence the pKa of side chains would be dramatically affected by the nature of its microenvironment (as we have already seen with the pKa of acetic acid in solvents of different polarity). NMR spectroscopy has been used to determine the pKa values of specific side chains in proteins whose crystal structure is known. Pace et al (2009) summarize data on the properties of ionizable side chains in a series of proteins whose structure has been determined. The intrinsic pKa, pKaint or prototypical pKa value for a side chain exposed to water can be determined using a pentapeptide containing the target amino acid X surrounded by 2 Ala on either each side with both the N and C termini of the peptide blocked so they are uncharged. Table $1$ below shows the pKa values of ionizable side chains in a series of proteins compared to that in the control pentapeptide. Group Content % Buried % pKa int in AAXAA pKa avg low pKa high pKa # measurements Asp 5.2 56 3.9 3.5 + 1.2 0.5 9.2 139 Glu 6.5 48 4.3 4.2 + 0.9 2.1 8.8 153 His 2.2 72 6.5 6.6 + 1.0 2.4 9.2 131 Cys 1.2 90 8.6 6.8 + 2.7 2.5 11.1 25 Tyr 3.2 67 9.8 10.3 + 1.2 6.1 12.1 20 Lys 5.9 34 10.4 10.5 + 1.1 5.7 12.1 35 Arg 5.1 56 12.3 C term     3.7 3.3 + 0.8 2.4 5.9 22 N term     8.0 7.7 + 0.5 6.8 9.1 16 Table $1$: pKa values of side chains in actual proteins A quick glance a the table shows a huge variation in the pKas of ionizable side chains in proteins with the pKa of Asp varying over a range of 8.7 pH units, showing that it can act at physiological pH as either a strong acid or a moderate base. Three majors effects can perturb the pKa of ionizable side chains: 1. Dehydration of the side chain as it is buried in a protein (Born Effect): The stability of a charged group depends on the polarity of the medium in which it exists. Ions are more stable in water than in nonpolar solvents as the water molecules can reorient and interact with the ion through ion-dipole or ion-H bond interactions, which effectively shields the ion from other counter ions. The shielding effect of water is related to the dielectric constant, ε, of the solvent. Coulomb's law can be written as: $\mathrm{F}=\frac{\mathrm{k} \mathrm{Q}_{1} \mathrm{Q}_{2}}{\mathrm{r}^{2}}=\frac{\mathrm{Q}_{1} \mathrm{Q}_{2}}{4 \pi \varepsilon \mathrm{r}^{2}} \nonumber$ Epsilon is the dielectric constant of the solvent. Water has a higher dielectric constant (80) than nonpolar solvents (4-10) and shields opposing charges more, stabilizing them. Hence the pKa of side chains of those amino acids whose deprotonated states are charged will have their pKa values raised (so they are less acidic) in nonpolar environments. The reverse holds for side chains whose protonated form is charged. Pace cites as an example two mutants of staphylococcal nuclease in which a buried Val 66 is changed either to Asp or Lys. The buried Asp has a pKa of 8.9 compared to 5.5 for the buried Lys. These changes were not compensated for with new charge-charge interactions, so the change can be attributed to the dehydration (or Born) effect. 2. Ion-Ion interactions with another charged side chain through Coulombic forces: This effect can be most readily observed at the surface of the protein. Pace cites a study of RNase S that is devoid of Lys and has a pI of 3.5. Five Asp and Glu were replaced on the surface using site-specific mutagenesis with Lys, which changed the pI of the protein to 10.2. At pH 7, the protein without Lys had a charge of -7 while the protein with 5 Lys had a charge of +3. The crystal structures were similar so Coulombic interactions would determine the differences in the pKa of the 11 common side chains. On average the mutant pKas were higher by 0.75 pH units, which makes sense as the mutant had a high pI. Calculated pKa values were similar to those determined by NMR. These data are consistent with the idea that Coulombic interactions are the chief cause of pKa changes in surface side chains. 3. Charge-dipole interactions and H bonds: It should be obvious that charge states of ionizable side chains would be adjusted to optimize H bond (and more generally charge-dipole) interactions in proteins. If the interactions are optimal in the charged state, pKa values for His and Lys would be increased and for Asp, Glu, Cys, and Tyr they would be decreased. Pace cites the buried Asp 76 in RNase T1 in which the Asp is charged but does not form an internal salt bridge. It has a depressed pKa of 0.6 and has 3 H bonds to the side chains of Asn 9, Tyr 11 and Thr 91. Mutants were made to remove the H bonds to see the effect on the pKa of Asp 76. Removing 1, 2, or 3 H bonds changed the pKa to 3.3, 5.1, and 6.4 respectively. The 6.4 value is much higher than the pKint, which can be attributed to the Born effect. Quaternary Structure Primary structure is the linear sequence of the protein. Secondary structure is the repetitive structure formed from H-bonds among backbone amide H and carbonyl O atoms. Tertiary structure is the overall 3D structure of the protein. Quaternary structure is the overall structure that arises when separate protein chains aggregate with self to form homodimers, homotrimers, or homopolymers OR aggregate with different proteins to form heteropolymers. Most protein subunits in a larger protein displaying quaternary structure are held together by noncovalent interactions (intermolecular forces), although in some, they are also held together by disulfide bonds (an example includes immunoglobulins). Figure $3$ shows an interactive iCn3D model of a homodimer, the variable domain of the T cell receptor delta chain (1tvd). Carefully rotate the model to see the two identical chains held together by noncovalent interactions Figure $3$: variable domain of the T cell receptor delta chain (1tvd) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...yN6B43P7tvHcR7 Figure $4$ shows an interactive iCn3D model of a heterodimer, reverse transcriptase (1rev). Carefully rotate the model to see the two identical chains held together by noncovalent interactions Figure $4$: variable domain of the T cell receptor delta chain (1tvd) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...2mmkgdR9Z2dTQ6 Macromolecule Oligomer Formation and Symmetry Many proteins are found in aggregated states and hence have quaternary structure. Hemoglobin consists of two alpha and two beta monomers (or protomers) which assemble to produce the biologically relevant heterotetrameric protein. A given monomer can self-aggregate to form homooligomers such as dimers (M2), trimers (M3), tetramers(M4), or or higher oligomers (Mn). The oliogomers often display symmetry with respect to the geometric arrangement of the subunits. Symmetry is an important component of the many kinetic models for catalysis. Most oligomeric proteins contain protomers that are symmetrically arranged. What mechanism determines whether a monomeric protein forms a homooligomer? Why do they stop at a certain n value? Can proteins be engineered to do so? If mutation can induce oligomer formation, then fewer mutations would be required to produce a symmetric oligomer from subunits since fewer mutations would be required. Why? A single mutation in a single monomer would be represented n times in a single oligomer of n monomers. This fact probably underlies the reason that oligomers display exquisite symmetry. Hence a basic knowledge of the symmetry of protein oligomers is necessary. In the study of small molecules, chemists describe symmetry through the use of mathematical symmetry operations and elements, which find great use in analysis of structure and in molecular spectroscopy. These concepts are usually first encountered in physical and inorganic chemistry classes. They are a bit complicated so we will offer a limited introduction. A symmetry operation is a movement of an object like a molecule that leads to an identical, superimposable molecule. Each operation has a symmetry element (point, line, or plane) about which the motion occurs. Some examples are shown in Table $2$ below: Element (with Jmol link) Operation inversion center (i) projection through center (point) of symmetry of point x,y,z to point -x,-y,-z proper rotation axis (Cn) rotation around a Cn axis by 360o/n where C denotes Cyclic horizontal (σh) and vertical (σv) symmetry plane (reflection) reflection across a horizontal (h) or vertical (v)plane improper rotation axis (Sn) rotation around a Sn axis by 360o/n followed by reflection in plane perpendicular to the axis. Table $2$: Symmetry Elements and Operations Luckily for students trying to apply these rules to protein oligomers, biomolecules made up of chiral monomers containing L-amino acids, can not be converted to identical structures using inversion or reflection since the chirality of the monomer would change. For proteins, this would entail an L to D amino acid change. That excludes all but proper rotation axes (Cn) from the list above. A point group is a collection of symmetry operations that define the symmetry about a point. The 4 types of symmetries around a point are those described above: rotational symmetry, inversion symmetry, mirror symmetry, and improper rotation. We'll just consider two versions of rotational symmetry. Cyclic (Cn) - Single Cn rotation axis. These are very common in proteins that form dimers, trimers, tetramers, etc of identical monomers. These are called homo n-mers. In this point group note that the n in Cn is equal to the number of monomers and the angle of rotation is 360o/n. Figure $5$, adapted from Voet and Voet, shows a cartoon model for C2 symmetry. Here are some protein examples with Cn symmetry with the symmetry axis shown as a red vertical line. Symmetry/ homo n-mer Protein ( click link for new window) Symmetry click for popup model C2: homo 2-mer alcohol dehydrogenase (1HSO) C3: homo 3-mer porin (2POR) C4: homo 4-mer neuramindase (2HTY) Symmetric complexes can also have empty interior volumes designed for specific function. Take for example, the human mitochondrial Hsp60-Hsp10 chaperonin complex (6MRD), which assists in protein folding in the mitochondria. It is a hetero 14-mer (A7B7) and displays C7 symmetry, as shown in Figure $6$, an interactive iCn3D model below. The cyan surface is from 7 A monomers while the red is from 7 B monomers Figure $6$: Hsp60-Hsp10 chaperonin complex (6MRD) with C7 symmetry (Copyright; author via source). Click the image for a popup (long load) or use this external link:(long load) https://structure.ncbi.nlm.nih.gov/i...jEEY5tu19nXcp6 Dihedral (Dn) - Mutually perpendicular rotation axes. These display higher symmetry as they contain (a) C2 axis(es) perpendicular to a single Cn axis. The minimal number of subunits is n. Most protein oligomers fall into this category. The packing (or asymmetric) unit does not have to be a single monomer but could be a heterodimer. Dn symmetries are more difficult to see but structures in the PDB conveniently provide the type of global symmetry and stoichiometry when symmetry is present. A D2 point group has 1 C2 axis and 2 perpendicular C2 axes, and 4 monomers (like Hb). These proteins can dissociate into two dimers (such as two α/β dimers for Hb). Note that a different arrangement of 4 monomers could produce an oligomer with C4 symmetry instead of D2. Also note the hemoglobin, a tetramer (α2β2) displays pseudo D2 symmetry since the α and β subunits are slightly different in sequence but their folds are almost identical. It also displays C2 symmetry. Figure $7$, adapted from Voet and Voet, shows a cartoon model for D2 symmetry. A D4 point group has 1 C4 axis and 4 C2 axes, along with 2n=8 subunits. An example of a D4 point group is ribulose bisphosphate carboxylase/oxygenase (RuBisCO) which has 8 subunits (where a subunit, or more technically the asymmetric subunit, is a dimer of a small and large molecular weight protein). This point group could arise from quaternary structure of two C4 tetramers or four C2 dimers. Here are some homo n-mer protein examples with Dn symmetry. Symmetry/ homo n-mer Protein ( click link for new window) Symmetry click for popup model D2: homo 4-mer phosphofructokinase (4XYJ) D3: hetero 12-mer A6B6 asparatate carbamoyltransferase 1Q95 D5: homo 10-mer glutamine synthetase (2OJW) Cubic Groups Now let's consider some cases in which a large number of rotation axes exist that fit the symmetry of simple geometric shapes called the Platonic solids, in which all faces, edges and angles are congruent. The five Platonic shapes, described by Plato, are shown inscribed in spheres in Figure $8$: These can also be inscribed in cubes. A tetrahedron and octahedron (actually the overlap of 2 tetrahedron) are shown inscribed in a cube in Figure $9$. Since all of the Platonic solids can be inscribed in a cube, they all have basic cubic symmetry. Cubes have a total of 13 symmetry axes: three C4 axes passing through the centers of opposite faces, four C3 axes passing through opposite vertices (diagonals), and six C2 axes passing through the centers of opposite edges. The other Platonic solids have related C3 axes (diagonals connecting opposite corners for cubes, diagonals from a vertex to the opposite face for tetrahedrons, lines connecting two opposite faces for octahedron, etc ) but they all can be considered to be part of the cubic point group. The rotation axes for each of the Platonic solids are shown in Table $3$below. # and type rotation axes # monomers/asymmetric units Shape 3 C2, 4 C3 12 tetrahedron 6 C2, 4 C3, 3 C4 24 cube/octahedron 15 C2, 10 C3, 6 C5 60 dodecahedron/icosahedron Table $3$: Cn axes and number of monomers in Platonic solids symmetries We'll consider examples of tetrahedral, octahedral and icosahedral symmetries, which depend on the overall shape and number of monomers in the functional structure. In some cases, the symmetry of the packed monomers is not perfect. This applies to monomers in clathrin since the monomers can form different structures) and hemoglobin (in which the four subunits are very similar (alpha and beta) but not identical, as described above. When many monomers form oligomers, it seems that homomers are favored evolutionarily over heteromers. One explanation for this is that in symmetrical arrangements of monomers, there are fewer unique subunit-subunit interfaces that have evolved for complementary of fitness of shape and noncovalent interactions than for heteromers. The same argument applies to the formation of symmetric vs asymmetric arrangements of homo n-mers. A final note: The words tetrahedron, cube, octahedron, dodecahedron and isocahedron refer to the shape of structures. The words tetrahedral, cubic, octahedral, dodecahedral and isocahedral should be reserved for the type of symmetry. This can be a great source of confusion. Take the case of the E2p, dihydrolipoyl acyltransferase. The homo 24-mer version (from Azotobacter vinelandii, 1DPB) has the shape of a cube with octahedral symmetry, while the homo 60-mer (from Bacillus stearothermophilus, 1B5S) has the shape of a dodecahedron with the icosahedral symmetry. It is sometimes difficult to determine the actual biological structure and its symmetry from crystal structure given the artificial packing of the protein in the crystal state. Also, other than for icosohedral virus assemblies, there aren't that many examples of proteins that show tetrahedral, octahedral and dodecahedral symmetries. No structure in the Protein Data Bank is listed with a global symmetry of cubic. We'll describe a few structures with tetrahedral, octahedral and icosohedral global symmetries, knowing that they all fall in the cubic point group. Tetrahedral - 4 sided A tetrahedron has four C3 axes - diagonals from a four corners/vertices to the opposite faces as well as three C2 axes, which are the same as for the cube (since a tetrahedron can be inscribed inside a cube). Protein (pdb) Symmetry/Homo X-mer (click link) Structure - subunits Symmetry L-aspartate beta-decarboxylase (2zy2) tetrahedral/homo 12-mer ornithine carbamoyltransferase (1A1S) tetrahedral, homo 12-mer Octahedral - 8 sided C3 axes - line connecting two opposite faces for an octahedron. Since an octahedron can be aligned with a cube, it also has the same symmetry axes. Examples include human ferritin, octahedral, 24 asymmetric subunits. octahedral, homo 24-mer (4V6B) and dihydrolipoyl acyltransferase from Azotobacter vinelandii, octahedral, homo 24-mer (1DPB) Protein (pdb) Symmetry/Homo X-mer (click link) Structure - subunits Symmetry human ferritin (4V6B) octahedral/homo 24-mer dihydrolipoyl acyltransferase from Azotobacter vinelandii (1DPB) octahedral/homo 24-mer Icosohedral - 20 sided, 60 mers Examples include adenovirus Ad3 virus, Icosahedral , homo 60-mer 4AQQ and dihydrolipoyl transacetylase from Bacillus stearothermophilus, iscosohedral,homo 60-mer (1B5S) Protein (pdb) Symmetry/Homo X-mer (click link) Structure - subunits Symmetry Adenovirus Ad3 virus (4AQQ) Icosahedral/homo 60-mer dihydrolipoyl transacetylase from Bacillus stearothermophilus (1B5S) iscosohedral/homo 60-mer In summary, our study of symmetry is not in vain since almost half of all proteins appear to form complexes of identical or similar monomers. This probably stabilizes them and adds functional attributes such as kinetic regulation of their activities. Homo n-mers display symmetry, with Cn and Dn being the most common. The cubic symmetries are far less common. What is interesting about cubic symmetries is that structures displaying cubic symmetries have significant empty interior volumes, or "holes" which can be used to encapsulate chemical species. They include the icosahedral viruses, whose interiors contain proteins and viral genomes, and cellular ferritin, which houses up to 4500 Fe3+ (oxidized) ions in the form of hydroxide and phosphate complexes. Ferritin delivers Fe2+ into cells. Storing iron ions in ferritin prevents its spurious and harmful oxidation and precipitation in nonregulated environments. The encapsulated available volumes have diameters ranging from about 2-4 nm for tetrahedral, 4-8 nm for octahedral and 8-18 nm for icosahedral symmetries. Filaments Proteins, especially those involved in cytoskeletal filaments, can form fibers with helical symmetry which differs from those described above since the monomers at the ends of helical fibers, although they have the same tertiary structures as those in the middle of the helical fibers, do not contact the same number of monomers as monomers internal in the oligomer. Hence they have different microenvironments. Grueninger et al. address the question of whether the process of oligomerization can be programmed into the genome. Can simple amino acid substitutions lead to oligomerization? Oligomerization can be beneficial (formation of cytoskeleton filaments) or detrimental (formation of fibers in sickle cell anemia and prion disease). Oligomers with long half-lives (for example cytoskeletal filaments such as actin and tubulin) and short half-lives (for example proteins that are associated with transient biological activities) are regulated by oligomer formation. It has long been noted that if a protein chain forms oligomers, then a single amino acid change in the chain would be found n times in an oligomer of n chains. Mutations could either promote chain contact and oligomer formation or dissociation into monomeric or other asymmetric subunit composition if the mutation were in a region involved in subunit association (a contact region). Experimental work in this field of study is hampered by the fact that mutants made by site-specific mutagenesis to prefer the monomeric state often fail to fold (due to hydrophobic exposure and aggregation). Studies have shown that most contact areas between monomers or other asymmetric units are hydrophobic in nature and the contact regions must be complementary in shape. Obviously, mutations that replace hydrophobic side chains involved in subunit contact with polar, polar charged, or bulkier hydrophobic side chains would inhibit oligomer formation. Grueninger et al were able to successfully engineer dimer formation and oligomer formation as well. First, consider the simplest case of a mutation in a monomer that can produce a dimer with C2 symmetry. This is illustrated below in Figure $10$. The figure illustrates how a mutation that produces a weak interaction in a monomer could also produce a long helical aggregate (which can't be crystallized) without symmetry (as described above). A mutation at 2 could promote either oligomer helix formation or dimerization. It should be noted that mutation could lead to dimer or oligomer formation by producing a more global conformational change in the monomer (not indicated in the example above) which leads to aggregate formation, as we have seen previously in the formation of dimers and aggregates of proteins associated with neurodegenerative diseases (like mad cow disease). Grueninger produces mutants of two different proteins that showed dimer formation as analyzed by gel filtration chromatography (but did not crystallize so no 3D structures were determined). In addition, the group modified urocanase, a C2 dimer, at 3 side chains to form a tetramer with D2 symmetry. Also, they modified L-rhamnulose-1-phosphate aldolase, a C4 tetramer, at a single position to form an octamer with D4 symmetry. The latter two were analyzed through x-ray crystallography. Their work suggests ways that complex symmetric protein structures arose in nature from simple mutation and evolutionary selection.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/04%3A_The_Three-Dimensional_Structure_of_Proteins/4.03%3A_Tertiary_and_Quaternary_Structures.txt

Search Fundamentals of Biochemistry Common Structural Motifs Given the number of possible combinations of 1o, 2o, and 3o structures, one might guess that the 3D structure of each protein is quite distinctive. This is in general true. However, it has been found that similar substructures are found in proteins. For instance, common secondary structures are often grouped together to form common structural motifs, often called super-secondary structures. Often the same motif is found in proteins with similar functions (such as proteins that bind DNA, Ca2+, etc). Let's explore some of the common motifs. Alpha-loop-Alpha These are found in DNA-binding proteins that regulate transcription and also in calcium-binding proteins, in which the motif is often called the EF hand. The loop region in calcium-binding proteins is enriched in Asp, Glu, Ser, and Thr. Why? The EF hand shown below is from calmodulin. Figure \(1\) shows an interactive iCn3D model of a basic helix-turn-helix from the c-Myc protein (1NKP). The iCn3D model shows the helices interacting with the major grove of DNA, which is shown in spacefill. Figure \(1\): Basic helix-turn-helix from the c-Myc protein (1NKP). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...kDv9DGzWWWoMZ8 Figure \(2\) shows an interactive iCn3D model of the "EF hand" from the calcium-binding protein calmodulin (1cll) Figure \(2\): EF hand from Calmodulin (1cll): Secondary Structure Motif. (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...J9jefZYfcpkRu8 The EF Hand can be envisioned as a hand gripping a ball (calcium ion) with the index finger and thumb representing alpha helices, as shown in Figure \(3\). The EF hand motif of calmodulin is used in a variety of Ca2+ binding proteins. Figure \(4\) shows the alignment of the first 50 residues of human calmodulin with four other human calcium-binding proteins. The EF hand (F12-L29) of calmodulin consists of the second half of the first helix (F12-L18), an intervening loop (F19-T28), and the second helix (T29-L29). Sometimes, it is annotated to encompass a larger stretch (8-43) Part A shows the degree of conservation of amino acids in this first Ca2+-binding EF hand. Part B shows the general conservation of key hydrophobic (F12, F19, I27) as well additionally, those of similar polarity (36 and 39) Figure \(5\) shows an interactive iCn3D model of the first EF hand in human calmodulin with key amino acids labeled. Figure \(5\): First EF hand of Calmodulin (1cll) (Copyright; author via source). Click the image for a popup or use this external link:https://structure.ncbi.nlm.nih.gov/i...c6YcMn9dj77Wr9 Hover over the amino acid side chains that are coordinating the Ca2+ ion. Are they what you would expect? A linear connectivity "wiring" diagram showing secondary structure connected by connecting regions is shown in Figure \(6\). This particular wiring diagram shows a 2-residue beta strand, which is insignificant in length to be considered an actual strand. A more complicated 2D topology map is shown in Figure \(7\). In this case, it is linear given the small section of amino acids depicted. We will see more complicated 2D topology maps with more complicated structures below. It is presented on its side to save space on this page. Beta-hairpin or beta-turn This motif is present in most antiparallel beta structures, both as an isolated ribbon and as part of beta sheets. Figure \(8\) shows an interactive iCn3D model of the beta hairpin from bovine pancreatic trypsin inhibitor (1k6u) Beta hairpin from bovine pancreatic trypsin inhibitor (1k6u) Figure \(8\): Beta hairpin from bovine pancreatic trypsin inhibitor (1k6u) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...eMFdHkGogJHCCA Figure \(9\) shows the 2D homology map for the beta-hairpin. Greek Key The "Greek Key" symbol represents infinity and the eternal flow of things and resembles in part primitive keys. The Greek Key motif in proteins can be seen in the structure of antiparallel beta sheets in the ordering of four adjacent antiparallel beta strands as shown in Figure \(9\). The figure also shows the repetitive Greek key, which you will see many times if you visit Greece and tour its antiquities. Figure \(10\)s shows a partial 2D topology map of Staphylococcus nuclease (2SNS). Figure \(11\) shows an interactive iCn3D model of the Greek Key motif from Staphylococcus nuclease (2SNS). The involved beta strands are shown in yellow. Figure \(11\): Greek Key motif from Staphylococcus nuclease (2SNS) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...x2ef4xpttXrFb9 Beta-Alpha-Beta The motif is a common way to connect two parallel beta strands as compared to beta hairpins, which are used to connect antiparallel beta strands. Figure \(12\) shows an interactive iCn3D model of the beta-alpha-beta structure from triose phosphate isomerase (1amk). Figure \(13\) shows the 1D wiring diagram for the first beta-alpha-beta motif in triose phosphate isomerase. Figure \(14\) shows the 2D topology diagrams showing this motif. Larger Structural Motifs - Protein Architecture Some proteins combine larger secondary and supersecondary structural components, often in a repeated fashion to produce more complex structures. We've seen this with larger twisted sheets and beta barrels, such as the TIM barrel. Let's consider three of these, which can be considered examples of protein architectures without considering connectivity within the protein. The Rossman Fold Structural motifs can serve particular functions within proteins such as enabling the binding of substrates or cofactors. For example, the Rossmann fold is responsible for binding to nucleotide cofactors such as nicotinamide adenine dinucleotide (NAD+) as shown in Figure \(15\). The Rossmann fold is composed of six parallel beta strands that form an extended beta sheet. The first three strands are connected by α-helices resulting in a beta-alpha-beta-alpha-beta structure. This pattern is duplicated once to produce an inverted tandem repeat which contains six strands. Overall, the strands are arranged in the order of 321456 (1 = N-terminal, 6 = C-terminal). Five stranded Rossmann-like folds are arranged in the sequential order 32145. The overall tertiary structure of the fold resembles a three-layered sandwich wherein the filling is composed of an extended beta sheet and the two slices of bread are formed by the connecting parallel alpha helices. Image modified from: Boghog One of the features of the Rossmann fold is its co-factor binding specificity. The most conserved segment of Rossmann folds is the first beta-alpha-beta segment. Since this segment is in contact with the ADP portion of dinucleotides such as FAD, NAD and NADP it is also called as an "ADP-binding beta-beta fold". Figure \(16\) shows an interactive iCn3D model of the Rossman fold of malate dehydrogenase (5KKA) from E. Coli. The beta strands (yellow) and connecting alpha helices (red), and coil (blue) of the Rossman fold are shown in the context of the rest of the monomeric version of the protein, which is shown in gray. The TIM barrel revisited Interestingly, similar structural motifs do not always have a common evolutionary ancestor and can arise by convergent evolution. This is the case with the TIM Barrel, a conserved protein fold consisting of eight α-helices and eight parallel β-strands that alternate along the peptide backbone. It is illustrated in Figure \(17\). The structure is named after triosephosphate isomerase, a conserved metabolic enzyme. TIM barrels are one of the most common protein folds. One of the most intriguing features among members of this class of proteins is although they all exhibit the same tertiary fold there is very little sequence similarity between them. At least 15 distinct enzyme families use this framework to generate the appropriate active site geometry, always at the C-terminal end of the eight parallel beta-strands of the barrel. Image modified from: WillowW Although the ribbon diagram of the TIM Barrel shows a hole in the protein's central core, the amino acid side chains are not shown in this representation (Figure 2.26). The protein's core is actually tightly packed, mostly with bulky hydrophobic amino acid residues although a few glycines are needed to allow wiggle room for the highly constrained center of the 8 approximate repeats to fit together. The packing interactions between the strands and helices are also dominated by hydrophobicity and the branched aliphatic residues valine, leucine, and isoleucine comprise about 40% of the total residues in the β-strands. The figure \(18\) below shows an interactive iCn3D model of the TIM barrel (1WYI) from Chapter 4.2). As our knowledge continues to increase about the myriad of structural motifs found in nature's treasure trove of protein structures, we continue to gain insight into how protein structure is related to function and are better enabled to characterize newly acquired protein sequences using in silico technologies. Beta Helices These right-handed parallel helical structures consist of a contiguous polypeptide chain with three parallel beta strands separated by three turns forming a single rung of a larger helical structure which in total might contain as many as nine rungs. The intrastrand H-bonds are between parallel beta strands in separate rungs. These seem to prevalent in pathogens (bacteria, viruses, toxins) proteins that facilitate the binding of the pathogen to a host cell. Figure \(19\) shows an interactive iCn3D model of the C-terminal fragment of the phage T4 GP5 beta helix (4osd). Beta helices and found in the following organisms (with the diseases they cause in humans): Vibrio cholerae (cholera), Helicobacter pylori (ulcers), Plasmodium falciparum (malaria), Chlamyidia trachomatis (VD), Chlamydophilia pneumoniae (respiratory infection), Trypanosoma brucei (sleeping sickness), Borrelia burgdorferi (Lyme disease), Bordetella parapertussis (whooping cough), Bacillus anthracis (anthrax), Neisseria meningitides (menigitis) and Legionaella pneumophilia (Legionaire's disease). Beta Propellors Protein with this structure has 4-8 blade-shaped beta sheets arranged around a central axis, forming an active site shaped like a funnel. Figure \(20\) shows an interactive iCn3D model of the C-terminal domain of Tup1 (1ERJ), a yeast transcription factor, which has a seven-bladed beta propeller. Each blade contains a WD40 repeat sequence (around 40 amino acids) that often ends in tryptophan-aspartic acid (W-D). The particular protein has four WD dipeptides sequences, shown in sticks colored with CPK colors. The funnel provides binding sites for proteins and other molecules, with the ones with more blades usually acting as enzymes. Domains Domains are the fundamental unit of 3o structure. Domains can be considered a chain or part of a chain that can independently fold into a stable tertiary structure. Domains are units of structure but can also be units of function. Some proteins can be cleaved at a single peptide bond to form two separate domains. Often, these can fold independently of each other, and sometimes each unit retains an activity that was present in the uncleaved protein. Sometimes binding sites on the proteins are found in the interface between the structural domains. Many proteins seem to share functional and structural domains, suggesting that the DNA of each shared domain might have arisen from the duplication of a primordial gene with a particular structure and function. Evolution has led toward increasing complexity which has required proteins of new structure and function. Increased and different functionalities in proteins have been obtained with addition of domains to base proteins. Chothia (2003) has defined domain in an evolutionary and genetic sense as "an evolutionary unit whose coding sequence can be duplicated and/or undergo recombination". Proteins range from small with a single domain (typically from 100-250 amino acids) to large with many domains. From recent analyzes of genomes, new protein functionalities appear to arise from the addition or exchange of other domains which, according to Chothia, result from • duplication of sequences that code for one or more domains • divergence of duplicated sequences by mutations, deletions, and insertions that produce modified structures that may have useful new properties to be selected • recombination of genes that result in novel arrangement of domains. Structural analyzes show that about half of all protein-coding sequences in genomes are homologous to other known protein structures. There appear to be about 750 different families of domains (i.e. small proteins derived from a common ancestor) in vertebrates, each with about 50 homologous structures. About 430 of these domain families are found in all the genomes that have been solved. Proteins with multiple domains also are more likely not to misfold if each domain can fold somewhat autonomously. In addition, they provide a myriad of binding sites which increase the number of biological functions expressed in a single protein. Multidomain proteins can also express multiple catalytic activities, allowing for a reaction product from one domain to diffuse to another catalytic domain (or interface between domains). This would reduce the dimensionality of the search for a substrate from 3D to more of a 1D or 2D search, enormously speeding up the net reaction. The process is often called substrate channeling. Figure \(21\) shows an interactive iCn3D model of the three domains of the enzyme pyruvate kinase (1pkn). These include a nucleotide (ADP/ATP) binding domain (blue) made of beta strands, a substrate binding domain (green) in the middle composed of alpha/beta structure, and a regulatory domain (red) composed of alpha/beta structure. These domains were analyzed by a web program called CATH-Gene3D. The CATH programs offer a complete classification of protein structure based on the following hierarchy of organization: Class, Architecture, Topology, and Homologous Superfamilies - CATH. • Class: the highest level of organization which consists of four classes - mainly alpha, mainly beta, alpha-beta, and few secondary structures • Architecture (40 types): describes the shape of domain based on secondary structures but doesn't describe how they are connected. Ex: beta barrel, beta propeller • Topology (or fold group, 1233 types): members in topology groups have a common fold or topology in the "core" of the domain structure. • Homologous Superfamilies (2386 types): These groups are homologous in sequence or structure and derive from a common precursor gene/protein. An alternative computer program, Pfam, shows this enzyme as having 2 major domains, a pyruvate kinase beta barrel domain and a pyruvate kinase alpha/beta domain. Pfam domains are determined by sequence analysis while CATH are determined by structural comparisons. Domains determined by both programs show about a 75% overlap. At a simpler level, domains are built from the kinds of motif structures we discussed above. Since proteins are very packed structures, the organizational structure of proteins can be thought of as closely packed motifs, but not all possible combinations are found. For example, if you have one beta hairpin next to another to form a 2-unit Greek key, there are 24 likely ways to connect them but only eight are common. The two below appear to account for more than the sum of the other 22. These are shown in Figure \(22\). Figure \(23\) shows an example of the architecture of the multi-domain protein, human Attractin-like protein 1. This protein is an example of a lectin, a carbohydrate-binding protein, which we will explore in a subsequent chapter. It binds Ca2+, so it is considered a C-Lectin. Three different programs were used to analyze the domain structure. Figure \(23\): Architecture of the multi-domain protein, human Attractin-like protein 1

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/04%3A_The_Three-Dimensional_Structure_of_Proteins/4.04%3A_Secondary_Structural_Motifs_and_Domains.txt

Search Fundamentals of Biochemistry Proteins can also be classified as to the type and extent of secondary structure found in the protein. A detailed description of protein classes can be found at CATH. Here we will describe the basiv types with a few examples offered for each. Alpha proteins In these proteins, the core of the protein is composed of alpha helices. The CATH classification shows two major types a. Orthogonal bundles. Example: the Z[beta] Domain of the RNA-editing Enzyme ADAR1 (1xmk), shown in the interactive iCn3D model in Figure \(1\). Figure \(1\): Alpha protein, Orthogonal bundles: Z[beta] Domain of the RNA-editing Enzyme ADAR1 (1xmk) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...fxHFM9ErUptjY7 b. Updown bundles. Example: Phospholipase A2 from Agkistrodon acutus venom (1mc2), shown in the interactive iCn3D model in Figure \(2\). Figure \(2\): Alpha protein, Updown bundles: Phospholipase A2 from Agkistrodon acutus venom (1mc2) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...NGSeK9JAMe3Qh9 Beta proteins In these proteins, the core is typically antiparallel beta sheets. There are many types of these, including single sheets, rolls, beta barrels, clams, sandwich, propellers, etc (some of which we have already discussed). Here are a few interesting examples. a. Roll. Example: The second SH3 domain from ponsin (2O9S), shown in the interactive iCn3D model in Figure \(3\). Figure \(3\): Beta protein Roll: Second SH3 domain from ponsin (2O9S), (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...xYZBXHWpt3njf7 b. Sandwich. Example: Mcg immunoglobulin light chain variable domain (4unu), shown in the interactive iCn3D model in Figure \(4\). Figure \(4\): Beta protein Sandwich - Mcg immunoglobulin light chain variable domain (4unu) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...4vcY5svrBveJp7 Alpha/Beta proteins These are the most common class and contain both many beta-alpha-beta motifs with mostly parallel beta strands surrounded by alpha helices. Again, there are many variants for these proteins, including the alpha-beta barrel. We will show two other common ones. a. Two layer sandwich. Example: HIV-1 Nef-SF2 Core Domain (4U5W), shown in the interactive iCn3D model in Figure \(5\). Figure \(5\): Alpha-Beta Two layer sandwich - HIV-1 Nef-SF2 Core Domain (4U5W) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...6Fx6J3ecPJbF3A b. Three layer sandwich (aba). Example: Human biliverdin IX beta reductase (1hdo), shown in the interactive iCn3D model in Figure \(6\). Figure \(6\): Alpha-Beta Three layer sandwich (aba) - Human biliverdin IX beta reductase (1hdo) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...s8GrdasiecmZC8 Little Secondary Structures These proteins most likely can adopt different conformationdon binding to target molecules. Here are two examples a. HIV-1 TAT (Transactivating) Protein (1JFW), shown in the interactive iCn3D model in Figure \(7\). Figure \(7\): Few Secondary Structures - HIV-1 TAT (Transactivating) Protein (1JFW) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...zVYofj3HFasWBA b. Rat rat metallothionein-2 (4MT2), shown in the interactive iCn3D model in Figure \(8\). . Figure \(8\): Few Secondary Structures - Rat rat metallothionein-2 (4MT2) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...fYY4xF2RWd7pD7

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/04%3A_The_Three-Dimensional_Structure_of_Proteins/4.05%3A_Protein_with_Alpha_Alpha-Beta_Beta_and_Little_Secondary_Struct.txt

Search Fundamentals of Biochemistry Intrinsically Disordered Proteins (IDPs) and Metamorphic Proteins Many examples of proteins that are partially or completely disordered but still retain biological function have been found. At first glance this might appear to be unexpected, since how could such a protein bind its natural ligand with specificity and selectivity to express its function? Of course, one could postulate ligand binding would induce conformational changes necessary for function (such as catalysis) in an extreme example of an induced fit of a ligand compared to a "lock-and-key" fit. Decades ago, Linus Pauling predicted that antibodies, proteins that recognize foreign molecules (antigens), would bind loosely to the antigen, followed by a conformational change to form a more complementary and tighter fit. This was the easiest way to allow for a finite number of possible protein antibodies to bind a seemingly endless number of possible foreign molecules. This is indeed one method in which antibodies can recognize foreign antigens. Antibodies that bind to antigens with high affinity and hence high specificity are more likely to bind through a lock and key fit. (Pauling, however, didn't know that the genes that encode the proteins chains in antibodies are differentially spliced and subjected to enhanced mutational rates which allow the generation of incredible antibody diversity from a limited set of genes.) Intrinsically Disordered Proteins (IDPs) It's been estimated that over half of all native proteins have regions (greater than 30 amino acids) that are disordered, and upwards of 20% of proteins are completely disordered. Regions of disorder are enriched in polar and charged side chains which follow since these might be expected to assume many available conformations in aqueous solutions compared to sequences enriched in hydrophobic side chains, which would probably collapse into a compact core stabilized by the hydrophobic effect. Mutations in the disordered regions tend to preserve the disordered region, suggesting that the disordered region is advantageous for "future" function. In addition, mutations that cause a noncoding sequence to produce a coding one invariably produce disordered protein sequences. Disordered proteins tend to have regulatory properties and bind multiple ligands, in comparison to ordered ones, which are involved in highly specific ligand binding necessary for catalysis and transport. The intracellular concentration of disordered proteins has also been shown to be lower than ordered proteins, possibly to prevent occurrences of inappropriate binding interactions mediated through hydrophobic interactions, for example. Processes to accomplish this include more rapid mRNA and protein degradation and slower translation of mRNA for disordered proteins. For a similar reason, misfolded proteins are targeted for degradation as well. Figure \(1\) shows characteristics of intrinsically disordered proteins. Panel A shows the mean net charge vs the mean hydrophobicity for 275 folded and 91 natively unfolded proteins. Panel B shows the relative amino acid composition of globular (ordered) proteins compared to regions of disorder greater than 10 amino acids in disordered proteins. The two different grey bars were obtained with two different versions of the software used to analyze the proteins. Again the graph shows the enrichment of hydrophilic amino acids in disordered proteins. Many experimental methods can be used to detect disordered regions in proteins. Such regions are not resolved well in X-Ray crystal structures (have high B factors). NMR solution structures would show multiple, and differing conformations. CD spectroscopy likewise would show ill defined secondary structure. In addition solution measurements of size (light scattering, centrifugation) would show larger size distributions for a given protein. What types of proteins contain disorder? The above experimental and new computational methods have been developed to classify proteins as to their degree of disorder. There appear to be more IDPs in eukaryotes than in archea and prokaryotes. Many IDPS are involved in cell signaling processes (when external molecules signal cells to respond by proliferating, differentiating, dying, etc). Most appear to reside in the nucleus. The largest percentage of known IDPs bind to other proteins and also to DNA. These results suggest that IEPs are essential to protein function and probably confer significant advantages to eukaryotic cells as multiple functions can be elicited from the interaction of a single IEP (derived from a single gene) with different protein binding partners. This would greatly extend the effective genome size in humans, for example, from around 20,000 protein-encoding genes with specified functions, to many more. This doesn't even take into account the increase in functionalities derived from post-translational chemical modifications. Protein structure is fluid and complex and our simple notions and words to denote proteins as either native or denatured are misguided and constrain our ideas about how protein structure elicits biological function. For example, what does the word "native" mean, if proteins exist in multiple states in vivo and in vitro simultaneously? Dunker et al (2001) have coined the concept "Protein Trinity" to move past the notion that a single protein folds to a single state which elicits a single function. Rather each of the states in the "trinity", the ordered, collapsed (or molten globule) and extended (random coil) coexist in the cell, as shown in Figure \(2\).: Characteristics of Intrinsically Disordered Proteins. Hence all can be considered "native" and all contribute to the function of the cell. A single IDP could bind to many different protein partners, each producing different final structures and functions. IDPs would also be more accessible and hence susceptible to proteolysis, which would lead to a simple mechanism to control their concentrations, an important way to regulate their biological activity. Their propensity to post-translational chemical modification would likewise lead to new types of biological regulation. These ideas have profound ramifications for our understanding of the expression of cellular phenotype. In addition, a whole new world of drug targets is available by finding drugs that modulate the transitions between ordered, collapsed and extended protein states. Likewise, side effects of drugs might be understood by investigating their effects on these transitions in IDPs that were not initially targeted for anal. Several web database, including PONDR - Predictor of Naturally Occurring Disorder and Database of Protein Disorder are available. IDPs cover a spectrum of states from fully unstructured to partially structured and include random coils, (pre-)molten globules, and large multi-domain proteins connected by flexible linkers. They constitute one of the main types of protein (alongside globular, fibrous and membrane proteins). Figure \(3\) shows the conformational flexibility in SUMO-1 protein (PDB:1a5r), which is a composite of 10 NMR structures. The central part shows relatively ordered structure. Conversely, the N- and C-terminal regions (left and right, respectively) show ‘intrinsic disorder’. Figure \(3\): Conformational flexibility in SUMO-1 protein (1a5r) showing intrinsically disordered regions History of IDPs It's interesting to explore the history of our understanding of IDPs. In the 1930s -1950s, the first protein structures were solved by protein crystallography. These early structures suggested that a fixed three-dimensional structure might be generally required to mediate biological functions of proteins. When stating that proteins have just one uniquely defined configuration, Mirsky and Pauling did not recognize that Fisher's work would have supported their thesis with his 'Lock and Key' model (1894). These publications solidified the central dogma of molecular biology in that the sequence determines the structure which, in turn, determines the function of proteins. In 1950, Karush wrote about 'Configurational Adaptability' contradicting all the assumptions and research in the 19th century. He was convinced that proteins have more than one configuration at the same energy level and can choose one when binding to other substrates. In the 1960s, Levinthal's paradox suggested that the systematic conformational search of a long polypeptide is unlikely to yield a single folded protein structure on biologically relevant timescales (i.e. seconds to minutes). Curiously, for many (small) proteins or protein domains, relatively rapid and efficient refolding can be observed in vitro. As stated in Anfinsen's Dogma from 1973, the fixed 3D structure of these proteins is uniquely encoded in its primary structure (the amino acid sequence), is kinetically accessible and stable under a range of (near) physiological conditions, and can therefore be considered as the native state of such "ordered" proteins. During the subsequent decades, however, many large protein regions could not be assigned in x-ray datasets, indicating that they occupy multiple positions, which average out in electron density maps. The lack of fixed, unique positions relative to the crystal lattice suggested that these regions were "disordered". Nuclear magnetic resonance spectroscopy of proteins also demonstrated the presence of large flexible linkers and termini in many solved structural ensembles. It is now generally accepted that proteins exist as an ensemble of similar structures with some regions more constrained than others. Some people differentiate a particular type of IDP called Intrinsically Unstructured Proteins (IUPs), which occupy the extreme end of this spectrum of flexibility, whereas IDPs also include proteins of considerable local structure tendency or flexible multidomain assemblies. These highly dynamic disordered regions of proteins have subsequently been linked to functionally important phenomena such as allosteric regulation and enzyme catalysis. Many disordered proteins have their binding affinity with their receptors regulated by post-translational modification. Hence it has been proposed that the flexibility of disordered proteins facilitates the conformational requirements for binding their modifying enzymes as well as their receptors. Intrinsic disorder is particularly found in proteins implicated in cell signaling, transcription and chromatin remodeling functions. Here are some types or characteristics of IDPs. Flexible linkers Disordered regions are often found as flexible linkers or loops connecting domains. Linker sequences vary greatly in length but are typically rich in polar uncharged amino acids. Flexible linkers allow the connecting domains to freely twist and rotate to recruit their binding partners via protein domain dynamics. They also allow their binding partners to induce larger-scale conformational changes by long-range allostery. Linear motifs Linear motifs are short disordered segments of proteins that mediate functional interactions with other proteins or other biomolecules (RNA, DNA, sugars etc.). Many roles of linear motifs are associated with cell regulation, for instance in control of cell shape, subcellular localization of individual proteins and regulated protein turnover. Often, post-translational modifications such as phosphorylation tune the affinity (not rarely by several orders of magnitude) of individual linear motifs for specific interactions. Unlike globular proteins, IDPs do not have premade active pockets. Nevertheless, in 80% of IDPs (~3 dozen) subjected to detailed structural characterization by NMR, there are linear motifs termed PreSMos (pre-structured motifs) that are transient secondary structural elements primed for target recognition. In several cases, it has been demonstrated that these transient structures become full and stable secondary structures, e.g., helices, upon target binding. Hence, PreSMos are the putative active sites in IDPs. Coupled folding and binding Many unstructured proteins undergo transitions to more ordered states upon binding to their targets. The coupled folding and binding may be local, involving only a few interacting residues, or it might involve an entire protein domain. It was recently shown that the coupled folding and binding allow the burial of a large surface area that would be possible only for fully structured proteins if they were much larger. Moreover, certain disordered regions might serve as "molecular switches" in regulating certain biological functions by switching to ordered conformations upon binding small molecules, nucleic acids or ions. Disorder in the bound state (fuzzy complexes) Intrinsically disordered proteins can retain their conformational freedom even when they bind specifically to other proteins. The structural disorder in the bound state can be static or dynamic. In fuzzy complexes structural multiplicity is required for function and the manipulation of the bound disordered region changes activity. The conformational ensemble of the complex is modulated via post-translational modifications or protein interactions. The specificity of DNA binding proteins often depends on the length of fuzzy regions, which is varied by alternative splicing. Intrinsically disordered proteins adapt many different structures in vivo according to the cell's conditions, creating a structural or conformational ensemble. Therefore, their structures are strongly function-related. However, only few proteins are fully disordered in their native state. Disorder is mostly found in intrinsically disordered regions (IDRs) within an otherwise well-structured protein. The term intrinsically disordered protein (IDP) therefore includes proteins that contain IDRs as well as fully disordered proteins. The existence and kind of protein disorder is encoded in its amino acid sequence. As described above, IDPs are characterized by a low content of bulky hydrophobic amino acids and a high proportion of polar and charged amino acids, usually referred to as low hydrophobicity. This property leads to good interactions with water. Furthermore, high net charges promote disorder because of electrostatic repulsion resulting from equally charged residues. Thus disordered sequences cannot sufficiently bury a hydrophobic core to fold into stable globular proteins. In some cases, hydrophobic clusters in disordered sequences provide clues for identifying the regions that undergo coupled folding and binding (refer to biological roles). Many disordered proteins reveal regions without any regular secondary structure These regions can be termed as flexible, compared to structured loops. While the latter are rigid and contain only one set of Ramachandran angles, IDPs involve multiple sets of angles. The term flexibility is also used for well-structured proteins, but describes a different phenomenon in the context of disordered proteins. Flexibility in structured proteins is bound to an equilibrium state, while it is not so in IDPs. Many disordered proteins also reveal low complexity sequences, i.e. sequences with over-representation of a few residues. While low complexity sequences are a strong indication of disorder, the reverse is not necessarily true, that is, not all disordered proteins have low complexity sequences. Disordered proteins have a low content of predicted secondary structure. Silent Single nucleotide polymorphisms (SNPs) For some amino acids, multiple triplet nucleotide sequences (codons) in the coding regions of a gene for a protein lead to the incorporation of the same amino acid in the protein sequence. Hence two proteins identical in amino acid sequence might have slightly different nucleotide sequences in the gene that encodes them. Such single nucleotide polymorphisms (SNPs) in coding regions were thought to have no effect on the tertiary structure and biological function of a protein if the single nucleotide variation did not lead to the insertion of a different amino acid into the growing peptide chain (i.e the codons were synonymous and the mutations presumably silent with no effect). Recently single nucleotide polymorphisms (SNPs) in the gene for the product of the MDR1 (multidrug resistance 1) gene, P-glycoprotein, was shown to result in a protein with different substrate specificity and inhibitor interactions, and hence a different 3D structure. One possible explanation for this observation is a difference in the rate of translation of the mRNA for this membrane protein. Different rates might lead to different intra- and intermolecular associations, which could lead to different final 3D structures as the protein cotranslationally folds and inserts into the membrane. This would especially be true if two possible structures were close enough in free energy but separated by a significant activation energy barrier, precluding simple conformational rearrangement of one conformation to another. It has been shown in yeast that synonymous mutations (those that don't change the amino acid on mutation of the DNA encoding the particular amino acid) generally have the same effect on the "health" of yeast as do non-synonymous mutations (those that change the amino acid). This rather startling result upends much dogma. Some possible expected effects of synonymous mutation include alteration in gene expression of the mutated gene and possible effects on the stability of the transcribed RNA from the mutated RNA. mRNA levels are lowered from both types of mutations as well as fitness levels of the yeast, as defined by speed of growth. Metamorphic Proteins In addition to prion proteins, it appears that many proteins can adopt more than one conformation under the same set of conditions. In contrast to prion proteins, however, in which the formation of the beta-structure variant is irreversible since the conformational change is associated with aggregation, many proteins can change conformations reversibly. Often, these changes do not appear to be associated only with binding interactions that trigger the change. Murzin has described proteins that change conformations on change of the pH (viral glycoproteins), redox state (chloride channel), disulfide isomerization (lysozyme), and bound ligand (RNA polymerase as it initiates and then elongates the growing RNA polymer). He cites two proteins that appear to change state without external signals. These include Mad2, in which the two conformers share an extensive similarity, and Ltn10 (lymphotactin), in which they don't. One form of lymphotactin (Ltn 10) binds to similar lymphokine receptors, while the other (Ltn 40) binds to heparin. Folding kinetics may play a part in these examples as well, as proteins capable of folding to two conformers independently and quickly might prevent misfolding and aggregation that might occur if they had to completely unfold first before a conformational transition. Both Mad2 and Ltn10 alter conformation through transient formations of dimers, which facilitate conformational changes without widespread unfolding. Mutations in Ltn10 can cause the protein to adopt the Ltn40 conformation, Hence primordial "metamorphic" proteins could, by simple mutation, produce new protein functionalities. Metamorphic proteins, which display large structural changes, usually involving large changes in hydrogen bonding and hence secondary structure, are different from simpler allosteric proteins chose conformation changes are smaller. Few metamorphic proteins have been found but some speculate they could account for as much as 5% of proteins. A wonderful example of such a protein is the human chemokine XCL1 (lymphotactin) protein, an immune regulatory protein. It undergoes a huge transition from a form that has a typical chemokine fold to a dimer that has an extensive beta structure. Ltn lacks one of the two disulfide bonds found in all other chemokines, which allows greater conformation flexibility. Figure \(3\) shows interactive iCn3D model of the solution structures of monomeric and XCL1 (lymphotactin, PDB 2HDM) Monomeric XCL1 (lymphotactin PDB 2HDM) Dimeric XCL1 (lymphotactin PDB 2JP1) (Copyright; author via source). Click the image for a popup or use this external link:https://structure.ncbi.nlm.nih.gov/i...b8BwBXkuTAJvx6 (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...1fuU5jg76RAbVA

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/04%3A_The_Three-Dimensional_Structure_of_Proteins/4.06%3A_Intrinsically_Disordered_Proteins.txt

Search Fundamentals of Biochemistry Introduction Most proteins have a roughly spherical or "globular" tertiary structure. However, there are many proteins that form elongated fibrils with properties like elasticity, which allows deformation on the application of a force and subsequent return to the original state. Elastic molecules must store energy (go to a higher energy state) when the elongating force is applied, and the energy must be released on return to the equilibrium resting structure. Structures that can store energy and release it when subjected to a force have resiliency. Proteins that stretch with an applied force include elastin (in blood vessels, lungs and skins where elasticity is required), resilin in insects (which stretches on wing beating), silk (found in spider web and whose structure we showed in 4.2) and fibrillin (found in most connective tissues and cartilage). Some proteins have high resiliency (90% in elastin and resilin), while others are only partially resilient (35% in silk, which has a tensile strength approaching that of stainless steel). In contrast to rubber, which has an amorphous structure, which imparts elasticity, these proteins, although they have a dissimilar amino acid sequence, seem to have a common structure inferred from their DNA sequences. In some (like fibrillin), the protein has a folded beta sheet domain, which unfolds like a stretched accordion. Others, like elastin and spider silk, have a beta sheet domain and other secondary structures (alpha-helices and beta turns) along with Pro and Ala repetitions. Scientists are studying these structures to help in the synthesis of new elastic and resilient products. Fibrous Proteins are characterized by elongated protein structures. These types of proteins often aggregate into filaments or bundles forming structural scaffolds in biological systems. Within animals, the two most abundant fibrous protein families are collagen and α-keratin. Let's start our exploration of fibrillar proteins with these. Collagen Collagen is the most abundant protein in mammals, making 25% to 35% of the whole-body protein content. It is found predominantly in the extracellular space within various connective tissues in the body. Collagen contains a unique quaternary structure of three protein strands wound together to form a triple helix. It is mostly found in fibrous tissues such as tendons, ligaments, and skin. Depending upon the degree of mineralization, collagen tissues may be rigid (bone), compliant (tendon), or have a gradient from rigid to compliant (cartilage). It is also abundant in corneas, blood vessels, the gut, intervertebral discs, and dentin in teeth. In muscle tissue, it serves as a major component of the endomysium. Collagen constitutes one to two percent of muscle tissue and accounts for 6% of the weight of strong, tendinous, muscles. The fibroblast is the most common cell that creates collagen. Gelatin, which is used in food and industry, is collagen that has been irreversibly hydrolyzed. In addition, partially and fully hydrolyzed collagen powders are used as dietary supplements. Collagen also participates in many binding interactions with target proteins in addition to its role in the structure. The name collagen comes from the Greek (kólla), meaning "glue", and the suffix -gen, denoting "producing". This refers to the compound's early use in the process of boiling the skin and tendons of horses and other animals to obtain glue. Over 90% of the collagen in the human body is type I. However, as of 2011, 28 types of collagen have been identified, described, and divided into several groups according to the structure they form. The five most common types are: • Type I: skin, tendon, vasculature, organs, bone (the main component of the organic part of bone) • Type II: cartilage (the main collagenous component of cartilage) • Type III: reticulate (the main component of reticular fibers), commonly found alongside type I • Type IV: forms basal lamina, the epithelium-secreted layer of the basement membrane • Type V: cell surfaces, hair, and placenta Let's focus on Type I collagen, which has unusual amino acid composition and sequence: • Glycine is found at almost every third residue. • Proline makes up about 17% of collagen. • Collagen contains many hydroxyproline and hydroxylysine which are formed on post-translational modifications by different enzymes, both of which require vitamin C as a cofactor. • Some hydroxylysines are glycosylated, mostly with disaccharides. Figure \(1\) shows the post-translational hydroxylations of lysine and proline. Vitamin C deficiency causes scurvy, a serious and painful disease in which defective collagen prevents the formation of strong connective tissue. Gums deteriorate and bleed, with loss of teeth; skin discolors, and wounds do not heal. Prior to the 18th century, this condition was notorious among long-duration military, particularly naval, expeditions during which participants were deprived of foods containing vitamin C. Many bacteria and viruses secrete virulence factors, such as the enzyme collagenase, which destroys collagen or interferes with its production. Collagen has many (GXY)n repeats, where G is glycine (Gly), and X and Y are frequently proline (Pro) and hydroxyproline (Hyp). Three strands of collagen self-associate to for a triple-stranded helix with 10 GXY triplets in 3 complete turns of the helix. Others suggest that there are seven triplet units in 2 turns of the stands. Note that the helix of each strand in the triple helix is not an alpha helix and has different phi/psi angles. Each strand is "frameshifted" by one amino acid, resulting in a staggered arrangements of the individual stands and helices. The glycines are buried along the central axis so there is no essential hydrophobic core. The X, Y amino acids are solvent-exposed. All the other side chains, both hydrophobic and hydrophilic, are likewise exposed to solvent. Hydrogen bonding occurs between the amide hydrogen of the peptide bond of Gly and the carbonyl O of an X amino acid in another chain. Figure \(2\) shows an interactive iCn3D model of a triple helical collagen-like peptide (4Z1R). The main chain atoms, shown in CPK colors, are shown forming hydrogren bonds with neighboring chains. The side chains are color based on the three chains (blue, brown and magenta). Two sets of Pro-HPro-Gly repeats are labeled. Figure \(2\): Triple helical collagen-like peptide (4Z1R). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/icn3d/share.html?TCrji1wPhekypJtc6 α-Keratin α-keratin is the key structural element making up hair, nails, horns, claws, hooves, and the outer layer of skin. Due to its tightly wound structure, it can function as one of the strongest biological materials and has various uses in mammals, from predatory claws to hair for warmth. The first sequences of α-keratins were determined by Hanukoglu and Fuchs. These sequences revealed that there are two distinct but homologous keratin families which were named Type I keratin and Type II keratins. There are 54 keratin genes in humans, 28 of which code for type I, and 26 for type II. Type I proteins are enriched in Asp and Glu amino acids, while type II proteins contain more basic amino acids, such as lysine. This differentiation is especially important in α-keratins because in the formation of a keratin dimer, the coiled coil, one protein coil must be type I, while the other must be type II. Even within type I and II, there are acidic and basic keratins that are particularly complementary within each organism. For example, in human skin, K5, a type II α-keratin, pairs primarily with K14, a type I α-keratin, to form the α-keratin complex of the epidermis layer of cells in the skin. Coiled-coil dimers then assemble into a tetramer of two staggered coiled-coil dimers. Two tetramers can then pack together to form an elongated protofilament, a very stable, left-handed superhelical structure as shown in the figure below. The keratin filaments stay associated through hydrophobic interactions between apolar residues along the keratin's helical segments. This is illustrated in Figure \(3\). Figure \(3\): Assembly of Keratin Fibers. Wiki lectures. https://www.wikilectures.eu/w/Indivi...e_and_function Initially, two keratin monomers (A) form a coiled coil dimer structure (B) Two coiled coil dimers join to form a staggered tetramer (C), next, the tetramers start to join together (D), ultimately forming a sheet of eight tetramers (E). The sheet of eight tetramers is then twisted into a lefthanded helix forming the final intermediate filament (E) An electron micrograph of the intermediate filament is shown in the upper left hand corner. Figure \(4\) shows an interactive iCn3D model of a dimer of Type I alpha-keratin (magenta backbone) and Type II (blue backbone) (6JFV). Acidic (red) side chains (Asp and Glu) and basic (blue) side chains (Lys) can be seen projecting away from the dimer. Both A and B chains have negative and positive side chains. The A (more acidic) chain in this structure has 5 Lys , 7 Arg, 1 Asp and 16 Glu side chains for a net charge of +12 -17 = -5. The B (more basic) chain in this structure has 8 Lys , 9 Arg, 5 Asp and 12 Glu side chains for a net charge of +17 -17 = 0. It is clearly more basic with 17 Lys and Arg side chains, compared to the A chain with 12. Depending on their 3D orientation, they could present a positive face to the more negative monomer in the dimer. Figure \(4\): Dimer of a Type I and Type II alpha-keratin backbones (6JFV). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...g3L3sQh9dj5pw8 Acidic (red) side chains (Asp and Glu) and basic (blue) side chains (Lys, Arg) can be seen projecting away from the dimer. Both A and B chains have negative and positive side chains. The A (more acidic) chain in this structure has 5 Lys , 7 Arg, 1 Asp and 16 Glu side chains for a net charge of +12 -17 = -5. The B (more basic) chain in this structure has 8 Lys , 9 Arg, 5 Asp and 12 Glu side chains for a net charge of +17 -17 = 0. It is clearly more basic with 17 Lys and Arg side chains, compared to the A chain with 12. Depending on their 3D orientation, they could present a positive face to the more negative monomer in the dimer. Figure \(5\) shows an interactive iCn3D model of a spacefill model of the dimer. Note that a significant fraction of the nonpolar side chains is pointed inward between the two monomers and are much less exposed to solvent. Figure \(5\): Dimer of a Type I and Type II alpha-keratin backbones in spacefill (6JFV). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...uyJrw28mZ6x5h7 Elastin As its name implies, the protein confers elasticity in target structures such as connective tissue and blood vessels. It has low-complexity hydrophobic domains and the protein is cross-linked to form larger structures. It contains repeating hydrophobic amino acid sequences mostly of valine, proline, glycine and alanine, and mimetics of the repeating structure (LGGVG)6 have been studied. This protein also displays significant disorder. Resilin The following description of resilin is taken directly from an article under Creative Commons Attribution 4.0 International License available at http://creativecommons.org/licenses/by/4.0/. Balu, R., Dutta, N.K., Dutta, A.K. et al. Resilin-mimetics as a smart biomaterial platform for biomedical applications. Nat Commun 12, 149 (2021). https://doi.org/10.1038/s41467-020-20375-x Resilin-Mimetics Native elastomeric proteins are biomaterials that have been perfected over billions of years by natural selection to act as molecular springs in a wide range of biological systems to drive unique functions. Among native proteins, resilin is purported to be one of the most efficient elastic proteins known. It is essentially a structural protein, which exists mainly in insect exoskeleton structures and exhibits outstanding resilience and fatigue life. The first description of resilin was made in 1960s as a rubber-like protein observed in locust-wing hinge and dragonfly tendon. Early studies on the composition and structure of resilin revealed the protein to contain about 66% hydrophobic residues (much lower than elastin) with about 45% proline and glycine residues combined. In native state, resilins exist as di- and trityrosine crosslinked hydrogels, and exhibit highly amorphous structures when examined using X-ray diffraction and electron microscopy. During biosynthesis, pro-resilins (uncrosslinked) are secreted from the apical surface of the epidermal cells into the subcuticular space, where they are crosslinked by an enzyme-mediated process to form hydrogels. Over the course of next three decades, resilin was also identified in many other insects and arthropods, including copepods, reduviidae and moth. In arthropods, resilin is largely involved in a number of different functions, including the flexibility and deformability of membranous cuticle and joint systems, the storage of elastic energy in locomotion (jumping, flying, etc.) and catapulting systems, the adaptability to surface topography by multiple contact attachment, and prey catching systems and the reduction of fatigue and damage in feeding and traumatic reproductive system. The amino acid sequence of resilin was first identified in early 2000s from the CG15920 gene segment of the fruit fly Drosophila melanogaster, which opened up new opportunities for synthesis and development of biomimetic resilins. The CG15920 gene comprises N-terminal (exon-1), C-terminal (exon-3) and the middle chitin-binding (exon-2) domains, where exon-1 and exon-3 consist of 18 and 11 copies of consensus amino acid sequences: GGRPSDSYGAPGGGN and GYSGGRPGGQDLG, respectively. The first recombinant pro-resilin or resilin-like polypeptide (RLP), namely Rec1-resilin (encoded from the exon-1 of CG15920 gene) was synthesized in mid-2000s as a water soluble polypeptide expressed in the bacteria Escherichia coli. The synthesized pro-resilin was photo-crosslinked (dityrosine) using a ruthenium-persulfate crosslinking system to form hydrogels, which exhibited 97% resilience, outperforming native resilin dissected from dragonfly tendon (92%), natural elastin (90%) and synthetic polybutadiene rubber (80%) The synthesized RLPs have several advantages over other elastomeric polypeptides, such as elastin-like polypeptides (ELPs), silk-like polypeptides (SLPs) and collagen-like polypeptides (CLPs). These include: • Unique sequences rich in uncharged, polar amino acids and devoid of canonical hydrophobic residues, and contain high proportions of glycine- and proline-rich segments. • Average negative hydropathy index. • Intrinsically disordered protein structure with rapidly-interchangeable conformational ensemble in physiological conditions. • Multi-stimuli (pH, temperature, ions, mechanical stress, other molecules, etc.) responsiveness, including dual-phase transition behavior (existence of both upper critical solution temperature, UCST and lower critical solution temperature, LCST). • Low stiffness, high extensibility, outstanding resilience and excellent fatigue life. • No inflammatory response. Figure \(6\) shows possible transitions in the resilin protein which demonstrate such resiliency. Given its disordered nature, there are no available PDB structures for resilin. Fibrinogen This very large molecule is a hexamer of three monomers (Aα, Bβ and γ) each present in two copies (α2β2γ2). Disulfide bond connect one structural unit (αβγ) with another. When two fibrinopeptides (FpA and FpB) are cleaved from the amino ends of the α and β chains by the clotting enzyme thrombin, small structural "knobs" form that bind to "holes" on another fibrinogen, causing the formation of large fibrils of fibrin clots. Figure \(7\) shows an interactive iCn3D model of human fibrinogen (3ghg). The "floppy" parts of the alpha chain (αC region) and FpA and FpB peptides are not shown as they were not resolved (due to their disorder) in the crystal structure. Figure \(7\): Human fibrinogen (3ghg). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...S3dXzzsrkujKZ8 It is a very long, flexible molecule. The two alpha chains are shown in magenta and light green, the beta chains as dark blue and gray, and the gamma chains as brown and orange. Note the helical chains are actually alpha-helical for this molecule. Figure \(8\) shows the domain structure and hints at the flexibility of this long molecule, which is required to form a fibrous mesh clot as well as to be accessible to an enzyme, plasmin, which cleaves fibrin clots, facilitating their removal. The Aα, Bβ and γ are shown in blue, red and green, respectively. Carbohydrates are shown in orange in the spacefilling model (b). The coiled coils contain mostly alpha helical structures. The central E region is where the N-terminal ends of all of the fibrinogen chains are located and where the fibrinopeptides A (FpA, 16 residues) and B (FpB, 14 residues) are located, where they will be cleaved by thrombin in clot formation. The C-terminals of the chains are located at the distal D region which houses two C domains. After cleavage of the fibrinopeptides, conformational changes occur in the central E region to produce the "knobs" A (with starting sequence Gly-Pro-Arg) and B (Gly-His-Arg). These knobs bind through noncovalent interactions corresponding "holes" a and b at the distal D regions of another fibrinogen to form a dimer and subsequently a fibrin clot. Myosin Heavy Chain Myosin is a chief component of muscles and in complex with actin and other proteins allows muscle contraction. It has 2 clear domains and an elongated rod-like tail sequence, which has 28-residue repeats of 4 heptapeptides, characteristic of alpha-helical proteins that form the coiled-coils quaternary structure. Figure \(9\) shows the domain structure of human myosin heavy chain 1. The orange represents the motor domain of the protein, which binds and hydrolyzes ATP, providing the free energy that powers muscle contraction. Figure \(10\) shows a cartoon of myosin heavy chains (blue) associated with myosin light chains, and how they interact with actin in the actinomyosin complex in striated muscle cells. The motor domain also binds actin filaments. In a simplistic way, the myosin thick filaments can be considered to slide back and forth in muscle contraction and extension. Myosin can exist in two major conformations. One is the "6S" (extended tail) form that assembles into myosin filaments, which interacts with actin as shown in Figure \(10\) to transduce the chemical energy from ATP hydrolysis into mechanical forces and filament sliding. The other is the "10S" conformation which is folded on itself. The heads interact with each other and the tail. In this compact form, all necessary steps (ATP cleavage, filament assembly, actin activation) required for actin/myosin- mediated contraction are inhibited. Figure \(11\) shows an interactive iCn3D model of an inactive (nonextended) conformation of myosin heavy chain (6xe9) from smooth muscle. The long cyan and green chains are the myosin (II) heavy chains from smooth muscle. Figure \(11\): Inactive (nonextended) conformation of myosin heavy chain (6xe9). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...thwKbyQQ5ezB96 References OpenStax, Proteins. OpenStax CNX. Sep 30, 2016 http://cnx.org/contents/bf17f4df-605c-4388-88c2-25b0f000b0ed@2. File:Chirality with hands.jpg. (2017, September 16). Wikimedia Commons, the free media repository. Retrieved 17:34, July 10, 2019 from commons.wikimedia.org/w/index.php?title=File:Chirality_with_hands.jpg&oldid=258750003. Wikipedia contributors. (2019, July 6). Zwitterion. In Wikipedia, The Free Encyclopedia. Retrieved 21:48, July 10, 2019, from en.Wikipedia.org/w/index.php?title=Zwitterion&oldid=905089721 Wikipedia contributors. (2019, July 8). Absolute configuration. In Wikipedia, The Free Encyclopedia. Retrieved 15:28, July 14, 2019, from en.Wikipedia.org/w/index.php?title=Absolute_configuration&oldid=905412423 Structural Biochemistry/Enzyme/Active Site. (2019, July 1). Wikibooks, The Free Textbook Project. Retrieved 16:55, July 16, 2019 from en.wikibooks.org/w/index.php?title=Structural_Biochemistry/Enzyme/Active_Site&oldid=3555410. Structural Biochemistry/Proteins. (2019, March 24). Wikibooks, The Free Textbook Project. Retrieved 19:16, July 18, 2019 from en.wikibooks.org/w/index.php?title=Structural_Biochemistry/Proteins&oldid=3529061. Fujiwara, K., Toda, H., and Ikeguchi, M. (2012) Dependence of a α-helical and β-sheet amino acid propensities on teh overall protein fold type. BMC Structural Biology 12:18. Available at: Wikipedia contributors. (2019, July 16). Keratin. In Wikipedia, The Free Encyclopedia. Retrieved 17:50, July 19, 2019, from en.Wikipedia.org/w/index.php?title=Keratin&oldid=906578340 Wikipedia contributors. (2019, July 13). Alpha-keratin. In Wikipedia, The Free Encyclopedia. Retrieved 18:17, July 19, 2019, from en.Wikipedia.org/w/index.php?title=Alpha-keratin&oldid=906117410 Open Learning Initiative. (2019) Integumentary Levels of Organization. Carnegie Mellon University. In Anatomy & Physiology. Available at: Wikipedia contributors. (2019, July 16). Collagen. In Wikipedia, The Free Encyclopedia. Retrieved 03:42, July 20, 2019, from en.Wikipedia.org/w/index.php?title=Collagen&oldid=906509954 Wikipedia contributors. (2019, July 2). Rossmann fold. In Wikipedia, The Free Encyclopedia. Retrieved 16:01, July 20, 2019, from https://en.Wikipedia.org/w/index.php?title=Rossmann_fold&oldid=904468788 Wikipedia contributors. (2019, May 30). TIM barrel. In Wikipedia, The Free Encyclopedia. Retrieved 16:46, July 20, 2019, from en.Wikipedia.org/w/index.php?title=TIM_barrel&oldid=899459569 Wikipedia contributors. (2019, July 16). Protein folding. In Wikipedia, The Free Encyclopedia. Retrieved 18:30, July 20, 2019, from https://en.Wikipedia.org/w/index.php?title=Protein_folding&oldid=906604145 Wikipedia contributors. (2019, June 11). Globular protein. In Wikipedia, The Free Encyclopedia. Retrieved 18:49, July 20, 2019, from en.Wikipedia.org/w/index.php?title=Globular_protein&oldid=901360467 Wikipedia contributors. (2019, July 11). Intrinsically disordered proteins. In Wikipedia, The Free Encyclopedia. Retrieved 19:52, July 20, 2019, from en.Wikipedia.org/w/index.php?title=Intrinsically_disordered_proteins&oldid=905782287 Comprehensive Database for Protein Analysis - Biozon SCOP: Structural Characterization of Proteins - Database showing folds, superfamiles, families, and domains

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/04%3A_The_Three-Dimensional_Structure_of_Proteins/4.07%3A_Fibrillar_Proteins.txt

Search Fundamentals of Biochemistry Introduction We've seen many static and rotatable images of lipid aggregates (the micelle) as well as proteins. We have learned some rules about the disposition of amino acid side chains in a folded proteins. However, when we think about how proteins fold, we have to think dynamically as well as thermodynamically. Figure \(1\) shows a fun but clearly unrealistic animation of how a protein might fold from an unfolded state with exposed hydrophobic side chains (orange) to a folded state when they are mostly, but not fully, buried. Luckily we have the tools of molecular dynamics (MD) at our fingertips which helps us imagine how these processes take place and concomitantly how to probe protein folding experimentally. As protein folding occurs in 3D, let's explore a free energy (G) landscape for folding from an extraordinarily large number of unfolded states of higher free energy to a single low energy folded state. Two such landscapes, created by Ken Dill's group, are shown in Figure \(2\). The right image B shows a simplistic ("much-reduced frustration") funnel view with a simple one-step path for any unfolded protein to reach a single global free energy minimum state, a process that occurs without any intermediates. A more realistic view is shown in A in which there are a series of local minimums and one global minimum. Like in any activation energy curve you have encountered in chemistry courses, traversing from a local minimum to other local minimums or the global minimum is possible if enough energy is provided to overcome the activation energy for that step. Panel A implies that are many intermediates on the road to the final global free energy minimum native state but that local minimum could be populated and either stable or metastable depending on their activation energies. Intermediates might be "trapped" in these local minima. The view also conforms to our view that proteins are conformationally flexible and can adopt a variety of conformations. This is especially true for intrinsically disordered proteins. Now let's add some additional complexity. A protein on the path to a folded state has more hydrophobic exposure than the native state, so you would expect that it could aggregate with other self proteins and form intermediates and end products off of the normal folding pathway. These are illustrated the Figure \(3\) which shows the "normal" protein folding (blue) and a misfolding (orange) landscape. The misfolded landscape is populated with amorphous aggregates, oligomers, protofibrils and fibrils. We will discuss protein folding done in the lab (in vitro) as well as protein folding in the cell (in vivo). In vitro folding is done in very defined conditions, typically using low concentrations of small proteins to minimize misfolding opportunities. Folding in vivo occurs as a protein is being made on a ribosome. It also occurs when a fully-folded protein misfolds (such as during fevers in disease states) and has been prevented from folding by association with other molecules. Folding in vivo is often assisted by other proteins called folding chaperones. In either case, given the number of possibly nonnative states, it is amazing that proteins fold to the native state at all, let alone in a reasonable time frame. Consider this greatly simplified view of protein folding for a protein containing 100 amino acids. If each amino acid can adopt only 3 possible conformations, the total number of conformations could be 3100 = 5 x 1047. Assuming that it would take 10-13 s to change each conformation, the time required to "test" all conformations would be 5 x 1034 s or 1027 years, longer than the age of the universe (14 x 109 yr). Yet the protein can fold within seconds. This paradox is called the Levinthal paradox, after Cyrus Levinthal. Lubert Stryer, (in his classic Biochemistry text), shows a way out of this dilemma by using an analogy of a monkey sitting at a typewriter, and typing this line out of Hamlet: "Me thinks it is like a weasel." Random typing would produce that line after 1040 keystrokes on average, but if the correct letters were maintained, the number of keystrokes would be in the realm of a few thousand. Proteins could fold more quickly if they retain native-like intermediates along the way. Also, remember that much of conformational space is already restricted by allowed phi/psi angles (remember the empty areas in the Ramachandran plots). We will explore the classic study of the folding of RNase done by Anfinsen, for which he won the Nobel Prize. RNase is a small protein with 4 disulfide bonds and can refold from denaturing and reducing conditions in vitro. If he had chosen a larger protein, he might not have met with success as they are more prone to aggregation. We'll discuss mostly small proteins in this section. Before we start, let's anticipate what might happen to fully reduced and denatured RNase with 8 free Cys side chains. If the denatured protein is suddenly placed in a refolding solution without denaturant and in an oxidizing environment (such as oxidized glutathione), the reduced cysteine side chains could start forming disulfide pairs, but only one combination of such pairs would be native. What would be the probability that a purely random process of forming disulfides would produce 4 correct ones and a fully native state? To think about that, try this thought experiment. You have 4 pairs of socks, with each pair having a different color as shown in Figure \(4\). You threw them into a drawer unpaired after washing. Now without looking, take one sock out and then a second and tie them together to form a pair. Continue doing this without looking until all four are paired. What is the probability that all the socks will be correctly matched when you are done? Once you have the answer, you're ready to understand Anfinsen's classic experiment. To think about that, try this thought experiment. Exercise \(1\) Socks and Disulfides You have 4 pairs of socks, with each pair having a different color as shown in Figure \(4\). You threw them into a drawer unpaired after washing. Now without looking, take one sock out and then a second and tie them together to form a pair. Continue doing this without looking until all four are paired. What is the probability that all the socks will be correctly matched when you are done? Once you have the answer, you're ready to understand Anfinsen's classic experiment. Answer We'll calculate the probability that you get 4 perfect matches. You pick one sock initially. You have a 1/7 chance to get the match from the remaining 7 unpaired socks. You have 6 socks left. Pick one again. From the remaining 5 you have a 1/5 chance of getting a match. Now pick another. From the remaining 3 you have a 1/3 chance of getting a match. Now pick another. You have a 1/1 changes of getting the match. Each is independent so the total probability of getting 4 matched pairs of socks is given by the products of the probability at each step, or (1/7) x (1/5) x (1/3) x (1/1) = 1/105 or about 1% The classic experiment of Anfinsen showed that, at least for some proteins, all the necessary and sufficient information required to direct the folding of a protein into the native state is present in the primary sequence of a protein. Anfinsen studied the in vitro folding of a single chain protein, RNase, which has four intrachain disulfide bonds, as shown in the interactive iCn3D model in Figure \(5\). Figure \(5\): RNase with four intrachain disulfide bonds (yellow sticks) (1KF5). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...Risnvb1SfNrww6 We have previously discussed how chemical agents (such as beta-mercaptoethanol, a disulfide reducing agent) can covalently interact with specific protein functional groups. Some can bind through complementary intermolecular forces to the active site or other cavities on the surface. Other reagents, like urea, acting through generalized solvent changes or nonspecific interactions with the protein, can alter protein folding. Anfinsen used two different reagents, 8 M urea and beta-mercaptoethanol (βME), in combination to unfold, or denature, RNase to the nonnative or denatured state. He then removed the βME using dialysis, allowing the disulfides to reform. Next, he removed the denaturing reagent, urea. To monitor if the protein was correctly refolded or renatured, he tested the activity of the protein compared to the native protein. He found that the "refolded" protein retained only 1% of its initial activity (compare this to the matching socks activity). If, however, he added catalytic amounts of βME, the protein soon retained 100% of its initial activity. For his work, he was awarded the Nobel Prize in Chemistry in 1972. A general outline of his experiment is shown in Figure \(6\). Figure \(7\) presents a chemical mechanism to show how catalytic (non-stoichiometric) amounts of beta-mercaptoethanol can lead to full recovery of the most stable set of a protein with two disulfide bonds (right hand side). Scientists have investigated the folding of proteins both in vitro and in vivo. In vitro experiments involve denaturing the protein with urea, guanidine hydrochloride, or heat, then refolding the protein by removing the perturbant (denaturing agent), using spectral techniques to follow the process. In vivo experiments involve the study of intracellular proteins that assist folding. An understanding of protein folding can not be separated from an understanding of protein stability, and an understanding of the nature of the native and denatured state as illustrated in the protein folding landscapes shown in Figure \(1\) and Figure \(2\). In studying protein folding and stability/structure of the native and denatured states, both equilibrium (thermodynamic) and timed (kinetic) measurements are made. Folding occurs in the ms to second range, which limits the ability to study the presence of intermediates in the process. Some clever methods have been developed to study intermediates in protein folding by trapping specific intermediate structures and investigating their structure and stability in a "leisurely" fashion. Alternatively, intermediates can be studied as they occur using stop-flow kinetics. In this technique, a protein under denaturing conditions is rapidly mixed with a solution containing no denaturant or protein by injecting both solutions into a mixer/cuvette using syringes. The denaturant in the protein solution is now diluted such that renaturation can occur. Spectral measurements can begin at once. A diagram summarizing these methods is shown in Figure \(8\). Study it in conjunction with the text which follows. Folding appears to proceed not by an obligatory pathway but a probabilistic or stochastic search of possible conformations. Evolution has surely selected for sequences that can make it to the folded state. Localized secondary structure motifs (like a short alpha helix and beta turns) can form quickly (about 1 ms). The folding of small proteins occurs, depending on their structure, over a wide time frame (ms to minutes). Most likely, a small number of amino acids coalesce into a core, which serves as a nucleus for folding into structures that are similar to the native state. Finally packing interactions collapse the structure into the native state. In general, the more complex the fold of the backbone, the longer it takes the protein to fold. If complexity requires more interactions among distal regions of the polypeptide change, then the more complex the fold, the less probable that random interactions would lead to quick protein folding. The mechanisms of folding for larger proteins (greater than 100 amino acids) appear to proceed through intermediates, suggesting that different domains of the protein can fold independently. Protein Folding In Vitro Early studies of protein folding involved small proteins which could be denatured and refolded in a reversible fashion. A two state model, D ↔ N, was assumed. The denaturants were heat, urea, or guanidine HCl. Since the denatured states are less compact than the native state, the viscosity of the solution can be used as a measure of denaturation/renaturation. Likewise, the amino acid side chains in the differing states, would be in different environments. The aromatic amino acid Trp, Phe, and Tyr absorb UV light. After excitation, the electrons decay to the ground state through several processes. Some vibrational relaxation occurs, bringing the electrons to lower vibrational energy levels. Some of the electrons can then fall to various vibrational levels at lower principle energy states through a radiative process. The photons emitted are lower in energy and hence longer in wavelength. The emitted light is termed fluorescence. The wavelength of maximum fluorescent intensity and the lifetime of the fluorescence decay is very sensitive to the environment of the amino acids. Hence fluorescence can also be used to measure changes in protein conformation. Other spectral techniques like CD spectroscopy as well as simple absorbance measurements, are used. For small, single domain proteins (such as RNase) undergoing reversible denaturation, graphs showing the extent of denaturation using each technique above, as shown in Figure \(9\), are superimposable, giving strong validity to the two state model. Proteins that fold without easily discernable, long-lived intermediates and follow a simple two-state model, D ↔ N are said to undergo cooperative folding. This simple model needed to be expanded as more proteins were studied. Some intermediates in the process were detected. Some proteins show two steps, one slow one, and one quick one, in refolding studies, suggesting an intermediate. The longer a protein is kept in the denatured state, the more likely it is to display an intermediate. One accepted explanation for this observation is that during an extended time in the D state, some X-Pro bonds might isomerize from trans to the cis state, to form an intermediate. Alternatively, as in the case of RNase, which has a cis X-Pro bond in the native state, denaturation causes an isomerization to the trans-state. In the case of RNase, to refold, the accumulating intermediate I must reisomerize in a slow step to the cis state, followed by a quick return to the N state. Some proteins, which contain multiple disulfide bonds that must reform correctly after reductive denaturation could refold into intermediates with the wrong S-S partner. Such intermediates can be trapped by stopping further S-S formation during refolding with the addition of iodoacetamide as shown in Figure \(10\). By adding iodoacetamide at varying times along the folding pathway, and separating starting (unfolded), trapped intermediates on the pathway and the final native state by HPLC, the entire folding pathway can in principle be determined. This has been done for many proteins including bovine pancratic trypsin inhibitor (BPTI), small protein with 58 amino acids, a molecular weight of 6512, and three sets of disulfide bonds (C5-C55, C14-C38 and C30-C51). The structure of BPTI with native disulfides is shown in Figure \(11\). Given its characteristics, BPTI has been used as a model protein to study protein folding. These studies have shown that no non-native disulfides form as intermediates in the pathway. Two major intermediates form quickly, each with two of the three native disulfide bonds. These intermediates are named N',with disulfide pairs 14-38 and 30-51, and N*, with pairs 5-55 and 14-38. These two intermediates then slowly form a common intermediate NSHSH, with disulfide pairs 5-15 and 30-51, which then converts very quickly to the native N state with three correct pairs (5-55,14-38 and 30-51). This folding pathway is shown in part a and b of Figure \(12\). In going from N' or N* to NSHSH, the 14-38 disulfide, which is very solvent-exposed (see Figure \(11\)), must break before proceeding to the N (native state). Mousa et al replaced that disulfide with a stable methylene thioacetal bridge (MT, alternative name methylenedithioether, S-CH2-S link) with the idea that once this stable (i.e irreversible) 14-S-CH2-S38 bond formed in N' and N*, it would not break again. The formation of the methylenedithioether bond and its properties compared to disulfide bonds are shown in Figure \(13\). Hence the final last intermediate, NSHSH, would not form, leaving just N' and N*. This is illustrated in Part c an d in Figure \(12\). In actuality, MT-BPTI does fold to the native form, whose crystal structure is virtually identical to native BPTI. Figure \(12\) shows "2D" folding plots that show HPLC retention time of reactants, intermediates on one axis and folding time on the other for wild-type BPTI (part a) and MT-BPTI (part b). Hence the folding scheme is more complicated than the one shown in Figure \(14\). Some proteins form partially folded but stable intermediates when folded under partially denaturing conditions. A good example is lactalbumin, which under mildly acidic conditions (pH 4), low levels of guanidine HCl, or neutral pH and low ionic strength in the absence of calcium (which normally binds to the protein), forms a stable, isolatable intermediate (I) called the molten globule (MG). Figure \(15\) shows the folded state with a bound calcium ion and circular dichroism spectra of the protein in various states. Data show that the MG is about 50% larger in volume than the N state. This compares to the denatured state, which can be 300% larger than the native state. Hence, it is more like the native state as studied by hydrodynamic techniques, but with more solvent accessibility of hydrophobic side chains. The MG has a similar CD spectrum as the native state, but the aromatic side chains display the same UV absorption and fluorescent characteristics as the protein in 6 M guanidine HCl, suggesting that the final tertiary state has not yet completely formed. The secondary structure in the MG may not be the same as in the native state NMR can also be used to detect folding intermediates. Using this technique, proteins are unfolded in D2O, which will cause the exchange of all Cs with ionizable protons, including, the amide Hs. An amine is a weak base (pKB around 3.5) so its conjugate acid, the protonated amine, has a pKa of around 9.5. An amide or peptide bond would be a weaker base than an amine since it's lone pair is less available (due to delocalization through resonance) for sharing with a proton. The pKa for the conjugate acid of the amide (in which the amide N is protonated and has a plus charge) is much lower, around -0.5, than the pKa for the conjugate acid of an amine. At 2 pH units greater than its pKa, the charged amide N is close to 100% deprotonated The pka of the protonated group is important since the rate of H exchange is related to the pKa, holding other variables constant. The pka of an unprotonated amine (RNH2 → RNH- is very high (30s) and hence deprotonation of the RNH2 amine to form RNH- is not likely under normal conditions. Figure \(16\) shows NMR D/H exchange/protection experiments for the formation of an alpha helix in a protein folding experiment. Refolding is initiated by diluting the protein into a solution without the denaturatant, but still in D2O. As the protein folds and becomes more compact, the buried atoms are now sequestered from the solvent, and no longer readily exchange Ds. Then the protein is placed in H2O at pH 9.0 for 10 ms, after which the pH is changed to pH 4.0. D → H exchange is promoted at high pH, and quenched for the amide Ds and Hs at low pH. Amide Hs that continue to exchange must be accessible to water. Those that aren't are usually buried in secondary structure. How would you interpret these structure and data? Hen Egg white lysozyme: Radford et al, Nature 358, pg 303 (1992) 2YVB Cytochrome C: Elove et al, Biochemistry, 31, pg 6879 (1992). Cyan aromatic amino acids; Heme not shown. 5TY3 When the same techniques are applied to large, multidomain or oligomeric proteins, only a few percent refold in vitro. Incorrect intermolecular interactions and heterogeneous aggregation appear to be the main problems, which prevent correct protein folding in vitro Here is a movie of a 6 us molecular dynamics simulation of the small protein villin. The movie starts with the final crystal structure of villin and show how it folds into its final structure. With permission from the Beckman Institute for Advanced Science and Technology National Institutes of Health //National Science Foundation, Physics, Computer Science, and Biophysics at University of Illinois at Urbana-Champaign The Denatured State Although the structure of native and native-like states can be determined using x-ray crystallography and in solution using NMR, little detailed information exists on the actual structure of denatured and intermediate states. Intermediate states are difficult to trap in a way that allows detailed structural analysis. In contrast to the "native" state which consists of an ensemble of closely related states, intermediates and denatured states would consist of an ensemble of many different states, making structural analysis more difficult. Religa and others from Fersht's lab have engineered a mutant of the engrailed homeodomain (En-HD) from Drosophila melanogaster that allows such structural analyses to be performed. The mutation, Leu16Ala (L16A), destabilizes the protein such that it can be denatured simply by changing ionic strength. It is stable at high ionic strength and folds quickly under those conditions. However, at physiological ionic strength, it is "denatured" but contains significant alpha-helical structure but has nonnative contacts. It behaves like an early folding intermediate in that if placed in solutions of higher ionic strength it rearranges to form the native state. If placed in lower ionic strength, it progressively "unfolds" to yet other states. Given the ambiguities in how to define denatured and early folding intermediates states, Ferscht's group suggests an "explicit" definition of the denatured state. They define the unfolded state (U) as the "maximally unfolded state of a protein, in which the backbone NH groups have little protection against 1H/2H exchange". They define the denatured state, D, as the "lowest energy non-native state under a defined set of conditions". In this scenario, the denatured state could also be a folding intermediate if placed in conditions that promote folding. Previous work from the group showed that the denatured state of En-HD has three helices protected from 2H exchange and was 1kcal/mol l(4.1 kJ/mol) lower in energy than the unfolded state. In section 4.6, we discussed the added complexities to the notion of a simple 2-state D ↔ N model for protein folding. These include Silent Single nucleotide polymorphisms, Metamorphic Proteins and Intrinsically Disordered Proteins (IDPs). Protein Folding In Vivo There are many differences between how a protein might fold or unfold in a cell compared to a test tube. • The total concentration of all the proteins and nucleic acids in cells is estimated to be about 350 g/L, or 350 mg/ml. Most measurements in the lab are conducted in the range of 0.1 to 10 mg/ml • Proteins are synthesized in cells from an N to C terminal direction. Hence the nascent protein, as it emerges from its site of synthesis (the ribosome), might fold into intermediate structures since not all of the protein sequence is yet available for direct folding. • Proteins are synthesized in the cytoplasm, but they have to find their final place in the cell. Some end up in membranes, some must translocate across one or even two different membranes to end up in specific organelles like the Golgi, mitochondria, chloroplasts (in plant cells), nuclei, lysosomes, peroxisomes, etc. Do they translocate in their native state? Additional evidence suggests that protein folding/translocation requires assistance (i.e. catalysis) in the cell. • Mutant cells defective in certain proteins can lead to the accumulation in the cells of misfolded and aggregated proteins. • eukaryotic genes (taken from higher cells, which contain nuclei and internal organelles), when transferred into prokaryotes (bacteria, like E. Coli), can be expressed to form protein, but they often misfold and aggregate in the bacterial cells and form structures called inclusion bodies. Hence recombinant proteins expressed in vivo have the same problems in folding as larger proteins in vitro. In both cases, conditions favor the accumulation of nonnative proteins with exposed hydrophobic groups leading to aggregation. Aggregation also occurs in vivo when a protein is over-expressed or expressed at a higher temperature than normal. Why? Mutant cells have been selected that actually suppress inclusion bodies in vivo. This effect was mediated by a class of proteins, which are expressed by the bacteria and other cells when their temperature is raised. The function of these proteins, called heat shock proteins (Hsp), was unknown until it was realized that they facilitate correct protein folding, in part, by binding to denatured proteins in the cells before they aggregate into inclusion bodies. Further studies discovered a large number of proteins that seem to facilitate protein folding and prevent aggregation in vivo. These proteins are now called molecular chaperones. Many are still named Hsp#, where # is the approximate molecular weight of the protein in kD on a PAGE gel. In a broader sense, a protein homeostatic environment exists within cells to maintain the proteome. This system includes chaperones proteins which facilitate folding and refolding, the proteasome, which cleaves undesired proteins marked by ubiquination, and the lysosome, which facilitates the process of autophagy of structures such as damaged mitochondria. Here we will concentrate on chaperones, which also are involved in protein transport in the cell and preventing aggregation. Some have used novel names to describe the various activities of chaperones. These include holdases/translocases, unfoldases/foldases and disaggregases, which are used to process the various potential intermediates and end products that occur. A simplified 2D free energy folding diagram showing the involvement of chaperones is shown in Figure \(17\). Figure \(17\): 2D Free Energy Folding Diagram with Chaperone Assist. Moseck et al. Front. Mol. Biosci., 14 June 2021 | https://doi.org/10.3389/fmolb.2021.683132 Frontiers in Molecular Biosciences 8, pg 514, 2021 https://www.frontiersin.org/article/...lb.2021.683132. Creative Commons Attribution License (CC BY). The nomenclature used to categorize chaperones is confusing. In general, chaperones, found in all cells, interact with unfolded or misfolded proteins and essentially catalyze their folding. They can do so by removing them from environments that would inhibit folding. Some can prevent aggregate formation or remove them. Some interact with proteins exiting the ribosome as they are being synthesized while some escort damaged proteins to sites of proteolysis. Many chaperons are used to maintain proteostasis. They can be classified as to molecular size or groups based on overall structure and mechanism. We'll use the second approach. Here are some of the key player Cage Chaperonins: Oliogomeric High Molecular Nanoparticles Chaperonins, whose subunits can still be called chaperones, assemble into oligomeric, high molecular weight nanoparticle cages. These complexes are involved in the folding of larger proteins. The complexes consist of two stacked rings. Inside is an inner cavity in which larger proteins can fold in isolation (an easy way to remember the name chaperonin). A somewhat silly analogy would be a person dressing alone in a closet. These complexes are ATPase as the cleavage of ATP drives protein folding. There are two main classes of chaperonins, Class I and Class II Group I: These are found in bacteria, in some archaea and in mitochondria, which derived in evolution through an endosymbiotic relationship with bacteria. Proteins in this group include Hsp60, which forms a homo 14-mer, and the co-chaperone Hsp10, which forms a homo 7-mer. In E. Coli, hsp60 is also called GroEL, while the small Hsp10 is called GroES. GroEL and GroES together form the GroEL/GroES Complex, which is shown in the interactive iCn3D model in Figure \(18\) (note: it loads slowly given its large size). Figure \(18\): GroEL/GroES Complex (1pcq). (Copyright; author via source). Click the image for a popup (which loads slowly) or use this external link: https://structure.ncbi.nlm.nih.gov/icn3d/share.html?JNESMy2hS9ZBTST66 The 14 GroEL subunits form separate 7-mer (magenta) and 7-mer (gray) hollow rings, which stack on each other. The gray ring monomers each have an ADP/AlF3- (spacefill) bound. Seven GroES monomers, shown in cyan, form a "lid" over the end of the complex. This hetero 21-mer displays a C7 global symmetry. Lid binding requires ATP. Figure \(19\) shows a mechanism for the GroEL/GroES protein folding cycle (shown in a linear arrangement). In the left-most structure, a substrate protein (colored line drawing) binds in the cavity formed by the GroEL ring that does not have the GroES 7-mer cap (cyan). Rearrangement of the substrate protein and interactions with the interior wall trigger binding of ATP, binding of the top GroES cap on the cis end (same end as bound protein substrate), and release of the previously bound ADP, the distal trans GroES cap and a folded protein (not shown) from the trans end. Transient interactions and substrate protein conformation changes lead to the folding of the colored protein substrate within the time frame needed for the cleavage of the 7 bound ATPs. Binding of ATP and a new GroES cap at the bottom trigger release of the folded protein at the top along with the top GroES cap. The cycle then repeats with the binding of a new target protein substrate, ATP and a GroES cap leading to the dissociation of the ADP and the other GroES cap. The sequestration of the bound protein substrate clearly prevents aggregation of the folding protein. The binding of any polymer into a confined small volume must be entropically disfavored since the polymer's conformational flexibility is reduced. Somehow an entropic activation energy barrier is reduced inside of the cage, maybe in a fashion similar to the restraining effects of disulfides on protein folding, which allows less conformational space to be explored on folding. GroEL has also been shown to bind in its hydrophobic cavity a fluorescent CdS semiconductor nanoparticle which can be released on addition and cleavage of ATP Group 2: These are found in Archaea and in eukaryotes. The eukaryotic chaperonin in this group is CCT (cytosolic chaperonin containing TCP1) also known as TRic (tailless complex polypeptide 1 ring complex or TCP1-ring complex). These also contain two stacked rings but this time they are 8-mers of molecular weight 50-60K. This is similar to the Hsp60 monomers in the GroEL/GroES complex, but the monomer in Group 2 are not named with the beginning letters Hp. In addition, the complex does NOT have a cap like the GroES co-chaperone 7-mer in the GroEL/GroES complex. Rather, they have a built-in cap that closes on folding. The CCT/TRic chaperonin complex interacts with other "co-chaperones" including a prefoldin 6-mer complex, which inhibits aggregation and which "delivers" the protein substrate to CCT/TRic. It also binds to phosducin-like proteins. Well know substrate proteins for CCT/TRic include actin and tubulin (cytoskeletal proteins) and proteins involved in cell cycle control, but many other proteins (up to 10% of cytosolic proteins) might interact with it. The genes encoding the 8 monomers in CCT/TRic arose from gene duplication and subsequent mutation so they are different from each other (technically they are called paralogs). The subunits are named α, β, ξ, δ, ε, ζ, η and θ. Figure \(20\) shows generalized structures of CCT/TRic. Panel A shows a cryoEM of the complex with one subunit outlined in white and an adjacent PDB structure of the alpha subunit (1A6D).The red domain binds ATP. The relative arrangements of the monomers is shown in B. The Human TRiC/CCT complex (7LUP) with a protein substrate, reovirus outer capsid protein sigma-3, bound in the internal cavity is shown in the interactive iCn3D model in Figure \(21\) (note: it loads slowly given its large size). Figure \(21\): Human TRiC/CCT complex with reovirus outer capsid protein sigma-3 (7LUP) (Copyright; author via source). Click the image for a popup (which loads slowly) or use this external link: https://structure.ncbi.nlm.nih.gov/i...Dxr3n6oy6zLsZ7 The cyan spacefill protein substrate is the encapsulated reovirus outer capsid protein sigma-3. The top ring 8-mers are shown in gray ribbon while the bottom ones are shown in different colors. It appears that each ring binds 4 ATPs for a total of 8 when the "attached lids" are closed. Non-cage chaperones A variety of chaperones that are not part of the nanoparticle chaperonin complex are also involved in protein folding in vivo. Some classify chaperones on the basis of molecular weight into 5 classes, Hsp60 (which we discussed above as monomers in the chaperonin cage complexes), Hsp70, Hsp90, Hsp104, and the small Hsps. We'll focus mostly in Hsp70 and its "co-chaperone", Hsp 40, and their bacterial analogs, DnaK and DnaJ, respectively. Hsp70/Hsp 40: Hsp70 binds to hydrophobic regions of proteins which are more prevalent in unfolded and partially folded proteins and helps refold them through repeated cycles of binding and release, which is dependent on ATP cleavage. It also helps unfolded proteins through membranes and helps form/dissociate protein complexes. It's found in the cytoplasm and in a variety of organelles including chloroplasts, mitochondria, nuclei and the ER. There is a family of Hsp70 proteins (1, 1A, 1B, 2, 3, 4, 4L, 5–10, 12–18). Hsp70 proteins are made up of two regions. The nucleotide binding domain (NBD), which has intrinsic low ATPase activity, is located in the N-terminal region. The polypeptide substrate binding domain (SBD) is located in the carboxyl terminal end. It transiently recognizes short peptides in the target protein. A flexible stretch of conserved amino acids connects the two domains. The substrate binding domain has a beta sheet where peptide substrates bind and an alpha-helical region that acts like a lid. From an evolutionary sense, they are one of the most conserved proteins. This shows the critical importance of protein folding to life. Some members of the Hsp70 family include DnaK (a bacterial Hsp70 that has been widely studied along with its Hsp40 co-chaperone partner, DnaJ) and BiP (HSPA5) that assists protein folding in the ER. and alpha crystalline in eukaryotes. Alpha crystalline comprises 30% of the lens proteins in the eye, where it functions, in part, to prevent nonspecific, irreversible aggregates. Some Hsp70 proteins are constitutively expressed, while others are induced by increased temperatures or other stress perturbants that affect protein structure, including radiation, inflammation and exposure to heavy metals. They are key to the maintenance of proteostasis. Hsp70 proteins: • bind to growing polypeptide chains as they are synthesized on ribosomes. • express activity as monomers. • have ATPase activity - i.e. they cleave the phosphoanhydride ATP (which can drive reactions). • bind short, extended peptides, which stimulates the ATPase activity • release bound peptides after ATP cleavage Hsp70s are highly flexible so it is difficult to get detailed structural information comparing bound (to target peptides) and free states. Hsp70 appears to clamp onto hydrophobic regions of target proteins, making transient interactions characterized by multiple binding and release steps, until protein folding is complete. Hence the affinity for the Hsp70 proteins must be low enough (i.e. high dissociation constant KD where the KD is the inverse of the equilibrium binding constant as we will see in Chapter 5) for the target protein for many binding and release steps. The affinity of the SBD for the target protein is regulated by the nucleotide binding domain (NBD). When ADP is bound, the Hsp70 is in a closed state and has a high affinity for substrate, so the release rate for bound target protein is slow. When ATP is bound, Hsp 70 is in the open state in which the rate of association increases 100 fold and the rate of dissociation increases 1000 fold, so the affinity is lowered significantly. This enables multiple cycles of release, folding, and rebinding to occur. As mentioned above, Hsp70s have intrinsic but low ATPase activity. When ATP is cleaved to ADP, Hsp70 returns to the closed, high-affinity state. Hence ATP hydrolysis affects both the binding of protein substrate) and kinetics of folding. This activity increases with the binding of protein substrates as well as with the binding of co-chaperones containing what is called a J domain (as is found in the bacterial co-chaperone DnaJ). Hence considerable conformational changing and signaling occur between the NBD and SBDs in a process we will call allosterism in Chapter xx. The allosteric Hsp70 catalytic cycle is shown in Figure \(22\). Figure \(22\): Hsp70 Catalytic Cycle for Protein Folding. Stetz G, Verkhivker GM (2015) Dancing through Life: Molecular Dynamics Simulations and Network-Centric Modeling of Allosteric Mechanisms in Hsp70 and Hsp110 Chaperone Proteins. PLoS ONE 10(11): e0143752. https://doi.org/10.1371/journal.pone.0143752. Creative Commons Attribution License, Let's explore the catalytic cycle starting the left structure. (note that NBD subdomains are colored as follows: IA (in blue), IB (in red), IIA (in green), IIB (in cyan)) • Left structure (9 o'clock): ADP is bound so Hsp70 is in the closed (high affinity for protein substrate)state. The SBD consists of two parts or subdomains, the α-lid (magenta) section and the subdomain comprised of β-sheets (orange), where the actual protein substrate binds (not shown here). The α-helical lid of the SBD helps to keep the protein substrate from dissociating. The PDB ID for this structure is 2KHO, but if you explore this structure, no protein substrate is evident. The two domains of Hsp70, the SBD and the NBD, are connected by the flexible interdomain linker (black), are not interacting so they are shown in the domain undocked state. • Top (12 o'clock): This represents an "intermediate" in which ATP is exchanging for bound ADP and the complex is changing to the open form, but without the release of protein substrate. Note that the α-lid is no longer interacting as tightly with the β-sheet subdomain of the SBD. • Right (3 o'clock): The protein substrate (again not shown) is fully released, the α-lid is fully disengaged from the β-sheet subdomain of the SBD, and Hsp70 is in the open (to substrate binding) conformation. The SBD β-sheet subdomain and the NBD domains are still engaged and are shown in the domain-docked conformation. • Bottom (6 o'clock): Weak protein substrate binding leads to an "intermediate" in which the α-lid is starting to engage with the β-sheet subdomain of the SBD. Substate binding promotes ATP hydrolysis, which occurs as Hsp70 returns to the full closed conformation (left structure, 9 o'clock) with bound ADP Figure \(23\) shows the incredible conformational change that occurs on movement from the ADP-bound higher affinity closed form of E. Coli Hsp70 (DnaK) (2kho) to the ATP-bound lower affinity, domain-docked open(to substrate binding) form (4jne). Note how the alpha-helical lid in the left-hand side RBD moves to engage with the NBD on the right-hand side. Figure \(24\) how the chaperonins and Hsp70/40 (DnaK/DnaJ) work in concert. A few other chaperones acting downstream from Hsp70/Hsp40 and DnaK/DnaJ are shown in the image: • TF (Trigger factor) and NAC/RAC: these are ribosome-binding chaperones • Prefoldin (Pfd): bind unfolding proteins and shuffles them off to TRic. • Hop: mediates interactions between Hsp70/Hsp40 and Hsp90 • Hsp90: This chaperone targets protein receptors (including steroid hormone receptors, protein kinases, and proteins with nucleotide-binding site and leucine-rich repeat (NLR) domains as are found in inflammasomes) and has a cycle similar to Hsp70 with some differences. Hsp90 has an N-terminal nucleotide binding domains, middle domains that interact with protein substrates, and C-terminal dimerization domains so the protein exists as a homodimer, When bound to ADP or in the absence of nucleotides, the homodimer forms an open V-shape conformation (as shown in the figure above). When ATP binds a huge conformation change occurs that closes the V shaped clamp. The protein has intrinsic ATPase activity, which reverses the conformational change. Many different co-chaperones bond to the HSP90 dimer and modulate the cycle at different points. Additional Proteins also catalyze protein folding at key steps in the process. Here are two examples. • Protein Disulfide Isomerase (PDI) - catalyzes the conversion of incorrect to correct disulfides. The active site consists of 2 sets of the the following sequence - Cys-Gly-His-Cys, in which the pKa of the cysteines are much lower (7.3) than normal (8.5). How would this facilitate disulfide isomerization? • Peptidyl Prolyl-Isomerase - catalyzes X-Pro isomerization, by a mechanism, which probably involves bending the X-Pro peptide bond. How would this facilitate the process? Many proteins have been found to possess PPI activity. One class is the immunophilins. These are small proteins found in the cytoplasm that bind anti-rejections drugs used to prevent tissue rejection after transplantation. The immunophilin FK506 binding protein (FKBP) binds FK506 while the protein cyclophilin binds that anti-rejection drug cyclosporin. The complex of cyclophilin:cyclosporin or FKBP:FK506 binds to an inhibits calcineurin, an important protein (with phosphatase activity) in immune cells (T cells) required for T cell function. In this case, immunophilin:drug binding to calcineurin inhibits the activity of the T cell, preventing immune attack on the transplanted tissue and rejection. The immunosuppressant drugs (FK506 and cyclosporin) inhibit the PPI activity of their respective immunophilin. The extent to which the PPI activity of cyclophiin is required for its activity is unclear, but it seems to be important for some of its biological effects. Given the complexity of protein folding and the large number of chaperones involved in in vivo folding, it should not be a surprise that chaperone proteins bind to protein substrates through more than just hydrophobic interactions. Some chaperones (Spy and Trigger Factor) bind to charged regions on the surface of proteins, while the ER proteins calnexin and calreticulin bind to carbohydrates on glycoproteins. GroEL/GroES and TRiC/CCT also interact through electrostatic attractions with protein substrates. Nucleoplasm, a chaperone protein found in the nucleus, binds to histones which are strongly costively charged DNA binding proteins. Unfolded Protein Response As the site responsible for the folding of membrane proteins and proteins destined for secretion, as well as the major site for lipid synthesis, the endoplasmic reticulum (ER) must be able to maintain homeostatic conditions to ensure proper protein formation. Plasma cells that synthesize antibodies for secretion as part of the immune activation, show large increases in protein chaperones and ER membrane size The main pathway controlling ER biology is the unfolded protein response (UPR) signaling pathway. If demand for protein synthesis in the ER exceeds capacity, unfolded proteins accumulate. This ER stress condition activates a protein called IRE1, a transmembrane Ser/Thr protein kinase (which phosphorylates proteins). IRE1 activates a transcription factor that controls the transcription of many genes associated with protein folding in the ER. Another protein, ERAD (ER-associated degradation) moves unfolded proteins back into the cytoplasm where they are degraded by the proteasome. Proteins involved in lipid synthesis are also activated as lipids are needed for membranes as the ER increases in size. If the stress can not be mitigated the signaling pathway leads to programmed cell death (apoptosis). Schuck at al investigated the specific role and importance of UPR in the homeostasis of ER as modeled by the yeast Saccharomyces cervisiae. The UPR signaling pathway was analyzed using light and electron microscopy to visualize and quantify ER growth under various stress conditions. Western blotting procedures were performed to determine chaperone protein concentrations after stress induction and association with ER expansion after the ER was exposed to various treatment conditions. The authors found ER membrane expansion occurred through lipid synthesis since stress induction increased concentrations of proteins responsible for promoting lipid synthesis and expansion failed when the proteins were absent and lipid concentration was low. In addition, these lipid synthesis proteins were activated by the UPR signaling pathway. By separating ER size control and UPR signaling, they found that expansion occurred regardless of chaperone protein concentrations. However, if lipid synthesis genes were not available, raising the ER chaperone level helped alleviate stress levels in ER. Redox Chemistry and Protein Folding In general, we envision the interior of a cell to be in a reducing environment. Cells have sufficient concentrations of "b-mercaptoethanol"-like molecules (used to reduce disulfide bonds in proteins in vitro) such as glutathione (g-Glu-Cys-Gly) and reduced thioredoxin (with an active site Cys) to prevent disulfide bond formation in cytoplasmic proteins. For disulfide bonds to occur in a protein, a free sulfhydryl reacts with another one on a protein to form the more oxidized disulfide bond. This reaction occurs more readily if one of the Cys side chains had a lowered pKa (due to its immediate environment) making it a better nucleophile in the reaction. Most cytoplasmic proteins contain Cys with side chain pKa > 8, which would minimize disulfide bond formation as the Cys are predominantly protonated at that pH. Disulfide bonds in proteins are typically found in extracellular proteins, where they serve to keep multisubunit proteins together as they become diluted in the extracellular milieu. These proteins destined for secretion are cotranslationally inserted into the endoplasmic reticulum (see below) which presents an oxidizing environment to the folding protein and where sugars are covalently attached to the folding protein and disulfide bonds are formed (see Chapter 3D: Glycoproteins - Biosynthesis and Function). Protein enzymes involved in disulfide bond formation contain free Cys which form mixed disulfides with their target substrate proteins. The enzymes (thiol-disulfide oxidoreductases, protein disulfide isomerases) have a Cys-XY-Cys motif and can promote disulfide bond formation or their reduction to free sulfhydryls. They are especially redox-sensitive since their Cys side chains must cycle between and free disulfide forms. Intracellular disulfide bonds are found in proteins in the periplasm of prokaryotes and in the endoplasmic reticulum (ER) and mitochondrial intermembrane space (IMS) of eukaryotes. For these proteins, the beginning stage of protein synthesis (in the cytoplasm) is separated temporally and spatially from the site of disulfide bond formation and final folding. Disulfide bonds can be generated in a target protein by concomitant reduction of a disulfide in a protein catalyst, leaving the net number of disulfides constant (unless the enzyme is reoxidized by an independent process). Alternatively, a disulfide can be formed by the transfer of electrons to oxidizing agents such as dioxygen. In the ER, disulfide bond formation is catalyzed by proteins in the disulfide isomerase family (PDI). To function as catalysts in this process, the PDIs must be in an oxidized state capable of accepting electrons from the protein target for disulfide bond formation. A flavoprotein, Ero1, recycles PDI back to an oxidized state, and the reduced Ero1 is regenerated by passing electrons to dioxygen to form hydrogen peroxide. In summary, on formation of disulfides in the ER, electrons flow from the nascent protein to PDIs to the flavin protein Ero1 to dioxgen (i.e. to better and better electron acceptors). The first step is really a disulfide shuffle, which, when coupled to subsequent steps, leads to de novo disulfide bond formation. In the mitochondria, disulfide bond formation occurs in the intermembrane space (IMS) and is guided by the mitochondria disulfide relay system. This system requires two important proteins: Mia40 and Erv1. Mia40 contains a redox-active disulfide bond cys-pro-cys and oxidizes cys residues in polypeptide chains. Erv1 can then reoxidize Mia40 which can in turn get reoxized by the heme in cytochrome c. Reduced cytochrome C is oxidized by cytochrome C oxidase of electron transport through the transfer of electrons to dioxygen to form water. The importance of IMS protein oxidation is less understood, but it is believed that the oxidative stress caused by a dysfunction could lead to neurodegenerative diseases. A recent review by Riemer et al compares the ER and mitochondrial processes for disulfide bond formation: • Many more and diverse proteins form disulfides in the ER compared to the IMS. Most in the IMS have low molecular mass and have two disulfide bonds between helix-turn-helix motifs. These protein substrates include chaperones that facilitate the localization of proteins in the inner membrane, and in proteins involved in electron transport in the inner membrane. • There are many PDIs in the ER, probably reflecting the structural diversity of protein substrates in the ER. However, Mia40 appears to be the only PDI in the IMS. • "De novo" disulfide bond formation is initiated by Ero1 in the ER and Erv1 in the IMS. Convergent evolution led to similar structures for both - a 4-helix bundle that binds FAD with two proximal Cys. • The mitochondria pathway leads to water formation on reduction of dioxygen, not hydrogen peroxide, minimizing the formation of reactive oxygen species in the mitochondria. The peroxide formed in the ER is presumably converted to an inert form. • The IMS is in more intimate contact with the cytoplasm through outer membrane proteins called porins which would allow some glutathione access. The IMS presents a more oxidizing environment than the cytoplasm (with more glutathione). The ER, without a porin analog, would be more oxidizing. • The reversible formation of disulfides in the ER regulates protein activity. Disulfide bond regulation in the Periplasmic Space of Bacteria The redox sensitivity of the Cys side chain found in disulfide bonds is important in regulating protein activity. In particular, the thiol group of the amino acid Cys, an important nucleophile often found in the active site, can be modified to control protein activity. The formation of a disulfide bond or the oxidation of free thiols to sulfenic acid or further to sulfinic or sulfonic acid can block protein activity. The E. Coli periplasmic protein DsbA (disulfide bond A) converts adjacent free thiols into disulfide-linked Cystine, in the process becoming reduced. DsbB is reoxidized by DsbA back to its catalytically active form. What about periplasmic protein like YbiS with an active site Cys? Since the environment of the periplasm is oxidizing, YbiS is protected from oxidative conversion of the free Cys to either sulfinic or sulfonic acids causing the protein to become inactive. The mechanism involves two periplasmic proteins known as DsbG and DsbC which are similar to thioredoxin. These two proteins are able to donate electrons to the unprotected thiol preventing it from becoming oxidized, which allows YbiS to remain active in the periplasm. To maintain activity, DsbG and DsbC are reduced by another periplasmic protein, DsbD. Protein Transport Across Membranes How does a protein "decide" its final location after synthesis? Protein synthesis occurs in the cytoplasm, but proteins may end up outside of the cell, in cell membranes, internalized into various organelles, or remain in the cytoplasm. How is the decision made? There must be signals in the protein which target proteins to various sites in a cell, where processing can occur. Proteins that are destined for secretion or plasma membrane insertion typically have a signal peptide at the N-terminus which binds to a signal recognition particle in a cotranslational process, which temporarily arrests translation and nascent folding. This complex docks to signal recognition complex docking sites in the endoplasmic reticulum membrane, where translation continues as the nascent polypeptide extends through a protein pore in the ER membrane. Transport across the ER membrane can also occur partially or fully in a post-translational process if nascent proteins are partially or fully folded through interactions with cytoplasmic chaperones such as Hsp70/40. Figure \(25\) shows the cotranslational (a) and post-translation (b) pathways for uptake into the ER lumen. In both (a) and (b), the protein is shown during synthesis as it is bound to the ribosome (40S/60S) nanoparticles. The cytoplasmic signal recognition particle (SRP) binds to the hydrophobic signal sequence of the nascent protein. The signal is typically at or near the N-terminus of the growing protein. Either co- or post-translationally, the nascent protein is delivered to Sec61, which is both a ribosome receptor and a gated pore for passages of the target protein. Sec61 is part of a larger ER translocon complex which also includes Sec62 and Sec63. The membrane topology and subunit structure of the Sec proteins are shown in (c). Addition proteins including the chaperones BiP Hsp70/40 are also shown. Protein transport across the endoplasmic reticulum membrane. Mechanism of (a) co-translational and (b) posttranslational transport of precursor proteins through the Sec61 channel. (c) Topological domains of Sec61α1/ß/γ, (d) Sec62 and (e) Sec63. We note that (i) Sec63 interacts with Sec62 involving a cluster of negatively charged amino-acid residues near the C terminus of Sec63 and positively charged cluster in the N-terminal domain of Sec62, (ii) Sec62 interacts with the N-terminal domain of Sec61α via its C-terminal domain, (iii) BiP can bind to ER luminal loop 7 of Sec61 α via its substrate-binding domain and mediated by the ATPase domain of BiP and the J-domain in the ER luminal loop of Sec63, (iv) Ca2+-CaM can bind to an IQ motif in the N-terminal domain of Sec61α and (v) LC3 can bind to a LIR motif in the C-terminal domain of Sec62. 40S, 40S ribosome subunit; 60S, 60S ribosome subunit; SR, heterodimeric SRP receptor; SRP, signal recognition particle. Figure \(26\) shows an interactive iCn3D model of the Sec Complex from yeast (6ND1). Figure \(26\): Sec Complex from yeast (6ND1). (Copyright; author via source). Click the image for a popup (which loads slowly) or use this external link: https://structure.ncbi.nlm.nih.gov/i...6VoV8gTiUCQko8 The model shows Sec 61 and SEc 63 from Figure 25 above. Sec 61 is the ER protein-conducting channel (the analog in prokaryotes is Sec Y). The model is colored as follows: • Protein transport protein SEC61 (B)- magenta • Protein translocation protein SEC63 (A) - blue • Protein transport protein SSS1 - darker brown • Protein transport protein SBH1 - green • Translocation protein SEC66 - gold • Translocation protein SEC72 - salmon If the signal sequence of the protein to be imported not very hydrophobic, it doesn't bind the signal recognition particle, and hence is imported post-translationally. For these proteins, the Sec61 channel requires additional proteins, Sec62 and Sec63. These required also the BiP Hsp70/40 ATPase for import. Sec63 opens a gate on Sec61 leading to a wide opening, allowing proteins into the lipid bilayer. If destined for secretion, a protein enters the lumen of the ER. Proteins destined for insertion into the cell surface membrane gets "stuck" in the ER membrane, and through a process of vesiculation merges with the Golgi and eventually with the cell surface membrane. Proteins that are taken into organelles like mitochondria are done so in a post-translational process that requires facilitation by protein chaperones. Final protein folding occurs inside the organelle. In both cases, nonnative proteins pass through the membrane after which final folding occurs. An intriguing question is how the decision is made to keep a protein either in the membrane or allow it to pass through completely (in the case of proteins destined for secretion). Hessa et al investigated this "decision-making" process by studying the eukaryotic membrane pore protein complex, Sec 61 translocon (show in the above figures), whose activity must be closely regulated with the folding of the growing protein. In studying this process, they considered three local regions in a membrane: the hydrophobic region comprised of the nonpolar acyl tails of membrane lipids, the interfacial region in the vicinity of the polar head groups, and the aqueous regions (bulk water) on each side of the head groups. A 19 amino acid peptide was used as the experimental model protein which was added to the translocon. This size was chosen since it is just long enough to span the hydrophobic part of the membrane if the peptide were in an alpha-helical conformation (which is common in membrane-spanning proteins). They varied the proportion of amino acids that tend to partition into each of three regions and studied the disposition of the peptide after interaction with membrane and translocon. To test if the results were consistent with the thermodynamics of amino acid partitioning into nonpolar environments (and not kinetic considerations), they used the Wimley and White hydrophobicity scale, based on the free energy of transfer of amino acid side chains into nonpolar environments, to predict target peptide disposition with the membrane. The table below shows the propensity of amino acids to be in each region at equilibrium, based on this hydrophobicity scale. Table: Amino Acid Partitioning Into Membrane Regions Region Amino Acids Bulk water Arg, Asn, Asp, Gln, Glu, His, Lys, Pro Bulk water + interfacial Ala, Cys, Gly, Ser, Thr Interfacial Tyr Hydrophobic Ile, Leu, Met, Phe, Trp, Val Their experimental results were in concordance with those predicted using the above scale. If a polyalanine 19 mer was used, no insertion was observed. With five leucines in the peptide, almost 90% was inserted into the membrane. The results would be modeled using a two-state equilibrium: Peptide inserted ↔ Peptide translocated. They then substituted each of the twenty amino acids into a given position into a target peptide and used the results to develop an empirical scale for membrane transfer, not one based on the simple transfer to nonpolar medium. This new scale matched the hydophobicity scale, suggesting insertion and transfer decisions were based on the thermodynamics of side chain partitioning. They also varied the position of the varied amino acid in the test peptide. If the amino acid favored the bulk and/or interfacial region, the peptide would be inserted if that amino acid were at the end of the peptide, not the middle. For translocation, the peptide had to be amphiphilic with one face polar and the other nonpolar. They developed a simple equilibrium model to show the processes involved, as shown below in a top-down view of the membrane in Figure \(27\). The translocon, shown in green, has a water-filled pore but also a sidewise opening toward the membrane interior. The target peptide enters the pore. Transient conformational changes in the pore expose the peptide to the nonpolar membrane core. The target peptide samples both the aqueous and nonpolar environments and partitions into them based on the considerations mentioned above. If it partitions more favorably into the hydrophobic core, it will do so and cause the peptide to become membrane-bound. Otherwise, it will pass through to the other side. This can be modeled as an equilibrium process if the rate of translocation is slow compared to the rates of translocon conformational change and environmental sampling by the peptide. Obviously, the process becomes more complicated if the target is a large protein. Bacterial toxin proteins also have evolved ways to pass through a cell membrane, again in a nonnative state, through a protein channel in the membrane. Krantz et al have recently worked out details of how the anthrax toxin protein moves through eukaryotic cell membranes. Three anthrax proteins are involved. One is a "prepore" protein that binds to specific proteins on the cell membrane, where it is activated by limited proteolysis to form a pore protein, which assembles into the homoheptamer prepore in the membrane. Two other proteins secreted by the bacteria, lethal factor and edema factor, bind to the heptamer complex and the whole assembly is then taken up into the cell by invagination to form a vesicle with the pore complex in the membrane. This vesicle fuses with a lysosome in the cell, and upon acidification, a conformational change occurs in the prepore complex to activate it. The lethal and edema factors unfold partially, possibly to a molten globule state, and are then passed through the pore into the cell where they exert their toxic influences. An electrochemical potential gradient (which we will discuss later in the semester) is required for the passage of the factors through the membrane. The active pore further unravels the factor protein, facilitating transport. Krantz et al. studied the pore protein by mutating two amino acids, Phe427 and Ser 429, on each monomer of the pore to Cys. They then modified the Cys with [2-(trimethylammonium) ethylmethanethiosulfonate and observed effects on ion conductance of the pore and pore conformations. They noted that when both residues were mutated and chemically modified, that ion conductance was blocked, suggesting that these side chains were localized in the narrowest part of the channel. When Phe 427 alone was mutated to smaller side chains (Ala), ion conductance increased but the transfer of peptides from the factor proteins was inhibited. This suggested that an aromatic ring in the narrow part of the channel opening participates in the translocation of bacterial proteins through the membrane. They then analyzed the transport of a variety of small molecules with varying hydrophobicity through the wild-type pore. Their results were consistent with the binding of the molecules through hydrophobic and aromatic electron interactions. They suggest a mechanism of transport consistent with their data in which the unfolded protein "ratchets" through the pore, which promotes factor protein unfolding to expose more hydrophobic groups to the nonpolar aromatic ring in the pore. This mechanism is similar to how the chaperone complex GroEL/GroES unfolds protein in its large central cavity in a process which requires hydrolysis of ATP, not a transmembrane potential. In addition, the Sec61 translocon in the inner membrane of bacteria and in eukaryotic ER membranes also has a pore containing a ring of hydrophobic groups (Ile).

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/04%3A_The_Three-Dimensional_Structure_of_Proteins/4.08%3A_Protein_Folding_and_Unfolding_%28Denaturation%29_-_Dynamics.txt

Search Fundamentals of Biochemistry Introduction to Protein Stability This material is not easy, and is perhaps the most intellectually challenging of the entire book. Much of the organizational framework for this section comes from an article by Ken Dill, Biochemistry, 29, 7133-7155 (1990) as that article so clearly defined the factors that contribute to protein stability. Extrapolating from the results of studies of the transfer of small molecule H bond donors/acceptors and hydrophobic molecules from water to nonpolar solvents, it would appear that H bond interactions (as well as ion ..ion interactions) do not drive protein folding per se. Rather, the biggest contributors to the stabilization of the native state are the hydrophobic effect and the van der Waals interactions among the tightly packed buried atoms of the protein. However, using actual data from wild-type and mutant proteins of known structure, it appears that H bonds contribute significantly to protein folding and stability, and may make a greater contribution to the stability of the native state than the hydrophobic effect. The main factor which opposes folding is chain conformational entropy as folding proceeds from very populated denatured states to a "single" folded state. These positive and negative factors sum up to a small negative ΔG favoring protein folding, implying marginal stability of the native protein at normal temperatures. What types of noncovalent forces might act within a protein and between proteins and solvent molecules that would cause a protein to fold spontaneously to a unique 3D structure? These forces can be long range (ion-ion, ion-dipole, or dipole-dipole) or short range (van der Waals repulsive and attractive forces). The interactions can be local (between adjacent amino acids in the linear sequence) or nonlocal (between sequences separated in the linear sequence but brought close together in 3D space). Clues as to what stabilizes the tertiary structure of a native protein can be gained by subjecting proteins to agents that unfold or denature proteins. Such agents include extremes of pH, high concentrations of some salt solutions or organic solvents, and temperature extremes. Such experiments show that native proteins are only marginally stable (about 0.4 kJ/mol amino acid - or around - 10 kcal/mol (-42 kJ/mol) for a protein of molecular weight of 10,000 - about 100 amino acids). This is equivalent to the stability provided by 2 H bonds. We will consider the different types of noncovalent attractions (ion-ion, H bonds, van der Waals), and stabilizing influences arising from the hydrophobic effect. and ask if each is a significant driving force for protein folding. Figure $1$ shows the relative contributions to the ΔG for protein folding. Most of this chapter will deal with H bonding and the hydrophobic effect. A theme of any biochemistry course is that if you can understand the interactions among small molecules, you can apply that knowledge to the understanding of larger molecules like proteins. To understand if H bonds within proteins, often buried in the more hydrophobic interior of the protein, drive protein folding, we will first examine the thermodynamics of H bond formation of a small molecule, N-methylacetamide, in water and in a nonpolar solvent. To understand if the hydrophobic effect, mediated by the burying of nonpolar side chains within the more nonpolar center of the protein, drives protein folding, we will examine the thermodynamics of benzene solubility in water. Most recent studies involve the creation of specific mutants at amino acid positions that might reveal the contributions of H bonding and the hydrophobic effect to folding and protein stability. Ion - Ion Interactions These could be investigated by altering pH or ionic strength. Why is that? a. General Charge Interactions - Proteins denature at either low or high pHs where they have a maximal positive charge or negative charge, respectively. Small proteins that seem to fit a simple two-state folding model (F ↔ U) have a characteristic melting temperature (TM) at which the 50% of the proteins in the population are unfolded. The higher the TM, the more stable the protein. The graphs of the TM for different proteins at different pH values are shown in Figure $2$. Under extremes of pH (but not so great as to catalyze peptide bond cleavage, electrostatic repulsions would cause the protein to denature. The folded, compact state has an increasing charge density (q/Vol) at pH extremes, which could be alleviated by unfolding to a less dense state. But what about specific charge pair interactions? In contrast to the general charge interactions, these might actually stabilize a protein. Are they the predominant factor that determines stability? b. Specific Charge Interactions (charge pairs) - If ion pairs are the source of protein stability, you would expect that high salt could disrupt them, and lead to denaturation. Although some salts do denature proteins, others stabilize them. Other evidence argues against this idea. Ion pairs are not conserved in evolution. In addition, the number of ion pairs in proteins is small (approx. 5/150 residues, with one of those on average buried). Also, the stability of a protein shows little dependence on pH or salt concentration (at low concentrations) near the isoelectric point, the pH at which proteins have a net zero charge. The overall charge state affects not only the stability of a protein but also its solubility. Proteins are most insoluble at their isoelectric point pI since at that pH value (where they have a net 0 charge) the proteins experience the least electrostatic repulsion and are most likely to aggregate and precipitate. Low salt concentration also promotes insolubility. Mutagenesis studies show that solubility can be increased by replacing nonpolar groups on the surface with polar ones. Pace (2009) cites studies on RNase Sa which has a maximally exposed Thr 76. If it is replaced with aspartic acid, the solubility increased to 43 mg/ml but if it is replaced with tryptophan, it decreased to 3.6 mg/ml. His, Asn, Thr and Gln have a negative effect on solubility near the pI compared to Ala, a surprising result. Similar results were obtained compared to Ala when Arg and Lys were used. Smaller side chains, Asp and Ser, at position 76 increased the solubility over Ala. Hydrogen Bonding Linus Pauling first suggested that H bonds (between water and the protein and within the protein itself) would play a dominant role in protein folding and stability. It would seem to make sense since amino acids are dipolar and secondary structure is common. Remember, however, the H bonds would be found not only in the native state but also in the denatured state. Likewise, there are H bonds between water and proteins in both the native and denatured states. Do H bonds in proteins contribute differently to the stability of the D vs N states? Many experimental and theoretical studies have been performed investigating helix ↔ (random) coil transitions in small peptides. Remember all the intrachain H bonds in the helix? Are they collectively more stable than H bonds between water and the peptide in a (random) coil? In this section, we will explore the effects of H bonds on small molecules and infer from them the likely contributions of H bonds to protein stability. After all, if we can't understand small molecules and their interaction, how can we understand the same interactions in big ones like proteins? We will see however that when we use modern tools of site-directed mutagenesis to explore H bonds in proteins, we come to different conclusions as to their relative roles. So consider this immediate section an exercise in model building and critical thinking. In thinking about conformational studies involving small peptides, it is useful to apply Le Chatelier's Principle to the equilibrium below: random coil ↔ helix Anything perturbant (small molecules, solvent, etc) that would preferentially interact with the helical form would push the equilibrium to the helical form. Early models assumed that intrachain H bonds were energetically (enthalpically) more favorable than H bonds between peptide and water. But to form a hydrogen bond requires an entropy payback since a helix is much more ordered (lower entropy) than a random coil (higher entropy). At low temperatures, enthalpy predominates and helix formation in solution is favored. At high temperatures, the helix is disfavored entropically. Imagine the increased vibrational and rotational states permitted to the atoms at higher temperatures. Theoretical studies on helix-coil transitions predicted the following: • as the chain length increases, the helix gets more stable; • increasing the charge on the molecule destabilizes the helix, since the coil, compared to the more compact helix, has a lower charge density; • solvents that protonate the carbonyl oxygen (like formic acid) destabilize the helix; and • solvents that form strong H bonds compete with the peptide and destabilize the helix. In contrast, solvents such as CHCl3 and dimethylformamide (a nonprotic solvent), stabilize the helix. Likewise, 2-chloroethanol and trifluoroethanol, which form none or weaker H bonds to the peptide than water, stabilize the helix. (In the case of trifluoroethanol, molecular dynamics simulations have shown that TFE preferentially interacts with (solvates) the peptide, which inhibits H bonds from the peptide backbone to water, stabilizing the intrahelical H bonds. These helix-coil studies suggest that H bonds are important in stabilizing a protein. But do they really? Why should these H bonds differ from those in water? It's difficult to figure out whether they are since there are so many possible H bonds (between protein and water, water and water, and protein and protein), and their strength depends on their orientation and the dielectric constant of the medium in which they are located. If intrachain H bonds in a protein are not that much different in energy than intermolecular H bonds between the protein and water, and given that proteins are marginally stable at physiological temperatures, then it follows that the folded state must contain about as many intramolecular hydrogen bonds within the protein as possible intermolecular H bonds between the protein and water, otherwise the protein would unfold. To resolve this issue, and determine the relative strength of H bonds between the varying possible donors and acceptors, many studies have been conducted to compare the energy of H bonds between small molecules in water with the energy of H bonds between the same small molecules but in a nonpolar solvent. The rationale goes like this. If the interior of a protein is more nonpolar than water (lower dielectric constant than water), then intrastrand H bonds in a protein might be modeled by looking at the H bonds between small molecules in nonpolar solvents and asking the question, is the free energy change for the following process less than zero: Dw + Aw ↔ (DA)n, ΔGo, K where D is a hydrogen bond donor (like the H in N-H) and A is a hydrogen bond acceptor, (like the O in C=O), w is water (i.e. donor and acceptor are in water), and n is a nonpolar solvent, and ΔGo and K are the standard free energy change and the equilibrium constant, respectively, for the formation of a H-bond in a nonpolar solvent from a donor and acceptor in water. This reaction simulates H-bond contributions to protein folding, where a buried H-bond is mimicked by a H-bond in a nonpolar solvent. The reaction written above is really based on a thought experiment since it would be hard to set up the necessary conditions to make the measurement. However, we can calculate the ΔGo for this reaction since it is a state function and it really doesn't matter how one accomplishes this process. You learned about state functions in introductory chemistry courses so this should not be a new concept. Remember a state function is one in which the variable describing two connected states does not depend on the path between the states. A simple example involves potential energy and height. Imagine you climb a mountain to the peak. It doesn't matter which path you take up the mountain, since the difference in height (Δ height) is the same if you start at the same point and end up at the peak. Your change in potential energy is also the same. ΔGo for protein folding is also a state function for a given set of conditions (temperature, pressure, solvent) Let's consider a specific example, the formation of H bonds between two molecules of N-methylacetamide (NMA) in water and in a nonpolar solvent. N-methylacetamide is a good mimic for the H bond donors and acceptors of the peptide bond of a polypeptide chain as shown in Figure $3$ below. (3\): N-methylacetamide as a mimic for the peptide backbone The reaction scheme shown in Figure $4$ describes a set of reactions (a thermodynamic cycle) involving the formation of H-bonded dimers of NMA . A and B are both molecules of NMA, in either water (w) or a nonpolar solvent (n). N-methylacetamide is a good mimic for the H bond donors and acceptors of the peptide bond of a polypeptide chain as . K1 is the equilibrium constant for the dimerization of NMA in a nonpolar medium. This can be readily determined, and is >1, implying that ΔGo < 0. (Remember, ΔGo = -RTlnKeq) For the dimerization of NMA in CCl4, ΔGo1 = -2.4 kcal/mol (-10 kJ/mol). K2 is the equilibrium constant (think of it as a partition coefficient) for the transfer of two NMA molecules from water to a nonpolar solvent (again easily measurable). For NMA transferring from water to CCl4, ΔGo2 = + 6.12 kcal/mol (+25.6 kJ/mol). K3 is the equilibrium constant for the dimerization of NMA in water. This can be readily be determined and is <1, implying that ΔGo > 0. For the dimerization of NMA in water, ΔGo3 = +3.1 kcal/mol (+13 kJ/mol). K4 is the equilibrium constant (think of it as a partition coefficient) for the transfer of a hydrogen-bonded dimer of NMA from water to a nonpolar solvent. You try to think of a way to measure that! I can't. This is where thermodynamic cycles come in so nicely. You don't have to measure it. You can calculate it from K1-3 since ΔGo is a state function! \begin{aligned} &\Delta \mathrm{G}_{2}^{0}+\Delta \mathrm{G}^{0}{ }_{1}=\Delta \mathrm{G}_{3}^{0}+\Delta \mathrm{G}_{4}^{0} \text { or } \ &-\mathrm{RTInK}_{2}+-\mathrm{RT} \ln \mathrm{K}_{1}=-\mathrm{RT} \ln \mathrm{K}_{3}+-\mathrm{RT} \ln \mathrm{K}_{4} \ &\mathrm{InK}_{2}+\ln \mathrm{K}_{1}=\ln \mathrm{K}_{3}+\ln \mathrm{K}_{4}=\ln \left(\mathrm{K}_{2} \mathrm{~K}_{1}\right)=\ln \left(\mathrm{K}_{3} \mathrm{~K}_{4}\right) \text { or } \ &\mathrm{K}_{2} \mathrm{~K}_{1}=\mathrm{K}_{3} \mathrm{~K}_{4} \end{aligned} For NMA transferring from water to CCl4, ΔGo4 = + 0.62 kcal/mol (+2.6 kJ/mol). (Note: Biochemists like to talk about thermodynamic cycles which may seem new to you. However, believe it or not, you have seen them before in introductory chemistry in the form of Hess's Law!) From K1-4 and the corresponding ΔGo values, we can now calculate ΔGo5 for the formation of H-bonded NMA dimers in a nonpolar solvent from two molecules of NMA(aq). This reaction, which we hope simulates the formation of buried intrachain H bonds in proteins on protein folding, is: Dw + Aw ↔ (DA)n, for which ΔGo5= +3.72 (i.e. disfavored). If this model is a good mimic for studying H bond formation on protein folding, it suggests that the formation of buried H bonds during protein folding does not drive protein folding. However, if the transfer of D and A (from a large protein) from water to the nonpolar medium (modeled by K2) is driven by other effects (such as the hydrophobic effect), the negative value for ΔGo1 will significantly favor buried H bond formation. So, if this happens in proteins, it is clear why so many intrachain H bonds form, since K1 is favored. H bonds may not assist the collapse of a protein, but would favor internal organization within a compact protein. That is, H bonds don't drive protein folding per se, but form so that the folded protein would not be destabilized by too many unsatisfied H bonds. There are potential problems with this simple model. The interior of a protein is not homogeneous (i.e. the effective dielectric within the protein will vary). H bond strength is also very sensitive to geometry. Also, there are many H bonds within a protein, so slight errors in the estimation of H bond strength would lead to large errors in the determination of protein stability. Another argument against H bonds being the determining factor in protein folding and stability comes from solvent denaturation studies. If intrachain H bonds are so important, then should not solvents that can H bond to the backbone denature the protein? Shouldn't water (55 M) act as a denaturant? It doesn't, however. Dioxane (5 membered heterocyclic ring with O) which has only a H bond acceptor wouldn't be expected to denature proteins, but it does. H bonds also increase in nonpolar solvents. Peptides, which have random structures in water, can be induced to form helices when placed in alcohol solutions (trifluorethanol, for example), which are more nonpolar than water, as explained above in the helix-coil studies. If H bonds are the dominant factor in protein stability, the alcohols would stabilize proteins. At low concentrations of alcohol, proteins are destabilized. Hydrophobic Interactions: Introduction We have studied the role of the hydrophobic effect (involving the favorable entropic release of caged water molecules about solvent-exposed hydrophobic groups) in driving nonpolar molecule solubility in Chapter 2.5. Does this also drive protein folding? To explore this question, we will study the thermodynamics of small nonpolar molecules, especially benzene, with water and ask whether the thermodynamic parameter associated with benzene solubility are similar to those associated with protein stability. If this analogy holds, anything that will promote benzene solubility will lead to increased hydrophobic amino acid side chain exposure to water and hence protein denaturation. What is the evidence to support this? a. Crystal structures: PDB crystal structures show that most nonpolar side chains are buried inside a protein, which is tightly packed and which excludes water. Studies show that as the surface area of amino acid side chains increases, the free energy of transfer of amino acids from water to ethanol becomes more negative as shown in Figure $5$. b. Low-temperature denaturation of proteins - It has been observed that proteins can denature at low temperatures (less than 0o C), suggesting that nonpolar residues become more "soluble" in water at low temperatures (i.e. they move from the more hydrophobic interior of a protein to the more polar outside). Compare the solubility of nonpolar gases like CO2 or N2, which are more soluble at low temperatures. As you heat solutions of nonpolar gases in water, the gases become less soluble as evidenced by bubble formation (i.e. phase separation of dissolved gases as they become more insoluble, as you have observed with a bottle of soda). If protein behavior is governed by this same behavior (greater solubility of nonpolar groups at low temperatures), it would suggest that proteins might denature at low temperatures (leading to increased exposure to water of the nonpolar side chains). This phenomenum has been observed. c. Protein stability affected by different salt species - Over 100 years ago, Hofmeister determined the effectiveness of different cations and anions of salts to precipitate blood serum proteins in the 0.01 - 1 M concentration ranges. The series is shown below: Cations: NH4+ > K+ > Na+ > Li+ > Mg2+ > Ca2+ > guanidinium Anions: SO42- > HPO42- > acetate > citrate > Cl- > NO3- > ClO3- > I- > ClO4- > SCN- • A salt of pairs of the first ions in these series, for example, (NH4)2SO4, when added to aqueous solutions of proteins, precipitate the native form of the protein. We must account for the fact that it precipitates the protein, and that the protein is precipitated in the native, not denatured state. More on why it precipitates proteins in a moment. The first ion in each series increases the surface tension of water (making it harder to make a cavity in the water to fit the nonpolar molecule). This decreases the solubility of nonpolar molecules. These "salt-out" nonpolar molecules, promoting not dissolution in water but aggregation followed by a phase separation. By analogy, they will stabilize the native state since the buried hydrophobic side chains would have a decreased propensity to move out into the aqueous environment. • The last ions of the series have less effect on surface tension, and hence increase the solubility of nonpolar molecules ("salt-in"). By analogy, they will destabilize the native state since the buried hydrophobic side chains would have an increased propensity to move out into the aqueous environment. The Hofmeister Series and its effects on the chemical properties of water and solutes are shown in Figure $6$. The solubility of benzene in aqueous salt solutions of this series increases from left to right, just as native protein stability decreases from left to right (i.e. the protein's nonpolar core residues become more "soluble" in water, leading to its denaturation). d. Conservation of hydrophobic core residues - These residues are highly conserved and correlated with structure. e. Urea denatures proteins - Another additive, urea (H2N(C=O-)NH2), at high concentrations is often used to denature proteins. People used to think that urea competed with the intrachain H bonds and hence unraveled the protein. The arguments above with H bonds dispute this contention since water should then denature protein. How does urea denature proteins? It has been shown that the free energy of transfer of the nonpolar amino acids into 8M urea is increasingly negative as the side chains become bigger and more nonpolar. This is also true for denaturation by guanidine hydrochloride. Both also increase the solubility of nonpolar molecules in a manner proportional to their surface area. The structure of urea and guanidinium, along with the side chains of arginine and the Type II diabetes drug metformin, are shown in Figure $7$. Figure $8$ shows the free energy of transfer of the nonpolar amino acids into 8 M urea and 6 M guanidine HCl as a function of the surface area of amino acid side chains. Additives and Their Interactions with Protein Surfaces Additives to proteins that increase the stability of the folded state of the protein also tend to decrease their solubilities. These additives are excluded from the preferential water hydration sphere around the protein (negative binding of these agents). Denaturants in contrast tend to increase protein solubility and interact preferentially with the protein surface. In their presence, proteins respond by increasing their surface area by denaturation. For stabilizers, proteins try to minimize their surface area by staying "native" and aggregating to form a precipitate, both of which minimize the surface area from which the stabilizing agent is excluded. The main effect of dissolved ions on water structure has been thought to involve changes in H bonds (either enhancers/structure makers or inhibitors/structure breakers) which correlate with the salting-in or salting-out effects of various ions. Many techniques have been used to study these interactions: • viscosity: inferential information on the structure • diffraction (x-rays/neutrons): gives information on the coordination number of solvation shell (static information) • NMR: information on average relaxation of bulk and hydration sphere water around ions (dynamic information) • molecular dynamics simulations: which gives insight into short but not long-range interactions between ions and water. Recent studies have provided conflicting support for the notion of structure makers/breakers. Omta et al. 2003 used femtosecond mid-infrared pump-probe spectroscopy to study actual H-bonds between water molecules in salt solutions (Mg(ClO4)2, NaClO4, and Na2SO4). In pump-probe spectroscopy, a sample is excited with a short pulse (pump) and after a short time lag, with another pulse (probe), which interacts with the excited state. The linear-polarized infrared pulses (pump) were used to excite OH groups in solution, followed by a probe pulse which was polarized 45 degrees compared to the pump pulse. Only those excited OH groups that had rotated in the time interval between the pump and probe would be excited by the probe. Using this technique, the time frame for reorientation of the OH groups, which is related to the "stiffness" of the H bonds, can be determined. The salts had no effect on the rotational motion of bulk water outside of the first hydration shell, which suggests that salts have no effects on the H bond networks in bulk water. Mg2+ ions are considered structure makers, as the ions greatly increase the viscosity of water, brought about supposedly by increased H bonds among water molecules. This study does not support this model. Increased viscosity of Mg solutions must be attributed to those ions directly interacting with water molecules. The solution can be modeled as bulk water with small rigid spheres of ion + first hydration sphere. Clearly, much more experimental and theoretical work must be performed to gain structural insight into the role of salts on water structure. Until then, we will continue to try to understand the effects of different salt on water structure in descriptive terms and with the use of thermodynamic quantities. Studies have shown that urea binds preferentially to the protein surface, and hence tends to increase the protein's surface area and hydrophobic exposure, and denature proteins. However, note in the figure below, that glycerol, a bigger polar but uncharged molecule, stabilizes the native state. This pair of uncharged additives have correspondingly similar effects on protein stability as do the charged guanidine HCl/ammonium sulfate pair. Figure $9$ shows how solution additives might interact with the surface of the protein. Figure $10$ shows a thermodynamic cycle for urea denaturation of proteins A graphical summary of the types of surface changes that result from protein denaturation is given Figure $11$. The Hydrophobic Effect and Change in Heat Capacity Our understanding of hydrophobic interactions has changed dramatically in the last several years. This is not reflected in most textbooks. The hydrophobic effect means different things to different people. Some refer to the transfer of nonpolar solutes/solvents to aqueous solution. Some refer to the same phenomena only if the effects have a unique temperature dependency. Other refer to the ordering of water around nonpolar residues. The most recent explanation centers around the unique temperature dependencies of the transfer reactions. Before we can understand it, here is an interesting bit of data. If you dissolve one mole of methane in hexane, the volume of 1 L of hexane changes 60 ml, but if done in water, the water volume changes 37 ml, indicating that water molecules pack more efficiently around nonpolar molecules than in its absence. Let's now consider the thermodynamic aspects of the hydrophobic effect, as we did for micelle and bilayer formation. In a brief summary, we found that the free energy of transfer of a hydrocarbon or alcohol from aqueous solution to the pure hydrocarbon or alcohol, for example, was disfavored enthalpically (unexpectedly) but favored entropically (also unexpectedly until we included solvent in our model). These experiments were done at one temperature and gave us our first initial understanding of the hydrophobic effect. We will expand on this view by looking at the enthalpic and entropic contribution to the transfer of benzene into water as a function of temperature. This will lead us to a more modern view of the hydrophobic effect. To do this we have to think about the thermodynamics of mixing two substances with different properties, such as polarity which affects solubility. If you mix two substances A and B that aren't very soluble in each other, two opposing thermodynamic factors are relevant. • The tendency to mix is driven by an increase in entropy. • The mixing is usually opposed by enthalpy. The later makes "intuitive" sense since you might expect that van der Waal interactions between A-B might be less than those of A-A and B-B (i.e. the old adage "like dissolves like"). If AA and BB self-interactions are stronger, A would not dissolve in B and vice/versa. You would also expect no significant changes in entropy and enthalpy as a function of temperature in this ideal mixing. The most modern understanding of the hydrophobic interactions involves the mixing of A and B which is characterized by a unique temperature dependency for the value of the change in entropy and enthalpies. At room temperature, if one corrects the entropy changes for effects due just to mixing, the "excess" entropy is what principally opposes taking a nonpolar molecule into water. Enthalpy changes are small. We have modeled this effect using structured water around the nonpolar residues. We will now further our understanding of the hydrophobic effect by using benzene transfer from water as a model system (much as we did with N-methylacetamide for H bonding). Before we discuss entropy and enthalpy changes accompanying protein folding/unfolding, let's try to learn about the thermodynamic aspects governing benzene solubility in water. What happens to benzene solubility in water and the corresponding thermodynamic parameters as you raise the temperature? The graph in Figure $12$ shows the change in G, H and -TS for taking benzene from pure benzene to water, BB → BW. This is real data. Note that the graph shows -TΔS not just ΔS. When the values of each of these terms, ΔG, ΔH and -TΔS are negative, the transfer of benzene into water is favored. The blue highlighted region shows more values in a more physiologically relevant temperature range (0-1000 C). Two things to note for right now. ΔG for BB → BW is always positive and hence always disfavored. Another key feature is shown in the vertical dotted line at the temperature at which benzene has its greatest "aversion" to being in water, where the slope of the ΔG vs T curve (dΔG/dT) = 0. At this point, entropy plays no role in the solubility of benzene in water. This goes again the notion of the hydrophobic effect we discussed in Chapter 2.5. This maximum aversion to water is completely driven by enthalpy. If these axes showed three lines describing the number of ducks, geese and lunes in a lake as a function of lake temperature, no further explanations might be needed. But many years of teaching experience and research shows that thermodynamics parameters are examples of threshold concepts that students struggle with for many years. A graph showing three simultaneously might be uninterpretable to many. So let's deconstruct this graph into a series of stepwise graphs with targeted explanations to facilitate your understanding. These graphs with explanations are shown below. A graph of ΔG vs T for BB → BW ΔG for BB → BW is positive and hence disfavored over the entire temperature range shown. However note several important features: • There is a temperature (around 3750K) when ΔG is at a maximum (i.e. in the language of math, the slope of the curve ΔG/ΔT or dΔG/dT is a maximum. • As the temperature decreases from Tmax, the transfer of benzene becomes less disfavored. If you were to extrapolate the curve to really low but unrealistic temperatures, it looks like it would become favorable. A graph of ΔH vs T for BB → BW The graph of ΔH vs T for or BB → BW is linear with a positive slope and crosses the T axis at the point indicated by the arrow. • At temperatures below the point indicated by the arrow, the transfer of BB → BW is enthalpically favored. We saw this unexpected finding in Chapter 2.5 in considering the transfer of butanol, for example, from the pure liquid alcohol to water at one temperature, 250C = 298K. That data is consistent with this curve. • At temperatures above the point indicated by the arrow, the transfer of BB → BW is increasingly disfavored. • The slope of this line, ΔH/ΔT , has the units kJ/mol/K, which is the heat capacity of the system. A graph of -TΔS vs T for BB → BW The graph of -TΔS vs T for for BB → BW is almost linear with a negative slope which shows that it becomes less entropically disfavored. The curve crosses the T- axis at the point indicated by the arrow. • At temperatures below the point indicated by the arrow, the transfer of BB → BW is entropically disfavored. We saw this in Chapter 2.5 for the transfer of alcohols from the pure liquid into water. We called this the "hydrophobic effect" which is the main thermodynamic drive to move organic molecules out of water. We attribute this to the formation of a cavity in water for the nonpolar parts of the alcohol in a process that forms a more ordered set of H bonds compared to bulk water and which reduced the number of microstates available to the associated water molecules. • However, at the temperature indicated by the arrow, there is no entropic barrier to move benzene into water. Above those temperatures, it is favored. This seems to destroy our simple definition of the hydrophobic effect. Let's add a bit more to our molecular interpretation of this seemingly anomalous favorable entropy at higher temperatures. As the temperature is raised, the available positional and thermal entropy of water increases significantly. It would seem logical that to then put a nonpolar residue into this system of water would become easier than putting it into more structured water (characterized by fewer microstates and lower positional and thermal entropy) at a lower temperature! (Remember from our review of thermodynamics that If the Tsurr is high, a given heat transfer to or from the surroundings will have a smaller effect on the ΔSsurr. Conversely, if the Tsurr is low, the effect on ΔSsurr will be greater. Atkins, in a recent General Chemistry, uses the analogy of the effect of a sneeze in library compared to one in a crowded street. An American Chemistry General Chemistry text uses the analogy of giving $5 to a friend with$1000 compared to one who has just \$10. But look at the other temperature anomaly. It becomes increasingly difficult from an enthalpic point of view to put benzene in water. At a high temperature, -TΔS becomes zero, and there is no entropic barrier to putting benzene into water. The barrier is completely enthalpic. This is why a more nuanced definition of the hydrophobic effect has emerged. If you sum ΔH and -TΔS at each temperature, you get the curve shown for the total ΔG to take benzene from pure benzene to water. Notice that it is always positive, so it is always disfavored. The ΔG function is curved. It increases at low temperatures, and decreases at very high temperatures, implying that there will be a temperature at which there is a minimum solubility of benzene in water (a maximum in the positive ΔG). The minimum solubility of benzene (the max. positive ΔG) occurs when dΔG/dT = 0. This occurs when ΔS = 0, and the maximal aversion of benzene to water is completely enthalpic, a statement not in accord with our initial understanding of the hydrophobic effect. For those who have taken physical chemistry ... For just PV work, dG = VdP - SdT, so that dG/dT = V dP/dT - S. At the maximum of the curve of G vs T, dG/dT = 0 so V dP/dT = S. For this system dP/dT = 0 so S = 0. This is observed in the top graph above. Here is another equation for heat capacity that you derived in physical chemistry: Cp = dH/dT= TdS/dT. The last equality stems from Maxwell's relationships, which Physical Chemistry students should remember. Let's review some more thermodynamics that the physical chemists in the crowd should remember. Even introductory chemistry students should to a degree (a pun). (Molar) heat capacity, Cp, is defined as the heat required to raise the temperature of a mole of a substance 1oC. It has units of kJ (or kcal)/mol. The slope of the enthalpy curve ΔH vs T for BB → BW has units of kJ/mol/K and is the heat capacity, Cp, where Cp = dH/dT. To refresh your mind and demystify the term heat capacity, let's look at the heat absorbed as ice is converted to gas as a function of temperature (i.e. the phase transition for water). Figure $13$ a graph of the heat absorbed, Qabs/gram vs T (top), and a derivative plot of the top graph (slope of the top graph = heat capacity at each point on the T axis). We can now learn one final concept from the graphs for benzene transfer into water. The graph of ΔH vs T is linear for the transfer of benzene from the pure liquid into water. We now know that the slope of that graph, ΔCp is a constant positive number for the process when a hydrophobe is exposed to water. This positive Cp observed when a hydrophobic group is transferred to water, is the signature of our new understanding of the hydrophobic effect. A positive Cp occurs when both H and S are dependent on temperature, which is observed when a hydrophobe is transferred from a more nonpolar environment to water. Likewise, a negative Cp is observed when hydrophobes in water are transferred to a more nonpolar environment. Hydrophobic Effect Applied to Proteins Now let's return to the world of proteins and see how we might apply what we learned from the transfer of benzene from the pure liquid to water. An analogous transfer experiment would be the denaturation of proteins in which buried hydrophobic amino acids are "transferred" to water. Figure $14$ shows the derivative plot of heat absorbed vs T ( i.e a plot of heat capacity Cp vs T) for the thermal denaturation of a protein, obtained using differential scanning calorimetry. Look at the graph in Figure $15$ which shows the heat capacity of the protein lysozyme at various pHs vs temperature obtained using differential scanning calorimetry. As the protein is heated, it reaches a temperature at which a large amount of heat is suddenly absorbed, as the protein unfolds. The area under the curve represents the heat absorbed on denaturation (units of cal/g.K x K = cal/g). The temperature at the midpoint is the Tm of the protein. (Why would the Tm be dependent on the pH of the solution?) Notice that there are two ΔCps shown in the graph. One, ΔCd, is associated with the actual denaturation process and is analogous to the change in heat capacity observed in phase changes, such as solid to liquid water. The other is ΔCp which is the difference in heat capacity between the denatured and native state. As was the case for the transfer of benzene to water, the ΔCp for protein denaturation is also positive, suggesting that in protein denaturation, hydrophobes are transferred from the interior of the protein to water. Figure $16$ attempts to give a molecular description that applies to different regions of the denaturation curve and helps explain the positive ΔCp observed in thermal denaturation curves of proteins. What is the molecular basis for this large heat capacity change of transfer for benzene. One can show that the Cp is also proportional to the surface area of the nonpolar solute. Figure $16$ shows the smaller, more compact native state, with buried Phe (F) side chains denaturing to the more open D state with exposed F side chains. Since these are nonpolar, we can envision a "clathrate" or cage of ordered water around them. The heat capacity curves for both the native and denatured states are extrapolated into the region where T < Tm (even though there is very little denatured state in that region). The "caged" water around the exposed F in the D state is low energy due to the "ice-like" H bond network. More heat would be absorbed (as the temperature is increased) to break up that cage compared to the same amount of heat applied to the N state. Hence, Cp D > Cp N. At room temperature, water molecules surrounding the nonpolar residue are low in energy (lots of H bonds) and low in entropy (thermal and positional, fewer available microstates). As the temperature is raised, water populates higher energy states (fewer H bonds) and higher entropy (thermal and positional, more available microstates. The increase in temperature causes "melting" of surrounding water structure in so far as energy and entropy are concerned. The two different energetic states of water provide an energy storage mechanism. Here is another explanation that might help. Water molecules form "iceberg"-like cage of water around nonpolar molecules, which is often called a clathrate. The water is fully H-bonded (to itself, not to the nonpolar molecule) in a fashion analogous to ice but the geometry of the H bonds is nonideal. This structuring of water decreases its entropy. With increasing temperature, the structured water "melts" which produces the large heat capacity of a solution of a nonpolar molecule in water, just as the actual melting of ice showed a large heat capacity. This large heat capacity is the signature thermodynamic feature of the solution of a nonpolar molecule in water. For the actual part of the graph in which denaturation occurs (N ↔ D), the following equations can be derived. \begin{gathered} \mathrm{C}_{\mathrm{p}}=\mathrm{C}_{\mathrm{d}}=\frac{d \mathrm{H}}{d \mathrm{t}} \ d \mathrm{H}_{\mathrm{d}}=\mathrm{C}_{\mathrm{d}} \mathrm{d} \mathrm{t} \ \int_{\mathrm{T}_{1}}^{\mathrm{T}_{\mathrm{S}}} \mathrm{d} \mathrm{H}_{\mathrm{d}}=<\Delta \mathrm{H}_{\mathrm{d}}>=\int_{\mathrm{T}_{1}}^{\mathrm{T}_{2}} \mathrm{C}_{\mathrm{d}} \mathrm{d} \mathrm{T} \end{gathered} to help calculate the calorimetric enthalpy changes (ΔHcal = ΔHd) of denaturation from differential scanning calorimetry. where <ΔHd> is the average enthalpy change that occurs on denaturation which is represented by the blue and red shaded areas in the curve above (between T1 and T2). Compare this to the van 't Hoff enthalpy discussed in Laboratory Determination of ΔGo for Protein Folding/Unfolding. For the calorimetric determination of ΔCd and ΔHd, the values are temperature dependent, and no assumption about the temperature independence need be made, as in the case of the ΔHvhoff plot. H2O Clathrate Cages - An Extension Is there a more quantitative description of the ordered water than a cage? Sharp et al investigated the "structure" of the caged water around nonpolar and polar molecules in a theoretical analysis supported by molecular dynamics (Monte Carlo) simulations. The average bond angles and lengths of water-water H bonds in the first hydration sphere around a nonpolar molecule like benzene decreased, but increased for polar ones such as potassium ions. The average changes noted arose from two types of H bonds compared to bulk water, those that were shorter and more linear, and those that were longer and more bent. They stated that "nonpolar groups do not so much increase the ordering of water as decrease the disordering". A recent review by Silverstein suggests that an immobile clathrate cage is not a good representation for water surrounding a hydrophobe. Although we like to envision molecular models that allow us to "explain" experimental thermodynamic findings, such models themselves should be subjected to rigorous experimentation. An alternative explanation hinges on water's small size (compared to other solvents), its tight packing and high density. Consider the density of water compared to more nonpolar liquid solvents as seen in the table below. Table: Density of common solvents Solvent Volume (Emin) (A3) (Spartan) Density (g/ml) H2O 19.17 1.00 methanol CH3OH 40.66 0.791 ethanol CH3CH2OH 59.08 0.789 n-propanol CH3CH2CH2OH 75.35 0.804 n-butanol CH3CH2CH2CH2OH 95.68 0.810 hexane 124.8 0.654 Let's consider the density of water surrounding an exposed nonpolar. If we envision the surrounding water as a clathrate, we might assume it is "ice" like. So what are the physical properties of ice and water that might give us insight into the water surrounding a nonpolar molecule? Ice, of course, has a lower density than liquid water. This can't be simply explained by the number of H bonds since experimental evidence shows that ice has an average of 4 H bonds per water molecule compared with liquid water, with an average of 2.4. Experimental data also shows that to accommodate water molecules into a rigid network of interacting waters with tetrahedral symmetry, the H-O-H bond angle increases to 106 from 104.5. Liquid water molecules, with fewer packing constraints, can self-organize to maximize packing and hence macroscopic density. Studies suggest that ten water molecules solvate a buried methyl group and infrared studies show that four of these have significant barriers to rotational diffusion, suggesting they are effectively immobilized and hence "ice-like". Silverstein suggests then that the water surrounding a nonpolar group on the solution should be considered in a dynamic sense with some immobilized (as in ice) and the remaining more fluid-like. Now let's review the benzene graph and apply it to protein unfolding one more time. The graph for BB → BW (Figure $12$) shows a maximum in benzene insolubility. As the temperature is decreased from that maximum, benzene becomes more soluble in water. Alternatively, as temperatures rises to that temperature of maximal insolubility, the solubility of benzene decreases (just like nonpolar gases become increasingly insoluble with increasing temperature). If you extrapolate the ΔG curve in this range of decreasing temperature past the range shown on the graph, it would cross the X-axis and become <0, implying benzene would be favored to dissolve in water. Does the low temperature behavior of benzene/water interactions (becoming more soluble as the temperature is decreased from the maximum temperature for its insolubility) extend to and predict protein behavior at low temperature? Figure $17$ shows the analogy between benzene solubility in water at cold temperatures and cold temperature denaturation of proteins. In the figure, F stands for a Phe side chain, which can be buried, sequestered from water as it would be in the native state of the protein, and exposed to water, as it might be in the denatured state.) The answer is our question is yes, at low temperatures. The analogy to benzene being more soluble at low temperatures is the hydrophobic side chains in a protein becoming more likely to flip into water, denaturing the protein. The low temperature behavior would predict low temperature protein denaturation. This phenomena has been observed. Note that it doesn't require a change to a state when the nonpolar side chains prefer to be in water, just a change in that direction might be enough to tip the balance and lead to denaturation of the marginally stable protein. Please note that we are attempting to extrapolate the thermodynamic parameters associated with benzene solubility in water to the denaturation of a protein, NOT TO THE SOLUBILITY OF A PROTEIN IN WATER! What about high temperature? Proteins denature as the temperature increases in the range that the ΔG curve for benzene reaches a peak. If the buried hydrophobic residues behave like benzene, they would increasingly "prefer" to not flip out into water as the temperature rises to the maximum in the ΔG vs T curve. Hence the benzene predicts that the protein should become more stable. What then explains the observed denaturation of proteins at high temperatures? Another factor must account for it. What is it? As the temperature is increased, more protein conformational states will become available and occupied. At low temperatures, let's say that there is only one native state available and occupied and (to pick a number), maybe 10 non-native states that are energetically accessible. At high temperatures, there is still only one native state, but perhaps 1000s of accessible nonnative states. More accurately, think of the protein existing in an ensemble of conformations. As the temperature increases, more non-native states can be populated, compared to at lower temperatures, leading to an entropic driving force favoring unfolding. Which way would this chain conformational entropy drive the protein at high temperature? Clearly, it would be driven to the most number of states - to the denatured state. Hence a modern definition of the hydrophobic effect can explain low-temperature denaturation, but not high-temperature denaturation. Summary of studies from small molecules (N-methyacetamide and benzene) It is clear that proteins are not all that stable, and many contributions of varying magnitudes must sum to give the proteins marginal stability under physiological conditions. The hydrophobic effect clearly plays a major role in protein stability. Also, since proteins are so highly packed compared to a loser-packed denatured state, collective Van der Waals interactions must also play a significant part. Remember these interactions, especially induced dipole-induced dipole interactions, are short range and become most significant under conditions of closest packing. Opposing folding is the chain conformational entropy just described. Since proteins are so marginally stable, even one unpaired buried ionic side chain, or 1-2 unpaired buried H bond donors and acceptors in the protein may be enough to "unravel" the native structure, leading to the denatured state. Mutagenesis and Protein Stability In the last decade, the contributions to the overall stability of a protein from the hydrophobic effect and H bonds have been studied using site-directed mutagenesis. In this technique, the DNA coding sequence for a given amino acid in a gene can be altered so that the new mutant protein differs from the normal (wild-type) protein by one amino acid. To probe the hydrophobic effect, for example, a buried hydrophobic amino acid like Ile could be changed to Gly which is much smaller, and offers a lower hydrophobic contribution to the stability of the native state. The result of this mutation might leave a "hole" in the protein (not unlike the vacant holes in crystal structures of salts). This "hole" might be diminished in size by subtle rearrangement of the protein structure in the vicinity. Certain amino acids would not be used as replacements in such studies. For instance, an Ile would not be replaced with a positively-charge Arg which would clearly destabilize the protein. The extent of destabilization in mutant proteins can be determined by calculating the ΔGo for the native to denatured transition using urea as the denaturing agent as discussed in another section. Previously, the following statistics were presented concerning the distribution of amino acids in the tertiary structure of a protein. New values are shown below in red, based on much more crystallographic data, as summarized in Pace's article. • The side chain location varies with polarity. Nonpolar side chains, such as Val, Leu, Ile, Met, and Phe, are nearly always (83%) in the interior of the protein. • Charged polar side chains are almost invariably on the surface of the protein. (54% - Asp, Glu, His, Arg, Lys are buried away from water, a bit startling!) • Uncharged polar groups such as Ser, Thr, Asn, Gln, Tyr, and Trp are usually on the surface, but frequently in the interior. If they are inside, they are almost always H bonded (63% buried - Asn, Gln, Ser, Thr, Tyr, again startling) . • Globular proteins are quite compact, with water excluded. The packing density (Vvdw/Vtot) is about 0.75, which is like the NaCl crystal and equals the closest packing density of 0.74. This compares to organic liquids, whose density is about 0.6-0.7. Two articles by Pace suggeststhat Dills "influential review (from which much of the above derives) that concluded that hydrophobicity is the dominant force in protein folding" should be rethought. Using site-directed mutagenesis to change Asn (which can H bond through its side chain) to Ala (which can't) in a variety of proteins, he has shown that approximately 80 cal/mol/A3 of stability is gained if a side chain (in this case Asn) can form buried H bonds to buried amide links of the protein backbone. Similar studies of mutants in which Leu is replaced with Ala, and Ile with Val, suggest that only 50 cal/mol/A3 is gained from burying a hydrophobic -CH2- methylene group. Extending these results to protein folding suggests that protein stability is determined more by the formation of buried H bonds than by the hydrophobic effect! The investigators measured ΔGo for the N ↔ D transition (presumably by varying the urea concentration and extrapolating the ΔGo for unfolding to 0 M urea (see: 4.12: Appendix - Laboratory Determination of the Thermodynamic Parameters for Protein Denaturation). For the reaction as written, ΔGounfolding > 0 at room temperature and 0 M urea. The mutant protein, since they are destabilized, would have a less positive value for ΔGounfolding (They would also have a less negative value for folding since they are less stable). The difference in ΔGounfolding between the wild type and mutant (ΔΔG) is expressed as: ΔΔG = ΔGounfolding wild-type - ΔGounfolding mutant > 0 ΔΔG > 0 since ΔGounfolding wild-type > ΔGounfolding mutant. The more positive the ΔΔG, the more the mutant is destabilized in comparison to the wild type. The data for a series of mutants is shown below. Analysis of Mutants: H Bonds in Protein Folding mutation ΔVol side chain (Å3) % buried ΔΔG kcal/mol (kJ/mol) (destabilized) ΔΔG cal/mol/Å3 (J/mol/Å3) (destabilized) Asn to Ala 37.4 95 2.9 (12) 78 (326) Leu to Ala 74.5 99 3.6 (15) 48 (200) Ile to Val 25.8 100 1.3 (5.4) 50 (209) What leads to protein stabilization/destabilization when Asn is changed to Ala? One possible contributor to stability is the side chain conformational entropy. Since in the mutant, the Ala would find itself in a larger "hole" and have greater freedom for motion, it would have more conformational entropy that would stabilize the mutant over the wild type. Hence this effect can NOT explain the observed destabilization of the Asn to Ala mutant. In the proteins he studied, only one of eight Asn to Ala mutations involved an Asn in a helix, so the average change could not be attributable to differences in helix propensities for the two amino acids. In the mutants, assuming no rearrangement of the remaining side chains, there is an "unnecessary" and unoccupied 37 Å3 cavity. The creation of this cavity is thermodynamically unfavorable (about 22 cal/mol/Å3 obtained from values for hydrophobic mutations). If the same penalty were applied here, the Asn to Ala mutant would be destabilized by 0.8 kcal/mol (22 x 37.4), This is significantly less than the observed destabilization (2.9 kcal/mol, 12.1 kJ/mol), so this effect also could not account for the destabilization of the Asn to Ala mutants. If there were compensatory changes to minimize the cavity size, this would only help to stabilize the protein and hence can not account for the observed destabilization. Possible Explanation of Destabilization of Asn to Ala Mutants possible reasons explanation effect on mutant support observed destab. of mutant? residue conformational entropy Ala in a bigger hole: more freedom of motion; favored entropically stabilize mutant NO free energy change excess cavity formation energy penalty to make an unoccupied cavity approx. 0. 8 kcal/mol (3.3 kJ/mol) destabilize yes but of insufficient size compared to the observed effect (2.9 kcal/mol, 12 kJ/mol) free energy change protein conformational changes rearrange protein to fill the cavity stabilize mutant NO Hence these alternative sources to explain the destabilization of the mutant can't account for the data and we're left with the explanation that the stability of the native protein over the mutant is accounted for by burying the H bond donor and acceptors of the amide group and associated changes in van der Waals interactions. Pace argues that burying the amide group of Asn is similar to burying the peptide bond of the main chain. Their sizes are very comparable. Free amide groups can form four H bonds, but peptide (amide) groups can only form three. Even if the value of 78 for the ΔΔG (cal/mol/Å3) is adjusted for this, the new value of 62 is still larger than that for burying a methylene group. Analysis of 108 folded proteins has shown that hydrophobic groups contribute 118,200 Å3 of buried volume, compared to 92,000 Å3 for peptide groups. Multiplying these figures by 78 and 49 (from the above table) suggests that overall, burying peptide groups contributes more to protein stability than burying hydrophobic groups. Would electrostatic interactions of the buried peptide group with the surrounding environment destabilize a protein? Pace argues that this would be more than compensated for by favorable van der Waal's interactions (short range) at the buried site. This can be illustrated by comparing the ΔG transfer of an amide from water to the vapor (11.2 kcal/mol, 47 kJ/mol) compared from water to cyclohexane (7.6 kcal/mol, 32 kJ/mol). Transfer to the vapor is more unfavored (due to the desolvation required when it moves to the gas phase) than to cyclohexane, even though a cavity must be created in the cyclohexane (a process which would be unfavored entropically). Transfer to octanol is even more favored (1.4 kcal/mol, 5.9 kJ/mol) but all these values are still positive (disfavored). Similar experiments with the transfer of a methylene group (-CH2-) are negative, given the hydrophobic effect and the collective close packing van der Waal's interactions possible. These suggest that van der Waals interactions formed on burying an amide in any solvent are stabilizing. Now consider the packing density of atoms for various substances: Packing Densities substance packing density water 0.36 cyclohexane 0.44 closest packed spheres 0.71 protein interiors 0.75 From this table, it should be apparent that collective van der Waals interactions (short range) will be more stabilizing in the interior of the protein compared to the same groups in bulk water (or in the denatured state). Carbonyl groups are more polarizable than methylene groups, which should contribute to van der Waals interactions. One other addition. It has been noted that Gly peptides are not very soluble in water. The backbone, even with the polar peptide bonds appears to be solvophobic. If the backbone of any polymer can't interact well with the solvent - i.e. the solvent is "poor" - then the backbone interacts with itself, which drives collapse. If the backbone interacts well with a "good" solvent, it won't collapse as readily. Protein Stability in Thermophilic Organisms What kinds of modifications are made to the sequence of a protein as the temperatures at which the organism thrives increase? A recent study by Szilagyi and Zavodszky (Structure, 8, pg 493, 2000) studied 93 structures of 25 proteins, 29 from organisms that live at elevated temperatures (thermophiles, >50oC for optimal growth ) and 64 at nominal temperatures (mesophiles). Here are their results: • the number of H-bonds and secondary structure elements do not correlate with temperature, but the number of salt bridges do. • in hyperthermophiiles (>80oC for optimal growth organisms) that thrive at very high temperatures (100oC), few internal cavities were found. • in those that thrive at intermediate high temperatures (45-80oC), the surface had more polar residues. • in general, there was an increase in weaker ion pairs (increased distance between the charged side chains) in the hot group, but increases in strong and weak ion-ion bonds in the very hot group. Kashefi and Lovley recently reported the identification of a bacteria obtained from a hydrothermal vent in the northeastern Pacific ocean. In a laboratory setting, the strain grew in water at a temperature of 121oC under high pressure. These are the same conditions used in autoclaves to produce sterile samples. Cell doubling took place under these conditions in 24 hours. The authors suggest that this strain would be useful to determine molecules and their properties necessary for such high temperature growth. Using a computational program called Rossetta Design (PNAS, 97, 10383 (2003)), Korkegian et al determined mutations in buried side chains of the homodimer cytosine deaminase. Buried residues are presumably are important in the stability of a protein and are targets for mutagenesis experiments that would increase the melting temperature (Tm) of the protein. In the program, a target sequence was "threaded" onto the sequence of the template protein (the wild-type protein) and changes were made to side chains in the random sequence. Energies were calculated and those changes resulting in lower energies were saved. Target residues (88) within 4 angstroms of the active site and the dimer interface were fixed to those in the wild-type template in order to minimize alterations in the catalytic activity of the enzyme, cytosine deaminase, that they chose to study. Remember, the goal of the study was not to increase the catalytic activity of the enzyme, but rather to increase its themostability. The rest (65) were changed and energies calculated. 49% of the amino acids subjected to random change produced no change in amino acid compared to the template (wild-type) side chain. 16 changes on the surface were ignored. Two sets of changes were observed, one involving amino acids packed between an alpha helix and beta strands, and the other set between two alpha-helices. These later mutants, when prepared in the lab using recombinant DNA technology, were soluble at high protein concentrations, and could be studied. Three different mutants (A23L, I140L, V108I) were made which increased the TM by about 2 degrees. However, a triple mutation had TM values 10 degrees higher than the wild-type protein and a 30-fold longer half life (t1/2) at50 degrees C. When the triple mutant was introduced into bacteria, the bacteria grew better at higher temperatures. Crystal structures of both the wild-type and triple mutants show essentially an identical fold, with about 70 Å2 of additional surface area buried in the mutant protein. Beeby et al. analyzed sequence and structural data from P. aerophilum (archea) and Thermus thermophilus (thermophilic bacteria) and found that disulfide bonds stabilized proteins from these species. Cytoplasmic protein from eukaryotes don't have disulfides due to the presence of reducing agents (such as glutathione) in the cell. In those thermophiles with disulfides in proteins, a novel protein, protein disulfide oxidoreductase, was found, which catalyzes the formation of sulfide bonds. Finally, Berezovsky and Shakhnovich have also analyzed proteins from hyperthermophilic archaea and bacteria and compared them to analogous proteins from mesophilic bacteria. They found two types of stabilizations of hyperthermophilic proteins, depending on the evolutionary history of the organism. Proteins from cells that originally evolved in high temperature conditions (Archaea) were very compact (maximizing van der Waals interactions, had a high number of contacts per residue, and a high percentage of hydrophobic residues), but did not use specific structural stabilizing interactions (like electrostatic in salt bridges). In contrast, proteins from cells the originally evolved under mesophilic conditions, but later adapted to hyperthermophilic conditions had proteins that evolved specific sequences features that stabilized electrostatic interactions (more charged residues, salt bridges. .

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/04%3A_The_Three-Dimensional_Structure_of_Proteins/4.09%3A_Protein_Stability_-_Thermodynamics.txt

Search Fundamentals of Biochemistry Introduction to Protein Aggregates We have studied different types of protein aggregation, including aggregation of the native state (to form dimers, trimers, multimers, and filaments). We've also studied how misfolded proteins can aggregate and how a whole family of molecular chaperones help newly synthesized and misfolded proteins fold correctly. But what happens if a protein can fold two reasonably stable or metastable structures of starkly different conformation? A class of proteins called prions or amyloid proteins has this characteristic. their "alternative" conformation structures can bind to the "normal" structure and cause it to flip to the alternative conformation. This then can seed a continuation of the process which ends in the formation of large aggregates that are fibrillar in structure. This process leads to aberrant cell function and when it occurs in neurons can lead to a variety of brain diseases such as Alzheimer's Disease. Protein aggregates complicate the lives of people who study protein folding in vitro and who try to express human proteins in prokaryotes like E. Coli in vivo, which often end up in large protein aggregates called inclusion bodies. Instead of viewing these aggregates as unwanted "junk", some study them avidly. It turns out that these aggregates are not as non-specific as earlier believed. In addition, an understanding of how and when they form will give us clues into the etiology and treatment of some of the most debilitating and feared diseases. Specificity of Aggregate Formation In the early 1970s it was shown that chymotrypsinogen could not be folded in vitro without aggregates forming. An intermediate was presumed to have formed that if present in high concentration would aggregate irreversibly instead of folding to the native state. Refolding of tryptophanase showed that it aggregated only with itself, suggesting specificity. In the 1980s, a single amino acid folding mutant was found in a viral protein. Both the normal and mutant viral proteins unfolded at high temperature, but only the mutant would aggregate at high temperatures, suggesting that aggregation could be programmed into or out of a gene. Also, a single amino acid change in bovine growth hormone completely prevented aggregation without affecting correct folding. This knowledge of protein folding and aggregation was soon turned toward understanding several diseases in which protein aggregates were observed which either initiated or were associated with diseases. These protein aggregates were termed "amyloid deposits" and seemed to be associated and, perhaps causative of several neurodegenerative diseases. The name amyloid was first used by a German pathologist, Rudolf Virchow, who in 1853 described waxy tissue deposits associated with eosinophils (a type of immune cell). These deposits seemed to resemble starch (made of amylose and amylopectin) so he termed them amyloid. All known amyloid deposits are, however, composed of protein, not starch. It now appears that these diseases are likely caused by improper protein folding and subsequent aggregation. Except in certain rare inherited diseases, the amyloid deposits are composed of normal wild-type proteins (not mutants), which seem to undergo conformational changes to form monomers, which catalyze the formation of more altered normal monomers into the altered form, which polymerize into fibrils. Sometimes, in inherited conditions, or when mutations appear in a specific protein, the amyloid protein deposits consist of the mutant protein. The proteins in these deposited fibers are composed predominantly of β sheets which are perpendicular to the fiber axis. In some cases, the monomeric "normal conformation" of the protein has little beta sheet structure. Figure \(1\) shows a simplified model of how a normal protein with a "normal" conformation enriched in this case for illustration in alpha-helices can form fibrils of abnormal monomers which are highly enriched in beta sheets. These can self-associate to form large insoluble protein "amyloid" fibers. Note the green arrows representing beta-strands. In the misfolded state, proteins have an increased propensity to oligomerize through the association of their metastable beta-sheet domains. These can convert into more stable beta-sheet states and the ensuing oligomers act as the nuclei for the subsequent elongation reaction, which leads to the formation of so-called protofibrils. The final amyloid fibril usually consists of a number of intertwined protofibrils. Diseases of Protein Aggregates We'll now describe a series of diseases that are caused by or highly associated with fibril formation from normal soluble proteins. For each one, we will present the best available structure of the amyloid fiber obtained mostly through cryo-EM. What's amazing is that at first glance all of the amyloid fiber structures have an astonishingly similar structural appearance. We present them not to be redundant but to illustrate how natural processes can render from a great diversity of protein structures a common structural and often lethal outcome. That the amyloid fibers are so structurally similar suggests that a common therapy to prevent their formation may be developed. Familial amyloidotic polyneuropathy (FAP) This affects 1/10,00 to 1/100,000 people. The monomer protein involved is called transthyretin (147 amino acids, MW 15,887), which normally exists in the blood as a homotetramer (a dimer of dimers). Figure \(2\) shows the structure of the dimer (6fxu). Note each monomer contains mostly beta structure. The protein binds L-thyroxine and around 40% of blood plasma transthyretin is bound to retinol-binding protein. In mildly acid conditions in vitro, the equilibrium between tetramer and monomer is shifted to monomer, which can aggregate into fibrils. Monomer aggregation could be promoted by a possible transition to a molten globule (discussed previously with lactalbumin) like state. This has secondary structure but loosely-packed tertiary structure with more exposed hydrophobic groups. If the concentration is high enough the molten globules aggregate. In people with the disease, mutations in the protein destabilize the tetramer, pushing the equilibrium to the monomer, which presumably increases molten globule formation and aggregation. Specifically, Val30Met and Leu55Pro mutations promote the dissociation of the tetramer and the formation of aggregates. Conversely, Thr119Met inhibits tetramer dissociation. The aggregates deposit in the heart, lungs, kidney, etc, leading to death. Figure \(3\) shows a pictorial representation of how the transthyretin dimer could be stabilized to form monomers or dimers leading to fibril formation. Figure \(4\) shows the intramolecular hydrogen bonds bonds within monomers (-----) and intermolecular hydrogen bonds between monomers (-----) in the transthyretin dimer. We present this figure to remind readers that in all of the complex amyloid fibrillar structures presented below, the bulk of hydrogen bonds are "intermolecular" between adjacent monomers in their extended states (phi/psi angles consistent with beta-structure) within the fibrillar structure. Figure \(5\) shows an interactive iCn3D model of Cryo-EM structure of a transthyretin-derived amyloid fibril from a patient with hereditary ATTR amyloidosis (6SDZ). Each separate monomer is shown in a different color. The static image below shows arrows indicating beta strands, which form hydrogen bonds from the main chain amide Hs and carbonyls Os to another main chain atoms on an adjacent monomer. Note the beautiful but unfortunately deadly array of adjacent extended monomeric chains (each colored differently) that form hydrogen bonds with adjacent extend monomers to stabilize the fibrillar structure. Light Chain Amyloidosis - AL amyloidosis (amyloidosis from the light chain) The light chain (MW approx 25,000) is a normal component of circulating immunoglobulin antibody (protein) molecules. Each contains two light and two heavy chains. We will discuss the structure of antibodies in detail in Chapter 5.5. Needless to say, antibodies are very diverse molecules. Antibodies can be generated by the immune system to recognize almost any foreign molecules. The light chains of antibodies hence are incredibly diverse and variable, although they all have two immunoglobulin domains. Each domain has about 110 amino acids containing two layers of β-sheets each with 3-5 antiparallel β-strands with a disulfide bond connecting the two layers so they start with significant beta structure in the monomeric form. The large diversity in light chains is generated in part by the recombination of gene fragments to produce individual light chains. Some variants of the light chains (λ1, λ2, λ3, λ6, and κ1) are associated with AL amyloidosis, a potentially fatal disease. Mutants in the light chain can cause a destabilization of the native state to a state similar to a molten globule, which then conformationally converts to a structure that aggregates into amyloid fibers. These can deposit in various tissues. The pathway that a simple 25K monomer takes to produce such a complex but regular structure as shown above is must start with simple dimer formation between two monomers. Figure \(6\) shows an interactive iCn3D model of the normal conformation of a λ6a light chain dimer (6mg4). The λ6a light chain variant is more prone to aggregate to form fibrils. Figure \(6\): Full-length human lambda-6A light chain dimer showing IgG fold domains (6mg4). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...MNsiddm4RAdKn8 One light chain in the dimer is shown in blue, with one IgG fold domain in light blue and the other in dark blue. The other light in the dimer is shown in shades of magenta to show the two domains. Normally the light chains don't form dimers but rather associate with heavy chains to form full IgG antibodies. If not associated with a heavy chain, free light chains will form dimers, which can alter conformation and form amyloid fibrils. Figure \(7\) shows an interactive iCn3D model of the AL amyloid fibril from a lambda 3 light chain in conformation A (6Z1O). Each separate monomer is again shown in a different color. Figure \(7\): AL amyloid fibril from a lambda 3 light chain in conformation A (6Z1O). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...pSu2Nm714gvjh7 The Figure \(8\) shows a comparison of the secondary structures of the native conformation of the light chain and those found in the two different fibrillar forms (A and B) of the amyloid fibrils (panel a). Remember that each light chain has two IgG domains, each having two sets of β-sheets containing 3-5 antiparallel β-strands). Panel b shows the alignment of six fibrillar light chain proteins in conformation A. Panel c shows the trace of the backbones and the local environment of the side chains in a single light chain from the fibrillar A conformations. It shows cross-β-sheets interactions with parallel, hydrogen bonds across strands. Note the disulfide at connecting Cys 22 and Cys 87 on adjacent stands. The circles show surface side chains. Note that they are enriched in green (polar) and blue/red (basic/acidic) with some hydrophobic patches (example V10-L14). Some are pairs such as D24 and R28. Panel D shows the electrostatic surface of a single light chain from the fibrillar A conformation with red indicating negative and blue positive. Alzheimers' Disease (AD) This disease accounts for 60-80% of dementia cases. Brains in Alzheimer's patients have amyloid deposits of the amyloid beta (Aβ) protein as well as aggregates of a protein called tau. The origin of this disease is not fully resolved. Some believe aggregates of the amyloid beta protein cause the disease. Others suspect the role of tau in forming tau bundles. Still others suggest an infectious disease agent (more on that later). Irrespective of the fundamental cause, aggregates of the amyloid beta protein are neurotoxic and at minimum correlative if not causative of the disease. The amyloid aggregates in Alzheimer's start with a change in a monomeric protein normally found in the membrane of neurons. The protein, called β-amyloid precursor protein (BAPP or simply APP), is a transmembrane protein. A slightly truncated, soluble form is also found secreted from cells and is found in the extracellular fluid (such as cerebrospinal fluid and blood). The normal function of these APP proteins is not yet clear. An endoprotease cleaves a small 40-42 amino acid fragment from this protein named the amyloid beta (Aβ) protein. Figure \(9\) shows the normal processing of the amyloid precursor protein APP (left) and the abnormal, amyloidogenic form (right). In a "normal" processing pathway, the proteases α- and γ-secretase release two variant peptides, soluble amyloid precursor protein cleaved by α-secretase (sAPPα) and p3 fragments, into the extracellular environment. In the amyloidogenic processing pathway, β- and γ-secretases release soluble amyloid precursor protein cleaved by β-secretase (sAPPβ) and β-amyloid (Aβ) peptides. Both pathways release the same intracellular domain, AICD, which moves to the nucleus and acts as a transcription factor to regulate gene expression. The β-amyloid (Aβ) peptides aggregate to form the fibrillar aggregate plaque. It is the amyloid beta (Aβ) protein or a mutant form of it that aggregates to form beta-sheet containing fibrils in Alzheimer's disease. The NMR solution structure of the monomer amyloid beta-peptide (1-42) is shown in Figure \(10\). Note the absence of any beta structure. Several mutations in different proteins have been linked to Alzheimer's, but they all seem to increase the production or deposition or both of the amyloid beta protein. These deposited plaques are extracellular, and have been shown to cause neuronal damage. They are found in areas of the brain required for memory and cognition. The APP gene is found on chromosome 21, the same chromosome which is present in an extra copy (trisomy 21) in Downs Syndrome, whose symptoms include presenile dementia and amyloid plaques. Aggregate formation appears to be driven by increased expression of APP and hence amyloid beta protein. In addition, some mutants may serve to destabilize the amyloid beta protein, increasing its aggregation. Figure \(11\) shows an interactive iCn3D model of the prevalent amyloid-beta fibril structure from Alzheimer's disease brain tissue (6W0O). Figure \(11\): Amyloid-beta(1-40) fibril derived from Alzheimer's disease cortical tissue (6W0O). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...NZCX9sAhnFPiB6 The tau protein (758 amino acids, MW 78,928), which is much larger than the other proteins we discuss in this chapter, has also been implicated as a cause or factor in Alzheimer's Disease. It facilitates microtubule assembly and stability (see Chapter 5) along with other functions in neurons. Since its C-terminus binds microtubules in axons and the N-terminus binds the plasma membrane, it might link both. The cytoskeleton of neurons is disrupted in the neurons in Alzheimers' and in other neurodegenerative diseases patients. When tau tangles are involved, these diseases can also be termed tauopathies. One tauopathy is chronic traumatic encephalopathy (CTE) caused by repetitive head impacts (from contact sports or physical abuse) or concussions arising from explosions in combat. No full-length structure of tau has been determined. The structures of a predicted model of solution phase monomeric tau and tau fibers from the brain of patients with neurodegenerative diseases (Alzheimer's, CTE and Corticobasal Degeneration - CBD) are shown in Figure \(12\). The predicted structure of the monmeric protein (using AlphaFold) is almost completely devoid of secondary structure. The structures of tau from the other tauopathies are similar but clearly distinct. In CBD tau fibers there are 4 microtubule-binding repeats (4R). Picks Disease, another tauopathy, has three repeats (3R) while taus in AD and CTE are 3R or 4R. The fibril cores of tau in both CBD (Lys 274-Glu380, which contains the end of R1 and R2-R4) and Alzheimer's Disease (CD) contain around 13% glycine residue. These allow the main chain flexibility and intersheet packing to allow the large conformation changes necessary to adopt beta structures and fibril formation. Repeats of PGGG motif allow sharp turns or extended chains. Valine (around 10%) and isoleucine, leucine, and phenylalanine facilitate inter-sheet packing through induced dipole-induced dipole interactions as well as through the hydrophobic effect. Certain tau fibrils in CBD contain hydrophilic pockets that bind molecules yet to be elucidated that might seed the nucleation of fibrils. The cavity has three lysines and a histidine so the molecule with the pocket is probably linked to histidine might be a glycan or an ADP-ribose. In addition, recent evidence shows that different combinations of post-translational modification by ubiquitinylation and acetylation of lysines 311, 317, 321, 343, and 353 might lead to different tau fibril structures. CTE tau protein in tangles appears to be hyperphosphorylated. Figure \(13\) shows an interactive iCn3D model of a paired helical tau filament from Alzheimer's Disease human brain tissue (6VHL). Figure \(13\): Paired helical filament of Tau (6VHL) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...NHsZdhDJNentg9 Zoom into the interactive images to see the H bonds within one of the filaments. Note that the hydrogen bonds (green dotted lines) in the left filament are between side chains and not between backbone amide Hs and carbonyl Os. These are pointing above and below the plane of the backbone where they could interact with other of the chains above and below to create the multi-chain fibers seen in all of the examples above. Figure \(14\) shows an interactive iCn3D model of a singlet Tau fibril obtained from corticobasal degenerated human brain tissue (6VHA). Figure \(14\): Singlet Tau fibril from corticobasal degeneration human brain tissue (6VHA). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...Bbxr2Z1caGqnG8 The backbone of the C-terminal amino acid (367-380) of each of the three separate chains of the fibril is shown in CPK colors and hydrogen bonds between the strands that form the parallel beta strands are shown as green dashes. Progress has been incredibly slow on ways to treat Alzheimer's. Most methods focus on reducing amyloid beta production and aggregation by finding small molecules that inhibit steps in its production, including secretase cleavage, and the resulting conformational steps necessary to produce amyloid fibers. But what if the amyloid aggregates are secondary to the primary cause? What if the overproduction of the amyloid beta protein was the brain's response to defend against the cause? Lewy Bodies and Parkinson Disease α-Synuclein (140 amino acids, MW 14,460) is expressed in the brain and in presynaptic terminals in the central nervous system and is involved in the regulation of neurotransmitter release and in the synaptic vesicles that hold them. Its aggregation is a cause or consequence of Parkinson's Disease and Lewy Body Dementia. It's found in the cytoplasm and the nucleus and is secreted as well. Figure \(15\) shows an NMR solution structure (left) and AlphaFold-predicted structure (right) for this protein which in solutions is so disordered that no full crystal structure has been determined. Figure \(16\) shows an interactive iCn3D model of an amyloid fibril structure of alpha-synuclein determined by cryo-electron microscopy (6A6B). Two protofilaments with clearly Greek key topologies are shown. Figure \(16\): Amyloid fibril structure of alpha-synuclein determined by cryo-electron microscopy (6A6B). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...SbQq7fhFpQ9K4A Transmissible spongiform encephalopathies (TSEs) - Prion Diseases Prion diseases are another set of brain diseases resulting from aggregates of monomers of the prion protein (PrPc). As with the examples above the aggregates form amyloid beta fibrils. The prion diseases include scrapie in sheep, bovine spongiform encephalopathy (mad cow disease), and in humans Creutzfeld-Jacob Disease (CJD), Fatal Familial Insomnia (FFI), Gerstman-Straussler-Scheinker Syndrome, and Kuru (associated with cannibalism). In these fatal diseases, the brain, on autopsy, resembles a sponge with holes (hence the name spongiform). In contrast to the diseases described above, these diseases can be transmitted from one animal to another, but typically not between species. (However, consider the controversy with mad cow disease.) Also, the infectious agent can self-replicate in vivo. The logical conclusion is that a virus (slow-acting) is the causative agent. However, the infectious agent survives radiation, heat, chemical agents, and enzymes designed to kill viruses and their associated nucleic acids. Mathematical analyses suggested that the infectious agent in such diseases could be nothing more than a protein. Stanley B. Prusiner in the 80's isolated just such a protein which he named a prion, for proteinaceous infectious agent. In October 1997 he was awarded the Nobel Prize in Medicine. The normal monomeric prion protein, PrPc (253 amino acids, MW 27,661), is highly conserved in mammals, and is widely expressed in embryogenesis. Expression is highest in the central nervous system. The normal function of the protein is still unclear. It is a physiological substrate to a particular membrane receptor (the Gpr 126 G protein-coupled receptor). Knocking out the gene shows that the normal protein is involved in synapse structure/function, myelination of neurons and circadian rhythms probably by acting as a transcription factor. It also helps regulate Cu2+ and Zn2+ levels in the central nervous system. The protein is cleaved and a 209 amino acid fragment is bound to the extracellular side of the neuron membrane-anchored by attachment of a lipid (GPI) anchor. The protein N-terminal residues (23-124) are flexibleand are followed by residues 125-231 which are mostly alpha helical. There is a disulfide between Cys179 and Cys214. The PrPc (without the PI link) is water soluble, a monomer, protease-sensitive, and consists of around 45% alpha helix and 3% beta sheet. No full-length crystal structure of the protein has been determined given its highly disordered structure. The solution structure of residues 125-231 has been determined by NMR and the structure of the full protein has been modeled with Alpha Fold. These structures are shown in Figure \(17\). The blue helices and gold loops in the computer model consists of the same amino acids (125-231) as the NMR solution model in the left hand side of Figure \(2\). The problem in transmissible spongiform encephalopathies (TSE's) is that amyloid-like protein aggregates form, which are neurotoxic. The protein found in the plaques (in cases other than those that are inherited) has the same primary sequence as the PrPc but a different secondary and presumably tertiary structure. The protein found in the plaques, called the PrPsc (the scrapie form of the normal protein) is insoluble in aqueous solution, protease-resistant, and has a high beta sheet content (43%) and lower alpha helix content (30%) than the normal version of the protein PrPc. Figure \(18\) shows an interactive iCn3D model of the cryo-EM structure of an amyloid fibril formed by full-length human prion protein (6LNI). Each line represents a PrPsc chain from amino acids 170-229,which is the core of the fibril. Figure \(18\): Cryo-EM structure of an amyloid fibril formed by full-length human prion protein. (6LNI) (Copyright; author via source). Click the image for a popup or use this external link https://structure.ncbi.nlm.nih.gov/i...ByT4rL33brHRY7 The aligned zig-zag lines clearly indicated beta strands aligned through hydrogen bonding the adjacent strands. The two alpha helices in the C-terminal domain become beta strands . A genetic, inheritable form of the disease also exists, in which a mutant form of the PrPc occurs, whose normal structure is destabilized by the mutation. The aggregates caused by the mutant form of the disease are understandable in light of the other diseases which we discussed above. The question is how does the normal PrPc form PrPsc . Evidence shows that if radiolabeled PrP*c from scrapie-free cells is added to unlabeled PrPsc from scrapie-infected cells, the PrP*c is converted to PrP*sc! It appears that the PrPc protein has two forms not that much different in energy, one composed of mostly alpha helix and the other of beta sheet. A dimer of PrPc.PrPsc might form, which destabilizes the PrP*c causing a conformational shift to the PrPsc form, which would then aggregate. Exposure to the PrPsc form would then catalyze the conversion of normal PrPc to PrPsc . Hence, it would be transmissible by contact with just the PrPsc form of the protein. Likewise, species specificity could be explained if only dimers of PrPc.PrPsc formed from proteins of the same species could occur. The inherited form of the disease would be explained since the mutant form of the normal protein would more easily form the beta structure found in the aggregate. It has recently been found that the very same mutation in PrPc, Asp178Asn can cause two different diseases - CJD and FFI. Which disease you get depends on if you have 1 of two naturally occurring, nonharmful variants at amino acid 129 of the normal PrPc gene. If you have a Met at that position and acquire the Asp178Asn mutation, you get CJD. If, on the other hand, you have a Val at amino acid 129 and acquire the Asp178Asn mutation, you get FFI. This disease was first observed in 1986 and has been reported in five families in the world. It occurs in the late 50's, equally in men and women. It is characterized by a progressive loss of the ability to sleep and disrupted circadian rhythms. The brain shows neuronal losses. It is known that amino acids 129 and 178 occur at the start of alpha helices, as predicted from propensity calculations. Chronic exposure to micromolar levels of synthetic fragment 106-126 of PrPc kills hippocampal neurons. This peptide also has the greatest tendency to aggregate synthetic PrPc peptides. Kuru killed many members of the Fore tribe in New Guinea until the cannibalistic practice of eating dead relatives was stopped. Analysis of the genes for the prion protein in the Fore tribe and other ethnic groups in the world show two versions differing by just one amino acid in all people (remember that a single gene is represented in both maternal and paternal chromosomes. That these two forms exist throughout the world suggests that they have been selected for by evolution and confer some biological advantage. People who have just one form of the protein are more susceptible to the development of prion diseases. Mead and Collinge have shown that about 75% of older Fore women (who had lived through cannibalistic practices) had two different prion genes, compared to about 15% of women from other ethnic groups. This high percentage suggests that these women were protected from the disease, leading through natural selection to a high percentage of heterozygotes in this defined population. The general presence of two forms of the prion gene (which probably offers protection from prion disease) suggests that cannibalism might have been widespread in our early ancestors. There appears to be one main difference between the formation of amyloid fibers from prion proteins and others such as mutant lysozymes. If you add mutant lysozyme to normal lysozyme, the amyloid fibers contain only the mutant protein. However, if you incubate mutant prion proteins with normal prions, the normal proteins become pathological. Misfolding and Aggregation Summary Recent work has shown the proteins considered to be completely harmless can generate misfolded intermediates that aggregate to produce pre-fibril structures that are toxic to cells. This process is usually prevented in the cell by the interaction of nascent forms of the proteins with chaperones, which sequester exposed hydrophobic patches and prevent aggregation. (Obviously, prion proteins and the others mentioned above are exceptions). Amyloid fibers (characterized by subunits with an abnormal amount of beta-structure) can be made from many different types of proteins as noted above. Is this property specific to just a handful of proteins, or is it more common than expected from the limited examples noted so far? The new studies show that when a bacterial protein HypF is incubated at pH 5.5 in the presence of trifluoroethanol, aggregates (but not fibrils) form with enhanced beta structure. These aggregates slowly form into fibrils characteristic of amyloid protein fibers. The early aggregates (before fibril formation) proved cytotoxic. Similar results were seen with dimers and trimers (prefibril states) of the amyloid-b peptide released from cultured neurons. A diverse group of proteins that do not share significant secondary or tertiary structures can form amyloid-like protein aggregates. Even though their monomer forms share little in common, the insoluble amyloid aggregates have a common structure in which the monomer in the aggregates has significant beta structure with the strands running perpendicular to the aggregate axis. Since it has recently been shown that almost any protein, under the "right" set of conditions, can form such aggregates, the stabilizing feature of protein aggregates must be potentially found in any protein. Evidence suggests that it is the polypeptide backbone, and not the side chains, that are key in the formation of stable interstrand H-bonds in beta secondary structures in amyloid aggregates. In contrast, native, nonamyloid forms of normal proteins arise through specific interactions of unique side chain sequence and structure, which out-competes nonspecific interactions among backbone atoms found in amyloid structures. Nonspecific aggregation becomes more prevalent when buried hydrophobic side chains and buried main chain atoms become more solvent exposed. Such exposure occurs when native proteins form intermediate molten globule states when subjected to altered solvent conditions or when destabilizing mutants of the wild-type protein arise. Some mutations may alter the cooperativity of folding which would increase the fraction of nonnative protein states. Other mutations that decrease the charge on the protein or increase their hydrophobicity might enhance aggregation. In addition, chemical modifications to proteins (such as oxidation or deamination) might destabilize the native state, leading to the formation of the molten globule state. Once formed, this state may aggregate through sequestering exposed side chain hydrophobes or through inter-main chain H bond formation. Aggregate formation appears to proceed through the initial formation of soluble units (which may or not be more toxic to cells than the final aggregate). Aggregates are kinetically stable species. Since amyloid aggregates are cytotoxic and almost any protein can form them, albeit with different propensities, nature, through evolutionary selection, has presumably disfavored proteins with high tendencies to form such aggregates. Clearly, accurate protein folding is required for cell viability. Aberrant protein folding clearly can be the cause of serious illness. Given the extraordinary nature of the task and its failure, the process governing protein folding must be highly regulated. Figure \(19\) shows the steps that determine intracellular concentrations and locations of normal and aberrant protein structures. Potential therapies for diseases of proteostasis include replacing aberrant proteins, shifting the equilibria toward active forms with small ligands, or modulating the pathways with agents that influence pathways such as signal transduction, transcription, translation, degradation, and translocation using molecules like siRNAs to modulate concentrations of chaperons, disaggregases, and signal pathways. Binding, Intracellular Granules and Droplets The above structures are fascinating aggregates of specific proteins. The aggregates are quite large. In Alzheimer's Disease, they vary from around 150-500 μm2, which would give a length of 12-22 μm if they were squares. In comparison, intracellular "granules" are much smaller with diameters from 200-500 nm or 0.2-0.5 μm. The term granule describes particles in cells that are just barely visible by light microscopy. Granules are found in many cells and mostly contain protein. Platelet granules contain many proteins involved in clotting. Pancreatic beta cell granules contain insulin for secretion. Other types of granules in germ-line cells are called various names such as dense bodies, perinuclear P granules in Caenorhabditis elegans, germinal granules in Xenopus laevis, chromatoid bodies in mice, and polar granules in Drosophila. The contains RNA as well as proteins. Those are often called ribonucleoprotein (RNP) granules. Plants and livers also contain starch (a carbohydrate) granules. The granules don't appear to be surrounded by a membrane. Rather they are just aggregates of proteins, or RNA and proteins. In Chapters 10 and 11, we will see analogous particles for lipids, nonpolar "insoluble" molecules that self-aggregate into micelles and membrane bilayers. Lipid droplets, which contain TAGs and cholesterol esters, in contrast to the granules mentioned above, are surrounded by a phospholipid monolayer with adsorbed protein. Maybe an understanding of the structure and properties of phase-separated granules can shed light on the aggregates formed in neurodegenerative diseases. How do these granules form? What principle underlies the specificity of protein and RNA found in them? The aggregates are not toxic compared to the beta-amyloid aggregates discussed above. A quick review of the Cell Tutorial (scroll to bottom) shows granule formation can be caused by a classic "phase transition", not unlike gaseous water can self-associate through hydrogen bonds to form liquid drops, which can freeze with the formation of more hydrogen bonds to form solids. Soluble biomolecules in cells can reversibly aggregate through the summation of multiple, weak noncovalent interactions to form storage granules. This balance might be perturbed if storage granules aggregate further in a potentially irreversible process with health consequences as we saw in neurodegenerative diseases. Let's delve into new insights into the processes involved in droplet formation. Imagine small amounts of a sparing soluble oil added to an aqueous solution. Initially, it is in solution, but at a higher concentration, induced dipole-induced dipole interactions and the “hydrophobic effect” would drive the oil out of the solution into liquid drops. This phase separation could also be called liquid-liquid demixing as two liquids (solubilized oil in water and separated oil drops) separate. from each other. This process has been shown to produce many types of non-membrane bound droplets (not to be confused with membrane-bound vesicles) in the cell. This phenomenon has also been seen with intrinsically disordered proteins and proteins with such domains. These are characterized by amorphous structures with repeated, often positively charged amino acids and/or contain a limited number of different types of amino acids. An example of a protein with a domain that has low sequence complexity is the SP1 transcription factor, a DNA binding protein. One of its tranactivation domains is comprised of almost 20% glutamines with regions within it having even higher percent abundances. It has been estimated that up to 20% of eukaryotic proteins don't have a stable shape as they are in part intrinsically disordered and contain low complexity domains (LCDs). They are found in the N- and C-terminal ends of all mammalian intermediate filament proteins, almost all RNA binding proteins, lining the nuclear pore and in the cytoplasmic faces of mitochondrial, lysosomal, peroxisomal and Golgi integral membrane proteins. "They decorate both ends of all 75 intermediate filament proteins found in mammals, fill the central channel of nuclear pores, adorn almost all RNA-binding proteins, and occur on the cytoplasmic faces of integral membrane proteins associated with mitochondria, neuronal vesicles, peroxisomes, lysosomes, and the Golgi apparatus. They are the target of up to 3/4s of posttranslational modifications. LCDs hence appear to facilitate the promiscuous binding of a variety of proteins, especially those that lead to or remove covalent tags. Under the right condition, these can aggregate and “precipitate” from the solution. What is the nature of the precipitate? It might have properties more like distinct liquid droplets so this process could be called liquid-liquid demixing. Properties of demixed drops would include reduced rates of diffusion of material into an out of the drop, coupled movements of materials in the drop, and probable weak hydrophobic-dependent aggregation making drops sensitive to agents like detergents. Liquid-like diffusion inside the drop is observed as evident by the rapid recovery of fluorescence from partially photobleached internal components of the drop. As with the formation of a crystalline solid from a liquid solution, the process must be seeded. For intrinsically disordered proteins, this process can be “catalyzed” by poly-(ADP-ribose), a nucleic acid-like polyanion. The negative charges would counter the positive charges in the disordered protein domain, which without neutralization, would interfere with protein/protein contacts necessary for aggregation/droplet formation and demixing. Aggregation in these cases may arise from hydrophobic interactions (even though hydrophobic side chains are underrepresented in the disordered domains). The solubility of proteins in cells is a fascinating topic in itself. High concentration of ATP (5 mM) in the cell actually helps to solubilize proteins. ATP is considered a hydrotrope. It’s a small molecule with a very distinct polar part (polyphosphate and ribose) and a more nonpolar part (the adenosine ring). Hence it acts sort of like a mini-detergent (an amphiphile) but it doesn’t form micelles. It does help stabilize more nonpolar parts of proteins in solution and has been shown to inhibit aggregate formation and also disaggregate some aggregates. Figure \(20\) shows a nonprotonated form of energy-minimized ATP with its dipole moment shown as an arrow from + to - end. The dipole moment would only be larger if the ATP was deprotonated and had negative charges. Biochemists also use the term gel (examples include polyacrylamide gel or fibrin blood clots which are chemically cross-linked) and a "gel" form of a bilayer (Chapter 10), when they wish to describe a structure that is neither clearly solid nor liquid. Structures like the cytoskeleton or the actin-myosin network would be examples of the latter. Noncovalent gels would be characterized by the regulatable dissociation of subunits and hence short half-lives. A gel (either covalent or noncovalent) with a high-water content could be called a hydrogel which would contain hydrophilic components. An example would be RNA and protein-containing particles RNA granules Granules that contain RNA and proteins are called ribonucleoprotein bodies (RNPs) or RNA granules. Specific examples of these include cytoplasmic processing bodies, neuronal and germ granules, as well as nuclear Cajal bodies, nucleoli and nuclear dots/bodies). Some granules just contain proteins, including inclusion bodies with misfolded and aggregated proteins and those with active proteins involved in biosynthesis, including purinosome (for purine biosynthesis) and cellusomes (for cellulose degradation). Another feature found in some neurodegenerative diseases is a trinucleotide repeat. In Fragile X syndrome, there 230-4000 repeats of the CGG codon in the noncoding parts of the genome, compared to less than 50 in the normal gene. In Huntington’s disease, the repeat CAG is found in the protein-coding part of the affected gene. The translated protein has a string of glutamines which probably causes protein aggregation. Specific proteins may also bind to the string of CAGs. If the trinucleotide expansion is in the nonprotein-coding intronic DNA, deleterious effects are not associated with translated proteins but with the transcribed RNA in the nucleus. The intronic repeats would be spliced out of the primary RNA transcript. A CTG DNA repeat would produce a poly CUG containing RNAs (found in myotonic dystrophy), which could aggregate through non-perfect base pairing. In vitro experiments show that small complexes are soluble, but as the size increases, a liquid-liquid demixing phase separation (or alternatively a liquid-gel transition) can occur, forming spherical droplets of RNA particles. This would explain the observation that pathologies occur above a certain repeat length. If misfolded proteins are also present, these particles might combine to form larger gels. In the control experiment, when the repeats were scrambled, demixing and spherical particle formation were not observed. In an experiment similar to the addition of 1,6-hexanediol to intrinsically disordered proteins, if small antisense trinucleotide repeats, such as (CTG)8, which could interfere with the weak H bonds between G and C in the aggregates, were added, the size of RNA drops (foci) were reduced. In vivo experiments showed characteristic drop-like structures but only if the repeats were of sufficient size. Researchers found that in vitro, RNA drop formation was inhibited by monovalent cations. In the presence of 0.1 M ammonium acetate, which permeates cells without affecting pH, CAG RNA droplets in vitro disappeared. Aggregation of mRNA might be one way to regulate its translation and hence indirectly regulate gene activity. There are advantages to regulating the translation of a protein from mRNA, especially if the "activity" of the mRNA could be dynamically regulated. This would be useful if new protein synthesis was immediately required. Hence one way to regulate mRNA activity (other than degradation) is through reversible aggregation. Protein drops and granules The cytoskeletal proteins actin and tubulin (heterodimer of alpha and beta chains) can exist in soluble (by analogy to water gaseous) states or in condensed, filamentous states (actin filaments and microtubules respectively). GTP hydrolysis is required for tubulin formation. Actin binds ATP which is necessary for filament formation but ATP cleavage is required for depolymerization. Hence nucleotide binding/hydrolysis regulates the filament equilibrium which differentiates from simple phase changes such as in water. Since only certain proteins form granules, they must have similar structural features that facilitate reversible binding interactions. These proteins have multiple, weak-binding sites, but if they act collectively provide multivalent (multiple) binding interactions that allow robust but not irreversible granule formation. Here are some characteristics of proteins found in granules: • the protein NCK has 3 repeated domains (SH3) that bind to proline-rich motifs (PRMs) in the protein NWASP. These proteins are involved in actin polymerization. In high concentrations they precipitate from the solution and coalesce to form larger droplets; • repeating interaction domains are widely found especially among RNA-binding proteins; • some proteins contain Phe-Gly (FG) repeats separated by hydrophilic amino acids in portions of the protein that are intrinsically disordered. • a biotinylated derivative of 5-aryl-isoxazole-3-carboxyamide (Figure \(21\)) precipitates proteins, which are enriched in those that bind RNA (RBPs). In general, the precipitate proteins were intrinsically disordered and characterized by low complexity sequences (LCS). One such example contained 27 repeats of the tripeptide sequence (G/S)Y(G/S). The proteins could also form hydrogels (made of hydrophilic polymers and crosslinks) and transition between soluble and gel phases with extensive hydrogen bond networks. The hydrogel gel phase gave x-ray diffraction patterns similar to beta structure-enriched amyloid proteins. Short-range, weak interactions between LCS might then drive reversible condensation to gel-like granule states characterized by extensive hydrogen bonding (again similar to hydrogen bonding on ice formation). If this process goes awry, more continued and irreversible formation of a solid fibril (as seen in neurodegenerative diseases) might occur from the hydrogel state; • RNAs appear in granules when proteins bind them through their RNA binding domains, which interact through low complexity sequences leading to phase separation and hydrogel-like formation of granules. Around 500 RNA binding proteins have been found in the human RNA interactome. They are enriched in LCSs and have more tyrosines than average proteins in the whole proteome in which the Tyr are often found in the (G/S)Y(G/S) motif. Phosphorylation of tyrosines (Y) in LCS may decrease association and hydrogel stability. Given that so many neurodegenerative diseases are associated with unfolded/misfolded protein aggregates, the high protein concentrations in protein-containing liquid drops might pose problems to cells. If high enough, the equilibrium might progress from the liquid drop to a solid precipitate, which would have severe cellular consequences. The progression to the solid state may irreversibly affect the cell. Low complexity domains (LCD) and neurodegenerative disease The aggregation of alternatively-folded proteins is clearly associated with neurodegenerative disease. Mutations that lead to diseases lead to the association of low-complexity domains and aggregate formation, which is increasingly being described as phase separation. The demixed phases are stabilized by interchain backbone hydrogen bond as shown in the many beta-sheet aggregates described above. Evidence suggests that labile structures with potential for interchain H bonds and beta strand formation lead to fibril formation. The nascent interactions would involve short stretches of interchain H bonds. If so, mutations that enrich such nascent structural interaction would promote fibril formation while those that inhibit the nascent interactions would inhibit fibril formation. A study (Zhou et al, Science, 377, 2022. DOI: 10.1126/science.abn5582) verifies this. The investigators made single amino acid variants of the low complexity domains of an RNA binding protein TDP-43 RNA that prevented that single amino acid within a region involved in interchain beta strand formation from forming a hydrogen bond through its amide hydrogen. They did this by methylating single main chain amide nitrogen, which prevents its participation in a hydrogen bond. The modification is shown in Figure \(22\). Figure \(22\): Methylation of a single backbone nitrogen in a region involved in interchain hydrogen bond and beta sheet formation in low complexity domain of proteins Of the 23 variants they made, 9 within a continuous stretch inhibited phase separation. These 9 were at the same sites as hydrogen bonds between adjacent chains of the TDP-43 as determined by cryo-EM. Next, they looked at other proteins with low-complexity domains that form aggregates/polymers. The proteins were the neurofilament light (NFL) chain protein, the microtubule-associated tau protein, and the heterogeneous nuclear RNPA2 (hnRNPA2) RNA-binding protein. They found 10 mutations in LCDs that were known to be associated with neurological disease. Indeed these mutations allow one extra single hydrogen bond in the low complexity domain sequences, and display enhanced aggregate formation mediated presumably through the extra interchain H bond. Specifically, the known mutations replace individual prolines, a cyclic amino acid that lacks an amide H and hence cannot donate a hydrogen bond, with another amino acid, which allows one additional hydrogen bond. Each of the known mutations was associated with neurological disease and increased stable aggregate/polymer formation. This increased aggregation/polymer (phase separation) was reversed in vitro by chemical methylation of the single amino acid change in the mutant which prevented it from forming hydrogen bonds. The site-specific methylation was performed by linking synthetic peptides containing the single, Nα-methyl amino acid to the other synthesized peptides that comprise the protein. The semisynthetic NFL protein, for example, was incubated under conditions conducive to the assembly of mature intermediate filaments. In vitro experiments were conducted using different synthetic head domains of the neurofilament light (NFL) chain protein in which the P8 residue contained a different amino acid at those positions. The variant amino acids are shown in Figure \(23\). Figure \(23\): Variant amino acids used to at position P8 in the low complexity head domain of the neurofilament light (NFL) chain protein (after Zhou et al, ibid) Only variants containing Leu at position P8 were able to form filaments as measured by in vitro fluorescent studies. Experiments like this are key in ascertaining where phase separation/aggregation causes and are not merely correlated with the development of complex neurodegenerative diseases.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/04%3A_The_Three-Dimensional_Structure_of_Proteins/4.10%3A_Protein_Aggregates_-_Amyloids_Prions_and_Intracellular_Granule.txt

Search Fundamentals of Biochemistry Structure determines everything in biology and chemistry. Since you learned to represent molecules with Lewis structures, it's been drilled into you that the structure of a molecule determines its physical and chemical properties. Physical properties would include melting points, boiling points, and solubility. In contrast, chemical properties include acid/base, redox, precipitation, and general chemical reactivity determined by the presence of Lewis/Brønsted acids/bases and nucleophiles/electrophiles. More modern analyzes of reactivity would include molecular orbital theory descriptions of bonding. What makes chemistry and its fundamentally interconnected fields of biology and biochemistry so difficult to many is that we can't see molecules but make inferences from data (x-ray crystallography, NMR spectroscopy, and cryo-EM) about the structure of a molecule (atom type, atom/bond connectivity, and geometry). As the molecules get bigger (consider the muscle protein titin, also called connectin, with a molecular weight of around 3.8 million), we must use computer visualization to understand the structure and infer from it the resulting function and activity of the protein. As with small molecules, we can render the molecule in different ways to better understand various attributes of the molecule that confer function and activity. We ask students to view a biomolecule and infer its properties from the rendering without giving devoted attention and instruction as to how to do that. Small molecules Let's start with a small molecule like oleic acid, a long chain carboxylic acid with 18 carbon atoms and one cis (Z) double bond between carbons 9 and 10 as shown in Figure $1$. Table $1$ below shows multiple ways to render the molecule. Each rendering offers insight in the function/activity of the molecule but might at the same time leave students with difficulties in interpreting them and also reinforce or install misconceptions. Each representation below shows the very same molecule. The top row shows representations without H atoms, which the bottom row shows them. line stick ball sphere sphere surface Table $1$:Different renderings of oleic acid Here are some important things to remember about biomolecular structures including both small and large molecules: • Structures obtained using x-ray crystallography are constructed from relative electron densities calculated from diffraction patterns. Computer programs calculate the structure based on these electron density maps and known bond lengths, bond angles, atom types. Most structures used in this book are found in the Protein Data Bank. The structures seen in computer models are visualized data and can contain mistakes (missing atoms, steric conflicts, wrong atoms), although structural refinement techniques minimize such problems. • Structures derived from X-ray and cryomicroscopy analyzes are static structures and represent only one of a large ensemble of possible conformational structures. As you learned from the study of simple molecules in organic chemistry, bond lengths and angles changes can change within molecules. Bonds connecting two atoms can stretch, angles connecting three atoms can bend, and the torsional angle around the center bond in a four-atom, three-bond system can rotate to form eclipsed and staggered (gauche and anti) conformers. • PDB structures obtained by x-ray crystallography contain no H atoms as they are too small and contain too few electrons to diffract/scatter x-rays. So get used to adding them in your mind when you see a structure. Programs are used to calculate and show H bonds between a slightly positive H atom on an O or N atom in a protein, and another slightly negative Os or Ns on the same or different molecule. The H bonds are often shown between N and O atoms. You should look at the atoms involved and the distance between them and visualize a hydrogen atom connected to one of them. Figure $2$ shows an example of an H bond between two base pairs in a DNA molecule. • Double bonds or likely resonance structures are not typically shown in PDB structures. • Line, ball and stick, and stick renderings are useful for showing connectivity between atoms and bond angles. However, they are not particularly useful in showing how atom size might affect the molecules' structure and properties. This type of information is better shown when spacefill renderings that show the sizes of the atoms (based on their Van der Waals radii) is used, or when the surface of the molecule, calculated from contact surface created between the van der Waals surface of the atoms and a rolling probe (often an O atom mimicking water) is displayed. Large molecules As molecules get bigger, line, stick, and ball and stick renderings are increasingly useless. New ways of visualizing the structural features of the molecules become needed. The importance of multiple renderings to clarify structure/function relationships becomes apparent when you wish to understand protein structure. Various renderings of the protein superoxide dismutase (2sod) are shown in Table $2$ below. line stick cartoon sphere surface Table $2$: Multiple renderings of the protein superoxide dismutase • The same features and limitations described above for small molecules apply to large ones. It is best to leave out most of the atoms and use mixed renderings within a single display to reveal important structural feature of the biomolecule. The cartoon rendering of superoxide dismutase shows one tiny alpha helices (red) and many beta strands (yellow). All side chain and backbone atoms have been removed. The green line is the trace through the backbone of those amino acids not involved in secondary structure. The Cu and Zn ions are shown as spheres. Another type of surface rending, the electrostatic potential surface, is beneficial. Figure $3$ shows the electrostatic potential surface of superoxide dismutase taken from two different angles after simply rotating the protein. Figure $3$: Electrostatic potential surface of superoxidase dismutase from two perspectives The red represents minimal (most negative) potential. This part of the structure would be enriched in slightly negative Os and Ns or fully negative Os (i.e. have the highest electron density). The representesents the positive potential, centered in areas of the surface containing slight or full positive charge (I,.e.the lowest electron density). This enzyme binds superoxide, O2-, a toxic free radical reduction product of dioxygen, $\ce{O2}$. It catalyzes this reaction: $\ce{2 O2^{-} + 2H^{+} → O2 + H2O}. \nonumber$ The enzyme can effectively scavenge superoxide in its vicinity as the negative superoxide is drawn into the active site with the $\ce{Cu}$ and $\ce{Zn}$ atoms by the positive potential surrounding the active site, enhancing the normal diffusion encounter rate of the reactant with the enzyme pocket. Figure $4$ shows an interactive iCn3D model of the electrostatic potential of superoxidase dismutase (2sod). Figure $4$: Electrostatic potential of superoxidase dismutase (2sod) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...QQy2hAXehWLBW8

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/04%3A_The_Three-Dimensional_Structure_of_Proteins/4.11%3A_Biomolecular_Visualization_-_Conceptions_and_Misconceptions.txt

Search Fundamentals of Biochemistry Introduction Multiple methods can be used to investigate the denaturation of a protein. These include UV, fluorescence, CD, and viscosity measurement. In all these methods the dependent variable (y) is measured as a function of the independent variable, which is often temperature (for thermal denaturation curves) or denaturant (such as urea, guanidine hydrochloride) concentration. From these curves, we would like to calculate the standard free energy of unfolding (ΔGO) for the protein (for the reaction N ↔ D). It is relatively to calculate if the denaturation curves show a sigmoidal, cooperative transition from the native to the denatured state, indicating a two-state transition. The dependent variable can also be normalized to show fractional denaturation (fD). An idealized denaturation curve is shown below in Figure $1$. A more realistic denaturation curve might show a small linear change in the dependent variable (fluorescence intensity for example) for temperature or denaturant concentrations well before the major unfolding transition, as well as above those at which it is unfolded. In these cases, the mathematical analyses presented at the end is required. Denaturation with urea or guanidine hydrochloride For each curve, the value of $y$ (either A280, fluorescence intensity, viscosity, etc.) can be thought of as the sum contributed by the native state and from the denatured states, which are both present in different fractional concentrations from 0 - 1. Hence the following equation should be reasonably intuitive. $y=\left(f_{N} y_{N}\right)+\left(f_{D} y_{D}\right) \label{1}$ where $f_N$ is the fraction native and $y_N$ is the contribution to the dependent variable $y$ from the native state, and $f_D$ is the fraction denatured and $y_D$ is the contribution to the dependent variable $y$ from the denatured state. Conservation gives the following equation. $1=f_{N}+f_{D} \text { or } f_{N}=1-f_{D} \label{2}$ Substituting \ref{2} into \ref{1} gives $y=\left(1-f_{D}\right) y_{N}+\left(f_{D} y_{D}\right)=y_{N}-f_{D} y_{N}+\left(f_{D} y_{D}\right) \nonumber$ Rearranging this equation gives $\mathrm{f}_{\mathrm{D}}=\frac{\mathrm{y}-\mathrm{y}_{\mathrm{N}}}{\mathrm{y}_{\mathrm{D}}-\mathrm{y}_{\mathrm{N}}} \label{3}$ Notice the right-hand side of the equations contains variables that are easily measured. By substituting \ref{2} and \ref{3} into the expression for the equilibrium constant for the reaction $\ce{N <=> D}$ we get: K_{e q}=\frac{[D]_{e q}}{[N]_{e q}}=\frac{f_D}{f_N}=\frac{f_D}{1-f_D} From this, we can calculate ΔG0. \Delta \mathrm{G}^0=-\mathrm{R} \operatorname{Tln} \mathrm{K}_{\mathrm{eq}}=-\mathrm{R} \operatorname{Tln}\left[\frac{\mathrm{f}_{\mathrm{D}}}{1-\mathrm{f}_{\mathrm{D}}}\right] Remember that ΔGO (and hence Keq) depends only on the intrinsic stability of the native vs denatured state for a given set of conditions. They vary as a function of temperature and solvent conditions. At low temperatures and low urea/guanidine HCl concentration, the native state is favored, and for the N ↔ D transition, ΔGO > 0 (i.e., denaturation is NOT favored). The denatured state is favored at high temperatures and urea/guanidine-HCl concentrations and ΔGO < 0. At some temperature or urea/guanidine concentration value, both the native and denatured states would be equally favored. At this value, Keq = 1 and ΔGO = 0. The temperature at this point is called the melting temperature (Tm) of the protein. It is analogous to the Tm in the heat capacity vs temperature graphs for protein denaturation, as we saw in Chapter 4.9. This is Illustrated in Figure $2$. Ordinarily, at a temperature much below the Tm for the protein or at a low urea concentration, so little of the protein would be in the D state that it would be extremely difficult to determine the protein concentration in the D state. Hence it would be difficult to determine the Keq or ΔGo for the reaction N ↔ D. However, in the range where the protein denatures (either with urea or increasing temperature), it is possible to measure fD/fN.and hence ΔGo at each urea or temperature. Now we can calculate the ΔGow for N ↔ D in water without urea. For a simple two state N ↔ D, a plot of ΔGo vs [urea] is linear and given by the following equation, which should be evident from (1\). \Delta \mathrm{G}^0=\Delta \mathrm{G}_{\mathrm{w}}^0-\mathrm{m}[\text { urea }] It is important to know the Keq and ΔG0 for the N ↔ D transition in the absence of urea and under "physiological conditions". A comparison of the calculated values of ΔG0w in the absence of urea for a series of similar proteins (such as those varying by a single amino acid prepared by site-specific mutagenesis of the normal or wild-type gene, would indicate to what extent the mutants were stabilized or destabilized compared to the wild-type protein. ΔG0w for the N ↔ D transition of the protein in the absence of denaturant (i.e in water) can be determined by extrapolating the straight line to [urea] = 0. Admittedly, this is a long extrapolation, but with high-quality data and a high correlation coefficient for the linear regression analysis of the best-fit line, reasonable values can be obtained. Denaturation with heat Calculation of ΔHo and ΔSo for N <=>D at room temperature Keq values can be calculated from thermal denaturation curves in the same way as described above using urea as a denaturant by monitoring change in an observable (spectra signal for example) vs temperature. Knowing Keq, ΔH0, DS0 can be calculated from equation x below since a semi-log plot of lnKeq vs 1/T is a straight line with a slope of - ΔH0R and a y-intercept of + ΔS0/R, where R is the ideal gas constant. \begin{gathered} \Delta \mathrm{G}^{0}=\Delta \mathrm{H}^{0}-\mathrm{T} \Delta \mathrm{S}^{0}=-\mathrm{RTln} \mathrm{K}_{\mathrm{eq}} \ \ln \mathrm{K}_{\mathrm{eq}}=-\frac{\Delta \mathrm{H}^{0}-\mathrm{T} \Delta \mathrm{S}^{0}}{\mathrm{RT}} \ \ln \mathrm{K}_{\mathrm{eq}}=-\frac{\Delta \mathrm{H}^{0}}{\mathrm{RT}}+\frac{\Delta \mathrm{S}^{0}}{\mathrm{R}} \end{gathered} From these equations, it should be evident that all the major thermodynamics constants (ΔG0, ΔH0 and ΔS0 ) for the N ↔ D transition can be calculated from thermal denaturation curves. Equation (9) below shows that the derivative of equation (8) with respect to 1/T (i.e. the slope of equation 8 plotted as lnKeq vs 1/T) is indeed -ΔH0/R. Equation (9) is the van 't Hoff equation, and the calculated value of the enthalpy change is termed the van 't Hoff enthalpy, ΔH0vHoff. \frac{d \ln \mathrm{K}_{\mathrm{eq}}}{d(1 / \mathrm{T})}=-\frac{\Delta \mathrm{H}^{0}}{\mathrm{R}}=-\frac{\Delta \mathrm{H}_{\mathrm{vHoff}}^{0}}{\mathrm{R}} It is useful to compare the van 't Hoff enthalpy, ΔH0vHoff, with the enthalpy change determined directly using differential scanning calorimetry by analyzing a plot of Cp vs T. (Note that the area under the Cp vs T curve as the protein transitions to the unfolded state has units of kcal or kJ. The ΔH0 for the unfolding is inversely proportional to the width of the curve.) In contrast to the long extrapolation of the ΔG0 vs [urea] to [urea] = 0 to get ΔG0 (the y-intercept) in the absence of urea, which has some physical meaning, extrapolation of the straight line from the van 't Hoff plot from equation 8 to get ΔS0/R, the y-intercept, has little meaning since the 1/T value at the y-intercept is 0, which occurs when T approaches infinity. ΔS0 can be calculated at any reasonable temperature from the calculated value of ΔG0 at that temperature and the calculated ΔH0vHoff. 4.13: Chapter 4 Questions Section 1 Questions: Q1) Newman projection Q2) Explain what conformation peptide bonds predominately fall into, cis or trans. Can you think of a situation in which the nonpredominate form could be found in a biological system? A2) Most peptide bonds are in the trans conformation which helps to reduce steric hindrance R-groups of the amino acids but also decreases the strain on the peptide linkage. The cis form can be found in peptides, but at a low percentage. As the cis form causes strain on the peptide backbone. Enzymes can utilize this bond strain to overcome the activation energy for catalysis. Q3) Ramachandran Plot example (define the regions) Section 2 Questions: Q1) Which amino acid(s) is(are) considered to be alpha helix breaker(s)? Explain why this occurs. A1) Proline is consider to be the amino acids that "breaks" the secondary structure of an alpha helix. This occurs due to the amine ring in the proline R-group. This R-group is unable to participate in H-bonding needed for helix stabilization, and also is sterically locked in one conformation. Glycine is also an alpha helix breaker. Glycine has only a hydrogen as its R-group and therefore has no constrains for its Φ and Ψ angles. This makes glycine too flexible to hold the structure needed for am alpha helix. Q2) Helical wheel question Q3) Beta sheet/alpha helix counting from PDB file Q4)

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/04%3A_The_Three-Dimensional_Structure_of_Proteins/4.12%3A_Laboratory_Determination_of_the_Thermodynamic_Parameters_for_P.txt

Search Fundamentals of Biochemistry Reversible Binding of a Ligand to a Macromolecule Reversible, noncovalent binding of two or molecules is the first step in the expression of the biological properties of almost all biomacromolecules. If one of the molecules is small, it's often called a ligand. Ligands are often referred to by other names. Substrates are the reactants that bind to the active sites of enzymes. Hormones and neurotransmitters bind to solution phase or membrane-bound receptor proteins. Metal ions (simple like Ca2+ or molecular like CH3CO2-) are also considered ligands when bound to proteins or nucleic acids. You might be more familiar with the term ligand when it's applied to the coordination of a transition metal complex by electron pair donors (Lewis acids) on single or multidentate molecules, which for transition metal complexes are called ligands. Here is an interactive molecule model of a cobalt ion binding to EDTA, a multidentate ligand. The cobalt ion (dark grey ball) is octahedrally-coordinated to the multidentate ligand EDTA. Whether a macromolecule M and a ligand L bind to each other depends on their relative concentrations and how tightly they bind. Compare this to an acid. Its pKa and the pH of the medium determine if it deprotonates. Biochemists rarely talk about equilibrium constants to describe the strength of a binding interaction, but rather their reciprocals - the dissociation constants, $K_D$. For the reactions $M + L ↔ ML$, where M is free macromolecule, L is free ligand, and ML is macromolecule-ligand complex (which is held together by intermolecular forces, not covalent forces), the KD is given by \left.K_D=[M]_{e q}\right][L]_{e q} /[M L]_{e q} Figure $1$ shows free and bound M and L. Notice the unit of KD is molarity, M. • The lower the KD (i.e. the higher the [ML] at any given M and L), the tighter the binding. • The higher the KD, the looser the binding. KDs for biological molecules are finely tuned to their environments. KD values vary from about 1 mM (weak interactions) for some enzyme-substrate complex, to pM - fM levels. Examples of very tight, non-covalent interactions include the avidin (an egg protein)-biotin (a vitamin) and thrombin (enzyme initiating clotting)-hirudin (a leech salivary protein) complexes. The values are "tuned" so that the relative concentration of free and bound M and L are appropriate for a biological setting. To understand binding, it is important not only to know the noncovalent, intermolecular forces (IMFs) that lead to binding, but also to ask the simple question: are the macromolecule and ligand bound? If so, to what extent? To know if M or L is bound, we must use simple mathematics that you would have learned in Introductory or Analytical Chemistry courses. We'll start with the mathematical description which is harder for students to understand than the IMFs. We will start with three basic equations: For the Dissociation constant: K_D=([M] e q[L] e q) /[M L] e q=([M][L]) /[M L] (note that KD has units of molarity); For Mass Balance of M: M_0=M+M L where M0 is the total amount of macromolecule. (note: brackets and the eq subscript will be left off if the resulting equation is nonambiguous) For Mass Balance of L: L_0=L+M L where L0 is the total amount of ligand We would like to derive equations which give ML as a function of known or measurable values. The KD equation (5.1) shows that ML depends on free M and free L. From the equations above we can two derive two fundamental and equally valid equations which are useful under different experimental conditions. Case 1: This applies when you can readily measure free L OR when experimental conditions are such the Lo >> Mo (so L= Lo), which is often encountered in a lab setting. You don't have to measure free L since, for this case, it is approximately the total ligand that was added to the system. Substitute 5.1.3 into 5.1.1 gives \begin{gathered} \left.K_D=([M][L]) /[M L]=[M o-M L][L]\right) /[M L \ (M L) K_D=\left(M_o\right) L-(M L) L \ (M L) K_D+(M L) L=\left(M_o\right) L \ (M L)\left(K_D+L\right)=\left(M_0\right) L \end{gathered} or (M L)=\frac{\left(M_0\right) L}{K_D+L} This equation is ALWAYS TRUE for the chemical equation written above. L is the free ligand concentration at equilibrium. An interactive plot of the concentration of the ML complex (ML) vs free L (L) is shown below. Vary the sliders and note the changes in the graph. If L0 >> M0, then the equations simplify to: M L=\frac{\left(M_0\right)\left(L_0\right)}{K_D+L} Dividing this equation by Mo gives the fractional saturation Y of the macromolecule M. Y=[M L] / M_0=\frac{L}{K_D+L} where Y can vary from 0 (when L = 0) to 1 (when L >> KD) Note that the interactive graph above and graphs of ML vs L (equation 5.1.10) and Y vs L (equation 5.1.11) are all HYPERBOLAs To get a "gut" level understanding of the graphs of $(ML) = (M_0)(L)/(K_D + L)$ and $Y = L/(K_D+L)$, let's consider 3 different values or sets of values of free ligand: 1. L = 0: This obviously gives ML = 0 2. L = KD: $(ML) = (M_0)(L)/(L + L)= (M_0)(L)/(2L) = Mo/2$ which indicates that M is half saturated. In fact the operational definition of KD is the ligand concentration at which the M is half saturated. 3. L >> KD: ML = M0 and the macromolecule is saturated with ligand. Case 2 (more general): This applies when you know KD, but don't know free L or haven't measured it, and you just wish to calculate how much ML is present at equilibrium, given a KD value. In this case, L0 does not have to be much greater than M0. If where, like it is often in an experimental system, you would know that free L = L0 and you could use Case 1. In this case, we will substitute mass balance equations for both M0 (Eq 5.1.2) and L0 (Eq 5.1.3)and into the equation for KD (Eq. 5.1.1). This gives: \begin{gathered} K_D=([M][L]) /[M L]=\left[M_0-M L\right]\left[L_0-M L\right] /[M L] \ (M L) K_D=\left(M_0-M L\right)\left(L_0-M L\right) \ (M L) K_D=\left(M_0\right)\left(L_0\right)-(M L)\left(L_0\right)-(M L)\left(M_0\right)+(M L)^2 \end{gathered} or (M L)^2-\left(L_0+M_0+K_D\right)(M L)+\left(M_0\right)\left(L_0\right)=0 This can be rearranged into the form $ax^2 + bx + c = 0$ where • a = 1 • b = - (L0 + M0 +KD) • c = (M0)(L0) with the well known solution $x = [(-b) - (b^2 - 4(a)(c))^{1/2}]/2a$. Therefore, (M L)=\left[\left(L_0+M_0+K_D\right)-\left(\left(L_0+M_0+K_D\right)^2-4\left(M_0\right)\left(L_0\right)\right)^{1 / 2}\right] / 2 An interactive plot of the Y, fractional saturation, vs total L (L0) is shown below. Vary the sliders and note the changes in the graph. In the derivations, we came up with two equations for ML, Eq 5.1.10 which gives ML vs L and Eq 5.1.16 which gives ML vs L0. Both equations are valid. In the first, you must know free L which is often L0 if M0 << L0. In the second, you don't need to know free M or L at all. At a given Lo, Mo, and KD, you can calculate ML, which should be the same ML you get from the first equation if you know free L. Equations 5.1.10 and 5.1.16 are useful in several circumstances. They can be used to • calculate the concentration of ML if KD, M0, and L (for Eq. 5.1.10) or if KD, M0, and L0 (for Eq. 5.1.16) are known. This is analogous to the use of the Henderson-Hasselbalch equation to calculate the protonation state (HA) and hence the charge state of an acid at various pH values. In the former bind case, we are measuring the concentration of a reversibly bound ligand (ML) and in the latter case, the concentration of covalently bound protons (HA). • calculate KD if ML, M0, and L (for Eq. 5.1.10) or if ML, M0, and L0 (for Eq. 5.1.16) are known. Techniques to extract the KD from binding data will be discussed in A separate chapter section. Interpretation of Binding Analyzes It is important to get a mathematical understanding of the binding equations and graphs. It is equally important to get an intuitive understanding of their properties. Just as we used the +/- 2 pH rule in determining at a glance the charge state of an acid, you need to be able to determine the extent of binding (how much of M is bound with L) given their relative concentrations and the KD. The usual situation is that [M0] is << [L0]. What happens to the binding curves for M + L ↔ ML if the KD gets progressively lower? Intuitively, you should expect that binding will increase, especially as L gets greater. The curves below should help you develop the intuition you need with respect to binding equilibria. Figure $2$ show Y vs L0 at Varying KDs Figure $3$ shows Y vs L0 at a very low KD (0.001 uM = 1 nM, resulting in a sharp "titration" curve. Any increment of L added is bound so effectively none is present in the free form. The graph abruptly changes to a horizontal line when all the macromolecule is bound. This curve could be used to determine [M0]! Note that in the last graph, given the same M0 and L0 concentrations, the "titration curves" for a binding equilibrium characterized by even tighter binding (for example, a KD = 0.1 pM or 0.01 pM) would be indistinguishable from the graph when KD = 1 pM. It should be apparent that for all of these KD values, all of the added ligand is bound until [L0] > [M0]. To differentiate these cases, much lower ligand concentrations would be required such that on the addition of ligand, all is not bound. Also note that this curve is NOT hyperbolic, which makes sense since the graph is of Y vs L0, not Y vs L, and since L0 is not >> M0. The interactive graph below shows fractional saturation Y vs L at two different KD values It is quite interesting to compare graphs of Y (fractional saturation) vs L (free) and Y vs Lo (total L) in the special case when L0 is not >> M0. Figure $4$ when M0 = 4 μM, Kd = 0.19 μM . Under the ligand concentration used, it should be apparent the L can't be approximated by L0 Two points should be evident from these graphs when L is not approximated by Lo: • a graph of Y vs L0 is not truly hyperbolic, but it does saturate • a KD value (ligand concentration at half-maximal binding) can not be estimated by inspection from the Y vs L0, but it can be from the Y vs L graph. Figure $5$ shows a comparison of the extent of covalent binding of a proton to an acid at pH values around the pKa and by analogy the extent of noncovalent binding of a ligand at log[L] values around the log KD. Different Graphical Analyzes of Binding In addition to the hyperbolic plots of [ML] vs [L] and fractional saturation Y vs [L], a variety of derivative plots are often encountered. The equations and their graphs (for two different KD values, are shown below. The graphs are in the form of Y vs L0, when L0 is approximately equal to free L. Hyperbolic saturation plot: \mathrm{Y}=\frac{\mathrm{L}}{\mathrm{K}_{\mathrm{D}}+\mathrm{L}} Double reciprocal plot: \frac{1}{\mathrm{Y}}=\frac{\mathrm{K}_{\mathrm{D}}+\mathrm{L}}{\mathrm{L}}=\frac{\mathrm{K}_{\mathrm{D}}}{\mathrm{L}}+1=\mathrm{K}_{\mathrm{D}}\left(\frac{1}{\mathrm{~L}}\right)+1 A plot of 1/Y vs 1/L has a slope of KD and a y intercept of 1 (which is the number of binding sites for this simple mechanism) The Scatchard plot: \begin{aligned} \mathrm{Y}\left(\mathrm{K}_{\mathrm{D}}+\mathrm{L}\right) &=\mathrm{L} \ Y\left(\mathrm{~K}_{\mathrm{D}}\right)+Y L &=L \ Y\left(\mathrm{~K}_{\mathrm{D}}\right)=L-\mathrm{YL} &=\mathrm{L}(1-\mathrm{Y}) \end{aligned} which gives the final Scatchard plot equation: \frac{Y}{\mathrm{~L}}=\frac{1-\mathrm{Y}}{\mathrm{K}_{\mathrm{D}}}=-\frac{\mathrm{Y}}{\mathrm{K}_{\mathrm{D}}}+\frac{1}{\mathrm{~K}_{\mathrm{D}}} A plot of Y/L vs Y has a slope of -1/KD and a y intercept of 1/KD. Y vs logL Plotting Y vs L give a hyperbola, but a plot of Y vs log L give a sigmoidal plot. Plots of Y vs log L are often used in the research literature instead of traditional hyperbolic plots of Y vs L. There are several reasons for this: • the log [L] is more fundamentally related to the thermodynamic expression that relates ΔG0 and Keq or KD, namely \Delta G^0=-R T \ln K_{e q}=R \operatorname{Tln} K_D • plots of Y vs L plateau over a very large range of [L], but given the compression of the X axis values in a semilog plot, the plots reach a saturation plateau over a much narrower range of log [L]. A range from 1-100 on the [L] scale becomes 0-2 on the log [L] scale. Since it takes a very high concentration of ligand to truly reach saturation (100xKD), it's much easier to see if saturation is achieved in semilog plots. • multiple plots of binding data for different KD values have exactly the same shape on a semilog plot. Binding data for a ligand to a wild type and mutant proteins, all with different KDs, will give identical plots with curves for higher KD values shifted to the right. Semilog plots are also used routinely to display multiple plots of binding in the absence and presence of a binding inhibitor. Figure $6$ show different graphs for ligand binding to a macromolecule Figure $6$: Different graphs for ligand binding to a macromolecule Sigmoidal binding curves: A note of caution The graph of Y vs log [L] is sigmoidal but the same data would give a hyperbola if plotted as Y vs [L]. However, as we will see in section 5.3, there are some occasions when the graph of Y vs [L] is sigmoidal. For example, this can occur when the binding of a ligand to a multimeric binding protein affects the binding of the ligand to additional sites on the protein. This is an example of allosteric binding, which we will explore in great detail in section 5.3. So if you see a sigmoidal plot, be careful to examine the graph to see if it is a regular or semilog plot. Straight line transformations of the hyperbolic binding equations are useful to get approximate values of KD, but linear regression analysis to get slopes and intercepts is not statistically optimal as the errors in the y variable (Y) and in the y and x variables in the Scatchard plot are not identical across values. To determine KD, it is best to fit experimental data to the nonlinear function for the hyperbola. Dimerization and Multiple Binding Sites In the previous examples, we considered the case of a macromolecule M binding a ligand L at a single site, as described in the equation below: M + L ↔ ML where KD = [M][L]/[ML] We saw that the binding curves (ML vs L or Y vs L are hyperbolic, with a KD = L at half-maximal binding. But there are many other chemical equilibria than can mechanistically explain binding data. We'll consider just two cases here. Dimerization A special, yet common example of this equilibrium occurs when a macromolecule binds itself to form a dimer (D), as shown below: M + M ↔ M2 or D where D is the dimer, and where K_D=[M][M] /[D]=[M]^2 /[D] At first glance, you would expect a graph of [D] vs [M] to be hyperbolic, with the KD again equaling the [M] at half-maximal dimer concentration. This turns out to be true, but a simple derivation is in order. In the case of dimer formation, Mo, which superficially represents both M and L in the earlier derived expression, are both changing. So we have to invoke mass balance of M again: $[Mo] = [M] + 2[D]$, where the coefficient 2 is necessary since there are 2 M in each dimer. More generally, for the case of the formation of trimers (Tri), tetramers (Tetra), and other oligomers, $[Mo] = [M] + 2[D] + 3[Tri] + 4[Tetra] + ....$ Rearranging (12) and solving for D gives $D = ([M_0] - [M])/2$. Substituting this into the KD expression (1) gives K_D=\frac{M^2}{\frac{\left[M_0\right]-[M]}{2}}=\frac{2 M^2}{\left[M_0\right]-[M]}= This can be rearranged into quadratic form for M (not D): 2 M^2+K_D(M)-K_D\left(M_0\right)=0 which is of the form y = ax2+bx+c. Solving the quadratic equation gives [M] and with M0 , D can be calculated from $D = ([M_0]-[M])/2$. A value Y, similar to fractional saturation, can be calculated, where Y is the fraction of total possible D, which can vary from 0-1: $Y= 2D/M_0$ A graph of Y vs Mo with a dimerization dissociation constant KD = 25 uM, is shown in Figure $7$. Note that the curve appears somewhat hyperbolic. Half-maximal dimer formation does occur at a total M concentration M0 = KD. Also note, however, that even at M0 = 1000 uM, which is 40x KD, only 90% of the total possible D is formed (Y = 0.90). For the simple M + L ↔ ML equilibrium, if L0 = 40x the KD and M0 << L0, $Y = L/(K_D+L) = L/[(L/40)+L] = 0.976$ An interactive graph showing Y (the fraction of dimers) vs M0 is show. Move the sliders to show how changes in "KD" affect the dimerization. The aggregation state of a protein monomer is closely linked with its biological activity. For proteins that can form dimers, some are active in the monomeric state, while others are active as a dimer. High concentrations, found under conditions when proteins are crystallized for x-ray structure analysis, can drive proteins into the dimeric state, which may lead to the false conclusion that the active protein is a dimer. Determination of the actual physiological concentration of [Mo] and KD gives investigators knowledge of the Y value which can be correlated with biological activity. For example, interleukin 8, a chemokine, which binds certain immune cells, exists as a dimer in x-ray and NMR structural determinations, but as a monomer at physiological concentrations. Hence the monomer, not the dimer, binds its receptors on immune cells. Viral proteases (herpes viral protease, HIV protease) are active in dimeric form, in which the active site is formed at the dimer interface. Binding of a ligand to two independent sites What if a ligand L binds to two different sites on the same biomacromolecule? Assuming that the binding of ligand L to one site does not affect the binding of the ligand to the other site (and vice versa), the following equation can be simply derived: Y=\left[\frac{L}{\left(K_{D 1}+L\right)}+\frac{L}{\left(K_{D 2}+L\right)}\right] / 2 The numerator of the equation has a term for the fractional saturation of site 1 characterized by KD1 and a term for the fractional saturation of site 2 characterized by KD2. These two terms are divided by 2 so that the fractional saturation of all sites is 1 at saturating values of free ligand. Note that there can only be one free ligand concentration in solution so the only thing that differs in the two terms in the numerator is the KD value. The interactive graph below shows such binding to two independent sites with different KD values. Again, we'll assume the binding of one ligand does NOT influence the binding of the other. The Binding Continuum Binding affinities give us a way to measure the relative strength of binding between two substances. But how "tight" is tight binding? Weak binding? Let us exam that issue by considering a binding continuum. Consider two substances, A and B that might interact. Over what range of strengths can they actually bind to each other? It would helpful to set up the extremes of the binding continuum. At one end is no binding at all. At the other end, consider two things that bind covalently. We have discussed how Kd reflects binding strength. Remember, KD = 1/Keq. Also, we know that Keq is related to ΔGo by the equations: \Delta G^0=-R \operatorname{Tln} K_{e q}=R \operatorname{Tln} K_D Given these simple equations, you should be able to interconvert between Keq, KD, and ΔG0. (Keep your units straight.). No interaction: One end of the binding continuum represents no interaction. Let's assume that Keq is tiny (KD large), for example Keq~ 2.4 x10-72. Plugging this into the equation $ΔG^0 = - RTlnK_{eq}$, where R = 2.00 cal/mol.K, and T is about 300K, the ΔG0 ~ +100 kcal/mol (418 kJ/mo). That is, if we add A + B, there is no drive to form AB. If AB did form, then it would immediately fall apart. Covalent interaction: At the other end of the continuum consider the interaction of 1H atom with another to form H2. From a general chemistry book we can get ΔG0form. Using simple thermodynamics, we can calculate ΔGo for H-H formation. (ΔGo = ΣΔG0form prod. - ΣΔG0form react.) Doing this gives a value of -97 kcal/mol (-406 kJ/mol). Specific and Nonspecific Binding: Consider the interaction of a protein, the lambda repressor (R), with a small oligonucleotide to which it binds tightly (called the operator DNA, O). This is an example of a biologically tight, but reversible interaction. R can bind to many short oligonucleotides due to electrostatic interactions and H bonds between the positively charged protein and the negatively charged nucleic acid backbone. The tight binding interaction, however, involves oligonucleotides of a specific base sequence. Hence we can distinguish between tight binding, which usually involves specific DNA sequences and weak binding which involves nonspecific sequences. Likewise, we will speak of specific and nonspecific binding. R and O, which bind with a KD of 1 pM, represent an example of specific binding, while R and nonspecific DNA (D), which bind mostly through electrostatic interactions with a KD of 1 mM, are an example of nonspecific binding. You might expect any positively charged protein, like mitochondrial cytochrome C, would bind negatively charged DNA. This nonspecific interaction would presumably have no biological significance since the two are localized in different compartments of the cell. In contrast, the interaction between positively charged histone proteins, bound to DNA in the nucleus, would be specific. Rate constants for association and dissociation: When the reaction M + L ↔ ML is at equilibrium, the rate of the forward reaction is equal to the rate of the reverse reaction. From General Chemistry, the forward reaction is biomolecular and second order. Hence the vf, the rate in the forward direction is proportional to [M][L], or $v_f = k_f[M][L]$, where kf is the rate constant in the forward direction. The rate of the reverse reaction, vr is first order, proportional to [ML], and is given by $v_r = k_r [ML]$, where kr is the rate constant for the reverse reaction. Notice that the units of kf are M-1s-1, while units of kr are s-1. At equilibrium, $v_f = v_r$, or k_f[M][L]=k_r[M L] Rearranging the equation gives [M L] /[M][L]=k_f / k_r=K_{e q} Hence Keq is given by the ratio of rate constants. For tight binding interactions, Keq >> 1, KD << 1, and kf is very large (in the order of 108-9 ) and kr must be very small (10-2 - 10 -4 s-1). To get a more intuitive understanding of KDs, it is often easier to think about the rate constants which contribute to binding and dissociation. Let us assume that kr is the rate constant that describes the dissociation reaction. It is often times called koff. Likewise, kf is often called the on rate (kon). It can be shown mathematically that the rate at which two simple molecules associate depends on their radius and effective molecular weight. The maximal rate at which they will associate is the maximal rate at which diffusion will lead them together. Let us assume that the rate at which M and L associate is diffusion limited. The theoretical kon is about 108 M-1s-1. Knowing this, the KD and the fact that kon/ koff = Keq = 1/KD, we can calculate koff, which is a first-order rate constant. We can also determine koff experimentally. Imagine the following example. Adjust the concentrations of M and L such that Mo << Lo and Lo>> Kd. Under these conditions of ligand excess, M is entirely in the bound from, ML. Now at t = 0, dilute the solution so that Lo << Kd. The only process that will occur here is dissociation, since negligible association can occur given the new condition. If you can measure the biological activity of ML, then you could measure the rate of disappearance of ML with time, and get koff. Alternatively, if you could measure the biological activity of M, the rate at which activity returns will give you koff. Now you will remember from Introductory Chemistry that for a first-order rate constant, the half-life (t1/2) of the reaction can be calculated by the expression: k = 0.693/t1/2. Hence given koff, you can determine the t1/2 for the associated specie's existence. That is, how long will a complex of ML last before it dissociates? Given ΔGo or KD, and assuming a kon (108 M-1s-1), you should be able to calculate koff and t1/2. Or, you could be able to determine koff experimentally, and then calculate t1/2. Applying these principles, you can calculate the binding parameters. Table $1$ below shows calculated koff and t1/2 for binary complexes assuming diffusion-controlled kon. Complex KD (M) koff (s-1) H2 1 x 10-71 1 x 10-63 2 x 1055 yr RtV3 : Rt'L3(a) 10-17 1 x 10-9 2 yr Avidin:biotin 10-15 1 x 10-7 80 days thrombin:hirudin(b) 5 x10-14 5 x 10-6 2 days lacrep:DNAoper(c) 1 x 10-13 1 x 10-5 0.8 days Zif268:DNA(d) 10-11 1 x 10-3 700 s GroEL:r-lactalbumin(e) 10-9 0.1 7 s TBP:TATA(f) 2 x 10-9 2 x 10-1 3 s TBP:TBP 4 x 10-9 4 x 10-1 2 s LDH (pig): NADH(g) 7.1x10-7(j) 7.1 x 101 10 ms profilin: CaATP-G-actin 1.2 x 10-6 1.2 x 102 6 ms TBP: DNAnonspec(h) 5 x 10-6 5 x 102 1 ms TCR(i): cyto C peptide 7X10-5 7X103 100 us lacrep:DNAnonspec(h) 1 x 10-4 1 X104 70 us uridine-3P: RNase 1.4x10-4 (j) 1.4X104 50 us Creatine Kinase: ADP 8.2x10-4 (j) 8.2X104 10 us Acetylcholine:Esterase 1.2 x 10-3 1.2 x 105 6 us no interaction 4 x 1073 4 x 1081 - Table $1$: Calculated koff and t1/2 for binary complexes assuming diffusion-controlled kon 1. Trivalent Vancomycin derivative RtV3 + Trivalent D-Ala-D-Ala deriv, Rt'L3' 2. Hirudin is a potent thrombin inhibitor from leach saliva 3. lac rep is the E. Coli lac operon repressor protein, and DNAoper is the specific DNA binding region in the E. Coli genome that binds to the repressor 4. Zif268 is a mouse zinc-finger binding protein 5. GroEL is a chaperone protein; r-lactalbumin is the reduced form of lactalbumin 6. TBP is the TATA Binding Protein that binds to the TATA box consensus sequence 7. LDH is lactate dehydrogenase 8. DNAnonspec is DNA which does not contain the specific DNA sequence region involved in specific binding to a DNA binding protein 9. TCR is the T-cell receptor 10. calculated from equation: KD = koff/kon. What is usually measured is KD and/or koff (if the koff is reasonable). This analysis is very simplified. Electrostatic forces and other orientation factors may significantly change kon, while conformational changes in the complex may prevent ready unbinding of the bound ligand, dramatically altering koff. Figure $8$ shows an interactive iCn3D model of one of the tightest binding complexes, avidin and biotin (2avi). Figure $8$: Avidin-Biotin Complex (2avi) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...ZssSe5GoUfr9Q6 The blue shows the surface around the complex, which shows that the avidin in completely buried. Hydrogen bonds are shown to biotin (labeled as BTN) in green dashes. It is important to note that even reactions characterized by high KD (weak binding) can be specific. Specificity is ultimately defined as a binding interaction between a macromolecule and ligand that can be co-localized in the same environment and for which a biological function is elaborated upon binding.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/05%3A_Protein_Function/5.01%3A__Binding_-_The_First_Step_Toward_Protein_Function.txt

Search Fundamentals of Biochemistry It is often essential to determine the KD for a ML complex since given that number and the concentrations of M and L in the system, we can then predict if M is bound under physiological conditions. Again, this is important since whether M is bound or free will govern its activity. To determine KD, you need to determine ML and L at equilibrium. How can we differentiate free from bound ligand? The following techniques allow such a differentiation. Techniques that require the separation of bound from the free ligand. Care must be given to ensure that the equilibrium of M + L ↔ ML is not shifted during the separation technique. Gel filtration chromatography Add M to a given concentration of L. Then elute the mixture on a gel filtration column, eluting with the free ligand at the same concentration. The ML complex will elute first and can be quantitated. If you measure the free ligand coming off the column, it will be constant after the ML elutes, with the exception of a single dip near where the free L would elute if the column were eluted without free L in the buffer solution. This dip represents the amount of ligand bound by M. Membrane filtration Add M to radiolabelled L, equilibrate, and then filter through a filter that binds M and ML. For instance, a nitrocellulose membrane binds proteins irreversibly. Determine the amount of radiolabeled L on the membrane, which equals [ML]. Precipitation Add a precipitating agent like ammonium sulfate, which precipitates proteins (both M and ML). Determine the amount of ML. Techniques that do not require the separation of bound from free ligand. Equilibrium dialysis Place M in a dialysis bag and dialyze against a solution containing a ligand whose concentration can be determined using radioisotopic or spectroscopic techniques. At equilibrium, determine free L by sampling the solution surrounding the bag. By mass balance, determine the amount of bound ligand, which for a 1:1 stoichiometry gives ML. Repeat at many different ligand concentrations Spectroscopy Find a ligand whose absorbance or fluorescence spectra changes when bound to M. Alternatively, monitor a group on M whose absorbance or fluorescence spectra changes when bound to L. Isothermal titration calorimetry (ITC) In ITC, a high-concentration solution of an analyte (ligand) is injected into a cell containing a solution of a binding partner (typically a macromolecule like a protein, nucleic acid, or vesicle). Figure \(1\) shows an isothermal titration calorimeter cell, On binding, heat is either released (exothermic reaction) or adsorbed, causing a small temperature change in the sample cell compared to the reference cells containing just a buffer solution. Sensitive thermocouples measure the temperature difference (ΔT1) between the sample and reference cells and apply a current to maintain the difference at a constant value. Multiple injections are made until the macromolecules are saturated with the ligand. The enthalpy change is directly proportional to the amount of ligand bound at each injection, so the observed signal attenuates with time. The actual enthalpy change observed must be corrected for the change in enthalpy on simple dilution of the ligand into buffer solution alone, determined in a separate experiment. The enthalpy changes observed after the macromolecule is saturated with the ligand should be the same as the enthalpy of dilution of the ligand. A binding curve showing enthalpy change as a function of the molar ratio of ligand to binding partner (L0/M0 if L0 >> M0) is then made and mathematically analyzed to determine KD and the stoichiometry of binding. Figure \(2\) shows a typical isothermal titration calorimetry data and analysis It should be evident in the example above that the binding reaction is exothermic. But why is the graph of ΔH vs the molar ratio of L0/M0 sigmoidal (s-shaped) and not hyperbolic? One clue is that the molar ratio of ligand (titrant) to macromolecule centers around one, so, as explained above, when L0 is not >> M0, the graph might not be hyperbolic. The graphs below show a specific example of a KD and ΔH0 calculated from the titration calorimetry data. We will use them to show why the graph of ΔH vs molar ratio of L0/M0 is sigmoidal. A specific example illustrates these ideas. A soluble version of the HIV viral membrane protein, gp 120 (4 μM), was placed in the calorimetry cell. A form of its natural ligand, CD4, a membrane receptor protein from T helper cells, was placed in the syringe and titrated into the cell (Myszka et al. 2000). Enthalpy changes/injection were determined. The data was transformed and fit to an equation that shows the ΔH "normalized to the number of moles of ligand injected at each step". Fig."e \(3\) shows the raw data (top) and the best-fit model (bottom), assuming a 1:1 stoichiometry of CD4 (the "ligand") to gp 120 (the "macromolecule") and a KD = 190 nM. Note that the bottom curve is sigmoidal, not hyperbolic. A single experiment can determine the stoichiometry of binding (n), the KD, and the ΔH0. From the value of ΔHo and KD and the relationship ΔGo = -RTlnKeq = RTlnKD = ΔH0 - TΔS0, the ΔG0 and ΔS0 values can be calculated. No separation of bound from free is required. Enthalpy changes on binding were calculated to be -62 kcal/mol (260 kJ/mol). A series of plots can be derived using the standard binding equations (5, 7, and 10 above) to calculate free L and ML at various Lo concentrations and R = L0/M0 ratios. Two were shown earlier to illustrate differences in Y vs L and Y vs L0 when L0 is not >> M0. They are shown again below in Figure \(4\). A more precise understanding of the data analyses is shown in Figure \(5\), which shows a plot of ML vs R (= [L0]/[M0] (panel A1 right) was made. This curve appears hyperbolic, but it has the same shape as the Y vs L0 graph shown in Figure \(4\) (right). However, if the amount of ligand bound at each injection (calculated by subtracting [ML] for injection i+1 from [ML] for injection i) is plotted vs R (= [L0]/[M0]), a sigmoidal curve shown in Figure \(5\) (panel A2, left) is seen, which resembles the best-fit graph for the experimentally determine enthalpies in Figure \(4\). The relative enthalpy change for each injection is shown in red. Note the graph in Figure \(5\) (A2) shows the negative of the amount of ligand bound per injection to make the graph look the that in the graph showing the actual titration calorimetry trace and fit above. Surface Plasmon Resonance A newer technique to measure binding is called surface plasmon resonance (SPR), using a sensor chip consisting of a 50 nm layer of gold on a glass surface. A carbohydrate matrix is then added to the gold surface. A macromolecule that contains a binding site for the ligand is covalently attached to the matrix. The binding site on the macromolecule must not be perturbed significantly. A liquid containing the ligand is passed over the binding surface. The detection system consists of a light beam that passes through a prism on top of the glass layer. The light is reflected, but another component of the wave, called an evanescent wave, passes into the gold layer, where it can excite the Au electrons. If the correct wavelength and angle are chosen, a resonant wave of excited electrons (plasmon resonance) is produced at the gold surface, decreasing the total intensity of the reflected wave. The angle of the SPR is sensitive to the layers attached to the gold, and binding and dissociation of the ligand are sufficient to change the SPR angle, as seen in Figure \(6\). This technique can distinguish fast and slow binding/dissociation of ligands (as reflected in on and off rates) and be used to determine KD values (through measurement of the amount of ligand bond at a given total concentration of ligand or more indirectly through the determination of both kon and koff. Binding DB: a database of measured binding affinities, focusing chiefly on the interactions of proteiproteinsdered to be drug targets with small, drug-like molecules PDBBind-CN: a comprehensive collection of the experimentally measured binding affinity data for all biomolecular complexes deposited in the Protein Data Bank (PDB). Extreme Binding Affinities An incredibly tight binding interaction has recently been reported for the binding of Cu1+ to the CueR protein from E. Coli. Cu1+ ions are usually kept at very low concentrations in cells to prevent toxicity. Yet some enzymes require Cu. Free copper ions must be present in the cell to allow binding to appropriate sites in proteins. How are these competing concerns regulated in the cell? The total Cu concentration in E. Coli is about 10 μM (10,000 nM), which, given the small size of the bacterium, represents about 10,000 copper ions per cell. Cells have evolved many mechanisms to control and deliver Cu ions. Copper ions can be delivered to target proteins by copper chaperones (analogs of the chaperone proteins which guide protein folding). CueR in E. Coli appears to regulate the copper-induced expression of genes involved in copper biochemistry (including an enzyme that oxidizes Cu1+ to Cu2+, which is less toxic). One particular gene that is up-regulated is copA. CueR increases the transcription of copA in the presence of Cu, Ag, and Au (coinage metal) ions. Changela et al. developed an in vitro assay to determine the extent of expression of CueR-regulated genes under various ion types and concentrations. In the assay, purified CueR was added to a gene construct containing the promoter (a section of DNA immediately upstream of a gene start site where RNA polymerase binds) for copA. Initially, they found that transcription was always on, even in the presence of a ligand, glutathione, which binds Cu1+ avidly and should keep free Cu1+ levels very low. They switched to an even tighter binding Cu1+ coordinator, cyanide (CN-), to reduce the free Cu1+ levels to even lower levels. Extremely high levels of CN- (millimolar) stopped transcriptional activation, but if additional Cu1+ was added, activation ensued, suggesting that copper binding to the protein was reversible. At 1 mM CN-, transcription increased with the addition of copper ions up to a TOTAL Cu1+ concentration of 60 μm. Under these conditions, the free Cu1+ concentrations were much lower. Given the presence of CN- concentrations used, half-maximal activation occurred at a TOTAL Cu1+ concentration of 0.7 μM. Similar activation was observed by Ag1+ and Au1+, but not by Zn and Hg ions, showing the specificity for monovalent cations over divalent cations. Knowing the pKa of HCN, stability constants for Cu1+:CN- complexes, and CN- concentrations, Changela et al et al.ced a series of solutions buffered in FREE Cu1+ that extended from 10-18 to 10-23 M (pH 8.0). (For example, the log of the binding constant β, logβ, for the Cu1+ + 2CN- ↔ [Cu(CN)2]- is 21.7. You solved problems involving linked equilibrium if you have taken analytical chemistry.) The free Cu1+ concentration at half-maximal activation of gene reporter transcription, a measure of the dissociation constant, KD, was approximately 1 x 10-21 M (zeptomolar)! Now assume that the volume of the contents of an E. Coli cell is 1.5 x 10-15 L. If there were only one ion of Cu1+ in the cell, it would have a concentration of 10-9 M. The values suggest that there are no free Cu1+ ions in the cell and that only 1 Cu+1 ion in the cell is enough to ensure its binding to CueR and subsequent transcriptional activation of copA. It is essential for survival that bacterial cells get the correct metal to metalloproteins. A recent review by Waldron and Robinson illustrates how. The cell has many mechanisms for restricting specific binding sites, so metals can get to the right proteins. In addition, the natural order of stability for transition metal complexes must be considered in understanding metal affinities. That stability is given by the Irving-William series shown below (along with Group 2A metal ions). The trend parallels the size of the cation (going from largest to smallest): Mn2+ < Fe2+ < Co2+ < Ni2+ < Cu2+ > Zn2+ (tightest binding) • The ability of a protein to change shape on ligand binding allows different metals to bind. For example, cyanobacterium has a high demand for copper and manganese. Manganese might bind to a protein, followed by folding, which traps it in the protein. This unstable metal cannot be replaced by copper, which would ordinarily out-compete Mn2+ for the site. • Metal transporters help regulate how many ions of each metal are in the cell. Metal sensors are under the control of these metal transporters, regulating gene expression. Once a specific metal has a sufficient concentration for binding, the metal sensors target mRNA to repress specific genes and halt transcription. • Another enzyme can also be activated for the metal's export. By restricting the concentrations of the competing metals, weaker metal-binding sites remain available. • Metal sensors can also help to regulate what protein some metals use based on what is available. For example, E. coli switches metabolism to minimize the number of iron-requiring proteins expressed when iron is less abundant. • Metals are supplied by multiple pathways (in case a specific enzyme is not present) and are trafficked to the correct protein through many ligand-exchange reactions. • Certain enzymes bind specific metals that cause preferential conformational changes. Hence, if a metal comes along that binds more tightly but is not preferred by the enzyme, it will not trigger the enzyme because it binds in a different manner. Molecular Basis of High Affinity Interactions What differentiates high and low affinity binding at the molecular level? Do high affinity interactions have lots of intramolecular H-bonds, salt bridges, van der Waals interactions, or are hydrophobic interactions most important? Recently, the crystal structures of various antibody-protein complexes were determined to study the basis of affinity maturation of antibody molecules. It is well known that antibodies elicited on exposure to a foreign molecule (antigen) are initially of lower affinity than antibodies released later in the immune response. An incredible number of different antibodies can be made by antibody-producing B cells due to genetic mechanisms (combining different variable regions of antibody genes through splicing, imprecise splicing, and hypermutation of critical nucleotides in the genes of antigen binding regions of antibodies). Clones of antibody-producing cells with higher affinity are selected through binding and clonal expansion of these cells. Investigators studied the crystal structure of 4 different antibodies which bind to the same site (epitope) on the protein antigen lysozyme. Increased affinity was correlated with increased buried apolar surface area and not with increased numbers of H bonds or salt bridges. The data for these antibodies are shown below in Table \(1\). Table \(1\): Characteristics of Antibody:Hen Egg Lysozyme Complexes (HEL) from Li,Y. et al. Nature: Structural Biology. 6, pg 484 (2003) Antibody H26-HEL H63-HEL H10-HEL H8-HEL KD (nM) 7.14 3.60 0.313 0.200 Intermolecular Interactions H bonds 24 25 20 23 VDW contacts 159 144 134 153 salt bridges 1 1 1 1 Buried Surface Area ΔASURF (A2) 1,812 1,825 1,824 1,872 ΔASURF-polar (A2) 1,149 1,101 1,075 1,052 ΔASURF-apolar (A2) 663 724 749 820 Electrostatic interactions between biological molecules are still very important, even though we may consider them often nonspecific. Consider the binding of DNA binding proteins with positive domains to the negative polyanion, DNA. The initial encounter will be electrostatic in origin and important in targeting the proteins to DNA where other specific interactions may take place. In a similar example (Yeung, T et al.), it was recently reported that moderately positively charged proteins are directed to endosomes and lysosomes through interactions with negatively charged membrane phosphatidylserine (PS), whereas more positively charged proteins are targeted to the inner surface of the plasma membrane, which is enriched in PS and phosphorylated phosphatidyl inositol derivatives (PIP2, PIP3), as shown in Figure \(7\). To study this they used the C2 domain of lactadherin (Lact-C2) from milk that binds PS in the presence of calcium. The C2 domain was covalently linked to the green fluorescent protein. This protein contains an internal fluorophore comprised of three amino acids (Ser65-Tyr66-Gly67) that cyclize spontaneously on folding to produce a fluorophore that emits green light. A fusion gene of Lact-C2 and GFP was introduced in wild-type (WT) and mutant yeast lacking PS. It bound to the inner leaflet in WT cells and to endosome and lysosome vesicles but found diffused through the cytoplasm in mutant cells. They also made cationic probes with farnesyl tails that could anchor the soluble probes to membranes. The most positively charged probes were recruited to the plasma membrane inner leaflet, while less charged ones were recruited to internal vesicles. The authors speculate that PS on cytoplasmic membrane layers can target signal transduction proteins to these regions. Antibodies with Infinite Affinity. Chmura et al. PNAS. 98, pg 8480 (1998) Docking The quantitative methods described above do not elucidate the mechanism of binding. Computer programs have been developed that allow the docking of a ligand (small molecule or even another protein) to another protein. The automatic docking of flexible ligands to proteins can be modeled using free programs such as Autodock. Molecular dynamics simulations can also be used to study the actual binding and unbinding processes. The Crowded Cell Most binding studies are performed in vitro with dilution concentrations of both macromolecule and ligand. Are these conditions illustrious of conditions inside a cell? The answer is no! Cells are crowded with organelles, macromolecular complexes, and cytoskeletal components that provide an internal architecture to the cell. Total macromolecule concentration in the cell has been estimated to be as high as 400 g/1L = 400 g/1000 mL = 0.4 g/mL = 400 mg/mL. Try to dissolve a water-soluble protein like albumin at those concentrations! From 5 to 40% of the entire cellular volume is occupied with large molecules, and at the upper range, very little space exists for other large macromolecules. A representation showing the crowdedness of a bacterial cell at the atom level is shown in Figure \(8\). Imagine trying to diffuse through that!

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/05%3A_Protein_Function/5.02%3A_Techniques_to_Measure_Binding.txt

Search Fundamentals of Biochemistry Myoglobin, Hemoglobin, and their Ligands Almost all biochemistry textbooks start their description of the biological functions of proteins using myoglobin and hemoglobin as exemplars. On the surface this is a rational approach since they have become model systems to describe the binding of simple ligands, like dioxygen (O2), CO2, and H+, and how the structure of a protein determines and is influenced by binding of ligands. Yet in most ways, these globin-binding "ligands" are dissimilar to the majority of both small ligands, such as substrates (for enzymes), inhibitors and activators as well as large "ligands", such as other proteins, nucleic acids, carbohydrates and lipids that bind to proteins through noncovalent interactions (described in detail in Chapter 2.4). In contrast, dioxygen (O2), CO2, and H+ bind reversibly, but through covalent interactions. Dioxygen binds to a heme Fe2+ transition metal through a coordinate covalent or dative bond, protons obviously bind covalently to proton acceptors (Lewis bases like histidine), while CO2 binds covalently as it forms a carbamate with the N terminus of a hemoglobin chain. In typical covalent bonds, each bonded atom contributes to and shares the two electrons in the bond. In coordinate or dative covalent bonds, the ligand, a Lewis base, contributes both electrons in the bond. For simple analyses and four counting electrons, both electrons can be considered to be "owned" by the ligand and not by the transition metal ion, a Lewis acid, unless you analyze the interactions using molecular orbital (ligand field) theory. Hence the ligand can readily dissociate from the metal ion, much as a ligand bound through classical noncovalent interactions does. This analogy can be extended to protons which are also Lewis acids (with no contributing electrons) as they react with Lewis bases (lone pair donors) on atoms such as nitrogen on a histidine side chain. H+ readily leave (ionize) from a Lewis acid if the pH of the microenvironment is conducive to ionization. Even though we disagree with starting the discussion of protein structure and function with the covalent binding of small gaseous and marginally soluble ligands to myoglobin and hemoglobin, we will anyway to make the book consistent with most other texts and allow easier use without shuffling the order of chapters. Let's start with myoglobin (Mb), a monomeric protein containing 8 α−helices (A-H) and with hemoglobin, a heterotetramer with two α -and two β−subunits, each which also contains 8 α−helices. Both are oxygen binding proteins. Both contain heme (one in myoglobin, and 4 for the four subunits of hemoglobin). Each heme has a central Fe2+ ion, which forms a coordinate covalent bond with dioxygen. Dioxygen is transported from lungs, gills, or skin of animals to the capillaries, where it can be delivered to respiring tissue. O2 has a low solubility in blood (0.1 mM). Whole blood contains 150 g Hb/L, and can achieve a dissolved oxygen concentration of 10 mM. Invertebrates can have alternative proteins for oxygen binding, including hemocyanin, which contains Cu and hemerythrin, a non-heme protein. On binding dioxygen, solutions of Hb change color to bright red. Solutions of hemocyanin and hemerythrin change to blue and burgundy colored, respectively, on binding dioxygen. Some Antarctic fish don't require Hb since dioxygen is more soluble at low temperatures. Myoglobin is found in the muscles, and serves as a storage protein for oxygen transported by hemoglobin. The structure of heme in myoglobin and hemoglobin, is shown in Figure $1$. The heme group contains protoporphyrin IX, with four tetrapyrrole rings linked by methene bridges. Attached to the tetrapyrrole structure are four methyl, two vinyl, and two propionate groups. These can be arranged in 15 ways, of which only one (IX) occurs in biological systems. Protoporphyrin IX with bonded ferrous (Fe2+) iron is called heme and has a nitrogen atom on each of the four pyrrole rings that form a coordinate covalent bond to Fe2+. The heme fits into a hydrophobic crevice in heme-bindin proteins with the propionate groups exposed to solvent. Myoglobin (Mb) Myoglobin is an extremely compact protein with 75% alpha helical structure. It has 8 α−helices labeled A-H. Four are terminated by a proline, a helix breaker. The interior amino acids are almost entirely nonpolar. The only polar amino acids found completely buried are two histidines. One is called the proximal His as it is nearer the heme and serves the 5th ligand to the heme Fe2+. The other is called the distal His, which is too far to coordinate the heme Fe2+. This last potential 6th ligand-binding site forms a coordinate covalent bond with O2 in oxy-myoglobin. Figure $2$ shows an interactive iCn3D model of deoxymyoglobin from wild boar. The heme is shown in sticks along with the proximal and distal histidines. Hemoglobin Hemoglobin has an illustrious history. It is the first protein whose molecular weight was determined and the first assigned a specific function (dioxygen transport). It was the first protein in which a mutation in a single amino acid caused by a single base pair change in the DNA coding sequence was shown to cause a disease (sickle cell trait and disease). The mathematical theories developed to model dioxygen binding are used to explain enzyme activity. It also binds H+, CO2, and bisphosphoglcyerate which bind to sites (allosteric) distant from the oxygen binding site which regulates its dioxygen binding affinity. As with myoglobin, the Fe2+ ion is coordinated to 4 Ns on the 4 pyrrole rings, The 5th ligand is supplied by proximal His (the 8th amino acid on helix F) of the protein. In the absence of dioxygen, the 6th ligand is missing. and the geometry of the complex is somewhat square pyramidal, with the Fe slightly above (0.2 Å) the plane of the heme ring. A distal His (E7) is on the opposite side of the heme ring, but too far to coordinate with the Fe2+. When dioxygen binds, it occupies the 6th coordination site and pulls the Fe into the plane of the ring, leading to octahedral geometry. These changes that occur on oxygenation are shown in Figure $3$. The proximal histidine that provides the imidazole nitrogen ligand is shown. Dioxygen is shown as red spheres. Fe2+ ion is shown as a small orange sphere. Its size has been dramatically reduced in this image so its movement can be more readily observed. Carbon monoxide (CO), nitric oxide (NO), and hydrogen sulfide (H2S) also bind to the sixth coordination site, but with higher affinity than dioxygen, which can lead to CO poisoning for example. The distal histidine keeps these ligands (including dioxygen) bound in a bent, non-optimal geometry. This minimizes the chances of CO poisoning. Figure $4$ shows an interactive iCn3D model of human oxy-hemoglobin (2dn1) Fe2+ ion ligand interactions When the 6th ligand, dioxygen, binds to heme Fe2+, the geometry of the complex becomes octahedral. The Fe2+ ion has 6 electrons in d orbitals. The electronic configuration of atomic Fe is 3d64s2 while the Fe2+ ion has a 3d6 configuration, as shown in Figure $5$. Each of the orbitals would have the same energy except for the doubly occupied one which would have slightly higher energy due to the extra repulsion of the two electrons in the orbital. This effect is minimal so to a first approximation, the orbitals are considered to have the same energies (they are degenerate). The figure below shows them having the same energy You will remember from introductory chemistry classes that transition metal complexes and their solutions are highly colored. Since oxygenated hemoglobin (found in arteries) appears bright red/orange, it must absorb blue/green light more than deoxyhemoglobin, which is darker red (but still reddish). These absorbed wavelengths are removed from the spectrum, making hemoglobins shades of red. Veins contained more deoxygenated blood returning to the heart to be reoxygenated. The visible veins in your arms and legs appear blue not because of the spectral properties of deoxyhemoglobin. Rather, blue light doesn't penetrate into the tissue as far as red, so red is preferentially removed from the remaining light which is reflected, making veins appear blue. Figure $6$ shows a partial absorbance spectrum of deoxy- and oxy-hemoglobin from 280-1000 nm. The dashed light blue vertical line shows the approximate wavelength (around 450 nm) of the largest molar extinction coefficient difference between the two forms. Note that the y axis is a log scale. At 450 nm, oxyhemoglobin has an extinction coefficient of about 600,000 while deoxyhemoglobin is about 60,000, so much more blue light is removed from a solution of oxyHb, making it appear bright red. Note also that the same differences appear in the red region of the spectra, where the extinction coefficients only vary from 3000 for oxyhemoglobin to 200 for deoxyHb. Hence absorption in this region has little effect on the visible color of blood. The spectrum shown in Figure $6$ also shows the near-infrared region (denoted by a rectangle) of the spectra. Inexpensive pulse oximeters (some built into watches) have been increasingly used by people at home to their measure their oxygenation status during the COVID-19 pandemic. These use two pulsed LEDs, one at 660 nm, where oxyhemoglobin has a higher extinction coefficient, and one at 940 (infrared region), where deoxyhemoglobin has a higher extinction coefficient. Binding of ligand to the heme Fe2+: crystal and ligand fields Most biochemistry books offer minimal coverage of bioinorganic chemistry, even though a large percentage of proteins bind metal ions. Since biochemistry is an interdisciplinary field, it is important to use past learning in other biology and chemistry courses and apply it to biochemistry. Most students study transition metal chemistry in introductory chemistry courses. You should familiar with transition metal ions, their electronic configuration, crystal field theory, high and low spin states, paramagnetism and diamagnetism. Hence it is appropriate and important to bring these ideas into biochemistry and extend them when necessary. The basis of the material below is modified from Structure & Reactivity in Organic, Biological and Inorganic Chemistry by Chris Schaller (Creative Commons Attribution-NonCommercial 3.0 Unported License). Let's look at the electronic structure of Fe2+ in oxyhemoglobin. The Fe2+ is coordinated to six ligands (4 pyrrole rings of the heme in a plane, one axial imidazole ring each from the proximal histidine and bonded dioxygen). The geometry of the electron clouds around the Fe2+ is octahedral. The six d orbitals are oriented in the same x, y, and z axes direction as the heme ligands. This geometry and the shapes and orientations of the Fe2+ d atomic orbitals are illustrated in Figure $7$. Two of the orbitals, dz2 and dx2-y2, appear different in that they are oriented directly along the x, y and z axes while the other three are in-between the axes. Now imagine six anionic ligands with lone pairs approaching along the axes of the Fe2+ atomic orbitals, a postulate of crystal field theory used to explain bonding in transition metal complexes. The energy of the Fe electrons in the dz2 and dx2-y2 atomic orbitals would be raised higher than the others due to electron-electron repulsion. This is illustrated in Figure $8$. Two different outcomes can arise 1 (left panel): If the electrostatic interaction between the atomic orbitals of the Fe ion and the incoming ligands is low, the energy of the dz2 and dx2-y2 orbitals would be a bit higher (by an amount Δ) due to the great repulsion along those axes from anions oriented along them. This is illustrated in the left part of Figure $8$. When filling the orbitals with the six Fe2+ 3d6 electrons, you would add one electron to each of the 5 orbitals and then pair one for the sixth orbital. In that case there would be 4 unpaired electrons and the complex would be paramagnetic. We call this a high spin state. 2 (right panel): If however, the electrostatic interactions of the incoming ligands with the d atomic orbital electrons is high, the Δ would be large. When filling the orbitals in this case with the six Fe2+ 3d6 electrons, the electron would be paired in the three lower energy orbitals and there would be no electrons unpaired, so the ion is diamagnetic. This is the low spin case. In either case, if light of energy equal to the Δ interacts with the metal ion, electrons would be promoted to the higher energy level. For the low spin case (larger axial electrostatic interactions, Δ is large, the energy of the required photon is larger (more blue shifted) than in the high spin case. This could make a solution of the molecule in the low spin case appear redder than for the high spin case (as blue light is removed on absorption). This is the case for dioxygen, which interacts strongly with the Fe2+ axially-oriented orbitals. Hence oxy-Fe2+ heme complex is low spin and diamagnetic. Ligand Field Theory You studied basic crystal field theory in introductory chemistry courses. The theory is simplistic in several ways. The ligands might not be anions. More importantly, bonding is best described by molecular orbitals. A more comprehensive ligand field theory takes into account the effect of the donor electrons and the d orbitals of the transition metal ion. These atomic orbitals combine to produce molecular orbitals (MOs), which better describes bonding. Take the simple case of a covalent bond between two singly occupied adjacent p atomic orbitals the two carbons of ethylene (C2H4). Simplistically, you could imagine two carbon atoms with their two p orbitals approaching each other. The two p orbitals (or more accurately their wave functions) could combine constructively or destructively to form two new molecular orbitals (MO). One is a pi bonding MO, π, which is lower in energy (promoting bond formation) than the atomic p orbitals. The other is a pi antibonding MO, π*, which is higher in energy (antagonizing bond formation), as shown in Figure $9$. Two atomic orbitals form from two MOs! Let's use ligand field theory with its MOs to describe ligands binding to the heme Fe2+ d orbitals. This is illustrated in the MO diagram in Figure $10$. Figure $10$: Ligand field molecular orbtials for the d6 Fe2+ ion for two axial ligands For simplicity, for Fe2+ we will consider only d orbitals and focus on the dz2 and dx2-y2 (also called the eg) orbitals, since these are most affected by the ligands as described in crystal field theory. Let's assume these interact with lone pairs (a simplistic assumption as well) in two ligand orbitals, one on the dioxygen and one on the proximal histidine imidazole N. Seven atomic orbitals (5 Fe d2+ orbitals and 2 ligand orbitals) combine to produce 7 MOs. Since the orbitals (in sp2- or sp3-like orbitals) on the ligands are closer in energy to the lower energy bonding MOs, their electrons would go there. Since O2 interacts strongly with the Fe2+ d orbitals, the system is in a low spin state, with the unoccupied dz2 and dx2-y2 (eg orbitals) now considered antibonding orbitals. The fully occupied dxy, dxz and dyz are considered nonbonding orbitals since they have the same relative energy atomic orbitals. We will return to molecular orbitals occasionally throughout this book when they offer the best explanation for biological events. Here are some important things to note. When dioxygen binds to the heme iron, the oxidation state of the Fe2+ ion does not change, even though dioxygen is a great oxidizing agent. Hence the Fe2+ ion is a reversible carrier of dioxygen not of electrons. Free heme in solution is oxidized by dioxygen, forming a complex with water which occupies the 6th position, with the iron in the Fe3+ state. An intermediate in this process is the formation of a dimer of 2 hemes linked by 1 dioxygen. This can't occur readily when the heme is in Hb or Mb. Other heme proteins (like Cytochrome C), which we will explore in future chapters, are designed to be carriers of electrons. A small amount of the Fe2+ ion can get oxidized to Fe3+ ion myoglobin and hemoglobin, resulting in met-Hb and met-Mb. The brown color of old meat results in large part from Met-Mb. A enzyme is required to reduced the iron back to the Fe2+ state. The differences between hemoglobin and myoglobin are equally important. Hemoglobin is a heterotetramer of two α and two β subunits held together by noncovalent interactions (an example of quaternary protein structure), with 4 bound hemes, each of which can bind a dioxygen. In a fetus, two other subunits make up hemoglobin (two zeta - ζ and two epsilon - ε subunits -analogous to the two α and two β subunits, respectively). This changes in development to two α and two γ subunits. Fetal hemoglobin has a higher affinity for dioxygen than does adult hemoglobin. Myoglobin is a single polypeptide chain and has a higher affinity for dioxygen than hemoglobin. The α and β chains of hemoglobin are similar to that of myoglobin, which is unexpected since only 24 of 141 residues in the α and β chains of Hb are identical to amino acids in myoglobin. This suggests that different sequences can fold to similar structures. The globin fold of myoglobin and each chain of hemoglobin is common to vertebrates and must be nature's design for dioxygen carriers. A comparison of the sequence of hemoglobin from 60 species shows much variability of amino acids, with only 9 identical amino founds. These must be important for structure/function. All internal changes are conservative (e.g. changing a nonpolar for a nonpolar amino acid). Not even prolines are conserved, suggesting there are different ways to break helices. The two active site histidines are conserved, as is glycine B6 (required for a reverse turn). http://www.umass.edu/molvis/tutorials/hemoglobin/ Normal and Cooperative Binding of Dioxygen - Structural Analyses Plots of Y (fractional saturation) vs L (pO2) are hyperbolic for Mb, but sigmoidal for Hb, suggesting cooperative binding of oxygen to Hb (binding of the first oxygen facilitates binding of second, etc). Figure $11$ shows fractional saturation (Y) binding curves vs dioxygen concentration (PO2) for both myoglobin and hemoglobin. Note that hemoglobin is saturated with O2 at the high concentration found in the lung, but it releases much of its bound O2 in respiring tissues in which O2 is much lower. In contrast, myoglobin only releases significant bound oxygen at much lower O2 concentrations. Hence myoglobin is designed for dioxygen storage. In another difference, the affinity of Hb for dioxygen, but not Mb, depends on pH. This is called the Bohr effect, after the father of Niels Bohr, who discovered it. Figure $12$ shows binding curves for hemoglobin in the presence of increasing and decreasing concentrations of H+ (pH) as well as for CO2 and another ligand, 2,3-disphosphoglycerate (2,3-DPG). Michał Komorniczak (Poland). https://commons.wikimedia.org/wiki/F...ohr_Effect.svgCreative Commons 3.0. Attribution-ShareAlike (CC BY-SA 3.0). Protons (decreasing pH), carbon dioxide, and bisphosphoglycerate, all allosteric ligands which bind distal to the oxygen binding sites on the heme, shift the binding curves of Hb for oxygen to the right, lowering the apparent affinity of Hb for oxygen. The same effects do not occur for Mb. These ligands regulate the binding of dioxygen to Hb. From these clues, we wish to discern the • molecular and mathematical bases for the sigmoidal binding curves • mechanism for the exquisite regulation of O2 binding by allosteric ligands. The two obvious features that differ between Mb and Hb are the tetrameric nature of Hb and its multiple (4) binding sites for oxygen. Regulation of dioxygen binding is associated with conformational changes in hemoglobin. Based on crystallographic structures, two main conformational states appear to exist for Hb, the deoxy (or T - taut) state, and the oxy (or R -relaxed) state. The major shift in conformation occurs at the alpha-beta interface, where contacts with helices C and G and the FG corner are shifted on oxygenation. Figure $13$ shows conformation changes on O2 binding to deoxy-hemoglobin (files aligned with DeepView, displayed with Pymol). Dioxygen is shown as red spheres. The deoxy or T form is stabilized by 8 salt bridges which are broken in the transition to the oxy or R state. This is Illustrated in Figure $14$. 6 of the salt bridges are between different subunits (as expected from the above analysis), with 4 of those involving the C- or N- terminus. In addition, crucial H-bonds between Tyr 140 (alpha chain) or 145 (on the beta chain) and the carbonyl O of Val 93 (alpha chain) or 98 (beta chain) are broken. Crystal structures of oxy and deoxy Hb show that the major conformational shift occurs at the interface between the α and subunits. When the heme Fe binds oxygen it is pulled into the plan of the heme ring, a shift of about 0.2 nm. This small shift leads to larger conformational changes since the subunits are packed so tightly that compensatory changes in their arrangement must occur. The proximal His (coordinated to the Fe2+) is pulled toward the heme, which causes the F helix to shift, causing a change in the FG corner (the sequence separating the F and G helices) at the alpha-beta interface as well as the C and G helices at the interface, which all slide past each other to the oxy-or R conformation. Decreasing pH shifts the oxygen binding curves to the right (to decreased oxygen affinity). Increased [proton] will cause protonation of basic side chains. In the pH range for the Bohr effect, the most likely side chain to get protonated is His (pKa around 6), which then becomes charged. The most likely candidate for protonation is His 146 (on the β chain - HC3), which can form a salt bridge with Asp 94 of the β(FG1) chain. This salt bridge stabilizes the positive charge on the His and raises its pKa compared to the oxyHb state. Carbon dioxide binds covalently to the N-terminus to form a negatively-charge carbamate, which forms a salt bridge with Arg 141 on the alpha chain. Bisphosphoglycerate (BPG), a strongly negatively charged ligand, binds in a pocket lined with Lys 82, His 2, and His 143 (all on the beta chain). It fits into a cavity between the β subunits of the Hb tetramer in the T state. Notice all these allosteric effectors lead to the formation of more salt bridges which stabilize the T or deoxy state. The central cavity where BPG binds between the β subunits becomes much smaller on oxygen binding and the shift to the oxy or R state. Hence BPG is extruded from the cavity. The binding of H+ and CO2 help shift the equilibrium to the deoxyHb form, which facilitates the release of oxygen to the tissues. It is in respiring tissues that CO2 and H+ levels are high. CO2 is produced from oxidation of glucose through glycolysis and the Krebs cycle. In addition, high levels of CO2 increase H+ levels through the following equilibrium: \mathrm{H}_{2} \mathrm{O}+\mathrm{CO}_{2} \leftrightarrow \mathrm{H}_{2} \mathrm{CO}_{3} \leftrightarrow \mathrm{H}^{+}+\mathrm{HCO}_{3}{ }^{-} In addition, H+ increases from weak acids such as pyruvic acid produced in the central metabolic pathway (glycolysis) to produce energy from glucose oxidation. The binding of CO2 and H+ to hemogl serves an additional function: it removes excess CO2 and H+ from the tissues where they build up. When deoxyHb with bound H+ and CO2 reaches the lungs, they leave as O2 builds and deoxyHb is converted to oxyHb. Hemoglobin exhibits allosterism. Allosterism occurs when a regulatory ligand (like CO2 and H+) binds to a site distal to the binding site of a main ligand (like O2) and changes the affinity for the main ligand. We will define in our own convention two kinds of allosterism: Type I occurs when a ligand such as dioxygen binds to multiple ligand binding sites on the same protein and gives sigmoidal binding plot as a function of ligand concentration. Multiple binding sites for a main ligand can be found on a multimeric protein with identical or similar subunits (as in the case of hemoglobin but not myoglobin). In the case of hemoglobin, the main ligand O2 binds to the same active site in each monomer. This site is called the orthosteric binding site. For the hemoglobin tetramer, the orthosteric site of course is the heme Fe2+ ion. Type I allosterism occurs when an allosteric ligand binds at the active sites of the monomeric subunits of the protein. Most texts call this homotropic allosterism and the ligands homotropic ligands. Hence O2 and CO are homotropic ligands for hemoglobin. Type II occurs when a chemical modulator binds to a site different from the binding site for the main ligand. In doing so, it modulates (activates or inhibits) the binding affinity of the main ligand for the active or orthosteric site and shifts the binding curve for ligand binding. In addition, binding plots at a fixed main ligand concentration with varying modulator concentrations are also sigmoidal. The modulator binds at an allosteric (other) binding site. In the case of hemoglobin, the allosteric modulators are H+, CO2 and BPG. H+ and CO2 binding shift the O2 bind curve in ways that lower the affinity for O2, leading to its release. Most texts call this type of allosterism heterotropic. Protons (H+) and CO2 hence are heterotropic ligands. We prefer Type I and Type II over the more jargonistic less intuitive terms homo- and heterotropic. Mathematical Analysis of Cooperative Binding How do the sigmoidal dioxygen binding curves for Hb arise? Mathematics can offer clues that complement and extend structural information. At least three models (Hill, MWC, and KNF) have been developed that give rise to sigmoidal binding curves. Remember, sigmoidal curves imply cooperative binding of oxygen to hemoglobin. As oxygen binds, the next oxygen seems to bind with higher affinity (lower KD). We will discuss the mathematics behind two of the models. Both models are routinely applied to binding phenomena that give sigmoidal curves. Previously we have shown that the binding of oxygen to myoglobin can be described by chemical and mathematical equations. \mathrm{M}+\mathrm{L} \leftrightarrow \mathrm{ML} Y=\frac{L}{K_D+L} These mathematical equations are that of a hyperbola where Y is fractional saturation. Let's now explore two models that give sigmoidal curves. Hill Model In this model, we base our mathematical analysis on the fact that the stoichiometry of binding is not 1:1, but rather 4 to 1: Perhaps a more useful equation to express the equilibrium would be M + 4L ↔ ML4. We can derive an equation analogous to the one above: Y=\frac{L^4}{K_D+L^4} For any given L and KD, a corresponding Y can be calculated. A plot of Y vs L is not hyperbolic but sigmoidal (see the next link below). Hence we're getting closer to modeling that actual data. However, there is one problem. This sigmoidal curve does not give a great fit to the actual oxygen binding curve for Hb. Maybe a better fit can be achieved by altering the exponents in the equation. A more general equation for binding might be M + nL ↔ MLn, which gives the Hill equation: Y=\frac{L^n}{K_D+L^n} A derivation of the Hill Equation Some books/sources offer a different Hill equation. The one above is correct. Click the derivation below, which requires only a background from the preceding chapter sections and maybe a bit from Chapter 6.1. Derivation Let's consider this reaction: For a minute, assume n=2, and the reaction was written as M + 2L ↔ ML2. Also assume that the reaction occurs when M, L, and a second L all collide simultaneously (unlikely) to form the product, ML2. From introductory chemistry, you would write the rate equation for just the forward "ternary" reaction as: ratef = d[ML2]/dt = kf[M][L][L] = kf[M][L]2 The rate of just the reverse reaction would be: rater = d[ML2]/dt = kr[ML2] Subtract the two rates to get the net rate. Now let's switch from n= 2 back to just n and proceed without further explanation. At equilibrium, the forward and reverse rates are equal, so \frac{d\left[M L_n\right]}{d t}=k_f[M][L]^n=k_f[M][L]^n-k_r\left[M L_n\right]=0 \text { at equilibrium } KD is equal to the ratio of kr/kf, so solving for KD gives K_D=\frac{k_r}{k_f}=\frac{[M][L]^n}{\left[M L_n\right]} Solve for [MLn] gives \left[M L_n\right]=\frac{[M][L]^n}{K_D} The fractional saturation is given by Y=\frac{\left[M L_n\right]}{[M]+\left[M L_n\right]} Plug in the previous equation for [MLn] to obtain the correct form of the Hill Equation. Y=\frac{\frac{[M][L]^n}{K_D}}{[M]+\frac{[M][L]^n}{K_D}}\left(\frac{K_D}{K_D}\right)=\frac{[M][L]^n}{K_D[M]+[M][L]^n}=\frac{[L]^n}{K_D+[L]^n} QED! If n is set to 2.8, the theoretical curve of Y vs L gives the best, but still not perfect, fit to the experimental data. It must seem arbitrary to change the exponent which seems to reflect the stoichiometry of binding. What molecular interpretation could you give to 2.8? Consider another meaning of the equilibrium described above: M + 4L ↔ ML4. One interpretation is that all 4 oxygens bind at once to Hb. Or, alternatively, the first one binds with some low affinity, which through associated conformational changes alters the remaining 3 sites to very high affinity sites. This model implies what is described as infinitely cooperative binding of oxygen. (Notice that the Hill equation becomes: Y = L/[KD + L], when n =1 (as in the case with myoglobin, and in any equilibrium expression of the form M+L↔ML. Remember plots of ML vs L or Y vs L gives hyperbolas, with KD = L at Y = 0.5.) Does KD = L at Y = 0.5? The oxygen concentration at which Y = 0.5 is defined as P50. We can substitute this value into equation 3 which gives an operational definition of KD in terms of P50. \mathrm{Y}=0.5=\frac{\mathrm{P}_{50}^{\mathrm{n}}}{\mathrm{K}_{\mathrm{D}}+\mathrm{P}_{50}^{\mathrm{n}}} multiply both sides by 2 give \begin{gathered} 1=\frac{2 \mathrm{P}_{50}^{\mathrm{n}}}{\mathrm{K}_{\mathrm{D}}+\mathrm{P}_{50}^{\mathrm{n}}} \ \mathrm{K}_{\mathrm{D}}+\mathrm{P}_{50}^{\mathrm{n}}=2 \mathrm{P}_{50}^{\mathrm{n}} \ \mathrm{K}_{\mathrm{D}}=\mathrm{P}_{50}^{\mathrm{n}} \end{gathered} Note that for this equation, KD is not the ligand concentration at half-saturation as we saw in the case with hyperbolic binding curves. This gives a modified version of the Hill equation for hemoglobin binding of dioxygen: Y=\frac{L^n}{P_{50}^n+L^n} Is the Hill equation still useful? This Hill equation with the Hill coefficient n that is empirically determined to obtain the best fit to the binding data might seems a bit contrived (especially after seeing the more mechanistically and chemically intuitive MWC equation described below). Is it useful in any other circumstance? Indeed it is and it is used often in modeling more complex interconnected binding and kinetic pathways that show similar "exquisite" sensitivity to concentrations and resulting sigmoidal binding and kinetic plots. We will see its use in Chapter 30.13 when we model protein kinases that regulate the cell cycle! Use the sliders in the interactive graph below to explore the effect of changes in KD and n on fractional saturation. The Hill equation for hemoglobin gives sigmoidal dioxygen binding curve that fit the actual binding data. MWC Symmetry Model In the MWC (Monod, Wyman, and Changeux) model, in the absence of ligand (oxygen), hemoglobin is assumed to exist in two distinct conformations, the T state (equivalent to the crystal structure of deoxyHb) and the R state (equivalent to the crystal structure of oxyHb without the oxygen). In the absence of dioxygen, the T state (T0) is greatly favored over the unliganded R state (R0) at equilibrium. In the presence of increasing oxygen, the R state is favored. A constant (somewhat equivalent to a dissociation constant) can be defined. \mathrm{L}=\mathrm{T}_{0} / \mathrm{R}_{0} (Note: L is not the ligand concentration so don't get confused.) In addition, let us assume that hemoglobin can not exist with some of the monomers in the tetramer in the T state while others in the same tetramer are in the R state. Hence this model is often called the symmetry model. Finally, let's assume that each oxygen can bind to either the T or R state with the dissociation constants KT and KR respectively. These constants do not depend on the number of dioxygens already bound to the tetramer. Hence \mathrm{K}_{\mathrm{R}}=\frac{\left[\mathrm{R}_{0}\right][\mathrm{S}]}{\left[\mathrm{R}_{1}\right]}=\frac{\left[\mathrm{R}_{1}\right][\mathrm{S}]}{\left[\mathrm{R}_{2}\right]}=\ldots \frac{\left[\mathrm{R}_{\mathrm{n}}\right][\mathrm{S}]}{\left[\mathrm{R}_{\mathrm{n}+1}\right]} and \mathrm{K}_{\mathrm{T}}=\frac{\left[\mathrm{T}_{0}\right][\mathrm{S}]}{\left[\mathrm{T}_{1}\right]}=\frac{\left[\mathrm{T}_{1}\right][\mathrm{S}]}{\left[\mathrm{T}_{2}\right]}=\ldots \frac{\left[\mathrm{T}_{\mathrm{n}}\right][\mathrm{S}]}{\left[\mathrm{T}_{\mathrm{n}+1}\right]} where the subscript on R and T refers to the number of dioxygens bound to that form of R or T. A cartoon representation of the T and R forms and accompanying dioxygen binding is shown in Figure $15$. Now define two new parameters: \alpha=\frac{\mathrm{pO}_{2}}{\mathrm{~K}_{\mathrm{R}}}=\frac{[\mathrm{S}]}{\mathrm{K}_{\mathrm{R}}} where α is really a normalized ligand concentration describing how many times the KR the ligand concentration is, and \mathrm{c}=\frac{\mathrm{K}_{\mathrm{R}}}{\mathrm{K}_{\mathrm{T}}} the ratio of the dissociation constants for the R and T forms. If oxygen binds preferentially to the R form of hemoglobin, c would be a small fractional number. In the limiting case, when oxygen didn't bind to the T form, KT would be infinite, and c = 0. Using these definitions and equations, the following equation for Y, fractional saturation vs α can be derived, with n, the number of binding sites per molecule, = 4 for Hb. \mathrm{Y}=\frac{\alpha(1-\alpha)^{\mathrm{n}-1}+\operatorname{Lc} \alpha(1+\mathrm{c} \alpha)^{\mathrm{n}-1}}{(1+\alpha)^{\mathrm{n}}+\mathrm{L}(1+\mathrm{c} \alpha)^{\mathrm{n}}} Figure $16$ shows how fractional saturation (Y) vs alpha varies with L and c for the MWC model. When L is set at 9000 and c = 0.014, the Y vs α curve fits the experimental oxygen binding data well. Figure $17$ shows the best experimental dioxygen binding data that we could find (obtained from a graph, not from a table), the best fit of the Y vs L data using a Hill coefficient of n=2.8 (fitting equation 3 above), and the best fit of Y vs L using the MWC model, with L=9000, c=0.014, and Kr = 2.8 torr. Use the sliders in the interactive graph below to explore the effect of changes in L and c on fractional saturation. Hence, like the Hill equation, the MWC equation gives sigmoidal dioxygen binding curves. It does not require an empirical Hill-like coefficient, which has no clear physical meaning! Another way to think about the MWC Model The MWC model assumes that oxygen binds to either the T or R form of Hb in a noncooperative fashion. Hence KT and KR are constant, independent of the number of dioxygens bound to that form. If so, what is the basis of the cooperative oxygen binding curves? The answer can be seen below. The cyan curve might reflect the binding of a ligand to the T form of a macromolecule, with KD = 100 uM (low affinity), for example. The binding curve looks linear, but it really is just the initial part of a hyperbolic binding cure. Likewise, the magenta curve reflects the binding of a ligand to the R form of the macromolecule with KD = 10 uM. The T and R forms are linked through the T↔ R equilibrium. That equilibrium will be shifted to the tighter binding (lower KD) R form with increasing ligand concentration, assuming the ligand binds preferentially to the R form. This shifts the actual binding curve from that resembling the T form at low ligand (cyan) to one resembling the R form (magenta) as the ligand increases, imparting sigmoidal characteristics to the "observed" binding curve (gray). Figure $18$ shows how sigmoidal binding curve could arise from a switch from a low affinity to high affinity form. KNF Sequential Model The KNF (Koshland, Nemethy, and Filmer) Sequential model was developed to address concerns with the concerted model. One of the major problems with the concerted model is that it seemed unrealistic to expect all the subunits to change conformation together. Why shouldn't there be some differences in subunit conformation? The KNF model also fits the experimental data well. Figure $19$ shows the linked equilibria in the KNF model. Data suggests that the MWC model better explains the transition in proteins on ligand binding and that there is an all‑or‑none interconversion between the two states. Allosterism in other multisubunit protein complexes Changeux (of the MWC model) has written eloquently about the occurrence and effects of allostery in other proteins. We will encounter these proteins in other chapters, but present some here, in advance of the chapter in which they are usually discussed. We do this to show that other proteins display allostery and that the MWC can often be used in describing their behaviors. This offers a rationale to discuss allosterism using hemoglobin with its nonstandard covalent ligands as a model for allosteric binding proteins and enzymes. Environmental factors such as ligands and allosteric modulators can shift the degree of cooperativity for ligand binding, promote allosteric rearrangements and T ↔ R transitions of proteins other than hemoglobin. We offer several examples of multimeric proteins (complexes) that display allosterism. Many of these allosteric proteins not only bind ligands, but acts as catalysts. One protein, a ligand-gated ion channel, moves ions across a membrane. Others catalyze the chemical transformation of a substrate to a product. Another is a structural viral protein. The examples involving catalysis are more complex, since an additional step (transport of ions or alteration in covalent bonds) after binding is added to effect protein function. This extra step can be described as a rate, so we explore rate vs ligand concentration, not just fractional saturation vs ligand concentration curves. Lactate dehydrogenase (LDH) LDH is an enzyme that catalyzes the reversible reduction of the 3-carbon carboxylic acid pyruvate to lactate by the oxidizing agent NAD+, as shown in the reaction below (which is written in reverse as the reduction of pyruvate, the normal function of the enzyme). pyruvate + NADH + H+ ↔ lactate + NAD+ Its activity is modulated by the allosteric activator fructose 1,6-bisphosphate (FBP). The kinetics can be modeled using the MWC model, in which the enzyme exists in T (tense/taut) and R (relaxed) allosteric states. FBP binds preferentially to the R state. Figure $20$ shows an interactive iCn3D model comparing the T state of bacterial L-lactate dehydrogenase with bound NAD+ from Bifidobacterium longum (1LLD), and the R state of the enzyme from Geobacillus stearothermophilus (2LDB) with bound NAD+ and the allosteric activator fructose 1,6-bisphosphate (F6P). Toggle between the two states using the "a" key. Figure $20$: Comparison of the T (1LLD) and R (2LDB) states of bacterial L-lactate dehydrogenase with bound NAD+ and allosteric activator F6P (in R state) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...YEXKSp5J1ErFT6 The enzyme substrate NAD+, the allosteric activator F6P for the R state, and SO42- (from ammonium sulfate used to crystallize the protein) are shown in spacefill and labeled. Aspartate transcarbamylase (ATCase) This enzyme catalyzes the addition of aspartate and carbamoyl phosphate to form carbamoyl aspartate, the first step in the pathway for the synthesis of the pyrimidine nucleotides cytidine triphosphate (CTP) and uridine triphosphate (UTP). The end products of the pathway, CTP and UTP, feedback and allosterically inhibit the enzyme. In contrast, ATP is an allosteric activator. This prevents a buildup of pyrimidine nucleotides over purine nucleotides since equal amounts are needed for nucleic acid synthesis. Figure $21$ shows an interactive iCn3D model comparing the T (4FYW) and R (1D09) states of asparatate transcarbamylase (ATCase). Toggle between the two states using the "a" key. Figure $21$: Comparison of the T tense (4FYW) and R (1D09) relaxed state of asparatate transcarbamylase (ATCase). Toggle between the two states using the "a" key. (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...MHLtC9ALwWVia6 Each of the subunits is shown in a different color. The T state (4FYW) has bound CTPs (at the periphery, shown in spacefill) while the R state has a bound substrate analog, N-(phosphonacetyl)-L-aspartic acid. High levels of the substrate (or substrate analog) shifts the equilibrium to the active R state. Pentameric ligand-gated ion channels (bacterial) Protein channels in membrane bilayers are needed to "catalyze" and regulate the flow of ions across the hydrophobic membrane. Hence it makes sense that channels exist in closed and open states. One example is the bacterial GLIC pentameric ligand-gated ion channel, which is opened by ligand binding, often called ligand-gating.. Figure $22$ shows an interactive iCn3D model comparing the GLIC pentameric Ligand-Gated Ion Channel Loop2-22' oxidized mutant in a locally-closed conformation (LC3 subtype) (3TLV) and the A237F mutant channel in the open conformation (3LSV). Toggle between the two states using the "a" key. Figure $22$: Comparison of the GLIC pentameric Ligand-Gated Ion Channel Loop2-22' oxidized mutant in a locally-closed conformation (LC3 subtype) (3TLV) and the A237F mutant of the pentameric ligand gated ion channel from Gloeobacter Violaceus in the open conformation (3LSV). Toggle between the two states using the "a" key. (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...GKiPY9Jh7tQXTA Note that the outer (red) and inner (blue) membrane leaflets are shown only in the closed channel (3TLV). The green spheres represent chloride ions. Nudaurelia capensis ω virus capsid This hallow viral protein structure surrounds the internal viral genome, so it is an example of allostery in a protein complex that is neither a transporter nor an enzyme. This hetero 480-mer with icosahedral symmetry undergoes a global shape change when the immature capsid undergoes selective and limited proteolysis to form the mature capsid, as illustrated in Figure $22$. The R form is more open. This is a wonderful example to envision the global conversion of all subunits from a "T" to an "R" state, clearly necessary in this case to preserve the exquisite symmetry! Now, let's look at binding and rate curves for some multimeric allosteric enzymes. Since this is all a bit complicated, let's review again the difference between what we call Type 1 and Type II allosterism: Type I: Increasing the amount of a substrate can induce conformational changes in a multisubunit protein to a form that has apparently higher (or potentially lower as well) affinity for the substrate in the remaining unoccupied substrate binding sites. In this case, the substrate is binding to the orthosteric site. These sites are where substrates bind but also competitive inhibitors (if the protein is an enzyme), and agonists or competitive antagonists of receptors. We will explore enzymes and receptors later in this book. In Type I allosterism, binding or kinetic curves would show sigmoidal fractional saturation (or kinetic curves) with increasing concentrations of substrate. Type II: Increasing amounts of a chemical species (an inhibitor or activator) can bind to an allosteric site, which affects the binding of the substrate to the orthosteric site. The regulators shift and change the shape of the Y or rate curves vs substrate. In experiments to show this kind of allosterism, you wouldn't change the substrate and allosteric effector concentrations simultaneously since the resulting data and graphs would be hard to interpret. You could change the ligand or substrate that binds to the orthosteric site over a large range of concentrations (hopefully over a 1000 - 10,000 fold change, or 4 log units) in several different experiments, with each experiment having a different fixed concentration of the allosteric effector. Alternatively, you could conduct the experiment over a large concentration range of a given allosteric effector (again a 1000-10,000 fold change if possible) in several different fixed concentrations of ligand or substrate in a series of experiments. Rate vs ligand curves for allosteric proteins that catalyze chemical reactions Since we have already seen an example of Type I allosteric binding curves (hemoglobin binding dixoygen), let's look at a few examples of Type II allosteric in multisubunit proteins since their graphs are a bit more complicated. We realize the curves below show relative rates of enzymes and not relative fractional saturation of enzymes, but the same principles are present. Phosphofructokinase Figure $23$ shows an example of allosteric kinetic curves for Phosphofructokinases A (Pfk A) and B (Pfk B) from Mycobacterium tuberculosis. The enzyme catalyzes the phosphorylation of fructose-6-phophate (F6P) by ATP to produce fructose-1,6-bisphosphate (F1,6-BP) and ADP. F6P + ATP → F1,6-BP + ADP Figure $23$: The dependence of Pfk A and Pfk B activities on concentration of Mg2+. Individual reactions were performed in buffers containing fixed initial concentration for both substrates (1 mM F6P and ATP) with the concentration of Mg2+ varied. Snášel, J. et al. Int. J. Mol. Sci. 2021, 22, 1483. https://doi.org/10.3390/ijms22031483. Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Only Pfk A shows allosteric activation of the enzyme by Mg2+, run under fixed initial (and probably saturating) concentrations of the substrates F6P and ATP. The Hill coefficient is 3.3 for Pfk A, which suggests that Mg2+ is important in maintaining/promoting the active site and formation of the enzyme tetramer. Pfk B shows hyperbolic kinetics and no allosterism, with a Hill coefficient close to 1. These curves are modeled with the Hill equation and not the MWC equation. Lactate Dehydrogenase Again this enzyme catalyzes the following reaction: pyruvate + NADH + H+ ↔ lactate + NAD+ The graphs in Figure $24$ show relative inhibition (graph A, top) and double-reciprocal plots (C, bottom) for the enzyme lactate dehydrogenase B (LDHB) in the presence of an allosteric inhibitor, AXKO0046. This enzyme catalyzes the reduction of pyruvate by NADH (the substrate) to form lactic acid and NAD+ (the products). We'll discuss the graphs below. Figure $24$: Biochemical characterization of AXKO-0046. LDHB inhibition by AXKO0046 was studied using varying concentrations of (a) NADH (c) Double reciprocal (Lineweaver-Burk) plots of the kinetic data. Shibata, S. et al. Sci Rep 11, 21353 (2021). https://doi.org/10.1038/s41598-021-00820-7. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. Now, these graphs are a bit more complicated since in this case, the initial concentration of one substrate is varied, while the other concentration is fixed (in contrast to the PfkA experiments in which the initial concentrations of both substrates were held constant). Let's look at graph C (bottom) first. Instead of showing graphs of rate vs [NADH], the authors showed double-reciprocal kinetic plots, run with varying [NADH] and at one fixed concentration of pyruvate (not given) and several fixed concentrations of inhibitor. The graphs look like they are generally straight lines except at the end, which occurs at a low concentration of NADH (which gives the highest value of 1/[NADH] = 0.10). At this point, 1/rate (given as 1/v) data points are higher than the best-fit line would suggest, implying that the rate v is "abnormally" low. That rate accelerates as [NADH] increases (as 1/[NADH] decreases in a manner consistent with allosterism. This suggests that at any given fixed concentration of inhibitor and fixed pyruvate concentration, the graphs of rate vs substrate (NADH) (i.e not the double-reciprocal plot) would be sigmoidal. It's different from the graph for PfkA (Fig 23), in which the x-axis variable is the allosteric activator Mg2+. Look at Graph A (top) which uses varying inhibitor concentrations and several different fixed concentrations of reactant NADH. This is analogous to the graph for PfkA but notice that on the x-axis the log [inhibitor] is plotted instead of the [inhibitor]. These are NOT plots of v vs [substrate], expected to be hyperbolic, or v vs log[substrate] which are expected to be sigmoidal. (A lesson here is to look carefully at the axes). But note something unusual about the curves. The plateau for inhibition is not the same at each NADH concentration. The highest % inhibition (red and purple curves) occurs when the substrate [NADH] is highest. (We will see in the next chapter that this is a sign of what is called uncompetitive inhibition). X-ray crystal structures show that the inhibitor (AXKO-0046) does bind to an allosteric site, not the active orthosteric site. It appears to bind in the interface of the LDHB tetramer. The graphs show that over four orders of magnitude of inhibitor (4 log units), the inhibition goes from 0 to about 100%, which is expected if the sigmoidal semi-log curves gave hyperbolic curves with [inhibitor] plotted on the x-axis. This may seem confusing, but such sigmoidal curves are found in plots of rate vs log concentration of allosteric activators and inhibitors, as discussed in Chapter 5.1. So don't immediately jump to the conclusion that a sigmoidal curve implies allosterism. Look at the reactions and relative concentrations carefully. Consider this example. What if a protein binds a ligand L and an inhibitor I at the same orthosteric site? If one bound, the other couldn't. This is an example of a classical competitive, non-allosteric, inhibition. Now, what if an inhibitor, I, binds to an allosteric site and when bound, it altered the conformation of the orthosteric site such that the ligand could not bind. Binding of L and I would be mutually exclusive. This would produce the same binding curves as the classical competitive inhibition. In either case, at very high ligand concentrations, the effect of the inhibitor would be lost, and full maximal binding would be observed. It would just take higher concentrations of ligand to get the same fractional saturation of the protein in the presence of the inhibitor than in its absence. In the presence of a fixed concentration of these competitive inhibitors, the effective KD would be higher. Y vs L curves for both would be hyperbolic, and double-reciprocal plots would be linear. Allostery within monomeric protein Allosterism can also occur in monomeric proteins: Type I (again our nomenclature) allosterism can occur in monomers that have more 2 or more binding sites for a ligand/substrate and if the binding of ligand/substrate to one site significantly alters the affinity of the other site for substrate enough to produce a nonhyperbolic, sigmoidal binding/kinetic curve for substrates. This case is different than the binding of substrate to two different preformed substrate binding sites, each with a different fixed affinity, which we discussed in Chapter 5.1 (scroll down to binding of a ligand to two independent sites). We show again the graph of fractional saturation Y vs L for the binding of a ligand to two different preformed sites of different affinities below. Note that the above graph doesn't look sigmoidal. It is essentially hyperbolic except in the extreme case when one of the KD is much much less than the other AND at low ligand concentration such that the higher affinity binding leads to an abrupt, titration curve-like saturation of the low KD site before the second site has much occupancy. Rec A is an OK example of a "possible" Type I allosteric monomer binding protein (if you have a better example, let us know!). This protein is required for homologous recombination in bacteria. It has ATPase activity and catalyzes ATP-driven homologous pairing and strand exchange of DNA required for DNA repair. The structure is known for the Mycolicibacterium smegmatis apo form of the enzyme, the enzyme:substrate (dATP, a substrate analog) complex, and the enzyme:substrate:allosteric effector (a second dATP and possibly citrate) complex. The enzyme has three domains (N-terminal 1-30, the major M domain (31-269) and the C-terminal (270-349). The M domain is the catalytic domain which has nucleotide triphosphate hydrolase activity. It binds nucleotides, DNA and also interacts with the N domain of another RecA to promote the polymerization of RecA into a filament. The C-terminal domain is disordered but becomes ordered when bound to a second dATP in the crystal structure. Figure $25$ shows conformational changes in RecA:dATP (the ES complex) on binding a second dATP (the ESA complex), where A is the likely allosteric activator (the second bound dATP). A result of this ordering on binding is likely the polymerization of the RecA into filaments. Figure $25$: Conformational changes in RecA:dATP (the ES complex) on binding a second dATP (the ESA complex). The dATP in the catalytic site is shown in spacefill with CPK colors. The ES complex is shown as a darker gray protein with one bound dATP (spacefill, CPK colors). The ESA complex is shown in lighter gray with dATP bound in the catalytic (orthosteric) site in CPK colors and a second dATP (spacefill, cyan) bound in the putative allosteric site in the C domain. Type II: increasing amounts of a chemical species (an inhibitor or activator) that binds to an allosteric site in a monomeric protein could affect the binding of the substrate to an orthosteric site in the monomer. In this case, as in Type II for multimeric proteins, you could again run two different types of experiments (one with varying substrate at 3-4 different fixed allosteric effector concentrations or vice/versa. One example is thrombin, the last protease in a cascade of clotting proteins. The proteins are synthesized as inactive precursors (zymogens) that become activated on limited proteolysis. Active thrombin is a procoagulant enzyme in that it can cleave circulating fibrinogen (and other procoagulant molecules) into fibrin. This then self-associates to form a fibrin clot. Paradoxically, thrombin also has anticoagulant properties in that it can cleave another circulating protein, Protein C, which e inhibits further clotting. Thrombin does so when it binds a transmembrane protein, thrombomodulin, present on endothelial cells that line blood vessels. These contrasting activities support the notion that thrombin has two interconverting conformations, each stabilized by different ligands or proteins. One such ligand is the simple monatomic ion Na+. Indeed, thrombin appears to have two main catalytic conformations, a high-activity “fast” form (with bound Na+) and a low-activity “slow” form (without bound Na+). The fast form with bound Na+ (15 Å from the active site) appears to be the procoagulant form while the slow form is the anticoagulant form. Figure $26$ shows an interactive iCn3D model comparing the anticoagulant slow form of thrombin (1SGI) and the procoagulant sodium-bound fast form of thrombin (1SG8). Toggle between the two states using the "a" key. Figure $26$: Anticoagulant slow form (1SGI) and the procoagulant sodium-bound fast form of thrombin (1SG8). Toggle between the two states using the "a" key. (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...kxnATvMdVD1566 The magenta represents the slow form and the cyan with bound Na+ the fast form which has enhanced coagulant activity Table $1$ below shows a list of monomeric allosteric proteins and their PDB file codes. The proteins (P) are enzymes that bind a substrate (S) and an allosteric effector (A) to form PS, PA, PAS complexes (adapted from Wang et al. J. Phys. Chem. Lett. 2021, 12, 5404−5412) protein P PA PS PAS Effect protein RecA (RecA) 2OES   2ODN 2G88 activation mitogen-activated protein kinase 8 (MAP8) 1UKH 3O2M 2XRW   inhibition cAMP-dependent protein kinase catalytic. subunit alpha (Prkaca) 4NTS 4NTT 4IAF inhibition cAMP-dependent protein kinase catalytic. subunit alpha (Prkaca) 4NTS 1BKX 4DG0 activation cyclin-dependent kinase 2 (CDK2) 3PXR 3PXF 1HCK   inhibition casein kinase II subunit alpha (CK2α) 5ZN5 3H30 2PVR   inhibition myosin-2 heavy chain (mhcA) 1FMV   2JJ9 2JHR inhibition tyrosine-protein phosphatase. nonreceptor type 1 (PTP1B) 4QBW 1T49 1PTV   inhibition 1T48 1T4J Hemoglobin variants and disease Many diseases have been associated with alternations in the amino acid sequence of hemoglobin. Around 300,000 babies are born each year with a genetic disorder causing an aberrant hemoglobin structure. Over 80% of these are in low/middle-income countries. The worst is sickle cell anemia followed by β-thalassaemias and the less serious α-thalassaemias. We will only focus on sickle cell disease. In this disease, red blood cells become distorted in share from a normal smooth discoid shape to a crescent-like shape under low oxygen concentrations found in capillaries. These impede blood flow as shown in Figure $25$ and lead to symptoms ranging from anemia, episodic pain, swelling of hands and feet, and vision problem. Complications often lead to premature death. Linus Paul and colleagues showed that the isoelectric point (pI) of oxy-Hb and deoxy-Hb in normal blood was 6.87 and 6.68, respectively, but were higher (7.09 and 6.91, respectively) in sickle cell disease. This was the first evidence that a disease was caused by a molecular alteration of a protein. Eventually, we learned that a single negatively charged amino acid, Glu 6, on the β-chain of hemoglobin was mutated to a nonpolar one, valine. This causes hemoglobin, which is present at a concentration of 150 g/L in blood to self-aggregate into a long polymer. This distorts the cell in a sickle shape. Humans have two genes for the beta chain of hemoglobin, one from the egg donor and the ohter from the sperm donor. If only one mutated, the disease is called sickle cell trait. If both are mutated, sickle cell disease is observed. The hydrophobic Val 6 in on the surface of both beta chains in sickle cell disease. It can bind to a hydrophobic patch comprised of Ala 70, Phe 85 and Leu 88 on another β-chain on another hemoglobin tetramer. Hence hemoglobin has two Val 6s and two hydrophobic patches, allowing first the formation of a sickle cell hemoglobin "dimer" of tetramers, followed by elongation of the growing fibril. This is a disease of aberrant induced dipole-induced dipole interactions and the "hydrophobic effect". Figure $27$ shows a "dimer" aggregate of two hemoglobin S tetramers, in which each β-chain has the D6V mutation. α chains are shown in grey, β chains in cyan, Val 6 in red spacefill and the surface hydrophobic patch of A70, F85 and L88 in orange spacefill (1hbs). Note the binding of the two tetramers is mediated by the interaction of the red Val 6 on the right tetramer with the orange surface hydrophobic patch on the left tetramer. Note also that there are the "dimer" aggregate has three more exposed Val 6 (red sphere) and three more hydrophobic patches. This would allow the extension of the dimer and the formation of long fibril-like polymers, with binding mediated by noncovalent interactions. Figure $28$ shows an interactive iCn3D model of one tetramer of hemoglobin S. As in Figure $21$, the α chains are shown in grey, the β chains are shown in cyan, and Val 6s are shown in red spacefill. The surfaces of the hydrophobic pockets where the Val 6 another HbS tetramer binds, comprised of amino acids A70, F85 and L88, are shown in orange. Sickle cell disease and trait are endemic in Sub-Saharan Africa, where over 4 million have the disease and over 40 million have the trait. Its geographic distribution, along with that of malaria, is shown in Figure $29$. The Plasmodium parasite reproduces in red bloods cells. Their ability to reproduced is compromised as red blood cells with sickle cell hemoglobin rupture more frequently. Also the parasite uses hemoglobin as a source of amino acids. The endocytose it and hydrolyze it to amino acids in digestive organelles. Sickle cell hemoglobin is more resistant to this process. Hence evolution appears to have maintained the sickle cell gene in these areas as protection against malaria. Sickle cell disease is a systemic problem, so treatment of the multitude of symptoms is important. Some of these treatments are described in Figure $30$. Of course the cure would be to use DNA editing to change the base pair for the mutated Val 6 gene back to the wild-type Glu 6 as shown in Figure $31$. The amino acid sequence encoded by the DNA and RNA shown below is Pro-Glu-Glu for the normal and Pro-Val-Glu for the sickle cell chain. Crisper gene editing trials are now underway to attempt a cure of this dreadful disease.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/05%3A_Protein_Function/5.03%3A_Oxygen-Binding_Proteins_and_Allosterism.txt

Search Fundamentals of Biochemistry Introduction to the Immune System Now let's consider the daunting task faced by the immune system - to recognize all possible "foreign" molecules and react to them, either by targeting them for elimination or, paradoxically, to recognize them but not react to them (a process called tolerance). The same can be said of "self-molecules., and the immune system must recognize them but not respond to them, otherwise autoimmune disease might arise in which the body's powerful immune system targets self. It is virtually impossible to give an in-depth description of the immune system in a short section. Our goal is to illustrate how the immune system recognizes such a vast number of molecules. We will briefly cover the innate and adaptive immune system and their differences, and how some cells (macrophages in particular) in the innate immune system and cells (B and T cells) in the adaptive immune response recognize and respond to target molecules and cells. Finally, we'll discuss how the immune system can respond to similar molecules through the recognition of common molecular patterns. Emphasis will be given to recognition. Ways to simplify the complexities of the immune system have been presented in a fantastic book written by Lauren Sompayrac, How the Immune System Works. (2003, Blackwell Publishing. ISBN: 0-632-04702-X) and adopted here. We realize that we have not yet reached the chapters on carbohydrates, membrane proteins, and nucleic acids. Nevertheless, we present the material in this section to organize it in one specific location. Users can revisit this page after they studied subsequent chapters. Before we start, think of the variety of chemical species that the immune system should recognize as foreign: • a bacterial glycan or glycolipid on the outside of the cell • a viral surface protein, such as the spike protein of the SARS Coronavirus 2 • bacterial dsDNA (and not host dsDNA) • viral dsRNA (which is not common in host systems • a self protein that has been modified in a tumor cell • a crystal of urea • extracellular ATP (a place where it is not usually found) • a silica particle found in particles like asbestos. How would you design an immune system to bind each of the "enemy" targets above? That is what we will explore in this section - the binding interactions. What happens after the binding is beyond the scope of this section and falls generally in the field of signal transduction - how binding events at the self surface are transferred into intracellular responses. Three lines of defense protect us from the "enemies", foreign substances (bacteria, viruses and their associated proteins, carbohydrates, and lipids) collectively called antigens. • physical barriers of cells that line our outside surface and our respiratory, GI tract, and reproductive systems • , the innate immune system (IS) that all animals have. Composed of scavenger cells like macrophages (MΦ), neutrophils, dendritic cells, and natural killer cells (NK) that can move around the body through the blood and lymph systems and burrow into tissues to meet the enemy where they can engulf and destroy bacteria and "cellular debris". Macrophages start off as immature circulating monocytes, which enter tissues by slipping through blood vessel walls. They differentiate into macrophages. There they lie in wait ready for the enemy. • the adaptive immune system, which, as its name implies, can change and adapt to new molecular threats. This branch is better at dealing with viruses, which do their damage inside host cells. The adaptive IS is comprised of B cells that make and secret protein antibodies that recognize specific foreign molecules, and T cells. In a world experiencing the most deadly pandemic (Covid) of the last 100 years, and with more to come, immune recognition must be an important part of any biochemistry text. This chapter section could be a whole chapter, but we'll leave it as a very long section. Let's start with the adaptive immune system, which we can coopt to make vaccines to the major threats we face. B Cells and Antibodies B cells and their differentiated forms (B memory and plasma cells) make antibodies. Antibodies bind to foreign molecules (proteins, glycans, lipids, etc), which might neutralize their effects. For example, an antibody can bind to the hemagglutinin molecule of the influenza virus and prevent its entry into cells. We all are now familiar with the utility of vaccines that create antibodies to recognize the spike protein of the acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Antibodies also bind to foreign cells like bacteria, which signals other host immune proteins and cells to come in for the kill. Antibodies are secreted by B cells, which also have a membrane-bound form of the antibody on their surface. This antibody acts like a receptor which binds antigen and, through a signal transduction process, helps to activate the B cell. Mature B cells (those that have previously seen antigen) can secrete lots of antibodies quickly. Surface and secreted antibodies can recognize and bind to almost any molecule. There are many forms of antibody, also called immunoglobins (Ig). These include IgA, IgG, IgM and IgD. We will concentrate on the structure of IgG. It consists of 4 chains (a tetramer) of two light chains and two heavy chains. The light chains form disulfide links with the heavy chains and disulfides also link the heavy chains. Effectively, it's one big protein molecule (about 160 K). Figure \(1\) shows a spacefill, secondary structure, and geometric cartoon rendering of a mouse IgG protein (pdb ID 1IGT). The antibody is shaped like a Y. Foreign molecules (antigens) bind at the end of the top tips of the Y with both chains contributing to antigen binding. The structures of both are dominated by antiparallel beta sheets. Each chain consists of a single N-terminal variable domain (VL or VH) which participate in antigen recognition. The light chains have an additional constant domain (CL) while the heavy chains have 3 constant domains (CH1-CH3). The constant domains are not involved in antigen recognition, but they are involved in effector functions (such as the binding of other immune molecules like complement proteins) to the antigen-bound antibody heavy chain constant regions. Each of the domains is about 100 amino acids. A cartoon structure with the domain structures is shown in Figure \(2\). Two other features are depicted in the above figure. In each variable region of both light and heavy changes, there are hypervariable regions, which contribute to the unique binding features of a given antibody. The regions are also called complementarity-determining regions (CDRs). Membrane-bound forms of antibodies that serve as "receptor" proteins have additional domains now shown in the figure above. The binding site on the antigen recognized by the antibody is called the epitope. The corresponding binding site on the Y-shaped ends of the antibody that recognized the antigen is called the paratope. In Figure \(2\), you can see that the intact full IgG molecule has in 12 variable V and constant C domains called the immunoglobulin domain. Each has about 110 amino acid in length, two layers of β-sheets each with 3-5 antiparallel β-strands with a disulfide bond connecting the two layers. When we discussed domain structure, we indicated that proteins with multiple binding domains can often be selectively cleaved with protease, with cleaved fragments often retaining binding and other functions properties. The same is true with antibodies. Cleavage with either the proteases pepsin or papain forms fragments with binding activities as illustrated in the figure above. Selected protease digestion was used to clarify structure/function relationships in antibody recognition. When antibodies targeting different antigens were sequenced, it was clear that much variability was found among antibodies in the variable domains of both the light and heavy chains. In those domains there were also hypervariable regions. The origin of the variability and hypervariability arise mostly from an extremely large number of gene segments (also exons) in the gene encoding the variable domains. The exons can be spliced together at both the DNA and RNA levels to produce many different DNA/RNA sequences. These are decoded into the variable and hypervariable regions of the light and heavy chain proteins. Somatic mutations are also enhanced in this region. In-depth: Generation of Antibody Diversity We mentioned above that both DNA and RNA splicing occurs as B cells mature to become antibody-secreting cells (plasma cells). For those who have studied the Central Dogma of Biology, splicing for primary RNA transcripts should come as no surprise. What's is surprising is that the DNA genome of B cells changes on their maturation due to splicing of a multiple of exons within the variable chain genes to produce unique coding sequences for each clone of a given B cell. There are sets of exons (V, D and J) or segments within the genes for the variable chain. As the immune cells terminally differentiate, a unique combination of a VDJ segment forms in the DNA genome, so each terminally differentiated B cell is different. When needed (i.e. when their unique antigen binds to membrane forms of the antibody), the cell secretes a monoclonal antibody. Figure \(3\) shows how the different segments become linked in the DNA and how they can be uniquely spiced in the RNA to produce a unique, monoclonal antibody. The first antibodies produced by the immune system are often of low affinity. Over time, high affinity (low KD) antibodies are produced. What differentiates high and low affinity binding at the molecular level? Do high affinity interactions have lots of intramolecular H-bonds, salt bridges (ion-ion interactions), or are hydrophobic interactions most important? Crystal structures of many antibody-protein complexes were determined to study the basis of affinity maturation of antibody molecules. Clones of antibody-producing cells with higher affinity are selected through binding and clonal expansion of these cells. Investigators studied the crystal structure of four different antibodies which bound to the same site or epitope on the protein antigen lysozyme. Increased affinity was correlated with increased buried apolar surface area and not with increased numbers of H bonds or salt bridges as described in Table \(1\) below. Antibody H26-HEL H63-HEL H10-HEL H8-HEL Kd (nM) 7.14 3.60 0.313 0.200 Intermolecular Interactions H bonds 24 25 20 23 VDW contacts 159 144 134 153 salt bridges 1 1 1 1 Buried Surface Area ΔASURF (A2) 1,812 1,825 1,824 1,872 ΔASURF-polar (A2) 1,149 1,101 1,075 1,052 ΔASURF-apolar (A2) 663 724 749 820 Table \(1\): Characteristics of Antibody:Hen Egg Lysozyme Complexes (HEL). Data from Y. et al. Nature: Structural Biology. 6, pg 484 (2003) Many crystal structures of antibody:antigen complexes have been determined. Especially interesting are those in which the antigen is a protein. It is important to understand antibody:protein antigen interactions to develop vaccines against key epitopes in proteins such as the spike protein of the SARS-CoV-2. Let's look in more detail at the antibody that binds to hen egg white lysozyme (HEWL). The crystal structures of many different IgG antibodies that bind HEWL are known. One recognizes a discontinuous epitope on lysosome consisting of the following amino acids: H15, G16, Y20, R21, T89, N93, K96, K97, I98, S100, D101, G102, W63, R73 AND L75. Most of these amino acids are polar, and five are charged. Figure \(4\) shows the interaction of part of the Fab fragment of an antibody that binds to the HEWL epitope just mentioned (3hfm). The light chain is shown in magenta, the heavy chain in dark blue, and the antigen lysozyme in gray. The side chains of the amino acids in the epitope of HEWL are shown in sticks. Note the complete complementary of HEWL and Fab surfaces. Water is excluded from the interface. Figure \(5\) shows an interactive iCn3D model of the same HEWL:Fab complex (3hfm). Lysozyme is shown in black. Here is an external link to an interactive iCn3D model showing a detailed view of the multiple interactions (salt bridges, hydrogen bonds, pi-cation) T Cells What happens if a virus makes it into a cell? Antibodies can not bind to them anymore to prevent their entry. Something must be able to recognize a virally-infected cell and eliminate it. What about a cancer cell? Wouldn't it be nice if something could recognize a tumor cell as foreign and eliminate it before it divides too much and metastasizes? Those "something" are T cells. There are many T cells in a person, and many different kinds, including T helper cells (Th), cytotoxic lymphocytes (CTL), and even suppressor T cell. The express different subsets of proteins that differentiate them and their functions. T cells also recognize antigens but unlike B cells, these antigens can only be proteins fragments. The membrane proteins that recognize protein fragments are called T cell receptors. In addition, they don't recognize protein antigens in isolation. They must be bound to a protein on the surface of an "antigen" presenting cell (such as a macrophage or dendritic cell). The T cell receptor recognizes and binds simultaneously to the foreign protein fragment and to the self "antigen-presenting" protein on the surface of the antigen-presenting cells. The self protein which binds and presents the foreign protein fragments (peptides) is called a Major Histocompatability Complex (MHC) protein. Antigen presenting cells like macrophages and dendritic cells have MHC Class II molecules on their surface. These bind protein fragments from engulfed bacteria, for example, and present them on the surface. T cell receptors bind to the peptide:MHC II complex. All cells in the body have MHC Class I proteins on their surface. If a cell is infected with a virus, protein fragments from the virus end up bound to the MHC Class I protein on the surface. Now a T cell can bind through its T cell receptor to the peptide:MHC Class I complex. By displaying a viral protein fragment on the surface, the immune cell can recognize a virally-infected cell without getting inside of the cell where the virus is. Sompayrac describes MHC molecules as looking like a hot dog bun. In the grove of the bun lies the peptide fragment - like the hot dog. The T cell receptor recognizes both the bun and the hot dog! Figure \(6\) shows an interactive iCn3D model of a MHC Class Class I heavy chain complexed with beta-2-microglobulin with a peptide fragment of the vesicular stomatitis virus nucleoprotein (2VAA). The T-cell receptor consists of two transmembrane protein chains, alpha and beta, each containing a single variable and constant domain, followed by a transmembrane domain. Hence they are less complicated than an antibody chain. They bind through their extracellular variable domains a peptide fragment bound to a MHC Class I or Class II membrane protein in the target cell. In-depth: Generation of T-Cell Receptor Diversity We described above how an undifferentiated B cell has the potential to produce an incredible diversity of different antibodies from a starting genetic sequence. This occurs through both DNA and RNA splicing. The same processes occur with the alpha and beta chains of T-cell receptors. This is illustrated in Figure \(7\). Note that the alpha chains have no D (diversity) coding sequences. Molecular T-Cell Repertoire Analysis as Source of Prognostic and Predictive Biomarkers for Checkpoint Blockade Immunotherapy. International Journal of Molecular Sciences 21(7):2378 (2020). DOI: 10.3390/ijms21072378. License CC BY Figure \(8\) shows an interactive iCn3D model of the T-cell receptor alpha and beta chains binding to MHC Class 1 protein with a bound peptide (6rp9). The MHC protein complex consists of the histocompatibility antigen, A-2 alpha chain and β-2-microglobulin, an 11K subunit of MHC Class I proteins but not Class II MHC proteins. Bound to it is the 9 amino acid cancer/testis antigen 1 (shown in spacefill). The peptide is sandwiched between the MHC protein complex and the T-cell receptor α and β chains. The actual functional structure in vivo is actually more complicated. The T cell receptor is found within the much larger T Cell receptor complex (TRC), which contains two copies of the CD3 complex, which itself consists of γ, δ, ε and ζ chains, as shown in Figure \(9\) (A). Part A shows the variable and C domains of the α and β chains of the T-cell receptor in green and dark. The rest of the T-cell receptor complex includes two copies each of the CD3 complex, which consist of one copy of εδζ chains and one copy of εγζ chains. Figure \(9\): T cell receptor structor. Kumaresan Pappanaicken R., da Silva Thiago Aparecido, Kontoyiannis Dimitrios P. Methods of Controlling Invasive Fungal Infections Using CD8+ T Cells. Frontiers in Immunology, 8, 1939 (2018). https://www.frontiersin.org/article/...mmu.2017.01939. DOI=10.3389/fimmu.2017.01939. Creative Commons Attribution License (CC BY). As mentioned earlier, one of the functions of the MHC Class I molecules is to present peptides derived from tumor antigens to T-cells, which leads to the activation of other immune cells and hopefully destruction of the tumor cells displaying the tumor antigen. Much work has gone into the study of immune surveillance and ultimate destruction of tumor cells with the hopes of improving on our own immune response to cancer cells. In early work, T-cells that had infiltrated tumors were isolated from a patient, amplified in the lab by adding a cytokine (ex interleukin 2 - IL2), a protein growth factor released by activated immune cells, and then re-infusing the tumor-specific T cells along with IL2 back into the patients. This adoptive cell transfer (ACT) therapy led to remissions in some patients but the therapy also could be lethal. One promising type of immune therapy is chimeric antigen receptor (CAR) T cell therapy or (CAR T) in which patients are treated with modified versions of their own T-cells. T cells are removed from a cancer patient's own blood. A gene is constructed to mimic the V and C domains of the alpha and beta chain of the T-cell receptor and inserted into the patient's T-cell using a viral vector. The gene construct contains as their tumor antigen binding motif the V and C domain of an antibody gene made to recognize the tumor antigen. The receptor is hence a chimeric (formed from parts of different proteins) antigen receptor (CAR) with antibody and T cell parts. The genetically modified cells are amplified and re-infused back into the patient. Once the collected T cells have been engineered to express the antigen-specific CAR, they are "expanded" in the laboratory into the hundreds of millions. Compare the structure of the chimeric antigen receptor (CAR) in Figure XX-C with normal T-cell receptors shown in A. The CAR contains two, single-chain variable fragment (scFv) derived from combining the variable domains of the light (VL) and heavy (VH) chains of an antibody recognizing the tumor cell. These domains are connected by a linker peptide (10-25 amino acids) enriched in glycines, which confers flexibility and serines/threonines for hydrogen bonding interactions. This is attached to an FC fragment and other intracellular effector domains to create the receptor. Figure \(10\) shows the scFv structure. We'll discuss the addition cytoplasmic CD28 domain in a bit. You can imagine this whole T-cell receptor complex involved in the binding of a tumor peptide antigen presented by a MHC I transmembrane protein on a tumor cell, as illustrated in Figure \(11\) (in different colors). The entire interacting structure is called the T cell immunological synapse. Figure \(11\): T cell immunological synapse of T cell with a cancer cell. Zhao Lijun, Cao Yu J. Engineered T Cell Therapy for Cancer in the Clinic. Frontiers in Immunology, 10, 2250 (2019) https://www.frontiersin.org/article/...mmu.2019.02250. Creative Commons Attribution License (CC BY). Protection Against Autoimmune Recognition - Coreceptors How can the immune system recognize and bind to any foreign molecule but not self? The subject of immune tolerance is too specialized to include here, but there are a few features we will discuss. The MHC Class I proteins do present "self" peptides in their binding pockets. Self-proteins are also cleaved into peptides in the cell by proteasomes. However, the T cell receptor does not recognize and bind to the self-peptide fragment bound to the MHC Class 1 protein. Hence T cells do not recognize self and turn against their own cells. Once and a while they do, however, and autoimmune diseases like MS, rheumatoid arthritis, and lupus result. B cells and T cells must be activated before they can carry out their function. It is important to regulate the "on" switch. If the cells were activated without need, they might turn against self. In addition to T cell receptor complex binding to foreign peptide:MHC complexes for immune cell activation, they must also bind yet another protein on the antigen-presenting cell. In the case of T helper cells. the T cell protein CD28 must also bind the B7 protein on an antigen-presenting cell like a macrophage expressing an MHC II protein:foreign peptide complex. Hence there is one specific signal (the peptide:MHC complex binding to the T cell receptor complex) and a nonspecific signal (B7 binding CD28). Why are two signals needed for activation? Again, Sompayrac has a great analogy. A safety deposit box at a bank takes two keys, a specific key (which you have) and a "nonspecific" key (which the bank uses for all boxes) to open the box. Think of it as double security. You don't want to activate immune cells for killing unless you really need to do so. Yet other proteins are involved to ensure correct T-cell activations. We'll consider T-cells expressing either the proteins CD4 or CD8. T-cells expressing these expand after antigen stimulation (infection or immunization). It depends on the subtype of T-cell. Let's consider two here: T cells expressing the protein CD4: After initial simulation, the differentiate and proliferate in T helper cells (TH1 if they produce the cytokine interferon (IFN)-γ) and T helper type 2 (TH2) cells (if they produce the cytokine IL4). CD4 is an integral membrane protein and acts as a co-receptor for MHC Class II:peptide complex found on cells like macrophages. T-cells expressing CD8: These cells produce cytokines (IFN-γ and tumor necrosis factor (TNF)-α) or secrete protein which form pore-forming complex on foreign cells, leading to their lysis of cells such as pathogens or tumor cells. The CD8 protein has an alpha and beta subunit. They serve as co-receptors for MHC Class I:peptide complex found on tumor cells for example. Cytotoxic T-cells (a type of T-cell) express CD8. Figure \(12\) shows the multiple co-signals that are required to activate the CD4 T-cell (blue sphere), which has the T-cell receptor complex, the co-receptor CD4 and the CD28 protein. It also displays a cytokine receptor, which binds cytokines released by the antigen-presenting cells (macrophage shown in pink). This leads to the proliferation and differentiation of the activated T-cell. Sompayrac asks another interesting question. Why is antigen presentation by MHC proteins necessary at all? B cells don't really need presentation since they can bind antigen with membrane antibody molecules. Why do T cells need it? He gives different reasons for Class I and Class II presentations: Class I MHC (found on most body cells): T cells need to be able to "see" what is going on inside the cell. When virally-infected cells bind foreign peptide fragments and present them on the surface, they can be "seen" by the appropriate T cell. It's a way to get a part of the virus, for example, to the surface. They can't hide out in the cell. T cells don't need to recognize extracellular threats since antibodies from B cells can do that. Presentation is also important since viral protein fragments found outside of the cell might bind to the outer surface of a noninfected cell, targeting them for killing by the immune system. That wouldn't be good. It also helps that peptide fragments are presented on the surface. This allows parts of the protein that are buried and not exposed on the surface, which would be hidden from interaction with outside antibodies, to be used in signaling infection of the cell by a virus. MHC Class II (found on antigen-presenting cells like macrophages): Two different cells (the presenting cell and the T helper cell) must interact for a signal for immune system activation to be delivered to the body. Again it is a safety mechanism to prevent the nonspecific activation of immune cells. Also, as in the case above, since fragments are presented, more of the foreign "protein" can contribute to the signal to activate the immune system. Recognition and Response in the Innate Immune System The B and T cell part of the immune system represents the more sophisticated branch of the immune system called the adaptive immune system. It can be trained to recognize any foreign chemical/cellular species. The other branch of the immune system is the innate immune system. The system recognizes common molecular structures found all many different organisms, so in this branch, there is no need to adapt to each foreign species individually. The adaptive immune response also must be activated by cells of the innate immune system. The innate immune system recognizes common structural features in viruses and living cells like bacteria, fungi, and protozoans like amoebas. The cells of the innate system (dendritic cells, macrophages, eosinophils, etc, which we talked about as antigen-presenting calls above) have receptors called Toll-like Receptors 1-10 (TLRs) that recognize the common pathoge- associated molecular patterns (PAMPs) , which leads to binding, engulfment, signal transduction, maturation (differentiation), antigen presentation, and cytokine/chemokine release from these cells. Dendritic cells, which reside in the peripheral tissues and act as sentinels, are an example. They can bind PAMPs which include: • CHO/Lipids on bacteria surface (LPS) • mannose (CHO found in abundance on bacteria, • yeast dsRNA (from viruses) • nonmethylated CpG motifs in bacterial DNA After entering an immune cell, bacterial and viral nucleic acids are recognized by intracellular TLRs. Dendritic cells phagocytize microbial and host cells killed through programmed cell death (apoptosis). During maturation, surface protein expression is altered, allowing the cells to leave the peripheral tissue and migrate to the lymph nodes where they activate T cells through the antigen presentation methods described above. They also control lymphocyte movement through the release of chemokines. Figure \(13\) shows the TLR family, their binding signals, and intracellular adapter proteins used to transmit signals into the cell. Figure \(14\) shows an interactive iCn3D model of the mouse Toll-like receptor 3 ectodomain (that sticks out into the cytoplasmic space from an internal organelle) complexed with double-stranded RNA (3CIY). Double-stranded RNA is found in the life cycle of many viruses so it makes great sense for evolution to create a binding protein to recognize this common structure (PAMP). The TLR3 ectodomains (ECDs) form dimers when the dsRNA is at least 40-50 nucleotides long. The dsRNA is shown in spacefill (cyan and magenta). Note the extensive glycosylation (colored cubes) in the structure. The protein looks like a "horseshoe-shaped solenoid " with lots of beta structure. It has 23 leucine-rich repeats (LRRs) with some conserved asparagines allowing for extensive hydrogen-bonding. One face appears to be free of carbohydrate residues and may be important in dimerization and function. TLRS and mRNA vaccines Messenger RNA vaccines against the SARS-Cov2 spike protein have probably saved up to 20 million lives in the first year of the COVID-19 pandemic. The development of mRNA vaccines will go down as a great scientific achievement that required decades of fundamental and applied research by many scientists. Vaccines usually are composed of target proteins from a virus, for example. Instead of delivering an actual protein, whose actual development and mass production takes years, why not use mRNA that encodes a viral protein or fragments of it? The idea has been around for a long time. The problem is that RNAs, with their 2'-OH on the ribose ring, are very labile and degrade easily. In addition, injecting RNA into a patient causes a significant immune response to the RNA. We mount immune responses to foreign viral RNA through our TLR receptors (TL3, 7 and 8) but what is needed is an immune response to the protein made from the injected mRNA, not to the RNA. Yet we don't make an immune response against our own RNA. Why? There are two major problems that had to be solved (and a host of others as well) to make mRNA vaccines, the problem of stability and our immune response against them. A hint comes from the observation that TLRs recognize non- or undermethylated DNA found in bacteria. Methylated CpG motifs in DNA to do not stimulate an immune response. Katalin Karikó,Michael Buckstein, Houping Ni and Drew Weissman reported in 2005 that the incorporation of methylated (m) and modified nucleosides m5C, m6A, m5U, s2U, and pseudouridine ablated the immune response to the RNA. This opened the door to mRNA vaccines. The paper was rejected by Nature and Science but published in Immunity (Vol. 23, 165–175, August, 2005. DOI 10.1016/j.immuni.2005.06.008). The last line from the paper was truly prophetic: "Insights gained from this study could advance our understanding of autoimmune diseases where nucleic acids play a prominent role in the pathogenesis, determine a role for nucleoside modifications in viral RNA, and give future directions into the design of therapeutic RNAs". Katalin Karikó and Drew Weissman were awarded the 2021 Lasker–DeBakey Clinical Medical Research Award (often a prelude to the Nobel Prize) for their fundamental research that has saved so many of us. They were awarded the Nobel Prize in Medicine in 2023.  See Chapter 9.1 for more details about the role of pseudouridine in mRNA vaccines. Inflammasome Think of the things you would want your immune system to protect you from. Of course, there are the pathogens like viruses, bacteria, and fungi. And of course, you want to be protected from yourself in that you don't want to activate your immune system with self-antigens. But what about "non-biological" molecules like silica or asbestos whose presence might be deleterious? What about normal biomolecules (proteins, nucleic acids) that suddenly find themselves in the wrong cellular location due to cell death by necrosis or physical injury? In the previous section, we discussed how innate system immune cells (dendritic cells, macrophages, eosinophils, etc) have receptors that recognize common pathogen-associated molecular patterns (PAMPs) such as lipopolysaccharides (LPS) on the surface of bacteria, mannose on bacteria and yeast, flagellin from bacterial flagella, dsRNA (from viruses) and nonmethylated CpG motifs in bacterial DNA. These antigens are recognized by pattern recognition receptors (PRRs) - specifically the Toll-like Receptors (TLRs) 1-10. These include plasma membrane TLRs (TL4 for LPS, TL5 for flagellin, TLR 1, 2 and 6 for membrane and wall components of fungi and bacteria) and intracellular endosomal TLRs (TLR3 for dsRNA, TLR 7 and 8 for ssRNA and TLR9 for dsDNA) Damage-associated molecular patterns (DAMPs) are typically found on molecules released from the cell or intracellular compartments on cellular damage (hence the name DAMP). Many are nuclear or cytoplasmic proteins released from the cells. These would now find themselves in a more oxidizing environment which would further change their properties. Common DAMP proteins include heat shock proteins, histones and high mobility group proteins (both nuclear), and cytoskeletal proteins. Think what non-protein molecules might be released from damaged cells that might pose problems. Here are some other common non-protein DAMPS: ATP, uric acid, heparin sulfate, DNA and cholesterol crystals. In the wrong location, these can be considered danger signals. If TLRs recognize PAMPs, what recognizes DAMPs? They are recognized by another type of intracellular pattern recognition receptor (PRR) called NOD (Nucleotide binding Oligomerization Domain (NOD)- Like Receptors or NLRs. NLRs also recognize PAMPs. The proteins also are named as the Nucleotide-binding domain (NBD) and Leucine-Rich repeat (LRR)–containing proteins (NLR)s. This family of proteins participates in the formation of a large protein structure called the inflammasome. (Sorry about the multiple abbreviations and naming systems!) As both PAMPs and DAMPs pose dangers, it would make sense that once they recognize their cognate PRRs (TLRs and NLRs, respectively), pathways leading from the occupied receptors might converge in a common effector system for the release of inflammatory cytokines from immune cells. Given that uncontrolled immune effector release from cells in an inflammatory response might be dangerous, it would be sometimes helpful to require two signals to trigger cytokine release from the cell. We've seen this two-signal requirement for the activation of T cells. Two such inflammatory cytokines are Interleukin 1-beta (IL 1-b) and IL-18. Activation of TLRs by a PAMP leads to activation of a potent immune cell transcription factor, NF-kbeta, which leads to transcription of the gene for the precursor of the cytokine, pro-interleukin 1-beta. Without a specific proteolytic cleavage, the active cytokine will not be released from the cell. The protease required for this cleavage is activated by a signal arising when a DAMP activates a NLR, which then through a sequence of interactions leads to the proteolytic activation of another inactive protease, procaspase 1, on a large a multi-protein complex called the inflammasome. (In later chapters we will see other such protein complexes with targeted activities - including the spliceosome, which splices RNA to produce mRNA and the proteasome which conducts controlled intracellular proteolysis). The activated inflammasome activates procaspase to produce the active protein caspase (a cysteine-aspartic protease). The convergence of the signals from the PAMP activation of a TLR and DAMP activation of a NLD at the inflammasome is shown in Figure \(15\). The active cytokine interleukin 1-beta helps recruit innate immune cells to the site of infection. It also affects the activity of immune cells in the adaptive immune response (T and B cels). Active IL-18 leads to the increase of another cytokine, interferon-gamma and it also increases the activity of T cells that kill other cells. The focus of this chapter is on binding interaction and their biological consequences. From that perspective, this section will address • the structure and activity of caspases, which activate the pro-cytokine prointerleukin 1 beta, • the structure and ligands for the NLRs, the structure and properties of the inflammasome, and finally • how "danger" molecules such as ATP and crystals (cholesterol, silica) activate the inflammasome. Unfortunately, there are many proteins involved with crazy acronyms for names. These proteins have multiple domains and many of the proteins often have multiple names. Sorry in advance! Caspases Caspases (Cys-asp-proteases), not to be confused with Cas9 (CRISPR associated protein 9, an RNA-guided DNA endonuclease) is a protease which when active can lead to cell death, or in a less austere fashion initiate the inflammatory response (sometimes good, often bad or even fatal). They have an active site nucleophilic Cys and cleave peptide bonds after an Asp in target proteins. All caspases (13 in humans) have an N-terminal pro-domain followed by large and small protease catalytic domain subunits. As with other proteases, it is found as an inactive zymogen. Why is this important? To become activated they are recruited to a scaffolding protein where they are activated by removal of the N-terminal domain of the zymogen and then a second cut between the large and small catalytic subunits. The enzyme that does this is caspase itself in an autocatalytic step. . There are 3 kinds of caspases, two of which are involved in programmed cell death. We'll discuss the inflammatory cytokine processing Caspase-1. Once activated, the initiators activate other effector (executioner) caspases in the cell) . Caspase 1 is activated by the inflammasome. Two major domains are found in Caspase 1, the caspase recruitment domain (CARD) which mediate self-interaction with scaffold and adaptor proteins in the inflammasome for activation, and a proteolytic catalytic domain, as shown in Figure \(16\). All domain structures in the section were obtained using Conserved Domains from the NCBI (https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi) or the Simple Modular Architecture Research Tool (SMART) at the EMBL ( http://smart.embl-heidelberg.de/smart/set_mode.cgi?NORMAL=1 ). Uniprot was used for protein (FASTA) sequences (http://www.uniprot.org/uniprot/). We will see the CARD domain often. NOD-like receptor proteins (NLRPs) The NOD-like receptor proteins (NLRPs) are a family of proteins with similar domain structures. The structures and abbreviations used for the molecular players in inflammasome activation are very complicated and confusing. Different programs show different domains, which adds to the complexity. We will attempt to reduce the confusion by just showing domain structure diagrams, even if some show different domains for the same protein. Remember domains are calculated from structure so different algorithms using different databases might return different domain structures. Table \(2\) belows shows the domain structure for NLRPs. NLRP1,2,3 NLRP1 (NALP1) NLRP3 (NALP3) NLRP4 NAIP1 NAIP2 Table \(2\): Domain structures of the NLRPs Another protein in the NLR family is NAIP (neuronal apoptosis inhibitor protein). The domain structure for NAIP1 is shown in Table \(2\) above. In contrast to the other NLR for which specific ligands have not yet been found, several NAIPs have been shown to bind specific PAMPs. NAIP1 binds the needle protein CprI from C.violaceum which starts to drive the assembly of the NLRC4 inflammasome. NAIP2 binds the inner rod protein of the bacterial type III secretion system (which for Salmonella typhimurium is the protein PrgJ). NAIP5 and NAIP6 bind bacterial flagellin (which for Salmonella typhimurium is the protein FliC). AAA in the second domain representation is ATP-associated activities in the cell (otherwise denoted as the NACHT domain in the top representation). NAIP2 interacts with another adapter NLR family protein, NLRC4 (NLR family CARD domain-containing protein), to form the inflammasome. The domain structure of NAIP2 is shown in Figure \(\PageIndex{x}\) below: Note that many of these proteins share common domains: • Pyrin-NALP - Pyrin domains on different proteins self-associate through inter-protein Pyrin:Pryin interactions • NACHT - This domain contains about 300-400 amino acids and can bind ATP and may cleave it (i.e. act as an ATPase) • LRR - for Leucine Rich Repeat. These 20-30 amino acid repeats may occur up to 45 times in a given protein. They fold into an arc shape and seem to facilitate protein:protein interactions. On the concave side of the arc they have a parallel beta sheet while on the convex side they have an alpha helix. They also appear to be involved in the binding of PAMPS and DAMPs; • CARD - for caspase activation and recruitment. CARD domains on different proteins self-associate through inter-protein CARD:CARD interactions; • BIR - Baculoviral inhibition of apoptosis protein repeat; • ASC - Apoptosis-associated speck-like protein containing a CARD Adapter domain, allowing it to interact with other proteins with a CARD domain. ASC Adaptor Protein Small adapter proteins like ASC with a CARD domain mediate binding of caspases in the apoptosome (involved in apoptosis or programmed cell death) and in the inflammasome. This smaller protein has two domains, a pyrin domain and a CARD domain as shown in Figure \(17\). It is required for the recruitment of caspase-1 to some inflammasomes (for example, ones that contain NLRP2 and NLRP3 The Active Inflammasome The active inflammasome, in general, consists of three different kinds of proteins, some present in multiple copies: NLRPs, adapter proteins like ASC, and procaspases. They may also contain additional recruitment and ligand sensor proteins. We'll discuss two types using different NLRPs, the NLRP4 and NLRP3 inflammasome. NLRP4 Inflammasome Some of the best structures (obtained by cryomicroscopy) are for the NAIP2:NLRP4 inflammasome. Figure \(18\) shows part of the complex consisting of 11 NLRP4 subunits arranged in a large ring. The actual biological complex has 1 NAIP2 subunit and 10 NLRP4s. Figure \(19\) shows an interactive iCn3D model of the activated NAIP2-NLRC4 inflammasome (3JBL)) How does this structure arise? Presumably, it would not exist in the absence of a PAMP or DAMP in order to minimize immune-mediated inflammatory damage. Data suggests that the bacterial protein PrgJ (denoted PrgX in the figure below) binds to its receptor, NAIP2, altering its conformation as shown in Figure \(20\). This binary complex presents an asymmetric electrostatic surface which allows a loose association with NLRP4, which leads, after a conformational change, to a tighter binding interaction. Nine more NLRP4s bind in a similar fashion to form the 11-subunit ring structure. The complementary electrostatic interactions between two of the many NLRC4 subunit monomers in the NLRP4 inflammasome are depicted in Figure \(21\). The top right panel shows two of the NLRC 4monomers (of the 12 in the Jsmol model) bound to each other, with the concave inner face of the A subunit interacting with the convex outer face of the B subunit. For simplicity, only the interactions at the top of the dimers are highlighted. The other panels show the electrostatic potential surface (red indicating negative and blue positive) for each monomer on a sliding scale of -5 to +5 (images created using the PDB2PQR Server and Pymol). The depicted negative (red) electrostatic potential outer surface on one NLRC4 monomer that is complementary to the positive (inner) surface on the other NLRC4 subunit are outlined in each panel in red or blue ellipses, respectively. The curved tertiary structure of the proteins and the opposing electrostatic surface potentials of opposite faces commit the subunits to form a large ringed 12-mer core of the nucleosome. Note that this assembled ring brings together the CARD (caspase recruitment domain, yellow circles/spheres) which can interact with the CARD domain of the procaspase protein through CARD:CARD inter-protein interactions (think of a stack of playing cards all stuck together in a deck of cards) as shown in Figure \(22\). Once assembled, proximal procaspases autocatalytically convert procaspase 1 into active caspase 1, which can activate, by proteolysis, the cytokines interleukin 1 beta and interleukin 18 to form active cytokines which are released from the cell. Remember that the procytokines are present only if their genes have been transcribed following activation of the transcription factor NF-kappa beta through PAMP binding to a TLR. NLRP3 Inflammasomes Figure \(23\) shows an interactive iCn3D model of the NLRP3 double-ring cage, 6-fold (12-mer) (7LFH) The full-length mouse NLRP3 consists of 12- to 16-mer organized in a double-ring cage. It is held together by interactions between the leucine-rich repeats (LRR) domains. The pyrin domains are shielded by the structure, so they will not be activated without appropriate signals. The complex is also localized to the membrane. In contrast to NLRP4 inflammasomes, which require specific PAMPs/DAMPs for activation, NLRP3 inflammasomes seem to be activated by cellular distress as well as cell exposure to pathogens. It is one of the main responders to a variety of microbial infections. Given the large number of microbes that lead to NLRP3 inflammasome activation, it has been suggested that the actual signal that triggers NLRP3 is indirect. One such indirect signal is K+ ion levels in cells. In normal cells, K+ ions are higher in the cytoplasm than in the outside of the cell. Potassium ion decreases in cells caused by efflux can activate NLRP3 inflammasomes. Other conditions include the rupture of lysosomes (perhaps associated with the cellular uptake of particles like silica, uric acid, cholesterol crystals, and other "nanoparticles"), altered mitochondrial metabolism (which can lead to reactive oxygen species within the cell), etc. Obviously, all of these danger triggers don't bind to NLPR3 but somehow lead to downstream activation of it. NLRP3 hence probably works by being a general sensor for cell stress. Inappropriate and chronic activation of inflammation has been associated with many disease such as cancer, cardiovascular disease, diabetes and autoimmune diseases. Given the multiple types of signals that can activate the NLRP3 inflammasome, this complex is the focus for active drug development to find inhibitors that would stop undesired inflammation. These inflammasomes are found in granulocytes, monocytes (macrophages), megakaryocytes, and dendritic cells. Activated NLRP3 recruits the ASC Adaptor Protein, which leads to recruitment and activation of procaspase 1. NLRP3 has a pyrin, NACHT, and LRR domain. ASC has a pyrin and CARD domain. Active LRP3 can then recruit ASC through pyrin:pyrin inter-protein domain interactions. This then allows the CARD domain of bound ASC to recruit procaspase through CARD:CARD interactions (remember that procaspase has a CARD domain as well), forming the active NLRP3 inflammasome. An added feature of NLRP3 inflammasome activation occurs when the transcription factor NFkb, which is activated by PAMPs (signal 1), leads to the transcription of both the procytokines (IL-1 beta and IL 18) and of NLRP3 itself. Hence two signals are again needed: Signal 1 The first signals are the bacterial and viral (influenza virus, poliovirus, enterovirus, rhinovirus, human respiratory syncytial virus, etc) PAMPs, which bind to TLRs and lead to the activation of the NFkb transcription factor. This activates not only the transcription of pro-interleukin 1-beat and interleukin 18, but also to the transcription of the NLRP3 sensor itself. Signal 2 Signal 2 is delivered by PAMPs and DAMPs indirectly to the sensor NLRP3. This leads to the assembly of the inflammasome. These DAMPs appear to prime the activation of NLRP3 protein and subsequent formation of the active NLRP3 inflammasome. But what activates NLR3P3? After many studies, it became clear that the typical bacterial ligands that would activate TLRs and perhaps NLRs only prime NLRP3 for activation. They don't bind to it directly. Extracellular ATP is a major activator of NLRP3. Nanoparticles are known to release ATP as well. Most studies show that K+ efflux from the cell is an early signal and that the NEK7, a protein that phosphorylates other proteins, binds to NLRP3 after potassium ion efflux and activates it. Removing NEK7 stopped NLRP3 but not NLRP4 inflammasome activation. Although NLRP3 bound to NEK7 through the NEK7 catalytic domain, the activity of the catalytic domain of NEK7 was not needed. What leads to K+ efflux? Let's back up to possiblde upstream events that could lead to efflux and try to find a link to ATP. The background for some of this material will be explored in future chapters. The following steps occur as shown in the figure and information below: - solids such as silica, cholesterol crystals, uric acid crystals, and even aggregated proteins such as prions can be engulfed by monocytes/macrophages (much as they engulf bacteria as part of their immune function) in a process called phagocytosis. The particles are enveloped in plasma bilayer-derived membrane which buds off into the cell. This vesicle merges with a lysosome which gets damaged in the process. They then release ATP into the cytoplasm; - cytoplasmic ATP can then move outside of the cell through the glycoprotein membrane channel called pannexin 1; - extracellular ATP can bind to another membrane protein called the P2X7 purinoceptor. This protein now becomes a cation channel which allows K+ efflux since the ion has a higher concentration inside the cell than outside, as illustrated in Figure \(24\). The extracellular ATP "gates" open the P2X7 cation channel. The pore-forming toxin nigericin from Streptomyces hygroscopicus also leads to potassium ion efflux. Likewise, pore-forming proteins from S. aureus (hemolysins) lead to potassium ion efflux and activation of the NLRP3 inflammasome. We will discuss membrane protein in great detail in a latter chapter. Other signals also activate the NLRP3 inflammasome. These include mitochondrial damage and the release of reactive oxygen species.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/05%3A_Protein_Function/5.04%3A_Complementary_Interactions_between_Proteins_and_Ligands_-_The_Immune_System_and_Immunoglo.txt

Search Fundamentals of Biochemistry 9/21/21: The material below is derived from (shortened and summarized): Overview of the mechanism of cytoskeletal motors based on structure. Kato et al. Biophys Rev. 2018 Apr; 10(2): 571–581. Published online 2017 Dec 12. doi: 10.1007/s12551-017-0368-1. Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0). Muscle Contraction The biochemistry of muscle structure and contraction involves many proteins, including myosin heavy and light chains, actin, three types of troponins, tropomyosin, and other regulatory proteins. These form complexes which engage in cyclic conformation changes leading to muscle contraction in a process that requires chemical energy in the form of ATP hydrolysis. In the process, chemical energy is converted to mechanical energy. Those who view biochemistry through a more chemical lens might find the biology somewhat confusing. We'll provide a short introduction before looking at the proteins in more detail. First let's start with a simplified version of the structure of myosin and actin to the basic repeating structural unit of the muscle, the sarcomere, which is shown in panel A of Figure \(1\). Myosin forms a function dimer of two intertwined heavy chains (blue), each associated with two different light chains. These are named the essential light chain (red) and the regulatory light chain (yellow). Overall the functional dimer of myosin is a hetero 6-mer. The heavy chain of myosin consists of two major domains and other regions as shown in panel A of Figure \(1\). It contains a • motor (head) domain (blue ellipse), which binds and cleaves ATP (i.e. it is an ATPase) and which also binds actin; • an alpha-helical tail domain that binds the tail domain of another myosin heavy chain to form the functional a coiled-coil structure in the hetero 6-mer. • an α-helical lever arm, which binds the two light chains. In the muscle sarcomere, many hetero 6-mers aggregate through their coiled-coil tails to form the large blue structure shown in panel B of Figure \(1\). The protruding heads that radiate away from the filament axis interact with actin filaments. This is shown in panel B of Figure \(1\). The actin filaments are shown as two intertwined actin strands (small gray circles). Note also that in panel B, the myosin thick filaments appear to be bipolar as their motor head domain are pointed in opposite direction (antiparallel) along the center of the thick filament that has no heads in the center. The central region without motor domains has aggregated hetero 6-mers pointed in opposite directions. However, the rest are packed in a parallel fashion and extend the rest of the way. The head domain of the myosin heavy chain can be removed, along with the associated light chains, from the rest of the using selective proteolysis to form the S1 (head), or S1 and part of the myosin light chain (called heavy meromyosin -HMM). This has enabled studies of these molecules under simpler conditions. The myosin heavy chain fragments are shown in Figure \(2\). Going in the direction from the rod domain to the motor domain, the myosin heavy chain has a long alpha-helical lever arm which binds the two myosin light chains (the regulatory light chain (yellow above) and the essential light chain (green above). This is followed by the converter domain and finally the motor domain, which binds ATP. The actin thin filament consists of two intertwined antiparallel strands of many G-actin (globular) monomers (1atn, molecular weight of 42K), which self-assemble in a process requiring ATP hydrolysis, to form the F (filamentous) actin structure. The structure of G-actin and the overall arrangement of the asymmetric monomers in the F filament are shown in Figure \(3\). Figure \(3\): The structure of monomeric actin in the F-actin thin filament. https://www.mechanobio.info/cytoskel...ilaments-grow/. Creative Commons Attribution-NonCommercial 4.0 International License. It would be nice if the structure of the sarcomere were as simple as illustrated in Figure \(1\). Many more proteins are involved which regulate its contraction. Let's add some more proteins (troponin subunits T, C and I as well as tropomyosin) to get a deeper understanding of the structure as shown in the cartoon representation shown in Figure \(4\). Figure \(4\): Modified from de Tombe PP et al. Global Cardiology Science and Practice 2016:21 http://dx.doi.org/10.21542/gcsp.2016.21. Creative Commons Attribution 4.0 International License. The trimeric troponin complex consists of • troponin-C which binds Ca2+ ions • troponin-I which inhibits contraction • troponsin-T which attaches the trimeric troponin to the thin actin filament. Ca2+ ions released from stores in the sarcoplasmic reticulum (the muscle equivalent of the endoplasmic reticulum) on neural stimulation of muscle cells by the neurotransmitter acetylcholine initiates sarcomere contraction. The Ca2+ ions binds to troponin-C (dark red) which leads to conformational changes that rotates tropomyosin, which occupies the groove between the two actin chains in the thin filament. This exposes a binding site on actin for the motor domain heads on the myosin thick filament. An interactive iCn3D model of the Actin-Myosin-Tropomyosin ADP complex (6X5Z) is shown in Figure \(5\). - actin magenta, myosin cyan, tropomyosin gray, ADP and Mg spacefill Figure \(5\): Actin-Myosin-Tropomyosin ADP complex (6X5Z). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...Q8KSdXLhjtNzt6 Now let's show an even larger cartoon view, which shows what is observed under a microscope. Figure \(6\) shows a cartoon representation of a sarcomere. • the myosin thick filament is shown as a yellow rectangle; • the actin thin filament is shown as a orange rectangle with tropomysin shown as a red line and the trimeric troponin complex shown in blue The M line provides anchors for the thick filament while the Z disc attaches to and anchors the thin filaments. The myosin filaments are crosslinked and anchored at the end M band, which contains a series of proteins including myomesin, creatine kinase and M-protein. The Z disc/line contains many proteins including alpha-actinin (predominant), actin, many other proteins, and the end of titin. This cartoon shows the features shown in an electron micrograph of the vertebrate striated muscle sarcomere, which extends between the two Z-lines (or disks), as shown in Figure \(7\). A single bidirectional myosin thick filament is shown as an orangish rectangle, while single thin actin filaments are shown in blue (attached to the Z disc). Figure \(7\): Electron micrograph of the sarcomere. John Squire. Int. J. Mol. Sci. 2019, 20, 5715; doi:10.3390/ijms20225715. Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Again note that the rod domains of the thick filaments are packed in an antiparallel fashion (ie. pointing in opposite directions) in the middle of the sarcomere near the end M line band, so in this region that are no myosin heads. However, the rest are packed in a parallel fashion and extend through the rest of the A-band. These myosin heavy chains are packed in a somewhat (quasi) symmetrical fashion, which repeats. Looking down the myosin fiber, the motor domains radiate outward with a 3-fold rotational symmetry and are staggered at 1200. Now let's visualize the contraction of the sarcomere since it's hard to display in a single figure. Imagine it contracting somewhat like an accordion. The M line, devoid of myosin head groups and where the myosin heavy chains extend in different directions toward the left and right Z-lines, remains stationary. Since the myosin chains in the thick filament are attached to it, they remain stationary as well. Imagine the left and right Z line (disk), which anchor the actin thin filaments, moving towards the center line as the actin thin filaments slide along the stationary myosin thick filaments. This contracts the sarcomere. The sliding is powered, of course, by ATP hydrolysis in the motor heads of myosin in a series of steps involving attachment, detachment, and reattachment of the flexible myosin head domain further along with the actin thin filament. Especially note the elongated protein called titin shown in cartoon form in Figure \(6\). This links the ends of the myosin filament and the Z-line. Titin is the largest single polypeptide known. Its molecular weight is close to 4 million with over 34,000 amino acids. It is highly elastic. It helps balance the forces on each side of the sarcomere. Its repetitive structure is shown in Figure \(8\). A series of structured Ig domains, comprised mostly of beta-strands are separated by flexible hinge structures which allow rearrangements and stretching. The chemical cycle of adenine nucleotides consists of discrete steps which repeat: binding of ATP, hydrolysis, and dissociation of ADP and Pi. These are linked to a mechanical cycle involving attachment and detachment of myosin heads with the actin thin filaments and sliding of the thin filaments with respect to the thick myosin filaments. Figure \(9\) shows the linked chemical and mechanical states in the cycle detailing how force is generated. The myosin S1 head is shown at different angles. Starting with the bottom left in the ATP bound form, the head is bent backward to the left toward the rod domain. On hydrolysis of bound ATP, the head rotates into a vertical "cocked" position (top left). In this orientation, the S1 head binds weakly to actin (top right). A power stroke ensues as ADP and Pi dissociate from the myosin S1 head, reorienting the head to an orientation similar to the ATP-bound form. As it is a cycle, you can start any place along it to follow it. Figure \(10\) shows a "linear" presentation of the cycle which starts with Ca2+ binding to troponin C. This exposes the myosin binding site on actin and allows the actin binding to the myosin S1 head to form the weakly bound M.ADP.Pi state. which is shown in the upper left of Figure \(9\). In this diagram: 1. In the resting (extended) sarcomere state, ADP and Pi are bound to the motor head of myosin. On neural stimulation of the muscle, Ca2+ ions are released from the sarcoplasmic reticulum and bind troponin-C. This leads to the movement of tropomyosin, which exposes the myosin motor head binding site on actin. 2. On binding of actin, the power stroke results, as the actin filaments slide in contraction, leading to the dissociation of ADP and Pi. 3. ATP then binds to the motor head, leading to the detachment of the myosin heads from the actin filament and ATP hydrolysis 4. The motor head cocks to a position where it can interact again with actin filaments and start the process again. More states also exist including an ATP-bound state that is detached from actin (post rigor), an ADP-bound state (M·ADP) without Pi, and a nucleotide-free myosin state that is bound to actin (rigor). Pi dissociation is the key step that leads to the movement of the lever arm when bound to actin, resulting in the power stroke. ADP dissociates slowly after this so the M·ADP lasts longer. Figure \(11\) shows one last alternative figure with some of the states missing from the diagrams above and with corresponding structural features. To make matters more complicated, there are 35 different classes of myosin (MW 227K) and it is involved in functions (vesicle transport for example) other than muscle contraction. We'll explore the structure of the S1 head more closely when we discuss another motor protein, kinesin. No two-dimensional illustration of the process of sarcomere/muscle contraction can rival the insight gained by viewing a quality simulation of the molecular process. The video found below offers such a simulation. We don't routinely incorporate public videos into this book, in part since the links might not last and they often come with advertisements. This video, which is not narrated but richly annotated so clearly illustrates the ideas presented above, that we choose to incorporate it. Here is a link to another video: Sliding movement of actin filaments The cytoskeleton: An Overview Before we explore two other motor proteins, kinesin and dynein, whose structures are very similar to myosin, we will offer some background on the proteins that make up the interior skeleton of the cell. Once again, the structure of the cytoskeleton is a bit amorphous and constantly changing. It structurally supports the inner organelles and other components of the cell. It facilitates the movement of these structures within the cell. It must dramatically rearrange when cells divide or when they move. More importantly for us, they interact with the motor proteins kinesin and dynein. If we don't know a bit about them, our understanding of motor proteins would lack context. Much of the following overview comes, unless otherwise noted, from a BioLibre introductory biology text by Easlon. The cytoskeleton is a network of different protein fibers that provides many functions: it maintains or changes the shape of the cell; it secures some organelles in specific positions; it enables movement of cytoplasm and vesicles within the cell; and it enables the cell to move in response to stimuli. There are three types of fibers within the cytoskeleton: microfilaments, intermediate filaments, and microtubules, as shown in Figure \(12\). Some of the cytoskeletal fibers work in conjunction with molecular motors, which move along the fibers within the cell to carry out a diverse set of functions. There are two main families of cytoskeletally-associated molecular motors: dyneins and kinesins. How can the cell purposely move and control the location of materials between these organelles? More specifically, how can a eukaryotic cell transport compounds from their place of origin (in most cases the cytoplasm) to where they are needed (perhaps the nucleus, the mitochondria, or the cell surface)? One possible solution is for the cell to create a network that can connect all the different parts of the cell together. This network could be used not only as a scaffold to hold components in place but also as a reference for direction. For example, we can use a map to determine the direction we need to travel and the roads to connect and travel from home to campus. Likewise, an interconnecting network inside the cell can be used to direct and move compounds from one location to a final destination. Some of the required characteristics of this network are listed below. Can you add to this list? Here are some characteristics of the cytoskeleton: • The network needs to be extensive, and connect every area of the cell. • The network needs to be flexible, able to change and adapt as the cell grows larger, divides into two cells, or physically moves from one environment to another. • The network needs to be strong, and able to hold up to mechanical pressure from inside or outside of the cell. • The network needs to be composed of different fibers and each of these fibers needs to be for a specific connection in the cell. For example, certain fibers might be involved in holding organelles in place, and other fibers would be involved in connecting two different organelles. • The fibers need to have directionality (or polarity), meaning they need to have a defined starting point and a defined end to help direct movement from one location to another. • The fibers need to work with proteins that can convert chemical energy into kinetic energy, to actively transport compounds along the fibers. Figure \(13\) is a bovine pulmonary artery endothelial cells that as been stained to show thin filaments composed of actin (red), and microtubules (green), composed of tubulin. Figure \(13\): Actin and microtubules in endothelial cells. https://sitn.hms.harvard.edu/art/201...tin-four-ways/. Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Microfilaments Microfilaments are cytoskeleton fibers composed of actin subunits. Actin is the most abundant protein in the cytosol of eukaryotic cells and comprises 20% of total cellular protein by weight in muscle cells. The actin amino acid sequence is highly conserved in eukaryotic cells, meaning that the protein amino acid sequence, and therefore its final 3-D shape, has changed little over the course of evolution, maintaining more than 80% similarity between algae and humans. We have already seen actin filaments as part of the sarcomere. Actin can be present as either a free monomer called G-actin (globular) or as part of a polymer microfilament called F-actin ("F" for filamentous).  Actin must be bound to ATP to assemble into its filamentous form and maintain the structural integrity of the filament. (Note that we discussed above ATP binding to the myosin head and its subsequent hydrolysis during the chemomechanical sarcomere contraction, but we didn't discuss the role of ATP in F-actin formation.) Recent Updates: Actin - 7/16/23 G-actin has 2 outer subdomains (1 and 2) and two inner ones (3 and 4).  Between the outer and inner subdomains are a nucleotide-binding cleft and an opposing hydrophobic cleft involved in actin/actin and actin/actin-binding protein interactions necessary for filament formation. An interactive iCn3D model of the rabbit F-Actin-ADP complex (2ZWG) is shown in Figure \(14\) below. Figure \(14\): Rabbit F-Actin-ADP complex (2ZWG). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...n4poXDwJuqmASA The large cleft between subdomains 2 (yellow) and 4 (cyan) is the nucleotide-binding cleft. The hydrophobic cleft is between subdomain 1 (salmon) and 3 (magenta).  Many of the hydrophobic amino acid side chains in hydrophobic cleft are shown as gray sticks. Note that this form of Actin-ADP is flat on rotation., Figure \(15\) below shows an animation of the transition from G-Actin-ATP/Ca2+ complex (magenta) to F-Actin-ADP/Ca2+ complex (cyan) Figure \(15\) below shows an animation of the transition from G-Actin-ATP/Ca2+ complex (magenta) to F-Actin-ADP/Ca2+ complex (cyan) Note the conformational changes and the changes in the cleft on the conversion of the G-ATP complex to the F-ADP one.  Although the G in G-actin is for "globular", it is not really spherically.  Both G- and F-actin are flat.  Imagine the proteins as resembling the letter "H" but with the top "cleft" of the H (representing the nucleotide-binding domain) bigger than the bottom "cleft" (representing the hydrophobic cleft).  In G actin, one of the long sides of the letter "H" is tilted compared to the other, while in F-actin, both sides are parallel, as shown in Figure \(16\) below.  Hence F-actin is flatter than G-actin. Figure \(16\): cartoon showing structural changes in G- to F-Actin transition (after https://www.science.org/doi/10.1126/science.adg6812) The actin filament itself has structural polarity implying that it has two distinct ends to the filament. These ends are called the "(-)" end and the "(+)" end. At the "(+)" or "barbed" end, actin.ATP subunits are added to the elongating filament.  At the "(-)" or "pointed" end, actin.ADP subunits dissociate from the filament. Actin hence is an ATPase.  G-actin is a slow ATPase while F-actin is a fast ATPase due to a conformational change in the protein forming a flatter subunit conformation with a more catalytically productive active site. This process of assembly and disassembly is controlled by the ATP to ADP ratio in the cytoplasm. The general structure of bare F-actin from Oryctolagus cuniculus is shown in an interactive iCn3D model Figure \(17\). The bottom strand (repeating subunits shown in cyan and light cyan) winds around the top strand (subunits shown in magenta and light magenta). They twist around each other in a right-handed sense. A complete turn is formed after in a 13-mer. You can see the twist in the figure below which shows only 4 monomers in each strand. Figure \(17\): The structure of bare F Actin (6bno). (Copyright; author via source). Click the image for a popup (slow load) or use this external link: https://structure.ncbi.nlm.nih.gov/i...QcXoreeyVaHhB7 Figure \(18\) below shows the free barbed end (magneta, 8F8R) and the free pointed end (cyan, 8F8S) of actin. Figure \(18\): Free barbed end (magneta, 8F8R) and  the free pointed end (cyan, 8F8S) of actin Proteins interacting at the barbed and pointed ends include CapZ and tropomodulin, respectively. The CapZ binds to the F-actin flat "H" form with the CapZ undergoing significant conformational changes and minor one for the actin end. At the free pointed end, the monomers are more like G-actin in the twisted "H" form. On binding of tropomodulin, the second to the end subunit forms a more F actin conformation. In the process of filament "treadmilling" (polymerization), actin-ATP binds to the + (barbed) end of F-actin.  The bound ATP is quickly hydrolyzed with the Pi slowly released.  Actin-ADP dissociates from the - (pointed_ end.  Free actin can bind ATP and be ready to add to the + (barbed) end.  Proteins like forming can catalyze the addition of actin monomers to the + (barbed) end while proteins like cofillin can promote dissociation from the - (pointed) end.  Also both ends can be capped to stop actin addition at the + (barbed) end (by CapZ)  or removal from the - (pointed) end (by tropomodulin). As with myosin-actin interactions, hydrolysis of ATP and release of Pi are key steps. Multiple nucleotide states of actin include A.ATP, A.ADP.Pi, and A.ADP. As the polymer form, a gradient of these nucleotide states exist. On fiber formation, it appears that the key general base, His161, is positioned closer to the labile terminal phosphate on ATP, enabling hydrolysis. Actin participates in many cellular processes, including muscle contraction, cell motility, cytokinesis during cell division, vesicle, and organelle movement, and the maintenance of cell shape. Actin filaments serve as a track for the movement of a family of motor proteins called myosins discussed above Intermediate filaments Intermediate filaments are made of several strands of fibrous proteins that are wound together as shown in Figure \(19\). These elements of the cytoskeleton get their name from the fact that their diameter, eight to ten nm, is between those of the smaller microfilaments and the larger microtubules. The intermediate filaments are the most diverse group of cytoskeletal elements. Several types of fibrous proteins are found in the intermediate filaments. You are probably most familiar with keratin, the fibrous protein that strengthens your hair, nails, and the epidermis of the skin. Intermediate filaments have no role in cell movement. Their function is purely structural. They bear tension, thus maintaining the shape of the cell, and anchor the nucleus and other organelles in place. The figure above shows how intermediate filaments create a cable-like supportive scaffolding inside the cell. Microtubules Microtubules are the largest component of the cytoskeleton and are found throughout the cytoplasm. These polymers are made up of globular protein subunits called α-tubulin and β-tubulin. Microtubules are found not only in eukaryotic cells but in some bacteria as well. Both the α-tubulin and β-tubulin subunits bind to GTP instead of GTP. When bound to GTP, the microtubule starts to form with the first tubulins "nucleating" the growth of the growing tubule. As more GTP tubulin dimers assemble onto the filament, GTP is slowly hydrolyzed by β-tubulin to form GDP. Tubulin bound to GDP is less structurally robust and can lead to disassembly of the microtubule. Much like the actin filaments discussed above, microtubules also have a distinct polarity that is critical for their biological function. Tubulin polymerizes end to end, with the β-subunits of one tubulin dimer contacting the α-subunits of the next dimer. These differences lead to different subunits being exposed on the two ends of the filament. The ends are designated the "(−)" and "(+)" ends. Unlike actin filaments, microtubules can elongate at both the "(+)" and "(-)" ends, but elongation is significantly more rapid at the "(+)" end. The structure of tubulin is shown in Figure \(20\). One turn of the tubulin (7diz) in cortical microtubules from the human parasite Toxoplasma gondii with proteins inside the tubulin polymers is shown. The alpha subunits are shown in cyan, beta in magenta and the inner proteins specific to this tubulin polymer in gray. Figure \(20\): The structure of microtubules. Microtubules are hollow. Their walls consist of 13 polymerized dimers of α-tubulin and β-tubulin (right image). The left image shows the molecular structure of the tube. Microtubules help the cell resist compression, provide a track along which vesicles move through the cell, pull replicated chromosomes to opposite ends of a dividing cell, and are the structural elements of flagella, cilia, and centrioles (the latter are the two perpendicular bodies of the centrosome). In fact, in animal cells, the centrosome is the microtubule organizing center. In eukaryotic cells, flagella and cilia are quite different structurally from their counterparts in bacteria, as discussed below. A key function of microtubules is to move molecular "cargo" along the microtubule which acts like a "railroad" track. Two key motor proteins are involved in binding to target cargos, and hauling them along the polymer. These motor proteins are kinesin, which moves cargo towards the (+) end, and dynein, which moves it to the (-) end. These interactions are illustrated in Figure \(21\). Figure \(21\): Kinesins and dyneins carry cargo along microtubules. Kinesin moves towards the plus end of the microtubule, whereas the dyneins move towards the minus end. Here is an animation of the dynamics of micotubule and cargo movement. Motor Proteins - Cargo movent along microtubule tracks We will explore the motors protein kinesin and dynein in more detail. As they serve similar functions, you might expect them to have similar structures. Let's see! Some of the material is from https://www.open.edu/openlearn/scien...nt-section-5.2 Kinesins Motor proteins bind to vesicles and organelles and use energy from ATP to move them along the microtubule or microfilament network. Two families of motor proteins, the kinesins and dyneins, move vesicles along microtubules, and members of the myosin family move them along microfilaments. The myosin family is also important in cell movement. The kinesin superfamily (KIF) consists of over 40 different proteins, divided into about 16 families. They bind both microtubules and ATP and are molecular motors. The first ones identified were involved in cargo (organelles, mRNAs, proteins) transport along the long axons of neuronal cells. This method of transport would be vastly superior than simple diffusion down the axons. The kinesin-1 family (members include KIF5A, KIF5B and KIF5C) are heterotetramers with two heavy and two light chains, somewhat similar to myosin. As with myosin, kinesins have a motor head domain, which binds and hydrolyses ATP. They have a microtubule-binding domain instead of an actin-binding region. The N-kinesins have their motor domain at the end terminal, while the domain for M- and C- kinesins are in the middle or carboxy end, respectively. Both N-kinesins and C-kinesins are responsible for plus end and minus end-directed motility, and M-kinesins are used for depolymerization of microtubules in tubulin molecules. As with myosin, the neck stalk and tail are coiled coils of the heavy chains. Cargos bind through intermediary adapter proteins although some bind directly to kinesin tails. The direction of movement of bound vesicles along the cytoskeleton is absolutely dependent on the polarity of the microfilaments and microtubules. Some motor proteins move from the minus end to the plus end and others in the opposite direction. For example, of the various myosins that have been discovered throughout the animal and plant kingdoms, all but one (myosin VI) move towards the plus end of the filament. Kinesins have a tertiary structure that is similar to myosin II, even though there is no significant similarity in the primary structure. Both molecules have two heads with motor domains formed around an ATP-binding core, and a coiled tail that binds to the cargo, as shown in Figure \(30\). A number of other molecules are related to kinesin, and all of them share the kinesin motor domain, but very little else. These are the kinesin-related proteins. Kinesin itself moves towards the plus end of microtubules, but other members of the kinesin family move to the plus or minus end depending on the protein. Some of the kinesin-related proteins are involved in moving microtubules during mitosis – in this way the motor protein and the microtubule act in an analogous way to myosin and microfilaments in cell movement. Figure \(22\) below shows an interactive iCn3D model of the neck and motor domains of dimeric Kinesin-3 KIF13B from rat (6A1Z) Figure \(22\): Neck and motor domains of dimeric Kinesin-3 KIF13B from rat (6A1Z). (Copyright; author via source). Click the image for a popup (slow load) or use this external link: https://structure.ncbi.nlm.nih.gov/i...LTKsQN4xcN9B99 One monomer is shown as a blue surface and the other as a gray cartoon.  ADP is shown in colored spacefill.  Amino acid side chains involved in nucleotide and microtubule binding are shown as coloreds sticks and labeled. (Dyneins, which we will explore below, are unrelated to either kinesins or myosins, and they move towards the minus end of microtubules. Each is composed of two or three heavy chains, with the cytoplasmic dyneins having two chains, each of which forms a large motor domain. In nerve cells, the axonemal dyneins, which have two or three motor domains, transport vesicles along microtubule bundles in the axons.) The speed of the movement mediated by dyneins and kinesins is signficant. In vitro, kinesins can move along microtubules at 2 μm s−1 and dyneins at up to 14 μm s−1. Although these high rates of movement would not be achieved in the complex environment of a cell, they can explain, for example, how caveolar transcytosis of molecules (from the luminal side of the blood vessel to the subendothelial space) across an endothelial cell can occur in 1–2 minutes. Movement and force generation by both classes of proteins involves ATP hydrolysis and allosteric shifts in the orientation of the motor domains, so that the proteins are thought to ‘step’ progressively down the microtubule. Figure \(23\) shows a cartoon of a kinesin dimer attached to a microtubule. Figure \(23\): Cartoon of kinesin dimer attached to a microtubule. Moez. https://commons.wikimedia.org/wiki/F...in_cartoon.png.   Creative Commons Attribution-Share Alike 3.0 Unported Figure \(24\) shows an animated cartoon of Kinesin walking on microtubule. Figure \(24\): Cartoon of Kinesin walking on microtubule.  By Jzp706 - Own work, CC0, https://commons.wikimedia.org/w/inde...=13188271jdkfj Figure \(25\) shows an animation showing a kinesin dimer bound to a tubulin microtubule . Figure \(26\) shows two motor domains of kinesin-1 bind to the microtubule lattice simultaneously. Figure \(26\):  Two motor domains of kinesin-1 bind to the microtubule lattice simultaneously. Qin, J.; Zhang, H.; Geng, Y.; Ji, Q. How Kinesin-1 Utilize the Energy of Nucleotide: The Conformational Changes and Mechanochemical Coupling in the Unidirectional Motion of Kinesin-1. Int. J. Mol. Sci. 202021, 6977. https://doi.org/10.3390/ijms21186977.  Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/) The neck linkers of the kinesin-1 dimer are colored green. The leading head is in the nucleotide-free state and has an undocked neck linker, which points to the minus end of the microtubule. The trailing head is in the ADP·Pi/ADP-bound state and has a plus-end pointed neck linker. Notice however that the ATP-binding site of kinesin is located at the distal tip of the motor domain, whereas in myosin the equivalent site is deep within the motor domain and covered by the myosin's actin-binding site. Therefore the mechanism of stepping is different in the two molecules. In particular, the α-helical linking region connecting the two motor domains of kinesin appears to transfer allosteric changes between them to coordinate ATPase activity and hence the stepping motion of the protein. It is interesting that the motor domains of kinesin-related proteins that move to the plus end and those that move to the minus end of the microtubule are similar but the linkage between them is quite different. Kinesin has its motor domain near the N-terminus, whereas the molecule Ncd, which moves to the minus end of the microtubule, has its motor domain located near the C-terminus. It seems that whether the protein is directed to the plus or the minus end is dependent on the configuration of domains and the coordination. Figure \(27\): shows a comparison of structures of the motor head domain of myosin (panel A) and kinesin.ADP (panel B) and kinesin.ATP (panel C). T the structure of these proteins varies with the form of the bound nucleotide. The myosin head has a 7-stranded central β-sheet and 3 α-helices on each side of the sheet. Nucleotides bind in the β-sheet regions which as a P (phosphate binding)-loop and two "switches". On the back side of the β-sheet are two 50K domains, in between which actin binds when exposed. A converter and lever arm follow leading to the coiled rod domain. The motor domain of kinesin is smaller but homologous with the tubulin binding site also on the back side of the β-sheet. These structures suggest similar mechanisms of action for myosin and kinesins. However, in contrast to the importance of Pi release in the power stroke for the sarcomere, it appears that ADP/ATP are the key players. When ADP is released, the central β-sheet twists, leading to binding to microtubules, implying that microtubules act to exchange ADP/ATP to generate the force. The linkage of conformational change and binding in microtubule:kinesin chemomechanical coupling is shown in Figure \(28\). Kinesin is permanently attached to the microtubule, by either one head or the other. By comparison, the myosin heads (which are arranged in bundles in myofibrils) are only in contact with actin filaments for about 5% of each movement cycle. Dynein Like myosin and kinesin, dynein is a motor protein that undergoes chemomechanical cycles to move cargo on microtubules in the direction of the (-) end. Less is known about dynein given its much larger size (MW 1.4M) than myosin and kinesin, and the difficulty in studying it. There are cytoplasmic and flagellar versions of the protein. The latter generates a force that leads to flagellar motion and propulsion of the cell. They are part of the ATPases Associated with diverse cellular Activities (AAA+ proteins) superfamily. These often form hexameric ring complexes that are involved in the nucleotide-dependent changes in protein structure. Some acts as protease, chaperone, transcription factors, etc. Dynein is different from other AAA+ proteins as it is a single protein that includes six domains that arrange to form a hexameric ring structure. The domain structure and model of dynein bound to a microtubule are shown in Figure \(29\). Figure \(29\): Overall structure of dynein. Upper panel Domain composition of dynein. The numbers of AAA+ modules (AAA1–AAA6) are indicated. ‘C’ indicates the C sequence. Lower panel The overall structure of dynein in the post-power stroke state. CC1 and CC2 indicate coiled-coil helices 1 and 2 of the stalk, respectively. Kato et al., ibid. The structure contains: • six AAA+ module domains with different sequences that nevertheless form a hexameric ring (labeled 1-6 in Figure \(\PageIndex{x}\) above; • a lever-like linker domain that stabilizes the ring domains, connects to the tail domain and produces the power stroke; • a coiled-coil antiparallel stalk domain contains two intertwined alpha-helices; • a globular microtubule-binding domain (MTBD) that in the primary sequences of the protein separates the two alpha-helices in the stalk and which binds the "track" (microtubule). • a tail-domain that binds to cargos, or if multiple dyneins interact, other dyneins. It is quite amazing that dynein has a 3D structure (globular cytoskeleton binding domain - stalk - tail) that mimics that of myosin and kinesin, but all in a single protein. The power stroke is linked to lever motion and ATP hydrolysis. We must use this structural information to understand ATP binding and hydrolysis, conformational changes in the linker during the power stroke, transmission of conformational changes through the stalk and ensuing changes in the interaction of the microtubule-binding domain with the microtubule. Nucleotide binding and hydrolysis • the lever-like linker swings between AAA+2 and the stalk base(Roberts et al. ). • AAA+ modules 1-4 can bind an cleave ATP so they acts as ATPases. This contrasts to both myosin and kinesis which have only 1 ATP binding site per heavy chain. AAA+ 5 and 6 have mutations, which prevent ATP hydrolysis. • The ATPase site of AAA1 is between AAA1 and AAA2 binds ADP with higher affinity than ATP since an arginine side chain on AAA2 is not close enough to interact with the gamma-phosphate of ATP. The ADP-bound form is called the open form. • Crystals structure of ADP.vanadate (an equivalent ATP analog) shows a closed active site in which the arginine side chains interact with the vanadate, a proxy for the gamma-phosphate of ATP. Hence ATP binding closes this active site. A Figure \(30\) shows the open (ADP bound) and closed (ATP equivalent) bound state of dynein. Linker conformational changes in power stroke Linker motion produces the power stroke and the ADP bound form represents a post-stroke state in which the linker has minimal contact with the ring domains and is mostly detached. A long α-helix, H10, within the linker appears to be involved in the power stroke as it extended in th ADP bound state, but bent in the ATP proxy state, where it is posed to initiate the power stroke. These conformations are illustrated in Figure \(31\). Communication between the domains for ATP hydrolysis and track binding Obviously, there needs to be coordination and correcting timing between changes in the ATPase hexameric ring and its communication through the linker to the stalk and to the microtubule-binding domain (MTBD), which is 140 Å distant. This communication is easier for kinesins and myosins since the motor domain, where ATP cleavage occurs, is also the domain that binds actin and tubulin, respectively. The communication between the distal domains occurs through the coiled-coiled stalk. This makes sense as the MTBD is in between the two helices of the stalk. The two antiparallel helices presumably slide over each other to form different alignments or registries between the helices. Indeed different alignments appear to affect the affinity of the MTBD for tubulin. This was experimentally verified by creating a new protein with the coiled-coil stalk of dynein with another protein, seryl-tRNA synthetase, as illustrated in Figure \(32\). Interactions with tubulin There also appear to be two main conformations for the MTBD for its interaction with tubulin. Figure \(\PageIndex{x}\) below shows the domain structure of dynein and various synthetic constructs used in experiments (panel A), a model structure for the interaction of dynein with the α/β-tubulin dimer (panel B) and a detailed rendering of the strong (alpha-helix registry) and weak (beta structure registry) of the MTBD and the α/β-tubulin dimer. During each full cycle, the MTBD must dissociate from and reassociate with the microtubule as it marches along the polymer (hence the need for weak and strong binding interactions).  Figure \(33\) below shows the domain organization of dynein and low- and high-affinity bound states of the MTBD. Figure \(33\): Domain organization of cytoplasmic dynein and low- and high-affinity MT-bound states of the dynein MTBD. a Organization of the full-length cytoplasmic dynein heavy chain (HC) with an N-terminal Halo-Tag, Halo-Dyn1471kDa (a.a. 1–4092) and the tail-truncated monomeric constructs, GFP-Dyn1331kDa and Dyn1331kDa-GFP (a.a. 1219–4092). b Dynein MD bound to α/β-tubulin in the strong binding state (merged from PDB entries 3VKG and 3J1T; see Supplementary Note 1). c MT-bound MTBD in the weak MT-binding β-registration of the stalk helices (top, PDB entry 3J1U) and in the strong MT-binding α-registration (bottom, PDB entry 3J1T). Lu Rao et al. Nature Communications (2019) 10:3332 | https://doi.org/10.1038/s41467-019-11231-8. Creative Commons Attribution 4.0 International License.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/05%3A_Protein_Function/5.05%3A_Protein_Interactions_Modulated_by_Chemical_Energy-_Actin_Myosin_and_Molecular_Motors.txt

Search Fundamentals of Biochemistry Conformational Selection In our study of hemoglobin structure in the MWC model, we developed the idea that there were two forms of hemoglobin in solution, the taut and relaxed form, which are pre-existing and interconvertible even in the absence of dioxygen. Oxygen was presumed to bind preferentially to the relaxed form. In the KNF model we saw that ligand binding can induce conformational changes in adjacent subunits, promoting cooperative binding of ligand. In general, these two models distill down to combinations of two simpler models. The first might be called the conformational selection in which ligand binds tightly to a preexisting conformations in a "lock and key manner" without inducing subsequent macromolecular conformational change. Alternatively, the ligand might bind loosely and alter the macromolecular conformation to produce tighter binding, an example of an induced fit model. For the binding of dioxygen to hemoglobin, thermodynamic cycles could be drawn showing either the binding of ligand and subsequent conformational changes in protein structure or conformational changes in protein structure proceeding binding. Is there additional evidence to support the conformational selection model of binding of ligand to a protein that can exist in two conformations in the absence of ligand? The answer is yes. Antibodies are immune system protein molecules than can bind "foreign" molecules and target them for biological neutralization. Many crystal structures have been determined of antibodies in the presence or absence of a "foreign" ligand molecule. In these cases, the conformation of the bound antibody is different from that of the free. Either an induced fit model for ligand binding or a lock and key model of binding of ligand to one of two different pre-existing antibody conformations could account for this observation. These different mechanisms could be differentiated experimentally by stop-flow kinetic technique since both display slow and fast phases that are affected differently by ligand concentration. Theoretically, in the induced fit model, only one ligand type could bind to the antibody which would undergo a conformational rearrangement to produce tighter binding. However, a different structural ligand might bind to the two main antibody conformations in the two preexisting conformational models. James et al. have recently shown through stop flow kinetics techniques (to investigate binding) and x-ray crystallography (to investigate final structures) that one antibody molecule can, through existing in two different preexisting conformations, bind two different ligands (antigens). One antibody conformation binds small aromatic molecules with low affinity (including the small molecule 2,4-dinitrophenol, the immunizing molecule or hapten) and then rearranges to produce a high affinity binding complex in which the DNP is bound in a narrow cavity (reducing the effective off rate (koff) of the bound ligand. A second antibody conformation binds a protein ligand over a broad, flat binding site of the antibody molecule. Lange (2008) et al, using a NMR technique, residual dipolar coupling, that allows sampling of structures in the microsecond time scale, have shown that the solution structure of ubiquitin (which we modeled in our first lab), in the absence of ligand, exists in an ensemble of conformational states. More importantly, these different conformational states are identical to those found in the 46 crystal structure of ligand complexed to various protein ligands, strongly supporting the concept of conformational selection. In all likelihood, a combination of both induced fit and conformational selection probably occurs within a 3D energy landscape in which an initial binding encounter by either a lock and key fit to the "optimal fit" conformer or to a higher energy conformer in which the bound state relaxes to a lower energy through the induction of shape changes in the binding protein. Figure $1$ shows a cartoon illustrating the differences between conformational selection and induced fit binding (after Boehr and Wright, Science 320, 1429 (2008)). Rea et al. offered an interesting experimental model to distinguish conformational selection versus induced ligand binding. They studied rabbit ileal bile acid binding protein (I-BABP). The wild-type protein has a helix-turn-helix motif at its N terminus. They produced a mutant (Δa-I-BABP) that replaced this motif with a Gly-Gly-Ser-Gly linker, causing the protein to unfold. Next, they conducted binding and folding studies on addition of taurochenodeoxycholate (TCDC) using stopped-flow fluorescence to measure the binding behavior. They wished to distinguish between two distinct mechanisms – folding before binding (or conformational selection) and binding before folding (or induced-fit model). The data support a two-phase model. One phase did not depend on ligand and one did, suggesting binding followed by a conformational change). Conformational Selection Equation$1$ below describes the equilibria involved in the conformation selection model. The forward rate constants are shown as kn while the reverse ones are shown as k-n. P \underset{k-1}{\stackrel{k_{1}}{\leftrightarrow}} P^{*}+L \underset{k_{-2}}{\stackrel{k_{2}}{\leftrightarrow}} P^{*} L P* in the conformational selection model represents a high affinity, pre-existing conformation of the protein. Induced Fit Equation$2$ below describes the equilibria involved in the induced fit model. P+L \underset{k-1}{\stackrel{k_{1}}{\leftrightarrow}} P L \underset{k_{-2}}{\stackrel{k_{2}}{\leftrightarrow}} P^{*} L P* in the induced fit models results when high ligand shifts the equilibrium to the right. One way to differentiate these models is to look at the dependency of the different kinetic phases on ligand. In the conformation selection model, the slow step is the formation of the high affinity form of the protein, P*. The first slow step has a nonlinear dependence in L while the fast second step has a linear dependence. The data did not fit this model well. \begin{aligned} &k_{\text {slow }}=k_{-2}+\frac{k_{2}}{1+\frac{L}{\left(\frac{k_{-1}}{k_{1}}\right)}} \ &k_{\text {fast }}=k_{-1}+k_{1} L \end{aligned} In the induced fit model, the ligand binds to a low affinity and perhaps unfolded form of the protein, which subsequently collapses to the bound form in a slow step. \begin{aligned} &k_{\text {slow }}=k_{-2}+\frac{k_{2} L}{\left(L+\frac{k_{-1}}{k_{1}}\right)} \ &k_{\text {fast }}=k_{-1}+k_{1}[L] \end{aligned} Both ligand-dependent and independent phases are evident in the equation for the slow step for the induced fit mechanism. At high ligand concentration (when L >> k-1/k1) , the slow step in the induced fit would be independent of ligand (kslow = k-2 + k2). The authors state the data is consistent with a variant of induced fit called the "fly casting model". In this model, the protein first encounters ligand and forms a hydrophobic collapse intermediate (PL) in a fast step characterized by a linear dependence on ligand concentration. Then the intermediate slowly interconverts into a wild type like complex through conformational re-arrangement. Wild-type protein binds the ligand 1000x as quickly, suggesting entropic barriers to binding of the ligand to the unfolded state and rearrangement of the protein thereafter. Junker et al used atomic force microscopy (AFM) to observe the effects of ligand binding on the folding/unfolding fluctuations of a single molecule of calmodulin (CaM), a calcium-binding protein that binds amphiphilic helicals peptides, leading to a large conformation change in the protein. To do this, they sandwiched a single CaM molecule between filamins that serve as attachment points for the AFM tip and a surface. A slow pulling force was then applied to the molecule, and the length gain was measured as the protein unfolded. The rapid fluctuations between folded and unfolded states were quantified and used to derive a complete energy landscape for the folding of CaM. They conducted these experiments in the presence of two ligands, Ca2+ and mastoparan (Mas), a wasp venom peptide. They found that Mas does not affect the folding rate of CaM, although it does stabilize the already folded form. This suggests that Mas does not bind to the transition state or the unfolded protein, but rather selects a particular conformation from an ensemble of possible choices. Ca2+ however, increases the folding rate, which suggests that it stabilizes both the transition state and the folded state. AFM offers a considerable degree of precision in drawing energy landscapes of protein folding and unfolding, and it has several applications that are yet to be explored. Binding to Intrinsically Disorder Protein and MORFs As described above, the binding of a protein to a ligand (including another protein) could occur by a lock and key mechanism, possibly through a conformational selection process, or through an induced fit when an initial binding event is followed by a conformation rearrangement to form a more tightly bound complex. But how does binding to completely intrinsically disordered protein (which has been documented) occur? These cases are quite removed from those envisioned in simple induced fit mechanisms. Binding to IDPs might occur through specific Molecular Recognition Features (MoRFs). MoRFs are typically contiguous but disordered sections of a protein that first encounter a binding partner (a protein for example). Mohan et al conducted a structural study of MoRFs in the Protein Data Bank by selecting short regions (less than 70 amino acids) from mostly disordered proteins that were bound to proteins of greater than 100 amino acids. They chose a sequence size of 70 amino acids and smaller since they would most likely display conformational flexibility before binding to a target. 2512 proteins fit their criteria. For comparison, they created a similar database of ordered monomeric proteins. The analysis showed that after they encounter a binding surface on another protein, the MoRF would adopt or "morp" into several types of new conformations, including alpha-helices (a-MoRFs), beta-strands (b-MoRFs), irregular strands (i-MoRFs) and combined secondary structure (complex-MoRFs), as shown in the figure below. Figure $8$ shows interactive iCn3D models of the types of molecular recognition features in intrinsically disordered pProteins (A) α-MoRF, Proteinase Inhibitor IA3, bound to Proteinase A (1DP5) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...tk45aWAeMrTct7 (B) A β-MoRF, viral protein pVIc, bound to Human Adenovirus 2 Proteinase (1AVP) (Copyright; author via source). Click the image for a popup or use this external link:https://structure.ncbi.nlm.nih.gov/i...EFmanRy2T7zjX6 (C) An ι-MoRF, Amphiphysin, bound to α-adaptin C (1KY7) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...JEBC4VHUA9C718 (D) A complex-MoRF, β-amyloid precursor protein (βAPP), bound to the PTB domain of the neuron specific protein X11 (1X11) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...XtQ7retidnksT9 Vacic et al have further characterized the binding surfaces between MoRFs and their binding partners using structural data from PDB files. Interfaces were studied by determining the differences in accessible surface area between MoRFs and their binding partners, and the protein in unbound states. These were compared to ordered protein complexes, including homodimers and antibody-protein antigen interactions that were not characterized by disordered interactions. Their findings are summarized below. • MoRF interfaces have more hydrophobic groups and fewer polar groups compared to the surface of monomers. This is true even as the overall amino acid composition of intrinsically disordered proteins are enriched in polar amino acids, which leads them to adopt a variety of unfixed solution conformations. • a-MoRFs have few prolines, which is expected as prolines are helix breakers. • Methionine is enriched in both MoRFs and in their binding partner interface. Methionine is unbranched, flexibile, and contains sulfur, which is large and polarizable, making it an ideal side chain to be involved in London forces in a hydrophobic environment. • Even though MoRFs have few residues, their binding interfaces were of similar or larger size than other protein binding interfaces, a result which also applies to IDPs as a whole. MoRFs interfaces also have a larger solvent-exposed surface area, similar to IDPs. This is consistent with the notion that MoRFs are disordered before binding and that a defined structure is not possible with little buried surface area. • As MoRFs have significant nonpolar character within a IDP that is highly enriched in polar amino acids, MoRFs should be highly predictable by search algorithms.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/05%3A_Protein_Function/5.06%3A__Binding_-_Conformational_Selections_and_Intrinsically_Disordered_Proteins.txt

Search Fundamentals of Biochemistry Introduction Enzyme-linked immunosorbent assays (ELISAs) are used widely in biotechnology, pharmaceutical and clinical medicine labs. At the same time, they appear to be underrepresented in chemistry and biochemistry curricula, even though their sensitivity, selectivity, and ease of use would argue for their widespread use ELISAs use primary antibodies specific to a target analyte (or antigen) as a central part of the assay. One of them is Immobilized in wells of a multiwell plate or on a strip. The species not immobilized is then added, and an immobilized analyte (antigen)-antibody complex forms. After washing the bound complex on the solid phase support to remove unbound species, a second labeled antibody is added for detection. This secondary antibody binds to the distal end (Fc domain) of the bound antibody and not the analyte. The label on the second antibody can either be a fluorophore or an enzyme that can interact with an added substrate that will generate a colored solution. The color development is then measured with a fluorometer, spectrophotometer, or if just visually if quantitation is not needed. There are several variants of ELISAs, including the traditional ELISA, in which the antigen is bound or fixed to the surface of the solid support, or a sandwich ELISA, in which the antibody is bound to the surface. In the latter case, a second labeled antibody that binds to the antigen must bind at a different site (or epitope) on the antigen. For sandwich ELISAs, the target analyte must be large enough to accommodate two antibodies binding to different sites on the same molecule. Cartoon diagrams showing the binding interactions in traditional and sandwich ELISAs are shown in Figure $1$. Figure $1$: Binding interactions in traditional and sandwich ELISAs. Abbreviations are: fixiertes (fixed or immbolized), substrat (substrate), farblos (colorless), farbig (colored), enzymegekoppelter (enyzme coupled), antikörper (antibody), zweitantikörper (second antibody), erstantikörper (primary antibody). https://commons.wikimedia.org/wiki/File:ELISA.svg. Creative Commons Attribution 4.0 International license. Note that both types have direct and indirect versions. Some of the steps in a traditional ELISA are shown in Figure $2$. Figure $2$: Steps in a traditional ELISA In (1), an analyte is added to a well (outlined in gray). The solution is removed after a predefined time, and then a specific amount of analyte (such as protein) is irreversibly adsorbed (2). A blocker such as bovine serum albumin (BSA) or milk is added to bind any sites on the plate that could still bind protein. A primary anti-analyte protein is added. Some bind to the immobilized protein, and the rest is free in solution. After washing, only the primary antibody-immobilized analyte remains. Now if a test solution (for example from a patient's blood) is added after blocking but before the addition of the primary antibody, it remains in the well unbound (5). If a primary antibody is added to this well, the immobilized antigen competes with the free antigen for binding, so less secondary antibody would be bound to the well (6). After washing, wells 4 and 7 remain. After additional rounds of washing and blocking, the secondary labeled antibody is added. Wells (such as 7) that contain patient analyte will bind less secondary antibody to the immobilized protein. When a substrate for the conjugated enzyme is added, the solution will be less colored after a defined incubation time. In assays, a standard curve is made using a range of known concentrations of the analyte. The more analyte in the standard, the greater the competition with the immobilized analyte for the solution phase primary antibody, which would lead, after washing away the solution phase antigen:primary antibody complex, to a lower absorbance in the well with the higher solution phase antigen in the sample. Now let's consider a sandwich assay. Instead of immobilizing a protein antigen, an antibody that binds the target antigen is immobilized. For example, an antibody against a surface SARS-Cov2 protein can be immobilized. Next, a specimen (saliva, nasal swab) containing the target surface protein recognized by the immobilized antibody is added. The great the viral load, the more SARS-Cov2 binds to the antibody. Then a second labeled antibody can be added that recognizes a different protein (the spike protein from SARS-Cov2) is added. After washing, the substrate is added, and the color development from the action of the enzyme on the substrate is measured. In this case, the more SARS-Cov2 present in the sample, the higher the signal (absorbance or fluorescence). ELISAs have detection limits varying between 0.01 pg/mL to 100 ng/mL [1]. Although they are extensively used in health fields, they are not widely used in undergraduate biochemistry or chemistry courses, nor are they mentioned in the ACS’s Guidelines and Supplements for either Analytical Chemistry or Biochemistry. Given their importance, we choose to discuss them in this ext. ELISA Data Analysis The most difficult parts about ELISAs are developing an understanding of the chemical and mathematical equations, choosing/using modeling and analysis software, and evaluating validity/reliability. The typical data analysis is based on the generic Hill equation instead of the classical hyperbolic binding curve analysis we derived for a single ligand to a single site on a macromolecule. The Hill equation has more empirical parameters to use when fitting bind curves. Here is the Hill Equation equation that we studied before. Y=\frac{L^{n}}{K_{D}+L^{n}} For more complicated ELISA data, when a standard curve of known concentrations is used, and output signals (fluorescence, absorbance) vary from some minimum to a maximum value, the similar but more empirically useful Logistic Equation is used: Y^{\prime}=M\left(\frac{x^{n}}{k+x^{n}}\right) where Y is the observable signal and M is the maximal observable signal. The maximal signal might not be actually observed in a ELISA assay as was the case in the binding of a ligand to a macromolecule when ligand concentrations >>KD could not be reached. Let's use a variant of the Hill equation using the L50 value, the ligand concentration at half-maximum binding. \begin{gathered} 0.5=\frac{L_{50}^{n}}{K_{D}+L_{50}^{n}} \ 1=\frac{2 L_{50}^{n}}{K_{D}+L_{50}{ }^{n}} \end{gathered} hence Y=\frac{L^{n}}{K_{D}+L^{n}}=\frac{L^{n}}{L_{50}^{n}+L^{n}}\left(\frac{\frac{1}{L^{n}}}{\frac{1}{L^{n}}}\right)=\frac{1}{\frac{L_{50}^{n}}{L^{n}}+1}=\frac{1}{\left(\frac{L_{50}}{L}\right)^{n}+1} An analogous somewhat similar equation can be derived from the logistic equation: Y=d+\frac{a-d}{1+\left(\frac{L}{c}\right)^{b}} where four empirical parameters define the curve: • a is the smallest measured absorbance value (blank); • d is the largest absorbance value when Y = 1; • c is the inflection point in the curve which can easily be seen to occur when [L]= L50= the ligand concentration at half maximal saturation; and • b is the slope of the curve at L50 which is the Hill coefficient n. (for many ELISA curves ≈ 1). An idealized graph of ELISA data is shown in Figure $3$. Figure $3$: Idealized ELISA signal (fluorescence, absorbance) vs log [L] curve The 4-parameter modified Logistic equation is ideal for fitting ELISA data. An interactive active graph for the 4-parameter Logistic curve is shown in Figure $4$. T Figure $4$: Interactive active graph for the 4 parameter Logistic curve. Two of the parameters, the minimum signal a, and the maximal signal d have been set to 0.01 and 2.0 respectively, and are not changeable in the figure. Note that the greater the value of b (slope of the curve at the inflection point), the more "sigmoidal" the semilog curve appears (similar to the Hill binding curves for hemoglobin). Lateral flow immunoassays During the COVID pandemic, home test kits (when they were available) were used. These tests are a modified version of the sandwich ELISA described above. They differ in two main ways. The assays were not done in wells but a planar sheet in which the samples flow laterally across the sheet. As it flows across a strip through capillary action, a sample containing the SARS-COVID-2 with its spike protein would first encounter a labeled antibody to the spike protein. It would then flow into a region which contained immobilized test (anti-spike protein) and control antibodies. The bound analyte would stop and build up to sufficient concentration to see an observable colored band on the strip, but only if the sample contained the viral spike protein. These events are illustrated in Figure $5$. Figure $5$: Lateral flow ELISA assay. https://en.Wikipedia.org/wiki/Latera...Flow_Assay.jpg Figure $6$ shows a lateral flow assay that detects the presence of serum or potentially salivary antibodies (either IgG or IgM) against the SARS-Cov2 proteins. Figure $6$ show a lateral flow assay which detect the presence of serum or potentially salivary antibodies (either IgG or IgM) against the SARS-Cov2 proteins. https://commons.wikimedia.org/wiki/F...0453-g002.webp. Creative Commons Attribution 4.0 International license. The green cube represents the virus with viral proteins. It has been labeled with a gold nanoparticle (blue sphere). Gold is widely used as a labeling reagent in lateral flow immunoassays because it is chemically inert and hence extremely stable. The concentrated gold particles found in positive samples at the end of the strip can be observed visually since the gold nanoparticles absorb light through surface plasmon resonance. In this process, light of certain tunable wavelengths (based on the size of the nanoparticle) is absorbed when it matches the oscillatory frequency of the electron clouds of the metal nanoparticle. Plasmons are the oscillations in the electrons (hence electromagnetic oscillation) that occur when the nanoparticle's surface interacts with photons, causing oscillations in the electrons at the same frequency as the light (resonance).

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/05%3A_Protein_Function/5.07%3A__Binding_-_Enzyme_Linked_Immunosorbant_Assays_%28ELISAs%29.txt

Thumbnail: Dihydrofolate reductase is inhibited by methotrexate which prevents binding of its substrate, folic acid. (CC BY 4.0 International; Thomas Shafee (modified) via Wikipedia) 06: Enzyme Activity Search Fundamentals of Biochemistry In this section, we will explore chemical and physical factors that speed up reactions, and begin to relate these effects to reactions catalyzed by enzymes. We will see that enzymes employ various chemical strategies to increase the rates of reactions, in addition to physical ones like reactant proximity and the introduction of strain. This can result in reactions that are 10 million (or more!) times faster than the uncatalyzed reaction. To put this 10 million-fold rate enhancement in perspective, if the catalyzed reaction takes one second, the uncatalyzed one would take nearly four months! This chapter section has been written by Kristen Procko and Henry Jakubowski. Reactions in solution that are not catalyzed are slow. Consider the hydrolysis of an ester in water, illustrated in Figure $1$. The ester is stabilized by resonance, and is therefore weakly electrophilic; thus, attack by weakly nucleophilic water is a slow process. Examining the transition state, we can see that charge development and separation occurs in the transition state for the uncatalyzed reaction, resulting in an intermediate (P) with both positive and negative charges. When bonds are made or broken, charged intermediates are often formed, which are higher in energy than the reactants. Consider the energy diagram for the first step of a generic endergonic reaction, shown in $2$. The transition state is closer in energy to the intermediae P, than it is to the reactant R. Therefore, the TS more closely resembles than it does the starting reactants. Applying this analysis to the ester hydrolysis reaction from Figure $1$, the transition state is closer in energy to the charge-separated intermediate P, and therefore more closely resembles the charge-separated species. In this example, the intermediate is higher in energy than the reactants, thus the transition state is even higher in energy than the intermediate. Anything that can stabilize the charges on the intermediate will also stabilize the developing charges in the transition state. This lowers the energy of the transition state and catalyzes the reaction. In this section, we will investigate the mechanism underlying the catalysis by small molecules of chemical reactions. Presumably, biological macromolecular catalysts (like protein enzymes) will use similar mechanisms in their catalytic effects (which will be discussed in the next section). Catalysts, including enzymes, can employ at least 5 different ways to stabilize transition states. Chemical Strategies for Rate Enhancement General Acid and Base Catalysis Considering intermediate P in Figure $1$, we can envision two strategies to reduce the charge separation: the negative charge on the anionic oxygen could be protonated, or the positive charge on the cationic oxygen could be removed by deprotonation. If the reaction is pH dependent, and the reaction rate solely depends on hydronium ion concentration, [H3O+], then specific acid catalysis is operative. Specific acid catalysis occurs when the hydronium ion concentration is the sole factor determining the reaction rate, and the rate is not influenced by the concentration of any buffer components present in the solution. In other words, the rate of the reaction depends specifically on the concentration of hydronium ion. Specific base catalysis occurs when the reaction rate is dependent solely on the concentration of hydroxide ion, and is again independent of any buffer components in solution. By contrast, general acid catalysis occurs when the reaction is not solely dependent on the [H3O+] concentration; that is, the reaction rate is influenced by the concentration of a buffer component. With general acid catalysis, the charge separation in the transition state is decreased by donation of a proton to the carbonyl from general acids (e.g., acetic acid or a protonated imidazole ring). Proton donation decreases the developing negative in the transition state. In Figure $3$, the first step of the ester hydrolysis mechanism is shown via specific acid catalysis, alongside the general acid catalysis mechanism using the weak acid, acetic acid. Alternatively, the first step of the ester hydrolysis mechanism can be base-catalyzed, which increases the strength of the nucleophile. In Figure $1$, the attacking water molecule develops a partial positive charge in the transition state as it begins to form a bond to the electrophilic carbon of the carbonyl. In the base-catalyzed mechanism shown in Figure $4$, hydroxide becomes the nucleophile in the specific base-catalyzed mechanism. The energy of the transition state can also be lowered by the presence of a general base (e.g., acetate, a deprotonated imidazole ring). Proton abstraction decreases the developing positive charge. General acid/base catalysis is common with enzymes because enzymes often use amino acid side chains to promote acid-base reactions within the active site, the region of the enzyme where the chemical reaction takes place. Acetic acid is similar to glutamic and aspartic acid side chains, and the imidazole ring shown in the general base catalysis reaction in Figure $4$ is present in the side chain of the amino acid histidine. Metal Ion or Electrostatic Catalysis A metal such as Cu2+ or Zn2+ can also stabilize the transition state. The metal must be able to bind the charged intermediate and hence the transition state. An oxyanion intermediate formed during the reaction of an electrophilic carbonyl C can interact with a metal, especially when there is an O on an adjacent atom which can help coordinate the metal ion. This charge stabilization of the developing negative in the transition state and the full negative in the intermediate is often called electrostatic catalysis, and is illustrated in the decarboxylation of a β-keto carboxylic acid Figure $5$. Coordination of Cu2+ to the β-keto carboxylic acid increases the electrophilicity of the carbonyl, making it a superior electron acceptor, which facilitates decarboxylation. The intermediate enolate formed in the decarboxylation is protonated, giving the more stable ketone as the product. Electrostatic catalysis is likely to occur in many enzymes since nearly 1/3 of all enzymes require metal ions. A classic example of an enzyme using metal ion catalysis is carboxypeptidase A. Figure $6$s shows an interactive iCn3D model of Zn and the inhibitor citric acid bound to carboxypeptidase A (3KGQ). Note the histidine and aspartate amino acid side chains of the active site coordinating to the Zn2+ ion, along with the carboxylate group of citrate. Metals can also act in a different way. They may coordinate a water and by further polarizing the H-O bond increase the acidity of the bound water. For instance, a water molecule in the hexaaquairon(III) ion has a pKa of 9.4, compared to pure water, with a pKa of 14 ($7$). The complexed hydroxide is a better nucleophile than bulk water. Figure $7$: Metal ion decrease of pKa of coordinated water Another enzyme that utilizes Zn2+ is carbonic anhydrase. It is among the fastest of all enzymes, with a kcat of 106 s-1 and a kcat/Km of 8.3 x 107 M-1s-1 (reference). It is diffusion controlled at low substrate (CO2) concentration and converts one million bound CO2 per second to HCO3-! The Zn2+ appears to bind a water molecule and reduce its pKa such that the bound form is OH-. This is illustrated in Figure $8$, which depicts the local environment of the bound Zn2+ (coordinated by 3 histidine side chains and an OH-) in the absence (left) and presence (right) of CO2. Covalent or Nucleophilic Catalysis One way to change the activation energy of the reaction is to change the reaction mechanism in ways which introduces new steps with lower activation energy. As shown in Figure $9$, the catalyzed reaction has a new lower energy well, representing formation of the covalent intermediate, and the activation energy is lowered overall. Formation of the new intermediate results in two transition states, represented by the two high energy points of the blue line in the plot. Figure $9$: Energy diagram for an uncatalyzed reaction compared to a catalyzed reaction that utilizes covalent catalysis. A typical way to achieve covalent catalysis is to add a nucleophilic catalyst, which forms a covalent intermediate with the reactant. Figure $8$ shows how pyridine (red) acts as a nucleophilic or covalent catalyst in the hydrolysis of an anhydride. The anhydride is very reactive to start with, and the charged pyridinium ion intermediate contains a very good leaving group. The desired nucleophile, water, can then interact with the intermediate in a nucleophilic substitution reaction. In these types of reactions in general, as long as the nucleophilic catalyst is a better nucleophile than the ultimate nucleophile (usually water) then the activation energy is lowered, and the reaction is catalyzed. The nucleophilic catalyst and the original nucleophile usually interact with a carbonyl C in a substitution reaction. Reactions involving iminium ions are a recurring theme in biochemistry Positively-charged nitrogen cations (iminium ions) form as intermediates in many biochemical mechanisms. The iminium ion is a powerful electron acceptor, and can promote the cleavage of bonds that would otherwise be difficult to break, such as C–C and C–H bonds. To begin our analysis of how iminium ions promote such cleavage reactions, let's revisit a common carbon-carbon bond cleaving reaction, the decarboxylation of a β-keto acid, which we examined briefly above in Figure $5$ with metal ion catalysis. Under acidic conditions, β-keto acids usually decarboxylate with gentle warming. A cyclic transition state is often invoked, and the presence of the carbonyl of the ketone, adjacent to the breaking bond, gives the electrons somewhere to go (Figure $11$). The product of the decarboxylation is an enol, which tautomerizes to the more stable ketone. Under the slightly basic conditions that characterize the medium for most biochemical reactions, the equilibrium favors the deprotonated carboxylate form. The adjacent carbonyl again gives the electrons somewhere to go in the decarboxylation reaction, and under these basic conditions, an enolate is formed. Protonation gives the final product, a ketone. In Figure $5$, we saw that a metal ion can promote the decarboxylation reaction by interacting with the electron-accepting ketone carbonyl, which makes it even more electrophilic. Another strategy to create a better electron acceptor involves forming a full positive charge on the electron accepting atom, which can be done by converting the ketone to an iminium ion. Amines react with aldehydes or ketones to form iminium ions. Figure $12$ illustrates this strategy, which involves covalent catalysis. The amine, RNH2, reacts to form a new intermediate, the iminium ion, with a full positive charge. The protonated nitrogen serves as an excellent electron "sink" for decarboxylation reactions of beta-keto acids. This iminium ion or protonated Schiff Basehas a pKa of about 7, so the protonated iminium and deprotonated imine are in equilibrium near pH 7. Figure $12$ also illustrates a simple way to view reaction mechanisms. Electrons in chemical reactions can be viewed as flowing from a source (such as a carboxyl group) to a sink (such as an electrophilic carbonyl O or a positively charged N in a Schiff base). Acid- and base-catalyzed reaction mechanisms for Schiff base formation are shown in Figure $13$. An amine is used as the nucleophilic catalyst, forming the initial addition product, a carbinolamine. The carbinolamine dehydrates, since the free pair of electrons on the N are more likely to be shared with the carbon to form a double bond than electrons from the original carbonyl O, which is more electronegative than the N. If catalyzed by a general acid, an iminium ion, and the base-catalyzed reaction forms an imine. Near pH 7.4, the imine easily protonated to form a positively charged N at the former carbonyl O center. An actual Schiff base intermediate between fructose-1,6-bisphosphate (2FP-400) and Lys 239 from the enzyme fructose bisphosphate aldolase from Leishmania mexicana is shown in Figure $14$. Only a single bond between the carbon and nitrogen in the Schiff base is shown. Figure $14$: Schiff base intermediate between fructose-1,6-bisphosphate (2FP-400) and Lys 239 from the enzyme fructose bisphosphate aldolase from Leishmania mexicana(2QDG) Transition State Stabilization In the middle of the 20th century, Linus Pauling postulated that the only thing that a catalyst must do is bind the transition state more tightly than the substrate. This can be discerned in Figure $15$ and a little math. The diagram shows how the substrate (S) and the transition state (S*) can react with an enzyme (E) to form a complex which then proceeds to product (following the diagram from "start here" in the right-hand direction), or can go to product in the absence of enzyme (E) (following the diagram from "start here" in the left-hand direction). Note that the diagram is arbitrarily drawn such that the standard Gibbs free energy (G°) of the free product P is higher than that of the free substrate, S. The colored large vertical arrows represent the ΔG° for the transition shown: • The red arrows A and B represent the ΔG°s for the binding of E + S (arrow A) and E + S* (arrow B), respectively. • The green arrows C and D represent the ΔG°s for the activation energy of the free substrate (arrow C) and the enzyme-bound substrate (arrow D). Now consider the two pairs of arrows, A,C and D,B and add each set like a vector in elementary physics. Since the distance between the two horizontal blue lines is the same for the left hand process (uncatalyzed) and the right hand one (catalyzed), it follows that C-A=D-B The negative signs for A and B are used since in the diagram both A and B have negative ΔG° values. Now for an enzyme to be a catalyst, the activation energy D for the reaction in the presence of the enzyme E must be less positive (i.e., smaller) than the activation energy C in the absence of enzyme. Therefore, after rearranging the equation: C-D=A-B>0 and substituting in the ΔG° values for A and B, we can directly compare the free energy of binding the substrate (S) vs. binding the transition state (S*): -R T \ln \mathrm{K}_{\mathrm{eq} \mathrm{S}}-\left(-\mathrm{RT} \ln \mathrm{K}_{\mathrm{eq} \mathrm{S}^{*}}\right)>0 Hence, the equilibrium constant for binding the transition state is larger than that for binding free substrate: \mathrm{K}_{\text {eq } S^{*}}>\mathrm{K}_{\text {eq } \mathrm{S}} Pauling was right. The enzyme just needs to bind the transition state more tightly than the substrate to catalyze the reaction. This is why chemists synthesize stable transition state analogs as potential tight binding inhibitors of target proteins. The stability of the transition state also affects the reaction kinetics (which makes sense given that the activation energy clearly affects the speed of a reaction). As you probably remember from organic chemistry, biomolecular nucleophilic substitution (SN2) reactions are slow when the central atom where the substitution will occur is surrounded by bulky substituents (sterics once again). We discussed this in context to nucleophilic substitution on a sp2 hybridized carbonyl carbon in carboxylic acid derivatives versus on a sp3 hybridized phosphorous in phosphoesters and diesters. The explanation for this phenomena has usually been attributed to hindered access of the central atom caused by bulky substituents (intrinsic effects). Is this true? Studies on SN2 reactions of methylchloroacetonitrile and t-butylchloroacetonitrile (with the reagent labeled with 35Cl) using 37Cl- as the incoming nucleophile in the gas phase shown that the more hindered t-butyl derivative's activation energy was only 1.6 kcal/mol (6.7 kJ/mol) higher than the methyl derivative, but in aqueous solution, the difference is much greater for comparable reactions (Figure $16$:). Figure $16$: SN2 reactions are characterized by a pentavalent transition state They attributed the differences to solvation effects of the transition state. The bulkier the substituents on the central atom, the more difficult it is to solvate the transition state since water can't reorient around it as well. In effect there is steric hindrance for both reactant and solvent. What does it take for a macromolecule (M) to be a catalyst - an enzyme? It seems the minimum criterion are: • M binds a reactant • M binds the transition state more tightly than the substrate Anything above these is just "icing on the cake". If different functional group are present in the "active" site of the enzyme that would allow electrostatic, intramolecular, covalent, general acid and/or base catalysis, the better the catalyst. A transition state analog case study: Abyzmes (Antibody Catalysis) Recall that antibodies are immune system proteins that bind foreign molecules (see Chapter 5.4). The usual role of an antibody is to initiate an immune response. When the antigen-binding site, located in the variable region of an antibody, binds to an antigen, it triggers the formation of new antibodies (within B-cells) in an effort to optimize the immune response to that antigen. These new antibodies are made with mutations in the antigen binding region, and those that bind better than the original antibody will be selected to form longer lived memory B-cells, ready for the next time the body encounters the antigen. Decades ago, Linus Pauling made a hypothesis that antibodies could be produced with an atypical role—to act as catalysts! If antibody molecules could be made to bind to a compound resembling the transition state of a chemical reaction, they should also presumably catalyze the chemical reaction. In 1987, his prediction was verified. Lerner et al. made a transition state analog of an ester. When an ester is hydrolyzed, as shown in Figure $17$, the sp2 hybridized carbonyl carbon is converted to an sp3 hybridized center in the intermediate, with the carbonyl oxygen becoming an oxyanion. The transition state presumably looks more like this unstable intermediate (sp3, oxyanion). Thus, Lerner synthesized a phosphonate, an ester mimic with a sp3 hybridized phosphorous replacing the carbonyl C. It also has a negatively charged oxygen as does the intermediate for the ester. This phosphonate ester is very resistant to hydrolysis. When injected into a mouse (after first being covalently attached to a carrier protein so the small molecule becomes "immunogenic"), the mouse makes a protein antibody which binds to the phosphonate. When the corresponding carboxylic acid ester is added to the antibody, it is cleaved with nominal kcat and KM values. Site specific mutagenesis can then be done to make it an even better catalyst! The antibody enzymes have been called abzymes. The structure shown in Figure $17$ shows how phosphonamides act as transition state analogs as well. Figure $17$: PHOSPHONAMIDES: TRANSITION STATE ANALOGS Figure $18$s shows an interactive iCn3D model of transition state analog 5-(para-nitrophenyl phosphonate)-pentanoic acid bound to a mouse Fab antibody fragment with esterase activity (1aj7) Transition state theory can be used to more clearly quantify the relationships described in the graphical analysis above. This analysis will use the equilibrium constant (in contrast to the last two chapters which used dissociation constants to characterize macromolecule, receptor, and enzyme binding to ligand). Let assume that a substrate S is in equilibrium with its transition state S. Hence Keq = [S]/[S]. The following reaction can be written: S → S → P. Based on our previous kinetic analysis and experience in writing differential equations, dP/dt = k1[S]. By analogy, enzyme bound S (ES) can be converted to (ES) and then on to product as shown in the following chemical equation: $\ce{E + S <=> ES -> ES^{†} -> E + P}. \nonumber$ For the non-enzyme catalyzed reaction, transition state theory can be used to show that the first order rate constant k1= kT/h where k is the Boltzman's constant, T is the Kelvin temperature, and h is Planck's constant. Hence, using Keq = [S]/[S], equation 1 can be derived \frac{d P}{d t}=\frac{k T}{h}\left[S^{\dagger}\right]=\frac{k T}{h} K^{\dagger}[S]=k_{n}[S] where kn (hereafter written as kN) =(kT/h)K is the effective first order rate for the non-catalyzed rate. Now let's create a more complicated linked equilibrium showing the same reaction in the presence of an enzyme. Figure $19$ Remember that the K values for this analysis are equilibrium constants not dissociation constants. Note two important equilibrium constants, KS, the equilibrium constant for the binding of free S to E, and KT, the equilibrium constant for the binding of free S to E (assuming that free S could bind to E before it converted to product). As we have seen for linked equilibrium before, since the Keq values are related to the standard free energy changes which are state functions, the sum of the standard free energies going from E + S to ES (by either the top or bottom paths) are path independent so the products of the Keq for the top path are equal to those for the bottom paths. This gives the following equation: \frac{K_{T}}{K_{S}}=\frac{K_{E^{\dagger}}}{K_{N^{\dagger}}}=\frac{k_{E}}{k_{N}} The right hand side is the ratio of the effective first order rate constant for conversion or ES → E + P, kE divided by the rate constant for the conversion of S → P for the noncatalyzed rate, kN. The final ratio of rate constants can be derived from the simple relationship that kx=(kT/h)Kx where x is either N (non catalyzed) or E (enzyme catalyzed). Equation 2 states that the equilibrium constant for the binding of S to E, KT, is greater than the equilibrium constant for the binding of S to E, KS (as kE > kN). KT/KR ranges from 108 - 1014. Given common values for the equilibrium constant for binding of S to E (103 – 105 M-1) which is equivalent to dissociation constant values Kd = 10 uM – 1 mM, the calculated value of KT = 1015 M-1 which gives a dissociation constant for the enzyme and transition state of Kd = 10-15 M (1 femtomolar). This is as tight as one of the highest affinity binding interactions in the biological world, the binding of avidin and biotin. As we noted in Chapter 5.1, assuming that the second order rate constant for avidin/biotin binding and as shown above for E/S is diffusion controlled (about 108 M-1s-1), the off rate for the avidin-biotin or ES complex is 10-7 s-1, equivalent to a half life of the complex of 80 days. It doesn't get much tighter than that. Figure $20$ represent an image of an enzyme and three different molecules, 1–3, that could bind to it. Using the analysis above, which molecule do you think represents substrate? Transition state? Product? Physical Strategies for Rate Enhancement Intramolecular Catalysis Consider the hydrolysis of phenylacetate. This reaction, a nucleophilic substitution reaction, could be catalyzed by the addition of the general base acetate to the solution, as described above. Since this reaction rate would double with the doubling of the solution acetate, the reaction is bimolecular (first order in reactant and catalyst). Now consider the same reaction only when the the general base part of the catalyst, the carboxyl group, is part of the reactant phenylacetate. Such a case occurs in the acetylated form of salicylic acid—i.e., aspirin. When the carboxy group is ortho compared to the acetylated phenolic OH, it is in perfect position to accept a proton from water, decreasing the charge development on the O in the transition state. The general base does not have to diffuse to the appropriate site when it is intramolecular with respect to the carbonyl C of the ester link. The rate of this intramolecular base catalysis is about 100 fold greater than of an intermolecular base catalyst like acetate. It is as if the effective concentration of the intramolecular carboxyl base catalyst is much higher due to its proximity to the reaction site. Another type of reactions involving a carboxyl group (in addition to simple proton transfer) is when the negatively charged carboxyl O acts as a nucleophile and attacks an electrophilic carbonyl carbon. When the carbonyl is part of an ester, the carboxyl group engages in a nucleophilic substitution reaction, expelling the alcohol part of the ester as a leaving group. The remaining examples below consider the nucleophilic (carboxyl) substitution on phenylesters, with phenolate as the leaving group. The reactions in effect transfer an acyl group to the carboxyl group to create an anhydride. First consider acyl transfer with aspirin derivatives. Aspirin, as you know, contains a carboxyl group ortho to an ester substituent. Hence the carboxyl group can act as a nucleophile and attack the carbonyl carbon of the ester in a nucleophilic substitution reaction. The net effect is to transfer the acetyl group from the phenolic OH to the carboxyl group converting it to an anhydride. This is an intramolecular reaction. Compare this reaction to a a comparable bimolecular reaction shown in Figure $21$. The first order rate constant of the intramolecular transfer of the acetyl group to the carboxyl group, k1 = 0.02 s-1. The analogous bimolecular reaction rate constant k2~ 10-10 M-1s-1. Dividing k1/k2 gives the relative rate enhancement of the intramolecular over the intermolecular reaction. With units of molarity, this ratio can be interpreted as the relative effective concentration of the intramolecular nucleophile. This makes the effective concentration of the carboxylate in the aspirin derivative 2 x 107 M. Now consider the cleavage of phenylacetate using acetate as the nucleophile as shown in Figure $22$. The products are acetic anhydride and phenolate. This is a bimolecular reaction (a slow one at that), with a bimolecular rate constant, k2 which I will arbitrarily set to 1 for comparison to some similar reactions. Now consider a monoester derivatives of succinic acid - phenyl succinate - in which the free carboxyl group of the ester attacks the carbonyl carbon of the ester derivative, as shown in Figure $23$. If you assign a second order rate constant k2 = 1 M-1s-1 to the analogous intermolecular reaction of acetate with phenylacetate (as described above), the first order rate constant for the intramolecular reaction of phenylsuccinate is 105 s-1. The ratio of rate constants, k1/k2 = 105 M. That is it would take 105 M concentration of acetate reacting with 1 M phenylacetate in the first bimolecular reaction to get a reaction as fast as the intramolecular reaction of phenylsuccinate. The intramolecular reaction of an even more sterically restricted bicyclic phenylcarboxylate shown in Figure $24$ has a k1/k2 = 108 M. Another example is anhydride formation between two carboxyl groups. The ΔGo for such a reaction is positive, suggesting an unfavorable reaction. Consider two acetic acid molecules condensing to form acetic anhydride. For this intermolecular reaction, Keq = 3x10-12 M-1. Now consider the analogous intramolecular reaction of the dicarboxylic acid succinic acid. It condenses in an intramolecular reaction to form succinic anhydride with a Keq = 8x10-7 (no units). The ratio Keq-intra/Keq inter = 3 x105 M. It is as if the effective concentration of the reacting groups because they do not have to diffuse together to react, is 3 x105 M. How does this apply to enzyme catalyzed reaction? Enzymes bind substrates in physical steps which are typically fast. The slow step is often the chemical conversion of the bound substrate, which is effectively intramolecular if the initial binding reaction is fast. These three kinds of reactions, intermolecular, intramolecular, and enzyme-catalyzed can be broken down into two hypothetical steps, a binding followed by catalysis as shown in Figure $25$. If the rate constants for the chemical steps are all identical, the advantage of the intramolecular and enzyme-catalyzed reaction over the intermolecular reaction is KINTRA/KINTER and KENZ/KINTER, respectively. The advantage of intramolecular reactions can be seen by studying the Ca-EDTA complex. Calcium in solution exists as a octahedrally coordinated complex with water occupying all the coordination sites. EDTA, a multidentate ligand, first interacts through one of its potential six electron donors to Ca in a reaction which is entropically disfavored from the the Ca-EDTA perspective, although one water is released. Once this first intramolecular complex is formed, the rest of the ligands on the EDTA rapidly coordinate with the Ca and release bound water as illustrate in Figure $26$. The former is no longer entropically disfavored since it is now an intramolecular process while the later is favored through the release of the remaining five water molecules. Figure $26$: Binding of Ca2+ and EDTA We've shown above the catalytic advantage offered by intramolecular reaction in terms of a dramatic increase in the effective concentration of reactants, which sometimes reached levels of 108 M. Another way is to look at entropy changes associated with dimer formation. The table below shows that an intramolecular reaction is favored over an intermolecular reaction since in the latter, significant decreases in translational and rotation entropy result. Translational, Rotational, and Internal Entropies for Dimer Formation: A + B ↔ A-B (cal/K.mol) System A B A-B ΔS Gas S trans 30 30 30 -30 S rot 20 20 20 -20 S int 5 5 20 +10 Gas → Solution -10 -10 -15 S sol 45 46 55 -35 (Correspond to 108-109 M) Strain Distortion In organic chemistry, you learned that certain structures such as three-membered and four-membered ring structures, such as epoxides were highly reactive due to the strain distortion inherent to the unfavored bond angles inherent to the ring. Enzyme active sites can also utilize strain distortion within a bound substrate to increase the reactivity of the molecule and favor the formation of the transition state. Many enzymes that function by the induced fit model also utilize strain distortion within their catalytic mechanism. Within the unbound state they remain in a low catalytic state, however the interaction with the substrate induces the destabilization of the enzyme active site or may induce strain within the substrate causing the initiation of the catalytic activity of the enzyme. A Note on Asymmetric Catalysis/Organocatalysis In a subsequent section, we will discuss how protein enzymes use the catalytic strategies described above. An intriguing question arises: how much of the structure of a large protein is really needed for catalysis? Much work has been directed to the development of small molecule catalysis mimetics of large protein enzymes. Just how small can you go in reducing the size of a protein and still get catalysis? One important feature of enzyme catalysis is that they catalyze reactions in which only one enantiomer is produced. That is, the synthesis is asymmetric. This is typically a consequence of the asymmetric enzyme (itself chiral) binding only one enantiomer as a reactant and/or the imposition of steric restrictions on the possible reactions of the bound substrate. L-Pro alone can act as such an asymmetric catalyst in an aldol condensation reaction. Figure $27$: Catalysts are vital in biological settings but also in the laboratory synthesis of molecules that sustain our culture and economy. Transition metal and, increasingly, protein enzymes have been used as industrial catalysts. They have now been joined by new asymmetric catalysts (a subset of organocatalysts). The work of Benjamin List and David MacMillan, who were instrumental in developing the ideas of asymmetric catalyst, has been recognized by the Nobel Commission which awarded the 2021 Nobel Prize in Chemistry to them. The enzyme triose phosphate isomerase catalyzes an asymmetric reaction, in which only one enantiomer of glyceraldehyde-3-phosphate is produced from the achiral dihydroxyacetone phosphate. Figure $28$ shows this enantiospecific reaction. Figure $29$s contains an interactive iCn3D model of a triose phosphate isomerase from Trypanosoma brucei brucei (1KV5), which shows a conserved active site Pro 168 (spacefill) and amino acid side chains within 4 Å (stick) within the context of one monomer (cartoon) of the dimeric protein.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/06%3A_Enzyme_Activity/6.01%3A_How_Enzymes_Work.txt

Search Fundamentals of Biochemistry Single Step Reactions First, we will explore the kinetics of non-catalyzed reactions, which is needed to understand the kinetic of the more complicated enzyme-catalyzed reactions. Calculus: Derivatives and Integrals In this book, we will refer to the change of concentration of a chemical species X as a function of time as dX/dt instead of ΔX/Δt, where dX/dt is the derivative of X with respect to time t. This is the language of calculus, which most readers would have studied. The use of calculus will be mainly limited to writing equations of the form dX/dt = f(t), which is a type of differential equation. We will also use a few integrals, but a working knowledge of calculus is not required. For readers who have not studied calculus, replace dX/dt with ΔX/Δt in your mind, and you will derive the same meaning. You studied two types of kinetic equations in introductory chemistry to analyze kinetic data: • Initial Rates: In this method, the initial rate, v0, is measured as a function of the concentration of reactants. The initial velocity, v0, is the initial slope of a graph of the concentration of reactants or products as a function of time, taken over a range of times such that only a small fraction of A has reacted. Under this condition, [A] over this short time range is approximately constant and equal to Ao. Initial rate graphs are often based on the measurement of product increase with time, ΔP/Δt, so v0 vs. A plots have positive slopes. The velocity at time t along the A vs. t curve, dA/dt, constantly changes as [A] decreases since the velocity depends on the [A]. To reiterate, the initial velocity of the reaction is the slope of the initial linear part of the decay curve when the rate is essentially linear over a narrow range of [A]. • Integrated Rates and Progress Curves: In this method, a differential equation that gives the change of A or P with time (dA/dt or dP/dt) is integrated to give an equation that shows the concentration of A or P as a function of time. For any given reaction, it is essential to be able to write the integrated rate equation, often called an ordinary differential equation (ODE). In almost every case, we provide the solutions to the ODEs in either a mathematical equation or a fitted graph of A vs. t. As the reactions get more complicated, we let computers solve them numerically and show the output. The ODEs are also called progress curves as they show how the concentration of reactants and products change with time. In either case, the mathematical equations describing the reaction are used to fit experimental data taken in the lab. Statistics are used to see how closely the experimental data fit the rate equations. The best-fit equation give the mostly likely chemical reaction equations for the interconversion of reactant and products. Most biochemistry textbooks focus on initial velocities when explaining enzyme kinetics. However, in many ways, this method is less intuitive than exploring how the concentration of a molecule changes with time. An analogy is the concept of density (mass/volume), which is more complicated than understanding mass or volume separately. We use both methods to develop both chemical and biological intuitions of kinetic properties. We will first explore simple irreversible reactions (1st and 2nd order), then make them reversible, and then couple them together to form more complex reaction schemes, much as we are compelled to do for enzyme-catalyzed reactions. First Order Reaction $\ce{A ->[k_{1}] P} \nonumber$ where k1 is the first-order rate constant. For these reactions, the velocity of the reaction, $v$, is directly proportional to [A], or $v=-\frac{d A}{d t}=+\frac{d P}{d t}=k_{1} A \label{6.3.A.1}$ The negative sign in -d[A]/dt indicates that the concentration of A decreases. The equation could also be written as:$v=\frac{d A}{d t}=-k_{1} A$ For the rest of the reactions in this book, we follow the convention of writing all velocities expressed as d[x]/dt as positive numbers. A negative sign for a term on the right-hand side of the differential equation will indicate that the concentration dependency of that term will lead to a decrease in [x] with time. Likewise, a positive sign for the term on the right-hand side of the equation will indicate that concentration dependency of that term will lead to an increase in [x] with time. Examples: dA/dt = -kA shows that the A will decrease with time. dA/dt = +kA shows that A will increase with time. Here is the solution to the differential Equation 6.3.A.1 for [A] as a function of t. A=A_0 e^{-k_1 t} A derivation of the first order rate equation Here it is! Derivation \begin{gathered} \int_{A_O}^A \frac{d A}{A}=-k_1 \int_0^t d t \ \ln A-\ln A_0=-k_1 t \ \ln A=\ln A_0-k_1 t \ A=A_0 e^{-k_1 t} \end{gathered} Equation 6.3.A.2 is an example of an integrated rate equation. The following graphs show plots o.f A vs t and lnA vs. t for a first-order process. Note that the derivative of the graph of A vs. t (dA/dt) is the velocity of the reaction. The graph of ln[A] vs. t is linear with a slope of -k1. The velocity of the reaction (slope of the A vs t curve) decreases with decreasing A, which is consistent with equation 1. Again, the initial velocity is determined from data taken in the first part of the decay curve when the rate is linear and little A has reacted. That is, [A] is approximately equal to [A0]. Figure $1$ shows two ways to plot 1st order reaction data. The left graph shows the exponential decay of A with time and the corresponding rise in P when A0 is 0 and k1=2. The other shows the linear fall of ln[A] vs time. Once again, for complete clarification, another way of analyzing the kinetics of a reaction, in addition to following the concentration of a reactant or product as a function of time and fitting the data to an integrated rate equation, is to plot the initial velocity, vo, of the reaction as a function of the concentration of reactants. The initial velocity is the initial slope of a graph of the concentration of reactants or products as a function of time, taken over a range of times such that only a small fraction of A has reacted, so [A] is approximately constant = Ao. From the first-order graph of A vs. t above, the slope approaches 0 with increasing time as [A] approaches 0, which indicates that the reaction velocity depends on A. For this first-order order process, two equivalent equations can be written showing the • disappearance of A as v = -d[A]/dt = k1[A], while • appearance of A as v = d[A]/dt = -k1[A], Both equations show that v is directly proportional to A. As [A] is doubled, the initial velocity is doubled. Velocity graphs used by biochemists often show the initial velocity of product formation (not reactant decrease) as a function of reactant concentration. Hence, as product concentration increases, the initial velocity are positive. A graph of v (= dP/dt) vs [A] for a first order process would have a positive slope and be interpreted as showing that the rate of appearance of P depends linearly on [A]. Second Order/Pseudo First Order Reactions A+B \stackrel{k_2}{\longrightarrow} P \text { or } A+A \stackrel{k_2}{\longrightarrow} P where $k_2$ is the second-order rate constant. For the first of these irreversible reactions, the velocity of the reaction, v, is directly proportional to [A] and [B], or v=\frac{d A}{d t}=-k_2[A][B] We will consider two special cases of this reaction type: 1. [B] >> [A]. Under these conditions, [B] never changes, so Equation 5 becomes v=-\left(k_2[B]\right)[A]=-k_1^{\prime}[A] where k1' is the pseudo first order rate constant (= k2[B] ) for the reaction. The reaction appears to be first order, depending only on [A]. 1. As illustrated in the second reaction above, the only reactant is A, which must collide with another A to form P. The following differential equation can be written and solved to find [A] as a function of t. v=\frac{d A}{d t}=2 \frac{d P}{d t}=-k_2 A^2 Solving the differential equation for A gives the following: \frac{1}{A}=\frac{1}{A_0}+k_2 t A derivation of the second order rate equation Here it is! Derivation \begin{gathered} \frac{d A}{d t}=-k_2 A^2 \ \int_{A_0}^A \frac{d A}{A^2}=\int_{A_0}^A A^{-2} d A=\int_{A_0}^A A^{-2} d A=-k_2 \int_0^t d t \ \left.\left.\left.\frac{A^{n+1}}{n+1}\right]_{A_0}^A=\frac{A^{-1}}{-1}\right]_{A_0}^A=-k_2 t\right]_{A_0}^A \ -\frac{1}{A}-\left(\frac{1}{A_0}\right)=-k_2 t \end{gathered} Figure $2$ shows plots of A vs. t and 1/A vs. t for a second-order process when A0 is 0 and k2=1. The right graph shows the linear rise of 1/[A] with time. Note that just from a plot of A vs. t, it would be difficult to distinguish a first from a second-order reaction. If the plots were superimposed, you would observe that at the same concentration of A (10, for example), the vo of a first-order reaction would be proportional to 10, but for a second-order reaction, to 102 or 100. Therefore, the second-order reaction is faster (assuming similarity in the relative magnitude of the rate constants), as indicated by the steeper negative slope of the curve. However, at low A (0.1 example), the vo of a first-order reaction would be proportional to 0.1 but second-order order reaction to 0.12 or 0.01. Therefore, at low A, the second-order reaction is slower. The interactive graphs below show the first and second-order conversion of reactant A to product. Change the sliders to see how the curves are different. By comparing these curves, you should see how difficult it is to differentiate between a 1st and 2nd order process unless the reaction progresses to almost completion. Multi-Step Reactions Reversible First Order Reactions \mathrm{A} \underset{\mathrm{k}_2}{\stackrel{\mathrm{k}_1}{\rightleftarrows}} \mathrm{P} Here is the differential equation for dA/dt.. \mathrm{v}=\frac{\mathrm{dA}}{\mathrm{dt}}=-\mathrm{k}_1 \mathrm{~A}+\mathrm{k}_2 \mathrm{P} Here are the solution for both A and P as a function of time \begin{gathered} \mathrm{A}=\frac{\mathrm{A}_0\left(\mathrm{k}_2+\mathrm{k}_1\left[\mathrm{e}^{-\left(\mathrm{k}_1+\mathrm{k}_2\right) \mathrm{t}}\right]\right.}{\mathrm{k}_1+\mathrm{k}_2} \ \mathrm{P}=\mathrm{A}_0-\left(\frac{\mathrm{A}_0\left(\mathrm{k}_2+\mathrm{k}_1\left[\mathrm{e}^{-\left(\mathrm{k}_1+\mathrm{k}_2\right) \mathrm{t}}\right]\right.}{\mathrm{k}_1+\mathrm{k}_2}\right) \end{gathered} Figure $3$ shows graphs of A and P vs t for this reaction at two different sets of values of k1 and k2. Change the sliders on the interactive graph below of a reversible reaction to see how changing the relative values of the forward and reverse rate constants affects the concentrations at which the concentration plateaus are reached. We all grew up on mathematical graphs that give you valuable insight into textual descriptions and data tables from which the graphs were made. These graphs are enhanced when you can use sliders to change constants as for the reversible reaction A ↔ P above. Even then, you might not infer that when the reaction has reached equilibrium, product is still being made from reactant, and reactant from product, since the equilibrium is dynamic. To add insight into simple and complex reactions, animations showing the continual disappearance of reactants and products are valuable. This book will incorporate many animations to visually show the changes in the reactant and product concentrations. Hui Liu and Shraddha Nayak (Animation Lab, University of Utah) made all the animations in this book using mathematical solutions to the progress curves for the reactions. Multiple modes of presentation are useful as learners with different backgrounds and preferred ways of learning attempt to understand complex materials. It is relatively simple to write the differential equations (differential) to show how the rate of disappearance of a reactant A (for example), dA/dt, depends on the concentration of its immediate participants in the reaction. It is not so easy to solve the equations (as we did above) for the progress curve, which shows how [A] changes with time t (i.e. [A] = f(t)). Luckily, many programs have been developed that produce numerical solutions to the differential equations and give progress curve graphs like [A] = f(t). Two interrelated, freely available programs, Copasi and Virtual cell (Vcell), can solve all the equations for hundreds of cellular reactions simultaneously. They use a format called Systems Biology Markup Language (SBML) for describing and storing computational models. We will use Vcell models in this book as they are straightforward to create. All the coding to describe the reactions is built into Vcell and this book and hidden from you. All you will see are the output results. You can change the progress curves by moving sliders to change constants and see the resulting changes in graphical outputs. The VCell models use a reaction diagram that shows all of the interconnected species. The first Vcell model we will run is for the reversible conversion of A to P (A ↔P), which we just discussed and displayed in the graph above. Here is the Vcell reaction diagram and a description of its components. • The reactant A and product P are called species and are shown as green spheres. • The yellow square indicates a reaction node connecting A to P. • Lines connect the species that participate in the reaction. The arrows appear unidirectional, BUT the equations describing the concentrations of A and P are derived assuming a reversible reaction with rate constants kf (forward reaction) and kr (reverse reaction). The program calculates A and P as a function of time (i.e. it solves the differential equations for both species). The graphs of concentration vs time are called progress curves. It also can calculate fluxes (J) (velocities) for each species. The flux at any given time is the slope of the concentration vs time curve at any given time. When we get to metabolism, we will talk about fluxes of metabolites through pathways. Also, fluxes are used to describe the rate of movement of solute through membranes. Here is the result of the simulation run in Vcell, exported as a sbml file, and displayed in the book using a program called MiniSideWinder. MODEL Reversible reaction A ↔ P Vcell reaction diagram (1-way arrows defined as reversible in actual mathematical model) and chemical equation Initial parameter values: kf = 2,  kr = 4 Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) Animations The video animations show particles representing A (red) and P (cyan) interconverting in a reversible process with embedded progress curves showing A (red) and P (cyan) vs. time. Reversible Rx A (red) ↔ B (cyan): kF = kR = 3 Reversible Rx A (red) ↔ B (cyan): kF=4, kR=2 Reversible Rx A (red) ↔ B (cyan): kF=2, kR=4 Consecutive Irreversible First Order Reactions The following differential equations can be written for these reactions: \begin{gathered} \frac{\mathrm{d} \mathrm{A}}{\mathrm{dt}}=-\mathrm{k}_1 \mathrm{~A} \ \frac{\mathrm{dB}}{\mathrm{dt}}=\mathrm{k}_1 \mathrm{~A}-\mathrm{k}_2 \mathrm{~B} \ \frac{\mathrm{dC}}{\mathrm{dt}}=\mathrm{k}_2 \mathrm{~B} \end{gathered} Here are the solutions to the differential equations: \begin{array}{c}{\mathrm{A}=\mathrm{A}_{0} \mathrm{e}^{-\mathrm{k}_{1} \mathrm{t}}} \ {\mathrm{B}=\frac{\mathrm{k}_{1} \mathrm{A}_{0}}{\mathrm{k}_{2}-\mathrm{k}_{1}}\left(\mathrm{e}^{-\mathrm{k}_{1} \mathrm{t}}-\mathrm{e}^{-\mathrm{k}_{2} \mathrm{t}}\right)} \ {\mathrm{C}=\mathrm{A}_{0}-\mathrm{A}-\mathrm{B}=\mathrm{A}_{0}\left[1+\frac{1}{\mathrm{k}_{1}-\mathrm{k}_{2}}\left(\mathrm{k}_{2} \mathrm{e}^{-\mathrm{k}_{1} \mathrm{t}}-\mathrm{k}_{1} \mathrm{e}^{-\mathrm{k}_{2} \mathrm{t}}\right]\right.}\end{array} Figure $4$ shows graphs of A, B, and C vs. t for these reactions for a fixed value of k1 and k2. Figure $4$: Graphs of A, B, and C vs. t for the irreversible reactions A → B → C for a fixed value of k1 and k2. Change the sliders on the interactive graph below to see how changing the forward and reverse rate constants affect the curves. Here are two different animations for the irreversible reaction using different rate constants. Irreversible Rx A (red) → B (cyan) → C (blue) k1 = 0.2, k2 = 0.6 Irreversible Rx A (red) → B (cyan)→ C (blue) k1= 0.6, k2 = 0.2 Consecutive Reversible First Order Reactions You can imagine that solving the equations for the completely reversible reactions of A ↔ B ↔ C would be very difficult. However, writing the differential equations for each step is straight-forward and can be done easily in Vcell by choosing the built-in equations for each separate reaction based on mass action. The program can then solve the equations numerically to produce progress curve graphs. Now let's look at the simulation for the fully reversible reactions A ↔ B ↔ C. Again, the model was built and solve in VCell, and then exported in the system's biology markup language (sbml) format. The interactive graphs are made using a program called miniSideWinder. A note: Arrows in VCell Diagrams - In the reaction diagram for the reversible reaction A ↔ B ↔ C below, the arrows go in only one direction, left to right, and simply show that the species are connected. However, in the Vcell program, the equations for the reversible reaction were used to produce the graphs below. To run the simulation of the irreversible reaction, the rate constants for the reverse reaction would be set to 0. MODEL Reversible reaction A ↔ B ↔ C. Vcell reaction diagram (1-way arrows defined as reversible in actual mathematical model) Initial parameter values: k1f = 0.2, k1r = 0.1, k2f = 0.6, k2r = 0.3 A0 = 1 Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) Here is the corresponding animation for the fully reversible reaction A ↔ B ↔ C. Reversible Rx A (red) ↔ B (cyan) ↔ C Blue) k1f = 0.2, k1r = 0.1; k2f =0.6, k2r = 0.3

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/06%3A_Enzyme_Activity/6.02%3A_Kinetics_without_Enzymes.txt

Search Fundamentals of Biochemistry An enzyme alters the pathways for converting a reactant to a product by binding to the reactant and facilitating the intramolecular conversion of bound substrate to bound product before it releases the product. Enzymes do not affect the thermodynamics of reactions. For reversible reactions (as an example), the equilibrium constant, Keq, is unchanged. What is charged is the rate at which equilibrium is achieved. Enzymes lower the activation energy for bound transition states and change the reaction mechanism. Figure $1$ shows the simplest chemical reaction that can be written to show how an enzyme catalyzes a reaction. Rapid Equilibrium Enzyme-Catalyzed Reactions We have previously derived equations for the reversible binding of a ligand to a macromolecule. Next, we derived equations for the receptor-mediated facilitated transport of a molecule through a semipermeable membrane. This latter case extended the former case by adding a physical transport step. Now, in what hopefully will seem like deja vu, we will derive almost identical equations for the chemical transformation of a ligand, commonly referred to as a substrate, into a product by an enzyme. We will study two scenarios based on two different assumptions, each enabling a straightforward mathematical derivation of kinetic equations: 1. Rapid Equilibrium Assumption - enzyme E (macromolecule) and substrate S (ligand) concentrations can be determined using the dissociation constant since E, S, and ES are in rapid equilibrium, as we previously used in our derivation of the equations for facilitated transport. Sorry about the switch from A to S in the designation of the substrate. Biochemists use S to represent the substrate (ligand) and A, B, P, and Q to represent reactants and products in the case of multi-substrate and multi-product reactions. 2. Steady State Assumption (more general) - enzyme and substrate concentrations are not those determined using the dissociation constant. Enzyme kinetics experiments, as we will see in the following chapters, must be used to determine the detailed mechanism of the catalyzed reaction. Using kinetic analysis, you can determine the order of binding/dissociation of substrates and products, the rate constants for individual steps, and clues to the mechanism used by the enzyme in catalysis. Consider the following reaction mechanism for the enzyme-catalyzed conversion of substrate S into product P. (We will assume that the catalyzed rate is much greater than the noncatalyzed rate.) $\ce{E +S <=>[K_s] ES ->[k_3] E + P} \nonumber$ As we did for the derivation of the equations for the facilitated transport reactions under rapid equilibrium conditions, this derivation is based on the assumption that the relative concentrations of S, E, and ES can be determined by the dissociation constant, KS, for the interactions and the concentrations of each species during the early part of the reaction (i.e. under initial rate conditions). Assume also the S >> E0. Remember that under these conditions, S does not change much with time. Is this a valid assumption? Examine the mechanism shown above. S binds to E with a second-order rate constant k1. ES has two fates. It can dissociate with a first-order rate constant k2 to S + E, or it can be converted to product with a first-order rate constant of k3 to give P + E. If we assume that k2 >> k3 (i.e. that the complex falls apart much more quickly than S is converted to P), then the relative ratios of S, E, and ES can be described by Ks. Alternatively, you can think about it this way. If S binds to E, most of S will dissociate, and a small amount will be converted to P. If it does, then E is now free, and will quickly bind S and reequilibrate since the most likely fate of bound S is to dissociate, not be converted to P (since $k_3 \ll k_2$). This also makes sense if you consider that the physical step, characterized by k2, is likely to be quicker than the chemical step, characterized by k3. Hence the following assumptions have been used: • $S \gg E_0$ • $P_0 = 0$ • $k_3$ is rate limiting (i.e. the slow step) We will derive equations showing the initial velocity v as a function of the initial substrate concentration, S0, assuming that P is negligible over the time period used to measure the initial velocity. Also assume that $v_{catalyzed} \gg v_{noncatalyzed}$. In contrast to the first-order reaction of S to P in the absence of E, v is not proportional to S0 but rather to Sbound. Therefore, $v \propto [ES]$, or $v = {const} [ES] = k_3 [ES] \label{EQ10}$ where $v$ is the velocity (i.e., reaction rate). Now, let's get $ES$ from the dissociation constant KS (assuming rapid equilibrium of E, S and ES) and mass balance for E (E0 = E + ES , so E = E0 - ES). We will use mass balance for S when we derive the equation of the steady state. In many cases, S is approximately equal to S0 or the total amount of substrate. This makes sense if you consider that the enzyme is a catalyst acts repeatedly to produce the product, so you don't need significant amounts of the enzyme. The resulting equation of [ES] is shown below. (E S)=\frac{\left(E_0\right)(S)}{K_S+(S)} A derivation of [ES] under rapid equilibrium conditions Here it is! Derivation \begin{gathered} K_S=\frac{[E][S]}{[E S]}=\left(\frac{\left.\left(\left[E_0\right]-[E S]\right)[S]\right)}{[E S]}\right. \ (E S) K_S=\left(E_0\right)(S)-(E S)(S) \ (E S) K_S+(E S)(S)=\left(E_0\right)(S) \ (E S)\left(K_S+(S)\right)=\left(E_0\right)(S) \end{gathered} This derivation assumes that we know S (which is equal to S0) and Etot (which is E0). Let us assume that S is much greater than E, as is the likely biological case. We can calculate ES using the following equations and the same procedure we used for the derivation of the binding equation, which gives the equation below: [E S]=\frac{\left[E_0\right][S]}{K_S+S} which is analogous to [M L]=\frac{\left[M_0\right][S]}{K_D+L} This leads to v_0=\frac{\left(k_3\right)\left(E_0\right)(S)}{K_S+(S)}=\frac{\left(V_M\right)(S)}{K_S+(S)} where V_M=k_3 E_0 This is the world-famous Henri-Michaelis-Menten Equation. It is a hyperbola just like the graph for binding of a ligand to a macromolecule with a given dissociation constant, KD. Move the sliders in the interactive graph below to see how changing Km and Vm alters the graph. Note that this graph is identical to the graph for M + L ↔ ML. Just as in the case with noncatalyzed first-order decay, it is easiest to measure the initial velocity of the reaction when [S] does not change much with time and the velocity is constant (i.e. the slope of the dP/dt curve is constant). A plot of [P] vs t (called a progress curve) is made for each different substrate concentration studied. From these curves, the initial rates at each [S] is determined. Alternatively, one reaction time that gives a linear rise in [P] with time is determined for all the different substrate concentrations. At that specified time, the reaction can be stopped (quenched) with a reagent that does not cause any change in S or P. Then initial rates can be easily calculated for each [S] from a single data point. Under these conditions: • a plot of v vs S is hyperbolic • v = 0 when S = 0 • v is a linear function of S when S<<Ks. • v = Vmax (or VM) when S is much greater than Ks • S = KS when v = VM/2. These are the same conditions we detailed for our understanding of the binding equation (M L)=\frac{\left(M_0\right) L}{K_D+L} Note that when S is not >> KS, the graph does not reach saturation and does not look hyperbolic. It should be apparent from the graph that only if S >> KS (or when S is approximately 100 x > Ks) will saturation be achieved. The KS constant is usually called the Michaelis constant, KM. We will see in a bit that the KM for most enzyme-catalyzed reactions is not equal to the dissociation constant for ES, which we called KS. Very often, these graphs are transformed into double reciprocal or Lineweaver-Burk plots as shown below. \frac{1}{v_0}=\frac{K_M+S}{V_M S}=\left(\frac{K_M}{V_M}\right) \frac{1}{S}+\frac{1}{V_M} These plots are used to estimate VM from the 1/v intercept (1/VM) and KM from the 1/S axis (-1/KM). These values should be used as "seed" values for a nonlinear fit to the hyperbola that models the actual v vs S curve. An interactive graph of 1/v0 vs 1/[S] is shown below. Change the sliders for KM and VM and note the change in the slope and intercepts of the plot As we saw in the graph of A or P vs t for a noncatalyzed, first-order reaction, the velocity of the reaction, given as the slope of those curves, is always changing. Which velocity should we use in Equation 5? The answer invariably is the initial velocity, v0, measured in the early part of the reaction when little substrate is depleted. Hence v vs S curves for enzyme-catalyzed reactions invariably are really v0 vs [S] curves. Steady State Enzyme-Catalyzed Reactions In this derivation, we will consider the following equations and all the rate constants, and will not arbitrarily assume that k2 >> k3. We will still assume that S >> E0 and that P0 = 0. An added assumption, however, is that d[ES]/dt is approximately 0. Look at this assumption this way. When an excess of S is added to E, ES is formed. In the rapid equilibrium assumption, we assumed that it would fall back to E + S (a physical step) faster than it would go onto product (a chemical step). In the steady state case, we will assume that ES might go on to product either less or more quickly than it will fall back to E + S. In either case, a steady state concentration of ES arises within a few milliseconds, and its concentration does not change significantly during the initial part of the reaction under which the initial rates are measured. Therefore, d[ES]/dt is about 0. For the rapid equilibrium derivation, v = k3[ES]. We then solved for ES using KS and mass balance of E. In the steady state assumption, the equation v= k3[ES] still holds, but now we will solve for [ES] using the steady state assumption that d[ES]/dt =0. \frac{d[E S]}{\mathrm{dt}}=\mathrm{k}_1[E][S]-\mathrm{k}_2[E S]-\mathrm{k}_3[E S]=0 We can solve this and obtain the Michaelis-Menten equation for reaction. \mathrm{v}=\mathrm{k}_3[\mathrm{ES}]=\frac{\mathrm{k}_3\left[\mathrm{E}_0\right][\mathrm{S}]}{\frac{\mathrm{k}_2+\mathrm{k}_3}{\mathrm{k}_1}+\mathrm{S}}=\frac{\mathrm{V}_{\mathrm{M}}[\mathrm{S}]}{\mathrm{K}_{\mathrm{M}}+\mathrm{S}} Derivation of the Michaelis-Menten Equation for the steady state To see how to derive this, click below. Answer Applying mass balance for E (i.e E = E0-ES) in appropriate term below gives \begin{array}{l}{\mathrm{k}_{1}[E][S]=\left(\mathrm{k}_{2}+\mathrm{k}_{3}\right)[E S]} \ {\mathrm{k}_{1}\left[\mathrm{E}_{0}-\mathrm{ES}\right][S]=\left(\mathrm{k}_{2}+\mathrm{k}_{3}\right)[E S]} \ {\mathrm{k}_{1}\left[\mathrm{E}_{0}\right][S]-\mathrm{k}_{1}[\mathrm{ES}][S]=\left(\mathrm{k}_{2}+\mathrm{k}_{3}\right)[E S]} \ {\mathrm{k}_{1}\left[\mathrm{E}_{0}\right][S]=\left(\mathrm{k}_{2}+\mathrm{k}_{3}\right)[E S]+\mathrm{k}_{1}[\mathrm{ES}][S]} \ {\mathrm{k}_{1}\left[\mathrm{E}_{0}\right][S]=[E S]\left(\mathrm{k}_{2}+\mathrm{k}_{3}+\mathrm{k}_{1} \mathrm{S}\right)}\end{array} Solving for [ES] gives [\mathrm{ES}]=\left(\frac{\left[\mathrm{E}_0\right][\mathrm{S}]}{\frac{\mathrm{k}_2+\mathrm{k}_3}{\mathrm{k}_1}+\mathrm{S}}\right) Substituting into the Henri Michaelis Menten equation gives \mathrm{v}=\mathrm{k}_3[\mathrm{ES}]=\frac{\mathrm{k}_3\left[\mathrm{E}_0\right][\mathrm{S}]}{\frac{\mathrm{k}_2+\mathrm{k}_3}{\mathrm{k}_1}+\mathrm{S}}=\frac{\mathrm{V}_{\mathrm{M}}[\mathrm{S}]}{\mathrm{K}_{\mathrm{M}}+\mathrm{S}} Note that V_M=k_3 E_0 and K_M=\frac{k_2+k_3}{k_1} Now let's look at a progress curve simulation that compares the Michaelis-Menten equation derived from rapid equilibrium assumptions (when k2>>k3) and in which KM is the actual dissociation constant to the steady state approximation, when k2 is not >>k3. MODEL A comparison of the rapid equilibrium (left) and steady state (right)Michaels-Menten reaction v_0=\frac{k_3\left[E_O\right][S]}{K_M+S}=\frac{V_M[S]}{K_M+S} v_0=\frac{k_3\left[E_O\right][S]}{\frac{k_2+k_3}{k_1}+S}=\frac{V_M[S]}{K_M+S} Initial Condition:  VM = 10  ; KM = 10;  S = 50 Initial Conditions: k1 = 10  ; k2 = 90 ;  k3 = 10 ;  E0 = 1 uM; S = 50 VM = k3E0 =10;   KM = (k2 + k3)/k2 = 10) Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) The initial conditions in the graph are set so the graphs of the rapid equilibrium and steady state are identical. Recommendations: • Scale the y-axis to 50 • change the slider k2_r2 for the steady state graph to other values. Watch the curves separate. Although these plots are only for 1 substrate concentration (50 uM), the effects of changing k2 for the steady state plot are very dramatic. This should convince you that in general, unless k3 << k2, the calculated value of KM is not equal to the thermodynamic dissociation constant, KD. Analysis of the General Michaelis-Menten Equation This equation can be simplified and studied under different conditions. First, notice that (k2 + k3)/k1 is a constant which is a function of relevant rate constants. This term is usually replaced by KM which is called the Michaelis constant. Likewise, when S approaches infinity (i.e. S >> KM, equation 5 becomes v = k3(E0) which is also a constant, called VM for maximal velocity. Substituting VM and KM into equation 5 gives the simplified equation: \mathrm{v}=\frac{\mathrm{V}_{\mathrm{M}}[\mathrm{S}]}{\mathrm{K}_{\mathrm{M}}+\mathrm{S}} It is extremely important to note that KM in the general equation does not equal the KS, the dissociation constant used in the rapid equilibrium assumption! KM and KS have the same units of molarity, however. A closer examination of KM shows that under the limiting case when k2 >> k3 (the rapid equilibrium assumption) then, \mathrm{K}_{\mathrm{M}}=\frac{\mathrm{k}_2+\mathrm{k}_3}{\mathrm{k}_1}=\frac{\mathrm{k}_2}{\mathrm{k}_1}=\mathrm{K}_{\mathrm{D}}=\mathrm{K}_{\mathrm{S}} If we examine these equations under several different scenarios, we can better understand the equation and the kinetic parameters: • when S = 0, v = 0. • when S >> KM, v = VM = k3E0. (i.e. v is zero order with respect to S and first order in E. Remember, k3 has units of s-1> since it is a first-order rate constant. k3 is often called the turnover number, because it describes how many molecules of S "turn over" to product per second. • v = VM2, when S = KM. • when S << KM, v = VMS/KM = k3E0S/KM (i.e. the reaction is bimolecular, dependent on both on S and E. k3/KM has units of M-1s-1, the same as a second order rate constant. More Complicated Enzyme-catalyzed Reactions A reversibly-catalyzed reaction You have learned previously that enzymes don't change the equilibrium constant for a reaction, but rather lowers the activation energy barrier to move from reactants to products. This implies that the activation energy to move in the reverse direction, from products to reactants, is also lowered. Hence the enzyme speeds up both the forward and reverse reaction. We haven't accounted for that yet in our kinetic equations. Many reactions in metabolic reactions that do not have large negative values of ΔG (ie. they are not significantly favored) are reversible, allowing the enzyme to be used in the reverse direction. Take for example the pathway to break down glucose to pyruvate (glycolysis). It has 9 different steps, of which 5 are reversible, allowing them to be used in the reverse pathway to take pyruvate to glucose (gluconeogenesis). Let's set up the equations for the reversible reaction of substrate S to product P catalyzed by enzyme E. Assume that the KM for the forward reaction is KMS (or KS) and for the reverse reaction is KMP (or KP) as shown in the reaction scheme in Figure $2$. The rate constant k2 is the kcat (forward rate constant) for conversion of ES to EP and k-2 is the kcat (reverse rate constant) for conversion of EP to ES The following simple Michaelis-Menten equations can be written for just the forward reaction and for the reverse reaction: \begin{aligned} &v_f=\frac{V_f S}{K_{M S}+S}=\frac{\frac{V_f S}{K_{M S}}}{1+\frac{S}{K_{M S}}} \ &v_r=\frac{V_r P}{K_{M P}+P}=\frac{\frac{V_r P}{K_{M P}}}{1+\frac{P}{K_{M P}}} \end{aligned} Now you might think that simply substracting the two would give the net velocity in the forward direction, but that is NOT the case. v \neq\left[\frac{\frac{V_f S}{K_{M S}}}{1+\frac{S}{K_{M S}}}-\frac{\frac{V_r P}{K_{M P}}}{1+\frac{P}{K_{M P}}}\right] The reason is that in the derivation, the equations for both the forward and reverse rates must have terms for the reverse and forward reactions, respectively. A simple derivation shows that this is the equation for the reversible conversion of substrate to product. v=k_2[E S]-k_{-2}[E P]=\frac{V_f \frac{[S]}{K_S}}{\left[1+\frac{[S]}{K_S}+\frac{[P]}{K_p}\right]}-\frac{V_r \frac{[P]}{K_P}}{\left[1+\frac{[S]}{K_S}+\frac{[P]}{K_P}\right]}=\frac{V_f \frac{[S]}{K_S}-V_r \frac{[P]}{K_P}}{\left[1+\frac{[S]}{K_S}+\frac{[P]}{K_P}\right]} This reversible form of the Michaelis-Menten equation and other equations, written in the format shown in equation 6.24, are commonly used in programs such as VCell and Copasi to model the kinetics of whole pathways of biological interactions and reactions. The figures below show a reaction diagram, graphical results showing S and P vs time for the selected KM and VM values shown, and animations for the reaction. The chemical (S and P) are shown as green spheres connected by a line. The red dot again represents the enzyme (shown as a node of connection between S and P). Equation 6.24 was used to model the reversible reaction (even though the arrows shown between S and P are unidirectional. MODEL Reversible Enzyme-Catalyzed Reaction:   E + S  ↔  ES  ↔  EP  ↔ E  + P . Vcell reaction diagram (1-way arrows defined as reversible in the actual mathematical model) and chemical equation Initial parameter values: as shown in above Select Load [Enz Rev] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) If you reflect on it, this reaction is very similar to the reversible reaction of A ↔ P in the absence of an enzyme, which we explored in Chapter section 6.2. Just for comparison, the graph for that reaction is shown below. Change the sliders to produce a curve similar to the enzyme-catalyzed reaction shown in the figure above. Reaction with intermediates Not all reactions can be characterized so simply as a simple substrate interacting with an enzyme to form an ES complex, which then turns over to form product. Sometimes, intermediates form. For example, a substrate S might interact with E to form a complex, which then is cleaved to products P and Q. Q is released from the enzyme, but P might stay covalently attached. This happens often in the hydrolytic cleavage of a peptide bond by a protease, when an activated nucleophile like Ser reacts with the sessile peptide bond in a nucleophilic substitution reaction, releasing the amine end of the former peptide bond as the leaving group while the carboxy end of the peptide bond remains bonded to the Ser as an Ser-acyl intermediate. Water then enters and cleaves the acyl intermediate, freeing the carboxyl end of the original peptide bond. This is shown in the written reaction in Figure $3$: Even for this seemingly complicated reaction, you get the standard Michaelis-Menten equation. To simplify the derivation of the kinetic equation, let's assume that E, S, and ES are in rapid equilibrium defined by the dissociation constant, Ks. Assume Q has a visible absorbance, so it is easy to monitor. Assume from the steady state assumption that: \frac{\mathrm{d}[\mathrm{E}-\mathrm{P}]}{\mathrm{dt}}=\mathrm{k}_2[\mathrm{ES}]-\mathrm{k}_3[\mathrm{E}-\mathrm{P}]=0 assuming that k3 is a pseudo first-order rate constant and that [H2O] doesn't change. The velocity depends on which step is rate-limiting. If k3<<k2, then the k3 step is rate-limiting. Then \mathrm{v}=\mathrm{k}_3[\mathrm{E}-\mathrm{P}] If k2<<k3, then the k2 step is rate-limiting. Then \ \mathrm{v}=\mathrm{k}_2[\mathrm{ES}] The following kinetic equation for this reaction can be derived, assuming v = k2[ES]. \mathrm{v}=\frac{\frac{\mathrm{k}_2 \mathrm{k}_3}{\mathrm{k}_2+\mathrm{k}_3}\left[\mathrm{E}_0\right][\mathrm{S}]}{\left(\frac{\mathrm{k}_3}{\mathrm{k}_2+\mathrm{k}_3}\right) \mathrm{K}_{\mathrm{S}}+\mathrm{S}}=\frac{\mathrm{k}_{\mathrm{cat}}\left[\mathrm{E}_0\right][\mathrm{S}]}{\mathrm{K}_{\mathrm{M}}+\mathrm{S}}=\frac{\mathrm{V}_{\mathrm{M}} \mathrm{S}}{\mathrm{K}_{\mathrm{M}}+\mathrm{S}} You can verify that you get the same equation if you assume that v = k3[E-P].he Derivation: This equation looks quite complicated, especially if you substitute for Ks, k-1/k1. All the kinetic constants can be expressed as functions of the individual rate constants. However, this equation can be simplified by realizing the following: • When $\mathrm{S}>>\frac{\mathrm{K}_{\mathrm{S}} \mathrm{k}_{3}}{\mathrm{k}_{2}+\mathrm{k}_{3}}, \mathrm{v}=\left(\frac{\mathrm{k}_{2} \mathrm{k}_{3}}{\mathrm{k}_{2}+\mathrm{k}_{3}}\right) \mathrm{E}_{0}=\mathrm{V}_{\mathrm{M}}$ • $\frac{\mathrm{K}_{\mathrm{S}} \mathrm{k}_{3}}{\mathrm{k}_{2}+\mathrm{k}_{3}}=\mathrm{constant}=\mathrm{K}_{\mathrm{M}}$ Substituting these into equation 7 gives: $\mathrm{v}=\frac{\mathrm{V}_{\mathrm{M}} \mathrm{S}}{\mathrm{K}_{\mathrm{M}}+\mathrm{S}} \nonumber$ This again is the general form of the Michaelis-Menten equation The expression for VM in the first bulleted expression above is more complicated than our earlier definition of VM = k3E0. They are similar in that the term E0 is multiplied by a constant which is itself a function of rate constant(s). The rate constants are generally lumped together into a generic constant called kcat. • For the simple reaction kcat = k3 • For the more complicated reaction above with a covalent intermediate, $\mathrm{k}_{\mathrm{cat}}=\frac{\mathrm{k}_{2} \mathrm{k}_{3}}{\mathrm{k}_{2}+\mathrm{k}_{3}}$ • For all reactions, VM = kcatE0. Figure $4$ compares the Michaelis-Menten kinetic equations for the rapid equilibrium, steady state assumptions, and covalent intermediate cases . Meaning of Kinetic Constants It is important to get a "gut-level" understanding of the significance of the rate constants. Here they are: • KM: The Michaelis constant with units of molarity (M), is operationally defined as the substrate concentration at which the initial velocity is half of VM. It is equal to the dissociation constant of E and S only in if E, S and ES are in rapid equilibrium. It can be thought of as an "effective" (but not actual) KD in other cases. • kcat: The catalytic rate constants, with units of s-1 is often called the turnover number. It is a measure of how many bound substrate molecules "turnover" or form product in 1 second. This is evident from equation v0 = kcat[ES] • kcat/KM: Under condition when [S] << KM, the Michaelis-Menten equation becomes v0 = (kcat/KM)[E0][S]. This really describes a biomolecular rate constant (kcat/KM), with units of M-1s-1, for conversion of free substrate to product. Some enzymes have kcat/Km values around 108, indicating that they are diffusion controlled. That implies that the reaction is essentially done as soon as the enzyme and substrate collide. The constant kcat/Km is also referred to as the specificity constant in that it describes how well an enzyme can differentiate between two different competing substrates. (We will show this mathematically in the next chapter.) Table $1$ below shows KM and kcat values for various enzymes KM values enzyme substrate Km (mM) catalase H2O2 25 hexokinase (brain) ATP 0.4 D-Glucose 0.05 D-Fructose 1.5 carbonic anhydrase HCO3- 9 chymotrypsin glycyltyrosinylglycine 108 N-benzoyltyrosinamide 2.5 b-galactosidase D-lactose 4.0 threonine dehydratase L-Thr 5.0 kcat values enzyme substrate kcat (s-1) catalase H2O2 40,000,000 carbonic anhydrase HCO3- 400,000 acetylcholinesterase acetylcholine 140,000 b-lactamase benzylpenicillin 2,000 fumarase fumarate 800 RecA protein (ATPase) ATP 0.4 Table $1$: KM and kcat values for various enzymes Table $2$ below show kcat, KM and kcat/KM values for diffusion-controlled enzymes Enzymes with kcat/KM values close to diffusion controlled (108 - 109 M-1s-1) enzyme substrate   kcat (s-1) Km (M) kcat/Km (M-1s-1) acetylcholinesterase acetylcholine   1.4 x 104 9 x 10-5 1.6 x 108 carbonic anhydrase CO2   1 x 106 1.2 x 10-2 8.3 x 107 HCO3-   4 x 105 2.6 x 10-2 1.5 x 107 catalase H2O2   4 x 107 1.1 4 x 107 crotonase crotonyl-CoA   5.7 x 103 2 x 10-5 2.8 x 108 fumarase fumarate   8 x 102 5 x 10-6 1.6 x 108 malate   9 x 102 2.5 x 10-5 3.6 x 107 triose phosphate isomerase glyceraldehyde-3-P   4.3 x 103 4.7 x 10-4 2.4 x 108 b-lactamase benzylpenicillin   2.0 x 103 2 x 10-4 1 x 108 Table $2$ below show kcat, KM and kcat/KM values for diffusion controlled enzymes Experimental Determination of VM and KM How can VM and KM be determined from experimental data? From initial rate data The most common way to determine VM and KM is through initial rates, v0, obtained from P or S vs time curves. Hyperbolic graphs of v0 vs [S] can be fitted or transformed as we explored with the different mathematical transformations of the hyperbolic binding equation to determine KD. These included: • Michaelis-Menten plot: nonlinear hyperbolic fit • Lineweaver-Burk double reciprocal plot • Scatchard plot • Eadie-Hofstee plot We discussed all of these plots except for the Eadie-Hofstee plot, in the chapter on binding. The Eadie-Hofstee plot is another linearized version of Michaelis-Menten equation Here is a derivation of that equation, which starts with each side of the double-reciprocal plot being multiplied by v0VM. \begin{aligned} \left(v_0 V_M\right) \frac{1}{v_0} &=\left(v_0 V_M\right)\left(\frac{K_M}{V_M}\right) \frac{1}{S}+\left(v_0 V_M\right) \frac{1}{V_M} \ V_M &=\left(v_0\right) K_M \frac{1}{S}+\left(v_0\right) \ v_0 &=-K_M\left(\frac{v_0}{S}\right)+V_M \end{aligned} Note that a graph of v0 vs v0/S is linear so slopes and intercepts can be used to obtain values for VM and KM. The double-reciprocal plot is commonly used to analyze initial velocity vs substrate concentration data. When used for such purposes, the graphs are referred to as Lineweaver-Burk plots, where plots of 1/v vs 1/S are straight lines with slope m = KM/VM, and y-intercept b = 1/VM. Figure $5$ common graphs used to display initial rate enzyme kinetic data. The straight-line plots shown above should not be analyzed using linear regression, since simple linear regression assumes constant error in v0 values. A weighted linear regression or even better, a nonlinear fit to a hyperbolic equation should be used. (Common Error in Biochemistry Textbooks: The Shape of the Hyperbola). A rearrangement of the corresponding Scatchard equations in the Eadie-Hofstee plot is also commonly used, especially to visualize enzyme inhibition data as we will see in the next chapter. An Extension: kcat and VM from integrated rate equations KM and VM could be theoretically extracted from progress curves of A or P as a function of t at one single A concentration by deriving an integrated rate equation for A or P as a function of t, as we did in equation 2 (the integrated rate equation for the conversion of A → P in the absence of enzyme). In principle, this method would be better than the initial rates methods. Why? It is not easy to be certain you are measuring the initial rate for each and every [S] which should vary over a wide range. It's also time intensive. In addition, think how much data is discarded if you take an entire progress curve at each substrate concentration, especially if you quench the reaction at a given time point, which effectively limits the data to one time point per substrate. In practice, the mathematics is complicated and it is not possible to get a simple explicit function of [P] or [S] as a function of time. A slight variant of a progress curve can be derived. Let us consider the simple case of a single substrate S (or A) being converted to product P in an enzyme-catalyzed reaction. The analogous equations for first-order, noncatalyzed rates were A=A0e-k1t or P = A0(1-e-k1t). We can derive the equation for the enzyme-catalyzed reaction shown below. Here it is! \frac{\mathrm{P}}{\mathrm{t}}=\frac{\mathrm{K}_{\mathrm{M}} \ln \left(\frac{\mathrm{S}_0-\mathrm{P}}{\mathrm{S}_0}\right)}{\mathrm{t}}+\mathrm{V}_{\mathrm{M}} Derivation: kcat and VM from integrated rate equation Click below to see the derivation Derivation \begin{array}{r} \mathrm{v}=-\frac{\mathrm{dS}}{\mathrm{dt}}=+\frac{\mathrm{dP}}{\mathrm{dt}}=\frac{\mathrm{V}_{\mathrm{M}} \mathrm{S}}{\mathrm{K}_{\mathrm{M}}+\mathrm{S}} \ \int_{\mathrm{S}_0}^{\mathrm{S}} \frac{\mathrm{K}_{\mathrm{M}}+\mathrm{S}}{\mathrm{V}_{\mathrm{M}} \mathrm{S}} \mathrm{d} S=-\int_0^{\mathrm{t}} \mathrm{t} \end{array} -\mathrm{t}=\frac{\mathrm{S}+\mathrm{K}_{\mathrm{M}} \ln \mathrm{S}-\mathrm{S}_0-\mathrm{K}_{\mathrm{M}} \ln S_0}{\mathrm{~V}_{\mathrm{M}}} On rearrangement, this gives: \mathrm{S}_0-\mathrm{S}+\mathrm{K}_{\mathrm{M}} \ln \frac{\mathrm{S}_0}{\mathrm{~S}}=\mathrm{V}_{\mathrm{M}} \mathrm{t} This equation is an implicit equation, not an explicit one, as it does NOT give S(t) explicitly as a function of t. Equation yy can be written with respect to product P as follows: \begin{aligned} &P=\mathrm{S}_0-S{ }^{\prime \prime} \text { or }{ }^{\prime \prime} S=\mathrm{S}_0-P \ &\mathrm{~S}_0-\left(\mathrm{S}_0-\mathrm{P}\right)+\mathrm{K}_{\mathrm{M}} \ln \frac{\mathrm{S}_0}{\mathrm{~S}_0-\mathrm{P}}=\mathrm{V}_{\mathrm{M}} \mathrm{t} \ &\mathrm{P}-\mathrm{K}_{\mathrm{M}} \ln \left(\frac{\mathrm{S}_0-\mathrm{P}}{\mathrm{S}_0}\right)=\mathrm{V}_{\mathrm{M}} \mathrm{t} \end{aligned} Rearranging this gives \frac{\mathrm{P}}{\mathrm{t}}=\frac{\mathrm{K}_{\mathrm{M}} \ln \left(\frac{\mathrm{S}_0-\mathrm{P}}{\mathrm{S}_0}\right)}{\mathrm{t}}+\mathrm{V}_{\mathrm{M}} S(t) explicitly as a function of t. This equation does not give P(t) explicitly as a function of time. Rather one can get a graph of P/t vs [ln (1-P/S0)]/t (shown below) from the derived equation, which does give a straight line with a slope of Km and a y-intercept of Vm. Note that the calculated values of VM and KM are derived from only one substrate concentration, and the values may be affected by product inhibition. Figure $6$ shows the comparison of a first-order noncatalyzed conversion of A → P to the enzyme-catalyzed rate. The VM for the enzyme-catalyzed reaction was chosen to be small to make the two graph comparable. Note that the curves are similar but not identical. If you didn't know an enzyme was present, you could fit the data to a first-order rise in [P] with time, but it would not be the optimal fit. The progress curves are a lot more complicated to analyze if the product, which shares structural similarities with the substrate, binds the enzyme tightly and inhibits it (called product inhibition). Comparison of progress Curves of uncatalyzed and catalyzed reactions. Let's explore progress curves for enzyme-catalyzed reactions a bit further. Students usually see v0 vs [S] Michaelis-Menten plots in textbooks. These plots are in some ways less intuitive than seeing P vs t curves, which are more in line with how we might contemplate how a reaction proceeds. Hence, it would be illuminating to compare progress curve graphs of A → P (irreversible) for the uncatalyzed and S → P for the enzyme-catalyzed reactions. What might you expect? We saw one example in Figure $6$. In each case, P should increase with time. In the uncatalyzed reaction, S exponentially decreases to 0 and P concomitantly rises to a value of P = S0. You also get a rise in P vs t for the enzyme-catalyzed rate, but you would think it would be a faster rise with time since the enzyme catalyze the reaction. Figure $7$ shows a comparison of the progress curves for the uncatalyzed first-order reaction of A → P1 (red) and S → P2 (blue) for enzym- catalyzed reaction (blue, right) for these conditions: uncatalyzed reaction A → P1, k1 = 0.1; Catalyzed reaction: S → P2, VM=10, KM=5. Note that the rate at which bound S (i.e ES) goes to P for the catalyzed rate is 100x faster than the rate constant for the catalyzed rate. Note that the curves are somewhat similar in shape but also clearly different in comparison to the one shown in Figure $6$. Now let's use Vcell to compare the reactions for different values for the kinetic constants for the uncatalyzed and the enzyme-catalyzed reaction. Change the constants and try to find a set of conditions so that the catalyzed and catalyzed rates for conversion of reactions to products are superimposable. How can that be? MODEL Irreversible reactions:  A → P1  and E + S ↔ ES → E + P Vcell reaction diagram (1-way arrows defined as reversible in actual mathematical model) and chemical equation Initial conditions Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) Animations Now let's look at an animation of the same irreversible reactions in which enzyme-catalyzed reaction is no faster than the noncatalyzed rate (a worthless enzyme!). Here are the reactions: A→P, k1 =0.1; S → P, VM=?, KM = ?. The animations show just the accumulation of product. Animations are by Shraddha Nakak and Hui Liu. Exercise $1$ Use the Vcell model above to find a set of values for KM and VM that would make the two graphs superimposable - i.e. when the graphs in the absence and presence of E are identical. (Hint: that would be a really bad enzyme if it didn't increase the reaction over the uncatalyzed rate!) Answer KM = 96, VM = 10

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/06%3A_Enzyme_Activity/6.03%3A_Kinetics_with_Enzymes.txt

Search Fundamentals of Biochemistry Irreversible Covalent Inhibition Given what you already know about protein structure, it should be easy to determine how to inhibit an enzyme. Since structure mediates function, anything that would significantly alter the structure of an enzyme would inhibit the activity of the enzyme. Hence extremes of pH and high temperature, all of which can denature the enzyme, would irreversibly inhibit the enzyme unless it could refold properly. Alternatively, we could add a small molecule, which interacts noncovalently with the enzyme to either change its conformation or directly prevent substrate binding. Finally, we could covalently modify certain side chains, that if they are essential to enzymatic activity, would irreversibly inhibit the enzyme. We discussed previously the types of reagents that would chemically modify specific side chains that might be critical for enzymatic activity. For example, iodoacetamide might abolish enzyme activity if a cysteine side chain is required for activity. These reagents will usually modify several side chains, however. Determining which is critical for binding or catalytic conversion of the substrate can be difficult. One way would be to protect the active site with saturating concentrations of a ligand that binds reversibly at the active site. Then the chemical modification can be performed at varying reaction times. The critical side chain would be protected from the chemical modification with the extent of protection depending on the KD, the concentration of the protecting ligand., and the length of the reaction. The rest of the chapter will deal with reversible, noncovalent inhibition Competitive Inhibition Reversible Competitive inhibition occurs when substrate (S) and inhibitor (I) both bind to the same site on the enzyme. In effect, they compete for the active site and bind in a mutually exclusive fashion. This is illustrated in the chemical equations and molecular cartoons shown in Figure $1$. v_0=\frac{V_M S}{K_M\left(1+\frac{I}{K is}\right)+S} There is another type of inhibition that would give the same kinetic data. If S and I bound to different sites, and S bound to E and produced a conformational change in E such that I could not bind (and vice versa), then the binding of S and I would be mutually exclusive. This is called allosteric competitive inhibition. Inhibition studies are usually done at several fixed and non-saturating concentrations of I and varying S concentrations. The key kinetic parameters to understand are VM and KM. Let us assume for ease of equation derivation that I binds reversibly, and with rapid equilibrium to E, with a dissociation constant KIS. The "s" in the subscript "is" indicates that the slope of the 1/v vs 1/S Lineweaver-Burk plot changes while the y-intercept stays constant. KIS is also named KIC where the subscript "c" stands for competitive inhibition constant. A look at the top mechanism shows that even in the presence of I, as S increases to infinity, all E is converted to ES. That is, there is no free E to which I could bind. Now, remember that VM= kcatE0. Under these conditions, ES = E0; hence v = VM. VM is not changed. However, the apparent KM, KMapp, will change. We can use LaChatelier's principle to understand this. If I binds to E alone and not ES, it will shift the equilibrium of E + S → ES to the left. This would increase the KMapp (i.e. it would appear that the affinity of E and S has decreased.). The double reciprocal plot (Lineweaver-Burk plot) offers a great way to visualize the inhibition as shown in Figure $2$. In the presence of I, VM does not change, but KM appears to increase. Therefore, 1/KM, the x-intercept on the plot will get smaller, and closer to 0. Therefore the plots will consist of a series of lines, with the same y-intercept (1/VM), and the x-intercepts (-1/KM) closer and closer to 0 as I increases. These intersecting plots are the hallmark of competitive inhibition. Here is an interactive graph showing v0 vs [S] for competitive inhibition with Vm and Km both set to 100. Change the sliders for [I] and Kis and see the effect on the graph. Here is the interactive graphs showing 1/v0 vs 1/[S] for competitive inhibition, with Vm and Km both set to 10. Note that in the first three inhibition models discussed in this section, the Lineweaver-Burk plots are linear in the presence and absence of an inhibitor. This suggests that plots of v vs S in each case would be hyperbolic and conform to the usual form of the Michaelis Menton equation, each with potentially different apparent VM and KM values. An equation for v0 in the presence of a competitive inhibitor is shown in the above figure. The only change compared to the equation for the initial velocity in the absence of the inhibitor is that the KM term is multiplied by the factor 1+I/Kis. Hence KMapp = KM(1+I/Kis). This shows that the apparent KM does increase as we predicted. KIS is the inhibitor dissociation constant in which the inhibitor affects the slope of the double reciprocal plot. If the data were plotted as v0 vs log S, the plots would be sigmoidal, as we saw for plots of ML vs log L in Chapter 5B. In the case of a competitive inhibitor, the plot of v0 vs log S in the presence of different fixed concentrations of inhibitor would consist of a series of sigmoidal curves, each with the same VM, but with different apparent KM values (where KMapp = KM(1+I/Kis), progressively shifted to the right. Enzyme kinetic data is rarely plotted this way. These plots are mostly used for simple binding data for the M + L ↔ ML equilibrium, in the presence of different inhibitor concentrations. Reconsider our discussion of the simple binding equilibrium, M + L ↔ ML. For fractional saturation Y vs a log L graphs, we considered three examples: 1. L = 0.01 KD (i.e. L << KD), which implies that KD = 100L. Then Y = L/[KD+L] = L/[100L + L] ≈1/100. This implies that irrespective of the actual [L], if L = 0.01 KD, then Y ≈0.01. 2. L = 100 KD (i.e. L >> KD), which implies that KD = L/100. Then Y = L/[KD+L] = L/[(L/100) + L] = 100L/101L ≈ 1. This implies that irrespective of the actual [L], if L = 100 Kd, then Y ≈1. 3. L = KD, then Y = 0.5 These scenarios show that if L varies over 4 orders of magnitude (0.01KD < KD < 100KD), or, in log terms, from -2 + log KD < log KD< 2 + log Kd), irrespective of the magnitude of the KD, that Y varies from approximately 0 - 1. In other words, Y varies from 0-1 when L varies from log KD by +2. Hence, plots of Y vs log L for a series of binding reactions of increasingly higher KD (lower affinity) would reveal a series of identical sigmoidal curves shifted progressively to the right, as shown below in Figure $3$. The same would be true of v0 vs S in the presence of different concentrations of a competitive inhibitor, for initial flux, Jo vs ligand outside, in the presence of a competitive inhibitor, or ML vs L (or Y vs L) in the presence of a competitive inhibitor. In many ways plots of v0 vs lnS are easier to visually interpret than plots of v0 vs S . As noted for simple binding plots, textbook illustrations of hyperbolas are often misdrawn, showing curves that level off too quickly as a function of [S] as compared to plots of v0 vs lnS, in which it is easy to see if saturation has been achieved. In addition, as the curves above show, multiple complete plots of v0 vs lnS at varying fixed inhibitor concentrations or for variant enzyme forms (different isoforms, site-specific mutants) over a broad range of lnS can be made which facilitates comparisons of the experimental kinetics under these different conditions. This is especially true if Km values differ widely. Now that you are more familiar with binding and enzyme kinetics curves, in the presence and absence of inhibitors, you should be able to apply the above analysis to inhibition curves where the binding or the initial velocity is plotted at varying competitive inhibitor concentrations at different fixed nonsaturating concentrations of ligand or substrate. Consider the activity of an enzyme. Let's say that at some reasonable concentration of substrate (not infinite), the enzyme is approximately 100% active. If a competitive inhibitor is added, the activity of the enzyme decreases until at saturating (infinite) I, no activity would remain. Graphs showing this are shown below in Figure $4$. Progress Curves for Competitive Inhibition In the previous section, we explored how important progress curve (Product vs time) analyses are in understanding both uncatalyzed and enzyme-catalyzed reactions. We are aware of no textbooks which cover progress curves for enzyme inhibition. Yet progress curves are what most investigators record and analyze to determine initial rates v0 and to calculate VM, KM and inhibition constants, as described above. We will use Vcell to produce progress curves for reversibly inhibited enzyme-catalyzed reactions. MODEL Competitive Inhibition with constant [I]: No inhibition (left) and competitive inhibition (right) Initial conditions for no inhibition Initial conditions for competitive inhibition I is fixed for each simulation (as it is not converted to a product) but can be changed in the simulation below. Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed.  For this model, select Vm, Km, Ki and I |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) The graphs from your initial run show the concentrations of S, P and I as a function of time for just the initial conditions shown above. In typical initial rate laboratory analyzes, of competitive inhibition, at least three sets of reactions are run with the same varying substrate concentrations and different fixed concentrations of inhibitor. In the analyses above, [I] is fixed at 5 uM. Conduct a series of run at different values of I. Vary the KI, the dissociation constant for the EI complex, as follows: • I << KI, the dissociation constant for the EI complex • I >> KI, the dissociation constant for the EI complex. Then download the data and determine the initial rate for each of the initial conditions. Figure $5$ shows an interactive iCn3D model of human low molecular weight phosphotyrosyl phosphatase bound to a competitive inhibitor (5PNT) Figure $5$: Human low molecular weight phosphotyrosyl phosphatase bound to a competitive inhibitor (5PNT). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...XsEacG2tixDDi9 The competitive inhibitor, the deprotonated form of 2-(N-morpholino)-ethanesulfonic acid (MES), is actually the conjugate base of the weak acid (pKa = 6.15) of a commonly used component of a buffered solution.  It is shown in color sticks with the negatively charged sulfonate sitting at the bottom of the active site pocket.  The amino acids comprising the active site binding pocket are shown as color sticks underneath the transparent colored surface of the binding pocket.  The normal substrates for the enzyme are proteins phosphorylated on tyrosine side chains so the sulfonate is a mimic of the negatively charged phosphate group of the phosphoprotein target. Two specials cases of competition inhibition Product Inhibition Let's look at an enzyme that converts reactant S to product P. Since P arises from S, they may have structural similarities. For example, what if GTP was the reactant and GDP was a product? If so, then P might also bind in the active site and inhibit the conversion of S to P. This is called product inhibition. It probably occurs in most enzymes, and when it does occur it will start bending downward the beginning part of the progress curve for P formation. If the product binds very tightly, it might cause a significant underestimation of the initial velocity (v0) or flux (J0) of the enzyme. Let's use Vcell to explore product inhibition. The model will explore two reactions: • E + R ↔ ER → E + Q (no product inhibition) • E + S ↔ ES → E + P (with product inhibition) Note that the chemical equation above does not explicitly show the product P binding the enzyme to form an EP complex. An actual reaction diagram showing the inhibition of an enzyme by an inhibitor I and by the product P is shown in Figure $6$ below. Figure $6$: reaction diagram showing inhibition of an enzyme by an inhibitor I and by the product P Vcell uses much simpler diagrams since it is most often used for modeling whole pathways or even entire cells. In the simpler Vcell reaction diagrams, the inhibitor is typically not shown since the inhibition is built into the equation for the enzyme, represented by the node or yellow square in the figure above. Let's now explore product inhibition in Vcell. R and Q are the reactant and product, respectively, in the reaction without product inhibition. S and P are used for the reaction with product P inhibition. MODEL Irreversible MM Kinetics - Without (left rx 1) and With (right, rx 2) Product Inhibition Initial Conditions:  No product inhibition Initial Conditions: With product inhibition Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) Inhibition by a competing substrate - the specificity constant In the previous chapter, the specificity constant was defined as kcat/KM which we also described as the second-order rate constant associated with the bimolecular reaction of E and S when S << KM. It also describes how good an enzyme is in differentiating between different substrates. If an enzyme encounters two different substrates, one can be considered to be a competitive inhibitor of the other. The following equation gives the ratio of initial velocities for two competing substrates at the same concentration is equal to the ratio of their kcat/KM values. \frac{\mathrm{v}_{\mathrm{A}}}{\mathrm{v}_{\mathrm{B}}}=\frac{\frac{\mathrm{k}_{\mathrm{catA}}}{\mathrm{K}_{\mathrm{A}}} \mathrm{A}}{\frac{\mathrm{k}_{\mathrm{cat} \mathrm{B}}}{\mathrm{K}_{\mathrm{B}}} \mathrm{B}} A derivation of the specificity constant for an enzyme with competing substrates Here it is! Derivation \mathrm{v}_{\mathrm{A}}=\frac{\mathrm{V}_{\mathrm{A}} \mathrm{A}}{\mathrm{K}_{\mathrm{A}}\left(1+\frac{\mathrm{B}}{\mathrm{K}_{\mathrm{B}}}\right)+\mathrm{A}} \quad \mathrm{v}_{\mathrm{B}}=\frac{\mathrm{V}_{\mathrm{B}} \mathrm{B}}{\mathrm{K}_{\mathrm{B}}\left(1+\frac{\mathrm{A}}{\mathrm{K}_{\mathrm{A}}}\right)+\mathrm{B}} \frac{\mathrm{v}_{\mathrm{A}}}{\mathrm{v}_{\mathrm{B}}}=\frac{\frac{\mathrm{V}_{\mathrm{A}} \mathrm{A}}{\mathrm{K}_{\mathrm{A}}\left(1+\frac{\mathrm{B}}{\mathrm{K}_{\mathrm{B}}}\right)+\mathrm{A}}}{\frac{\mathrm{V}_{\mathrm{B}} \mathrm{B}}{\mathrm{K}_{\mathrm{B}}\left(1+\frac{\mathrm{A}}{\mathrm{K}_{\mathrm{A}}}\right)+\mathrm{B}}}=\frac{\frac{\mathrm{V}_{\mathrm{A}} \mathrm{A}}{\mathrm{K}_{\mathrm{A}}+\frac{\mathrm{K}_{\mathrm{A}} \mathrm{B}}{\mathrm{K}_{\mathrm{B}}}+\mathrm{A}}}{\frac{\mathrm{V}_{\mathrm{B}} \mathrm{B}}{\mathrm{K}_{\mathrm{B}}+\frac{\mathrm{K}_{\mathrm{B}} \mathrm{A}}{\mathrm{K}_{\mathrm{A}}}+\mathrm{B}}} Now in the above equation: multiple the top half of the right-hand expression by \frac{\frac{1}{K_A}}{\frac{1}{K_A}} multiple the bottom half of the right-hand expression by \frac{\frac{1}{K_B}}{\frac{1}{K_B}} replace VA with kcatAE0 and VB with kcatBE0 This gives the following expression for vA/vB: \frac{\mathrm{v}_{\mathrm{A}}}{\mathrm{v}_{\mathrm{B}}}=\frac{\frac{\mathrm{k}_{\mathrm{catA}}}{\mathrm{K}_{\mathrm{A}}} \mathrm{A}}{\frac{\mathrm{k}_{\mathrm{cat} \mathrm{B}}}{\mathrm{K}_{\mathrm{B}}} \mathrm{B}} Uncompetitive Inhibition Reversible uncompetitive inhibition occurs when I binds only to ES and not free E. One can hypothesize that on binding S, a conformational change in E occurs which presents a binding site for I. Inhibition occurs since ESI can not form the product. It is a dead-end complex that has only one fate, to return to ES. This is illustrated in the chemical equations and molecular cartoon shown in Figure $7$. v_0=\frac{V_M S}{K_M+S\left(1+\frac{I}{K i i}\right)}=\frac{\left(\frac{V_M}{1+\frac{I}{K i i}}\right) S}{\left(\frac{K_M}{1+\frac{I}{K i i}}\right)+S} Let us assume that I binds reversibly to ES with a dissociation constant Kii. The second "i" in the subscript "ii" indicates that the intercept of the 1/v vs 1/S Lineweaver-Burk plot changes while the slope stays constant. Kii is also named Kiu, where the subscript "u" stands for the uncompetitive inhibition constant. A look at the top mechanism shows that in the presence of I, as S increases to infinity, not all of E is converted to ES. That is, there is a finite amount of ESI, even at infinite S. Now remember that Vm = kcatE0 if and only if all E is in the form ES. Under these conditions, the apparent Vm, Vmapp is less than the real Vm without the inhibitor. In addition, the apparent Km, Kmapp, will change. We can use LaChatelier's principle to understand this. If I binds to ES alone, and not E, it will shift the equilibrium of E + S ↔ ES to the right, which would have the effect of decreasing the Kmapp (i.e. it would appear that the affinity of E and S has increased.). The double reciprocal plot (Lineweaver Burk plot) offers a great way to visualize the inhibition. In the presence of I, both Vm and Km decrease. Therefore, -1/Km, the x-intercept on the plot, will get more negative, and 1/Vm will get more positive. It turns out that they change to the same extent. Therefore the plots will consist of a series of parallel lines, which is the hallmark of uncompetitive inhibition, as shown in Figure $8$. Here is an interactive graph showing v0 vs [S] for uncompetitive inhibition with Vm and Km both set to 100. Change the sliders for [I] and Kis and see the effect on the graph. Here is an interactive graph showing uncompetitive inhibition with Vm and Km both set to 10. Change the sliders for [I] and Kii and see the effect on the graph An equation, shown in the diagram above, can be derived which shows the effect of the uncompetitive inhibitor on the velocity of the reaction. The only change is that the S term in the denominator is multiplied by the factor 1+I/Kii. We would like to rearrange this equation to show how Km and Vm are affected by the inhibitor, not S, which obviously isn't. Rearranging the equation above shows that Kmapp = Km/(1+I/Kii) and Vmapp = Vm/(1+I/Kii). This shows that the apparent Km and Vm do decrease as we predicted. Kii is the inhibitor dissociation constant in which the inhibitor affects the intercept of the double reciprocal plot. Note that if I is zero, Km and Vm are unchanged. Progress Curves: Uncompetitive Inhibition Now let's compare the progress curves for an enzyme-catalyzed reaction in the absence and presence of an uncompetitive inhibitor. MODEL No inhibition (left) and Uncompetitive Inhibition (right) (Note the the Vcell reaction diagram is the same as for competitive inhibition.  However, the mathematical equations differ as shown below. Initial values No Inhibition Initial values With Uncompetitive Inhibitor I is fixed for each simulation (as it is not converted to a product) but can be changed in the simulation below. Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) Figure $9$ shows an interactive iCn3D model of an uncompetitive inhibitor of the cysteine protease caspase-6 (4HVA) Figure $9$:  Uncompetitive inhibitor of the cysteine protease caspase-6 (4HVA) . (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...TVfKio7NxcpX28 The "substrate" in this model (cyan, transparent surface, with labels on V, E, and I) is a substrate analog, VEI-CHO in which the tripeptide substrate VEI ends not in a free carboxyl or amide group but an aldehyde, which causes this "substrate" to become covalently attached to the enzyme and act as an inhibitor.  The uncompetitive inhibitor (gray transparent surface) binds externally to the blue surface.  Hence it binds to the ES complex.  Two active site residues, Cys 163, the active site nucleophile, and His 121, a catalytic acid/base are shown in colored sticks and labeled. Figure $10$ below shows a Lineweaver-Burk plot showing 1/vo vs 1/[S], where the substrate is the divalent compound (VEID)2R110.    Its N-terminus is capped with a benzyloxy (Z) group.  R110 is a rhodamine-type fluorophore, which on cleavage gives a strong fluorescent signal that was initially quenched by the benzyloxy group in the uncleaved substrate. Figure $10$: Double-reciprocal Lineweaver-Burk plot of compound 3 with (VEID)2R110 substrate showing uncompetitive inhibition.  Heise CE et al. (2012) Mechanistic and Structural Understanding of Uncompetitive Inhibitors of Caspase-6. PLoS ONE 7(12): e50864. https://doi.org/10.1371/journal.pone.0050864.  Creative Commons Attribution License. Noncompetitive and Mixed Inhibition Reversible noncompetitive inhibition occurs when I binds to both E and ES. We will look at only the special case in which the dissociation constants of I for E and ES are the same. This is called noncompetitive inhibition. It is quite rare as it would be difficult to imagine a large inhibitor that inhibits the turnover of a bound substrate having no effect on the binding of S to E. However covalent interaction of protons with both E and ES can lead to noncompetitive inhibition. In the more general case, the Kd's are different, and the inhibition is called mixed. Since inhibition occurs, we will hypothesize that ESI can not form the product. It is a dead-end complex that has only one fate, to return to ES or EI. This is illustrated in the chemical equations and in the molecular cartoon in Figure $11$. Let us assume for ease of equation derivation that I binds reversibly to E with a dissociation constant of Kis (as we denoted for competitive inhibition) and to ES with a dissociation constant Kii (as we noted for uncompetitive inhibition). Assume for noncompetitive inhibition that Kis = Kii. A look at the top mechanism shows that in the presence of I, as S increases to infinity, not all of E is converted to ES. That is, there is a finite amount of ESI, even at infinite S. Now remember that Vm = kcatE0 if and only if all E is in the form ES . Under these conditions, the apparent Vm, Vmapp is less than the real Vm without an inhibitor. In contrast, the apparent Km, Kmapp, will not change since I binds to both E and ES with the same affinity, and hence will not perturb that equilibrium, as deduced from LaChatelier's principle. The double reciprocal plot (Lineweaver Burk plot) offers a great way to visualize the inhibition. In the presence of I, just Vm will decrease. Therefore, -1/Km, the x-intercept will stay the same, and 1/Vm will get more positive. Therefore the plots will consist of a series of lines intersecting on the x-axis, which is the hallmark of noncompetitive inhibition. You should be able to figure out how the plots would appear if Kis is different from Kii (mixed inhibition). v_0=\frac{V_M S}{K_M\left(1+\frac{I}{K i s}\right)+S\left(1+\frac{I}{K i i}\right)} An equation, shown in the diagram above can be derived which shows the effect of the noncompetitive inhibitor on the velocity of the reaction. In the denominator, Km is multiplied by 1+I/Kis, and S by 1+I/Kii. We would like to rearrange this equation to show how Km and Vm are affected by the inhibitor, not S, which obviously isn't. Rearranging the equation as shown above shows that Kmapp = Km(1+I/Kis)/(1+I/Kii) = Km when Kis=Kii, and Vmapp = Vm/(1+I/Kii). This shows that the Km is unchanged and Vm decreases as we predicted. The plot shows a series of lines intersecting on the x-axis as shown in Figure $12$. Both the slope and the y-intercept are changed, which are reflected in the names of the two dissociation constants, Kis and Kii. Note that if I is zero, Kmapp = Km and Vmapp = Vm. Sometimes the Kis and Kii inhibition dissociation constants are referred to as Kc and Ku (competitive and uncompetitive inhibition dissociation constants. Move the sliders on this interactive graph below to show changes in Kis and Kii affect position on the graph where the lines intersect. Try to change their values to move the intersections of the graphs from the left top quadrant to the x-axis to the left bottom quadrant. Here is an interactive graph showing v0 vs [S] for mixed inhibition with Vm and Km both set to 100. Change the sliders for [I] and Kis and see the effect on the graph Here is an interactive graph showing mixed inhibition with Vm and Km both set to 10. Change the sliders for [I] and Kii and see the effect on the graph Progress Curves: Mixed/Noncompetitive Inhibition Now let's compare the progress curves for an enzyme-catalyzed reaction in the absence and presence of a mixed inhibitor. MODEL No inhibition (left) and Uncompetitive Inhibition (right) Note that the Vcell reaction diagram is the same as for competitive and uncompetive inhibition.  It doesn't explicitly show that the mixed inhibitor binds to both free and substrate-bound enzymes.  Those interactions are addressed in the mathematical equations for mixed inhibition. Initial values No Inhibition Initial values With Uncompetitive Inhibitor I is fixed for each simulation (as it is not converted to a product) but can be changed in the simulation below. Select Load [model name] below Select Start to begin the simulation. Interactive Element Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) Mixed and noncompetitive inhibition (as shown by the mechanism above) differ from competitive and uncompetitive inhibition in that the inhibitor binding is not simply a dead-end reaction in which the inhibitor can only dissociate in a single reverse step. In the above equilibrium, S can dissociate from ESI to form EI so the system may not be at equilibrium. With dead-end steps, no flux of reactants occurs through the dead-end complex so the equilibrium for the dead-end step is not perturbed. Other mechanisms can commonly give mixed inhibition. For example, the product released in a ping pong mechanism (discussed in the next chapter) can give mixed inhibition. A ping pong reaction mechanism is shown and superficially explained in Figure $13$. If P, acting as a product inhibitor, can bind to two different forms of the enzyme (E' and also E), it will act as a mixed inhibitor. Figure $14$ shows an interactive iCn3D model of a noncompetitive inhibitor of M. tuberculosis's class IIa fructose 1,6-bisphosphate aldolase (4LV4). Figure $14$:  Noncompetitive inhibitor of M. tuberculosis's class IIa fructose 1,6-bisphosphate aldolase (4LV4). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...wZDtkvtwqnrVy7 The non-competitive inhibitor, 8-hydroxyquinoline carboxylic acid (HCA), is shown as with a cyan surface.  Active site side chains of the Zn2+- containing enzyme are shown as colored sticks and labeled. The noncompetitive inhibitor does not enter into the pocket where the substrate (not shown in the model above) binds.  The binding of the noncompetitive inhibitor causes a conformational change that moves the Zn-binding loop (Z-loop) which contains His 212 which coordinates the Zn2+ ion.  Hence the Zn-loop is part of the active site and when it moves on binding of the inhibitor, the access to the empty pocket where the substrate binds is hindered.   When the Zn2+ moves away from the active site, it can no longer engage in catalytic activity. Enzyme Inhibition in Vivo The pharmaceutical industry is devoted to finding drug molecules that affect biological processes. Typically this means the development of small molecule inhibitors of target proteins. Recent work has expanded to the development of inhibitory RNA molecules that affect DNA transcription and mRNA translation. Using combinatorial synthetic techniques and computational modeling, it has gotten easier to develop small molecule inhibitors (especially competitive ones) that inhibit proteins in vitro using purified enzymes, substrates, and inhibitors in lab testing. Assuming that the inhibitor could pass through the membrane and accumulate to a sufficient enough concentration, would it have the same inhibitory properties in the cell as in the test tube? The answer turns out to be maybe. Remember that a cell is tightly packed with a multitude of other small molecules and macromolecules. In addition, the enzyme targeted for inhibition is most likely part of a pathway of enzymes that feeds reactant into the enzyme and removes the product. Hence, the flux of substrate and product is controlled by the entire pathway and not just the single target enzyme. The concentration of the product of the target enzyme is determined by kinetic parameters for the enzyme and available substrate concentration. The conditions under which the enzymes are studied (in vitro) and operate (in vivo) are very different. • In vitro (in the lab), the enzyme is held at a constant concentration while the substrate is varied (i.e the substrate concentration is the independent variable). The velocity is determined by the substrate concentration. When inhibition is studied, the substrate is varied while the inhibitor is held constant at several different fixed concentrations. • In vivo (in the cell), the velocity might be held at a relatively fixed level with the substrate determined by the velocity. To avoid a bottleneck in flux, the substrate can't build up at the enzyme, so the enzyme processes it in a steady state fashion to produce the product as determined by the Michael-Menten equation. What happens when an inhibitor is added in vivo? Let's assume that the enzyme is running at v = Vm/2. How might in vivo inhibition plots look at constant velocity (for example v=Vm/2) when both I and S can vary and in which S for an enzyme in the middle of a pathway is determined by v? The equations and graph below show the ratio of S/Km vs I/Kix for inhibition at constant v, a condition encountered when an enzyme in a metabolic pathway is subject to flux controls imposed by the entire pathway. The x-axis reflects the ratio of inhibitor concentration to its inhibition constant. Likewise, the y-axis reflects the relative amount of substrate compared to its Km. The graph for in vivo competitive inhibition is linear, but it "blows up" for uncompetitive inhibition as shown in Figure $15$. Here are derivations used to produce the graphs in Figure $13$. A derivation of the competitive and uncompetitive inhibition in vivo when v = constant Here it is! Derivation Competitive Inhibition at constant velocity v: Let's start with the equation of competitive inhibition. \mathrm{v}=\frac{\mathrm{V}_{\mathrm{M}} \mathrm{S}}{\mathrm{K}_{\mathrm{M}}\left(1+\frac{\mathrm{I}}{\mathrm{K}_{\mathrm{is}}}\right)+\mathrm{S}} Let \left(1+\frac{\mathrm{I}}{\mathrm{K}_{\mathrm{is}}}\right)=\mathrm{y} Then, \begin{gathered} v=\frac{V_M S}{K_M y+S} \ v\left(K_M y+S\right)=V_M S \ v K_M y+v S=V_M S \ v K_M y=V_M S-v S=S\left(V_M-v\right) \ K_M=\frac{S\left(V_M-v\right)}{v y} \end{gathered} which gives: \begin{gathered} \frac{S}{K_M}=\frac{v y}{\left(V_M-v\right)}=\frac{y}{\frac{V_M}{v}-1}=\frac{1+\frac{I}{K_{i s}}}{\frac{V_M}{v}-1}=\frac{1}{\frac{V_M}{v}-1}+\frac{\frac{I}{K_{i s}}}{\frac{V_M}{v}-1} \ \frac{S}{K_M}=\frac{1}{\frac{V_M}{v}-1}+\left(\frac{1}{\frac{V_M}{v}-1}\right) \frac{I}{K_{i s}} \end{gathered} Note from the last equation that the graph of S/KM vs I/Kis is linear (at a fixed v), as shown in the above figure. Uncompetitive Inhibition at constant velocity v: Let's start with the equation of uncompetitive inhibition. \mathrm{v}=\frac{\mathrm{V}_{\mathrm{M}} \mathrm{S}}{\mathrm{K}_{\mathrm{M}}+\mathrm{S}\left(1+\frac{\mathrm{I}}{\mathrm{K}_{\mathrm{ii}}}\right)} Let \left(1+\frac{1}{\mathrm{K}_{\mathrm{ii}}}\right)=\mathrm{y} then: \begin{gathered} v=\frac{V_M S}{K_M+S y} \ v\left(K_M+S y\right)=V_M S \ v K_M=V_M S-v S y=S\left(V_M-v y\right) \end{gathered} which gives: \frac{S}{K_M}=\frac{v}{V_M-v y}\left(\frac{\frac{1}{v}}{\frac{1}{v}}\right)=\frac{1}{\frac{V_M}{v}-y}=\frac{1}{v-1-\frac{I}{K_{i i}}} This graph is not a linear function of I/Kii as it was for in vivo competitive inhibition. • for competitive inhibition, the graph of S/KM is a linear function of I/Kis • for uncompetitive inhibition, the graph of S/KM is NOT a linear function of I/Kii but rather "blows up" to infinity. These graphs and associated equations are dramatically different from the very similar forms of inhibition equations and curves for in vitro inhibition at varying S and different fixed values of inhibitor. Consider the uncompetitive graph and equation. In the absence of an inhibitor, if S=Km, then Vm/v = 2 so the calculated value from the equation above of S/Km = 1. The y-intercept of the graph above is 1 for uncompetitive (and competitive) inhibition. If I is allowed to increase to a value of Kii (so I/Kii = 1), again at constant v=Vm/2, then the right-hand side goes to infinity. In a linked series of reactions, if the middle reaction is inhibited, the substrate for that enzyme builds, whether the inhibition is competitive or uncompetitive. With competitive inhibition, the substrate concentration can be raised to meet the requirements of the enzyme. But as the above figure shows, this can't happen for uncompetitive inhibition since as more substrate accumulates, the reaction reaches a point where the steady state is lost. Obviously, this limiting case can't be realistically reached but it does suggest that uncompetitive inhibitors would be more effective in vivo in controlling a metabolic pathway than competitive inhibitors. Cornish-Bowden argues that purely uncompetitive inhibitors are rare in nature because of the degree of inhibition they can hypothetically produce (1986). Likewise, he suggests that medicinal chemists should synthesize uncompetitive inhibitors if their goal is to maximally inhibit a metabolic pathway under the kind of flux control described above. Although it is more difficult to synthesize a purely uncompetitive inhibitor (as it can't be easily modeled after the structure of a natural ligand that binds to the active site and are competitive inhibitors), he notes that synthesizing mixed (and noncompetitive) inhibitors whose Kii values are of reasonable size compared to their Kis values, would be one approach. Move the sliders on the interactive graph below to show how the graphs change. Disregard the graph in the right lower quadrant as that location would require negative values of either S or K. Inhibition by Temperature and pH Changes From 0 to about 40-50o C, enzyme activity usually increases, as do the rates of most reactions in the absence of catalysts. (Remember the general rule of thumb that reaction velocities double for each increase of 10oC.). At higher temperatures, the activity decreases dramatically as the enzyme denatures. These features are illustrated in Figure $16$. Likewise, pH has a marked effect on the velocity of enzyme-catalyzed reactions as illustrated in Figure $17$. Think of all the things that pH changes might affect. It might • affect E in ways to alter the binding of S to E, which would affect Km • affect E in ways to alter the actual catalysis of bound S, which would affect kcat • affect E by globally changing the conformation of the protein • affect S by altering the protonation state of the substrate The easiest assumption is that key side chains necessary for catalysis must be in the correct protonation state. Thus, a sidechain, with an apparent pKa of around 6, must be deprotonated for optimal activity of trypsin which shows increases in activity with pH centered at pH 6. Which amino acid side chain would be a likely candidate? Table $1$ below shows how pH effects on enzyme kinetics can be modeled at the chemical and mathematical level. \begin{gathered} \mathrm{v}_{0}=\frac{\mathrm{V}_{\mathrm{m} \mathrm{app}} \mathrm{S}}{\mathrm{K}_{\mathrm{m}} \text { app }}+\mathrm{S} \ \mathrm{V}_{\mathrm{m} \text { app }}=\frac{\mathrm{V}_{\mathrm{m}}}{1+\frac{\mathrm{H}^{+}}{\mathrm{K}_{\mathrm{ES} 1}}+\frac{\mathrm{K}_{\mathrm{ES} 2}}{\mathrm{H}^{+}}} \ \mathrm{K}_{\mathrm{m} \text { app }}=\frac{\mathrm{K}_{\mathrm{m}}\left(1+\frac{\mathrm{H}^{+}}{\mathrm{K}_{\mathrm{ES} 1}}+\frac{\mathrm{K}_{\mathrm{ES} 2}}{\mathrm{H}^{+}}\right)}{1+\frac{\mathrm{H}^{+}}{\mathrm{K}_{\mathrm{ES} 1}}+\frac{\mathrm{K}_{\mathrm{ES} 2}}{\mathrm{H}^{+}}} \end{gathered}

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/06%3A_Enzyme_Activity/6.04%3A_Enzyme_Inhibition.txt

Search Fundamentals of Biochemistry This chapter section has been written by Kristen Procko and Henry Jakubowski. We can apply what we learned about catalysis by small molecules (e.g., acids and bases) to enzyme-catalyzed reactions. To understand the mechanism of an enzyme-catalyzed reaction, we try to alter as many variables, one at a time, and ascertain the effects of the changes on the activity of the enzyme. Kinetic methods can be used to obtain data, from which inferences about the mechanism can be made. Crystal structures of the enzyme in the presence and absence of a competitive inhibitor give abundant information about possible mechanisms. It is amazing, however, how much information about enzyme mechanism can be gained even if all you have is a blender, a stopwatch, an impure enzyme, and a few substrates and inhibiting reagents. Introduction to Enzymes Mechanisms Almost every chemical reaction in the biological world is catalyzed by protein enzymes. The human genome encodes for over 20,000 different proteins, thousands of which are enzymes. The total number of different enzymes in the biosphere must be staggering. Yet at the same time, all of these enzymes catalyze different sets of similar reactions. To bring order to the world of enzyme catalysis, the IUBMB has classified enzymes based on the type of chemical reactions they catalyze. There are 7 main categories as shown in the expandable Table $1$ below. Each reaction type is given a four digit Enzyme Commission number. For example, alcohol dehydrogenase, the enzyme that catalyzes the oxidation of ethanol, a primary alcohol, to acetaldehyde using an oxidizing agent called NAD+, is given the enzyme commission number EC 1.1.1.1. Other enzymes that oxidize primary alcohols to aldehydes or secondary alcohols to ketones are also give the same EC number. Class Subclass Type Description EC 1 [+] Oxidoreductases redox reactions EC 2 [+] Transferases transfer/exchange of group from one molecule to another EC 3 [+] Hydrolases hydrolysis reactions EC 4 [+] Lyases elimination forming double bond EC 5 [+] Isomerases conversions of geometric, stereo- or constitutive isomers EC 6 [+] Ligases condensation of two molecules into one EC 7 [+] Translocases movement of species across a semipermeable membrane Table $1$: ExplorEnz database that for the curation and dissemination of the International Union of Biochemistry and Molecular Biology (IUBMB) Enzyme Nomenclature. (Source: https://academic.oup.com/nar/article...1/D593/1000297) Enzymes with the same or similar EC numbers probably have similar reaction mechanisms. Throughout this book, we will explore the reaction mechanisms of many enzymes, but we can't and shouldn't explore all of them. You can take your acquired understanding of the reaction mechanism for key representative enzymes and apply them to others. Of course, experimental evidence is needed to validate a given mechanism. In this chapter section, we will focus on the mechanisms of a few transferases (EC2) and hydrolases (EC3) as prototypical examples. Arrow Pushing Conventions in Biochemical Mechanisms The rules for electron pushing in biochemical mechanisms mirror those from organic chemistry. However, because of the length of some biochemical mechanisms, abbreviated mechanisms are often accepted and will be presented in the literature and textbooks. The aim of this section is to review arrow pushing by presenting some simple biochemical mechanisms, and to familiarize you with acceptable alternative ways to show arrow pushing. Class I Methyltransferases Coenzymes are organic molecules that participate in some enzyme-catalyzed reactions (see Section 6.8 for a detailed discussion). Often, these "enzyme helpers" impart reactivity that an enzyme would not have on its own. We'll begin our investigation with a mechanism catalyzed by class I methyltransferases, which bind the coenzyme S-adenosylmethionine (SAM). SAM itself is formed from the reaction of methionine with ATP, resulting in the positively charged sulfur shown in Figure $1$. The blue methyl group attached to the sulfur is very electrophilic due to the sulfur cation, and is transferred to a nucleophilic substrate. The reaction in Figure $1$ shows the SAM-promoted conversion of norepinephrine to epinephrine. It is catalyzed by the enzyme phenylethanolamine N-methyltransferase (EC: 2.1.1.28). The nucleophilic nitrogen atom in norepinephrine is brought into proximity of the electrophilic methyl group when it binds to the enzyme. In an SN2 reaction, nitrogen attacks, and we show a second arrow to keep track of the electrons from the carbon-sulfur bond, becoming a lone pair on the sulfur atom. An amine has a pKa close to 30, but a protonated amine has a pKa around 8–10. Specifically, the conjugate acid of epinephrine has a pKa near 8. Therefore, following the attack step, a protonated amine is a reasonable product to show at physiological pH (near 7.4). However, biochemical products are often shown uncharged when depicting an overall reaction. Therefore, a general base can be shown deprotonating the ammonium ion. The general base could be an amino acid side chain, or a general base within the buffer components contained in a cell; we will represent the general base as B: here. Figure $1$: Class I methyltransferase mechanism with substrate norepinephrine To simplify the mechanism in Figure $1$, biochemists may abbreviate the arrow pushing steps. As shown by the one-step deprotonation in Figure $2$, the general base deprotates the amine. The electrons from that bond are used to attack, and deprotonated epinephrine is produced in a single step with that arrow pushing. It is important to keep in mind that an anionic nitrogen is not produced in the reaction mechanism. With pKa values near 30, most amines are only deprotonated in practice using very strong organic bases, such as butyllithiums. Because of this, the authors prefer the alternative arrow pushing mechanism shown at the bottom of Figure $2$. Partial bond formation between the nitrogen and the methyl group must occur before a biological general base is able to deprotonate norepinephrine, so showing the arrow coming from the lone pair on nitrogen is perhaps a better depiction of the biological reality. The arrow pushing shown in each of the methyltransferase mechanisms is acceptable, and you may see each of these in different contexts. Figure $2$: Class I methyltransferase mechanism alternative arrow pushing Kinases Transfer of a phosphate group is common in biochemistry. The phosphate group is often transferred from adenosine triphosphate (ATP) via kinase enzymes. The following excerpt from Chemistry LibreTexts describes the phosphate transfer mechanism: In a phosphate transfer reaction, a phosphate group is transferred from a phosphate group donor molecule to a phosphate group acceptor molecule as shown in Figure $3$. Figure $3$: Phosphate transfer to an acceptor A very important aspect of biological phosphate transfer reactions is that the electrophilicity of the phosphorus atom is usually enhanced by the Lewis acid (electron-accepting) effect of one or more magnesium ions. Phosphate transfer enzymes generally contain a Mg2+ ion bound in the active site in a position where it can interact with non-bridging phosphate oxygens on the substrate (Figure $4$). The magnesium ion pulls electron density away from the phosphorus atom, making it more electrophilic. Figure $4$: Phosphate interaction with magnesium ion Without this metal ion interaction, a phosphate is actually a poor electrophile, as the negatively-charged oxygens shield the phosphorus center from attack by a nucleophile. Note: For the sake of simplicity and clarity, we may omit the magnesium ion or other active site groups interacting with phosphate oxygens in some of the figures that follow—but it is important to keep in mind that these interactions play an integral role in phosphate transfer reactions. Mechanistically speaking, a phosphate transfer reaction at a phosphorus center can be thought of as much like a SN2 reaction at a carbon center. Just like in an SN2 reaction, the nucleophile in a phosphoryl transfer approaches the electrophilic center opposite the leaving group, from the backside as shown in Figure $5$. Figure $5$: Phosphate transfer mechanism and energy diagram As the nucleophile gets closer and the leaving group begins its departure, the bonding geometry at the phosphorus atom changes from tetrahedral to trigonal bipyramidal at the pentavalent (5-bond) transition state (Figure $6$). As the phosphorus-nucleophile bond gets shorter and the phosphorus-leaving group bond grows longer, the bonding picture around the phosphorus atom returns to its original tetrahedral state, but the stereochemical configuration has been 'flipped', or inverted. In the trigonal bipyramidal transition state, the five substituents are not equivalent: the three non-bridging oxygens are said to be equatorial (forming the base of a trigonal bipyramid), while the nucleophile and the leaving group are said to be apical (occupying the tips of the two pyramids). Figure $6$: Transition state of the phosphate transfer reaction Although stereochemical inversion in phosphoryl transfer reactions is predicted by theory, the fact that phosphoryl groups are achiral made it impossible to observe the phenomenon directly until 1978, when a group of researchers was able to synthesize organic phosphate esters in which stable oxygen isotopes 17O and 18O were specifically incorporated (Figure $7$). This created a chiral phosphate center. Figure $7$: Inversion of stereochemistry in the phosphate transfer reaction Subsequent experiments with phosphoryl transfer-catalyzing enzymes confirmed that these reactions proceed with stereochemical inversion. (Nature 1978 275, 564; Ann Rev Biochem 1980 49, 877). (The previous excerpt has been adapted from Chemistry LibreTexts.) We should note that, although the charge-separated resonance form shown above contributes to the structure and therefore contributes to the resonance hybrid, the phosphate group is almost always shown with a double bonded oxygen atom to the phosphorous. Note that we have also depicted the mechanism below with our preferred biochemical arrow pushing, where the attack of oxygen at phosphorous facilitates the deprotonation of the alcohol (R1OH) by a general base. Figure $8$: Common arrow pushing for phosphate transfer With a formal charge of negative four, ATP would be an extremely poor electrophile. Therefore, the phosphoryl transfer from ATP, shown for hexokinase (EC 2.7.1.1) in Figure $9$, requires binding of a magnesium ion with ATP. The magnesium ion partially neutralizes the negative charge, allowing for nucleophilic attack by the oxygen atom of glucose. Figure $9$: Hexokinase mechanism with phosphate transfer from ATP A crystal structure of the enzyme was solved with the substrate, glucose-6-phosphate, bound. The magnesium cofactor also occupies the active site, along with a non-hydrolyzable analog of ATP. In the substrate analog, called ANP, the oxygen of the terminal phosphoanhydride bond of ATP is replaced with a nitrogen atom, and allows us to view probable interactions that occur between the enzyme of the catalytic complex. In the iCn3D image shown in Figure $10$, ANP and glucose are bound in the active site. The magnesium ion is shown in green. Figure $10$: Interactive iCn3D image of the catalytic complex of human glucokinase (a hexokinase isoform, 3FGU). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/icn3d/share.html?EajeSCpF8GM16HYq5 Elucidation of Reaction Mechanisms Using Kinetic Data To to this point, we've presented mechanisms with the support of PDB structures alone. However, much was known about enzyme mechanisms prior to ready access to crystal structures in the Protein Data Bank. Systematically, the kineticists, medicinal chemists and molecular biologists (i.e., a well trained chemist) can change: 1. the substrate - for example, changing the leaving group or substituents of a hydrolyzable substrate; 2. the pH or ionic strength - which can give data about general acids/bases in the active site; 3. the enzyme - by chemical modification of specific amino acids, or through site-specific mutagenesis; 4. the solvent - an odd idea on the surface but it leads to new insights into enzyme catalysis. For the following enzymes, we will concentrate on reaction mechanisms based on a mix of structural data, alongside kinetic data to hypothesize a reaction mechanism consistent with the findings. Even with lots of data, there are often different proposed mechanisms for a given reaction. Kinetic data is vital as it can help to determine: • the order of binding/dissociation of substrates and products; • the rate constants for individual steps; • and clues to the nature of catalytic groups found in the enzyme. Chymotrypsin and Other Endoproteases Chymotrypsin (EC 3.4.21.1), an endoprotease, cleaves an internal peptide bond after aromatic side chains by hydrolysis. It also cleaves small ester and amide substrates after aromatic residues. As an example, in Figure $11$, cleavage occurs on the C-terminal side of the tyrosine residue, giving two peptide fragments. Figure $11$: Chymotrypsin cleavage of an example peptide substrate Chymotrypsin has a similar mechanism to a multitude of other proteases that used the same catalytic triad, Ser 195, Asp 102 and His 95, so we'll study it significant detail. In determining the mechanism of an enzyme, you have to change an experimental variable and see how catalytic activity changes. What can be changed? Turns out everything including the solvent! Let's explore these changes and how they affect chymotrypsin activity. 1. Changing the substrate (for example changing the leaving group or acyl substituents of a hydrolyzable substrate): In the lab, it's easier to study the enzyme using small substrate mimics of a protein than to use a full protein substrate. The mimics include both esters and amides. Data from the cleavage of small amide and ester substrates shown in Figure $12$ suggest that a covalent intermediate is formed during chymotrypsin catalyzed cleavage. Table $2$ below shows kinetic data for the cleavage of these substrates. Chymotrypsin substrate cleavage, 25 oC, pH 7.9 kinetic constants Acetyl-Tyr-Gly-amide Acetyl-Tyr-O Ethylester Ester/Amide kcat (s-1) 0.50 193 390 Km (M) 0.023 0.0007 0.03 kcat/Km (M-1s-1) 22 280,000 12,700 Kinetic constants for chymotrypsin cleavage of N-acetyl-L-Trp Derivatives - N-acetyl-L-Trp-X X kcat (s-1) Km x 103 (M) -OCH2CH3 27 0.097 -OCH3 28 0.095 -p-nitrophenol 31 0.002 -NH2 0.026 7.3 Table $2$: Cleavage of peptides and ethylester substrate analogs by chymotrypsin Here's how these data can be interpreted. 1. The kcat and kcat/Km are larger and the Km smaller for ester substrates compared to amide substrates, suggesting that amides are more difficult to hydrolyze (Table 2 above). This is expected given the poorer leaving group of the amide. 2. The kcat for the hydrolysis of ester substrates doesn't depend on the nature of the leaving group (i.e., whether it is a poorer leaving group such as methoxy or a better leaving group such as p-nitrophenolate) suggesting that this step is not the rate limiting step for ester cleavage. Without the enzyme, p-nitrophenyl esters are cleaved much more rapidly than methyl esters. Therefore deacylation must be rate limiting. But deacylation of what? If water was the nucleophile, release of the leaving group would result in both products, the free carboxyl group and the amine being formed simultaneously. Since they are not released simultaneously, this suggests an acyl-enzyme covalent intermediate. When the acyl end of the ester substrate is changed, without changing the leaving group (a p-nitrophenyl group), a covalent intermediate can be trapped. Specifically, the deacylation of a trimethyacetyl group is much slower than an acetyl group. It is so slow that a 14C-labeled trimethylacetyl-labeled chymotrypsin intermediate can be isolated after incubation of chymotrypsin with 14C-labeled p-nitrophenyltrimethylacetate using gel filtration chromatography. We have seen a kinetic mechanism previously consistent with these ideas before. The data suggest a mechanism based on the chemical equations shown in Figure $13$: In this reaction, a substrate S might interact with E to form a complex, which then is cleaved to products P and Q. Q is released from the enzyme, but P might stay covalently attached, until it is expelled. This conforms exactly to the mechanism described above. For chymotrypsin-catalyzed cleavage, the step characterized by k2 is the acylation step. The step characterized by k3 is the deacylation step in which water attacks the acyl enzyme to release product P (free phosphate in Lab 5). The mathematical equation for this reaction is shown below (without derivation) \mathrm{v}_{0}=\frac{\left(\frac{\mathrm{k}_{2} \mathrm{k}_{3}}{\mathrm{k}_{2}+\mathrm{k}_{3}}\right) \mathrm{E}_{0} \mathrm{~S}}{\mathrm{~K}_{\mathrm{S}}\left(\frac{\mathrm{k}_{3}}{\mathrm{k}_{2}+\mathrm{k}_{3}}\right)+\mathrm{S}} For hydrolysis of ester substrates, which have better leaving groups compared to amides, deacylation is rate limiting, ( k3<<k2). For amide hydrolysis, as mentioned above, acylation can be rate-limiting (k2<<k3). From this, equation 6.5.1 can be simplified as shown in Table $3$ below for ester and amide hydrolysis. Ester hydrolysis (deacylation rate limiting, k3 << k2) Amide hydrolysis (deacylation rate limiting, k2 << k3) \mathrm{v}_{0}=\frac{\mathrm{k}_{3} \mathrm{E}_{0} \mathrm{~S}}{\mathrm{~K}_{\mathrm{S}}\left(\frac{\mathrm{k}_{3}}{\mathrm{k}_{2}}\right)+\mathrm{S}} \mathrm{v}_{0}=\frac{\mathrm{k}_{2} \mathrm{E}_{0} \mathrm{~S}}{\mathrm{~K}_{\mathrm{S}}+\mathrm{S}} V_{M}=k_{3} E_{0} \mathrm{V}_{\mathrm{M}}=\mathrm{k}_{2} \mathrm{E}_{0} \mathrm{K}_{\mathrm{M}}=\mathrm{K}_{\mathrm{S}}\left(\frac{\mathrm{k}_{3}}{\mathrm{k}_{2}}\right) \mathrm{K}_{\mathrm{M}}=\mathrm{K}_{\mathrm{S}} Table $3$: Simplification of equation 6.5.1 Just as we saw before for the rapid equilibrium assumption (when ES falls apart to E + S more quickly than it goes to product, Chapter 6.3), KM = Ks in the case of amide hydrolysis. 1. Changing the pH or ionic strength - which can give data about general acids/bases in the active site: • a graph of kcat as a function of pH indicates that a group of pKa of approximately 6 must be deprotonated to express activity (i.e., Vmax/2 is at about pH 6). This suggests that an active site histidine is necessary, which, if it must be deprotonated to express activity, must be acting as a general base. • a graph of kcat/Km shows a bell-shaped curve indicating the necessity of a deprotonated side chain with a pKa of about 6 (i.e., the same His above) and a group which must be protonated with a pKa of about 10. This turns out to be an N terminal Ile in chymotrypsin, which must be protonated to form a stabilizing salt bridge in the protein. Note: This N-terminal Ile is actually at the 16 position in the inactive precursor of chymotrypsin (called chymotrypsinogen); upon activation of chymotrypsinogen loses the first 15 amino acids by selective proteolysis. (Note: The PKAD is a database of experimentally measured pKa values of ionizable groups in proteins. It is searchable by the PDB ID.) 1. Changing the enzyme - by chemical modification of specific amino acids, or through site-specific mutagenesis: Here are some specific examples. 1. Modification of chymotrypsin (and many other proteases) with diisopropylphosphofluoridate (DIPF) modifies only one (Ser 195) of many serines in the protein, suggesting that it is hypernucleophilic and probably the amino acid that attacks the carbonyl C in the substrate, forming the acyl-intermediate. This reaction is illustrated in Figure $13$. The figure also shows analagous molecules used in common insecticides, which act through a similar mechanism. 1. Modification of the enzyme with tos-L-Phe-chloromethyl ketone inactives the enzyme with a 1:1 stoichiometry which results in a modified His, as shown in Figure $14$. 1. Comparison of the primary sequence of many proteases show that three residues are invariant: a Ser, a His, and an Asp residue. 2. Site-specific mutagenesis show that if Ser 195 is changed to Ala 195, the enzymatic activity is almost reduced to background. The strongly suggests that Ser 195 is an active site nucleophile. D. Changing the solvent. Yes indeed you can take chymotrypsin and show that it is active in anhydrous organic solvents. Surely this is impossible you say! It is true and we will explore it at the end of chapter since its challenging enough to understand chymotrypsin activity in aqueous solution. No new chemistry is needed, just a change in what your mind can conceptualize. The Chymotrypsin Arrow-Pushing Mechanism The chymotrypsin mechanism will be presented to explore the different types of acceptable arrow pushing one can show for this nucleophilic acyl substitution mechanism. In the mechanism, only the peptide bond will be shown. It is only necessary to focus on this small portion of the molecule shown in Figure $15$ to show the arrow pushing. Figure $15$: Abbreviated chymotrypsin peptide cleavage reaction The active site of chymotrypsin contains a catalytic triad, three amino acids working together to carry out the reaction that cleaves the peptide bond. The amino acids involved are the aspartate, histidine, and serine residues mentioned earlier (Figure $16$). Figure $16$: Serine protease catalytic triad The deprotonated aspartate side chain acts to increase histidine’s basicity, allowing it to accept a proton from serine in the catalytic mechanism. In some mechanisms, Asp is shown accepting a proton from histidine; however, simplified arrow pushing can be shown without Asp acting as a proton acceptor, and that is how the mechanism will be represented here. In the first stage of the mechanism, histidine deprotonates serine, which acts as a nucleophile and attacks the partially electropositive carbon atom of the carbonyl functional group (Figure $17$). In the simplest form of arrow pushing that can be shown, histidine deprotonates the nucleophilic using the lone pair on nitrogen, and the electrons from the hydrogen-oxygen bond are shown attacking. The carbonyl double bond breaks, shifting the electrons onto oxygen. This forms a tetrahedral sp3 hybridized carbon atom from the sp2 hybridized carbonyl group, and is therefore called a tetrahedral intermediate. Figure $17$: First stage of the chymotrypsin mechanism Using the simple arrow pushing model again, the electrons from the negatively-charged tetrahedral intermediate reform a double bond, kicking out the amine leaving group, which accepts an H+ from protonated histidine, neutralizing the charge on histidine. The arrow from the N-H bond neutralizes the positive charge on histidine; please note that this arrow is important to show. In all deprotonations, it is a convention to show electrons from a breaking bond becoming a lone pair on the atom receiving them. Another acceptable form of arrow pushing for this stage of the reaction requires more arrows, but better depicts how the enzyme is interacting with the substrate in the active site (Figure $18$. Because an active site often uses entropy reduction, bringing substrates close together in a reactive orientation, the lone pairs on heteroatoms are already interacting favorably to form the new bond. Additionally, in the first mechanism, it almost appears that a serine alkoxide attacks the carbon of the peptide bond, and that R-NH- (a poor leaving group) departs the molecule, picking up a proton after the bond breaks. Therefore, in this second mechanism option, these subtleties are considered. In the first step, the proton on serine is deprotonated by histidine, and those electrons are pushed toward the serine oxygen atom. The serine oxygen is positioned close to the carbonyl carbon of the amide bond, and an arrow originating from the serine lone pair depicts the attack. This type of arrow pushing implies the attack and deprotonation steps are happening in concert. Figure $18$: Alternate arrow pushing for the first stage of the chymotrypsin mechanism In the second step, the electrons that push down from the negatively charged oxygen atom break the N-H bond, becoming a lone pair on oxygen. Simultaneously, the lone pair already present on nitrogen is shown deprotonating histidine. It is important to note that, because biochemical mechanisms are often lengthy, they may be shown with the tetrahedral intermediate omitted. This arrow pushing for the first step, which may be shown using either convention described above, shows the serine oxygen attacking and the amine leaving group departure in one step, as shown in Figure $19$ below. Note that this abbreviated style of arrow pushing, which does not show the tetrahedral intermediate, often uses generic acids and bases, so it does not keep track of protonation states, or account for the amino acid residues performing protonation and deprotonation steps (which is quite important in the chymotrypsin mechanism). Figure $19$: Abbreviated arrow pushing for the first stage of the chymotrypsin mechanism The covalent intermediate must be released from the enzyme in order for chymotrypsin to catalyze another reaction. This second nucleophilic acyl substitution also proceeds through a tetrahedral intermediate; this time, water is the nucleophile, as shown in Figure $20$. Figure $20$: Second stage of the chymotrypsin mechanism Now, that we've seen the steps in detail, let's put all this together to show the full mechanism for serine protease cleavage of protein, shown in Figure $21$. Here a summary of what the Figure $21$ mechanism shows: • The deprotonated His 57 acts as a general base to abstract a proton from Ser 195, enhancing its nucleophilicity as it attacks the electrophilic C of the amide or ester link, creating the oxyanion tetrahedral intermediate. Asp 102 acts electrostatically to stabilize the positive charge on the His. • The oxyanion collapses back to form a double bond between the O and the original carbonyl C, with the amine product as the leaving group. The protonated His 57 acts as a general acid donating a proton to the amine leaving group, regenerating the unprotonated His 57. • The mechanism repeats itself, only now with water as the nucleophile, which attacks the acyl-enzyme intermediate, to form the tetrahedral intermediate. • The intermediate collapses again, releasing the E-SerO- as the leaving group which gets reprotonated by His 57, regenerating both His 57 and Ser 195 in the normal protonation state. The enzyme is now ready for another catalytic round of activity. • The mechanism for the first nucleophilic attack (by Ser) is the same as for the second (by water). The reverse mechanism of condensation of two peptide would be the reverse of the above mechanism, and is an example of the principle of microscopic reversibility. In short, many of the catalytic mechanisms we encountered previously are at play in chymotrypsin catalysis. These include nucleophilic catalysis (with the Ser 195 forming a covalent intermediate with the substrates), general acid/base catalysis with His 57, and loosely, electrostatic catalysis with Asp 102 stabilizing not the transition state or intermediate, but the protonated form of His 57. An important point to note is that His, as a general acid and base catalyst, not only stabilizes developing charges in the transition state, but also provides a path for proton transfer, without which, reactions would have difficulty in proceeding. One final mechanism is at work. The enzyme does indeed bind the transition state more tightly than the substrate. Crystal structures with poor "pseudo"-substrates that get trapped as partial tetrahedrally-distorted substrates of the enzyme and with inhibitors show that the oxyanion intermediate, and hence presumably the TS, can form H-bonds with the amide H (from the main chain) of Gly 193 and Ser 195. These cannot be made to the trigonal, sp2 hybridized substrate. In the enzyme alone, the hole into which the oxyanion intermediate and TS would be placed is not occupied. This oxyanion hole is occupied in the tetrahedral intermediate. A crystal structure of a relative of chymotrypsin, trypsin, which cleaves after positively charged lysine and arginine side chains, has been determined with a bound transition state analog inhibitor. The transition state inhibitor is t-butoxy-Ala-Val-boro-Lys methyl ester shown in Figure $22$. Recall from introductory chemistry that neutral boron compounds like BH3 and BF3 are trigonal planar (sp2) and electron deficient. Although the boron is not charged, it has a significant partial positive charge (δ+) so it is electrophilic. The nucleophilic oxygen of Ser 195 can then attack the boron to form a tetrahedral intermediate. This intermediate is not an oxyanion, but one of the attached oxygens with a δ- charge occupies the oxyanion hole. Figure $23$ show the active site group in trypsin interacting with part of the transition state analog (1BTZ). The serine 195 side chain O is covalently attached to the boron, so the boron is now tetrahedral (sp3). The yellow dotted lines show hydrogen bonding between the backbone amide hydrogens of Ser 195 and Gly 193 with the methoxy oxygen of the now tetrahedral borate transition state analog inhibitor. The boron atom is the yellow/orange sp3 atom connected to 3 oxygen (red) atoms and one carbon (cyan) atom. Normally, the oxyanion O- from the tetrahedral intermediate in amide or ester cleavage would occupy the oxyanion hole. Figure $24$ shows an interactive iCn3D model of the active site of the phenylethane boronic acid (PBA) complex of alpha-chymotrypsin (6cha). Figure $24$: Active site of the phenylethane boronic acid (PBA) complex of alpha-chymotrypsin (6cha). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...qXUxbrakurYmx6 Many enzymes have active site serines which act as nucleophilic catalysts in nucleophilic substitution reactions (usually hydrolysis). One such enzyme is acetylcholinesterase, which cleaves the neurotransmitter acetylcholine in the synapse of the neuromuscular junction (Figure $25$). Figure $25$: Reaction catalyzed by acetylcholinesterase The neurotransmitter leads to muscle contraction when it binds its receptor on the muscle cell surface. The transmitter must not reside too long in the synapse, otherwise muscle contraction will continue in an uncontrolled fashion. To prevent this, a hydrolytic enzyme, acetylcholinesterase, a serine esterase found in the synapse, cleaves the transmitter, at rates close to diffusion controlled. Diisopropylphosphofluoridate (DIPF) also inhibits this enzyme, which effectively makes it a potent chemical warfare agent. Another fluoride-based inhibitor of this enzyme, sarin (Figure $26$), is the most potent lethal chemical agent of this class known. Only 1 mg is necessary to kill a human being. Serine proteases have unique specificities to allow cleavage after a different subset of side chains. They cleave the peptide bond on the carboxylic acid side of specific amino acids and the specificity is determined by the size/shape/charge of amino acid side chain that fits into the enzyme’s S1 binding pocket (Figure $27$). Three chymotrypsin-like family members that share high sequence homology are the pancreatic digestive enzymes, trypsin, chymotrypsin and elastase. The protein cleavage sites of these enzymes vary. Trypsin cleaves proteins on the carboxylic side of basic residues, such as lysine and arginine, while chymotrypsin cleaves after aromatic hydrophobic amino acids, such as phenylalanine, tyrosine, and tryptophan. Elastase cleaves after small, hydrophobic residues, such as glycine, alanine, and valine. As shown in Figure $27$, variations in the amino acid residues within the binding pocket of these proteases, enables electrostatic interactions with the substrate and determines sequence specificity. A schematic nomenclature developed by Berger and Schechter is often used to show the sites on the substrate (labeled P3, P2, P1, P1', P2' and P3') referring to the products made after cleavage of the peptide/protein that is cleaved between P1 and P' (the scissile bond) and the corresponding sites on the protease (S3, S2, S1, S1', S2' and S3'). This is illustrated in Figure $28$. Serine proteases are just one type of endoproteases. However, they are extremely abundant in both prokaryotes and eukaryotes. Protease A, a chymotrypin-like protease from Stremptomyces griseus, has a very different primary sequence than chymotrypsin, but its overall tertiary structure is quite similar to chymotrypsin. The positions of the catalytic triad amino acids in the primary sequences of the protein are very similar, indicating that the genes for the proteins diverged from a common precursor gene. In contrast, subtilisin, a serine protease from B. Subtilis, has both limited sequence and tertiary structure homology to chymotrypsin. However, when folded it also has a catalytic triad (Ser 221 - His 64 - Asp 32) similar to that of chymotrypsin (Ser 195 - His 57 - Asp 102). The alignment of the core structures of chymotrypsin (5cha, magenta) and subtilisin (1sbc, cyan), are shown in Figure $29$. The list of serine proteases is quite long. They are grouped in two broad categories - 1) those that are chymotrypsin-like and 2) those that are subtilisin-like. Though subtilisin-type and chymotrypsin-like enzymes use the same mechanism of action, including the catalytic triad, the enzymes are otherwise not related to each other by sequence and appear to have evolved independently. They are, thus, an example of convergent evolution - a process where evolution of different forms converge on a structure to provide a common function. Proteases have multiple functions, other than in digestion, including degrading old or misfolded proteins and activating precursor proteins (such as clotting proteases and proteases involved in programmed cell death). In general, four different classes of proteases have been found, based on residues found in their active sites. Proteases can also be integral membrane proteins, and carry out their activities in the hydrophobic environment of the membrane. For example, aberrant cleavage of the amyloid precursor protein by the membrane protease presenillin can lead to the development of Alzheimer's Disease. Table $4$ below shows a classification of proteases based on their active site nucleophiles. Class (active site) Active Site Nucleophile Location Examples Serine/Threonine Hydrolases Ser/Thr soluble trypsin, chymotrypsin, subtilisin, elastase, clotting enzymes, proteasome membrane Rhomboid family Aspartic Hydrolases H2O activated by 2 Asps soluble pepsin, cathepsin, renin, HIV protease membrane β-secretase (BACE), presenilin I, signal peptide peptidase Cysteinyl Hydrolases Cys soluble bromelain, papain, cathespsins, caspases membrane ? Metallo Hydrolases H2O activated by 1 or 2 metal ions soluble thermolysin, angiotensin converting enzyme membrane S2P family Glutamate Hydrolases Glu . eqolysins (fungal) Asparagine Lysases (EC4) (elimination rx which are self-cleavage and hence not catalytic) Asn . Tsh autotransporter E. Coli Table $4$: Protease classification How do integral membrane protease catalyze the hydrolysis (using water) of transmembrane domains in proteins, given the hydrophobic environment of the bilayer? The rhomboid class of membrane proteases, which are found in prokaryotic and eukaryotic cells, is one of the most conserved membrane proteins in nature. Instead of using a catalytic triad, these serine proteases use a dyad of Ser 201 as a nucleophile and His 254 as a general acid/base. The chief requirement for protein substrates of rhomboids is the presence of a transmembrane domain in the target protein. No specific amino acid sequence seems to be required for specificity of one particular substrate, the drosophila transmembrane protein spitz found in Golgi membranes. On cleavage of this protein, the remaining part of the protein is released as a water soluble protein to the lumen of the Golgi where it can eventually be released from the cell. The soluble protein fragment that is released from the cell contains an epidermal growth factor domain. The structure of a rhomboid protease, GlpG (EC:3.4.21.105), from E. Coli, was determined. It is a serine protease with a catalytic dyad (Ser 201 and His 254) instead of a triad as in most serine proteases. This transmembrane protein has 6 transmembrane helices. The enzyme has a polar active site at the bottom of a V-shape opening situated laterally in the membrane. The active site His and Ser residues are deep in this V-shaped cleft, well below the surface of the membrane. Access to the transmembrane strand of the protein substrate is blocked by a loop, which must be gated open to allow substrate access between the V-shaped gap between helices S1 and S3. Ser 201 (nucleophile) and His 254 (general base/acid) are essential for activity. The active site His 254 can be covalently modified with different chloromethylketone peptide derivatives. Figure $30$ shows an interactive iCn3D model of the Rhomboid intramembrane protease GlpG 4QO2. Figure $30$: Rhomboid intramembrane protease GlpG (4QO2). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...ft9vFn7WqcujM7 Proteolytic enzymes (also termed peptidases, proteases and proteinases) are found in all living organisms, from viruses to animals and humans. Proteolytic enzymes have great medical and pharmaceutical importance due to their key role in biological processes and in the life-cycle of many pathogens. Proteases are extensively applied enzymes in several sectors of industry and biotechnology, furthermore, numerous research applications require their use, including production of Klenow fragments, peptide synthesis, digestion of unwanted proteins during nucleic acid purification, cell culturing and tissue dissociation, preparation of recombinant antibody fragments for research, diagnostics and therapy, and the exploration of the structure-function relationships. Proteolytic enzymes belong to the hydrolase class of enzymes and are grouped into the subclass of the peptide hydrolases or peptidases. Depending on the site of enzyme action, the proteases can also be subdivided into exopeptidases (like chymotrypsin) or endopeptidases (like carboxypeptidase A) as we will discuss next. Exopeptidases, such as aminopeptidases and carboxypeptidases catalyze the hydrolysis of the peptide bonds near the N- or C-terminal ends of the substrate, respectively. Endopeptidases cleave peptide bonds at internal locations within the peptide sequence. These differences are illustrated in Figure $31$. Proteases may also be nonspecific and cleave all peptide bonds equally or they may be highly sequence specific and only cleave peptides after certain residues or within specific localized sequences. The action of proteolytic enzymes is essential in many physiological processes. For example, proteases function in the digestion of food proteins, protein turnover, cell division, the blood-clotting cascade, signal transduction, processing of polypeptide hormones, apoptosis and the life-cycle of several disease-causing organisms including the replication of retroviruses such as the human immunodeficiency virus (HIV). Due to their key role in the life-cycle of many hosts and pathogens they have great medical, pharmaceutical, and academic importance. It was estimated previously that about 2% of the human genes encode proteolytic enzymes and due to their necessity in many biological processes, proteases have become important therapeutic targets. They are intensively studied to explore their structure-function relationships, to investigate their interactions with the substrates and inhibitors, to develop therapeutic agents for antiviral therapies or to improve their thermostability, efficiency and to change their specificity by protein engineering for industrial or therapeutic purposes. The following section material (between the two horizontal lines) is not found in most biochemistry textbooks, so it could be considered optional. At the same time, it offers another unique way of understanding enzymes that ultimately will lead to a better understanding of how they function. Enzyme catalysis in organic solvents In our earlier lists, we mentioned changing the solvent and exploring its affect on enzyme catalysis. It might seem a bit wild, but as we saw with the rhomboid protease, some enzymes work in hydrophobic environments. Also, lipases work at the boundary between the aqueous and hydrophobic worlds. For those interested, let's see what happens when we change solvents. These including putting the enzyme in various solvents, or mixtures of solvents, as described below: • Water miscible solvents like ethanol and acetone were added. If the water concentration was high enough, activity remained. • Biphasic mixtures in which an aqueous solution of an enzyme was emulsified in a water immiscible solvent like chloroform or ethylacetate. The substrate would partition into both phases, while the product hopefully would end up into the organic phase. • Nearly nonaqueous solvents, with a few % water at less than the solubility limits of water. • Anhydrous organic solvents (0.01% water). It is this case that is most astonishing since enzymatic activity is often retained. It is important to realize that in this last case, the enzyme is not in solution. It is rather in suspension and acts as a heterogeneous catalyst, much like palladium acts as a heterogeneous catalyst in the hydrogenation of alkenes. The suspension must be mixed vigorously and then sonicated to produce small suspended particles, so diffusion of reactants into the enzyme and out is not rate limiting. Let's explore the activity of chymotrypsin in a nonpolar solvent. Why aren't the enzymes inactive? Surely it must seem ridiculous that they aren't, since as we learned earlier, proteins are not that stable. A 100 amino acid protein on average is stabilized only about 10 kcal/mol (41 kJ/mol) over the denatured state, or the equivalent of a few H bonds. Surely the hydrophobic effect, one of the dominant contributors to protein folding and stability, would not stabilize the native structure of enzymes in nonpolar organic solvents, and the protein would denature. It doesn't however! Maybe the real question should be not whether water is necessary, but rather how much water is necessary. The enzyme can't "see" more than a monolayer or so of water around it. The data suggests that the nature of the organic solvent is very important. The most hydrophobic solvents are best in terms of their ability to maintain active enzymes! Chymotrypsin retains 104 more activity in octane than pyridine (see kcat/Km below), which is more hydrophilic than octane. The more polar the solvent, the more it can strip bound water away from the protein. If you add 1.5% water to acetone, the bound water increases from 1.2 to 2.4%, and the activity of chymotrypsin increases 1000 fold. Table $5$ below shows chymotrypsin activity in organic solvents. Solvent Structure kcat/Km (M-1min-1) relative ratio kcat/Km H2O bound to enzyme (%, w/w) Octane 63 15000x 2.5 Toluene 4.4 1000x 2.3 Tetrahydrofuran 0.27 175x 1.6 Acetone 0.022 5.5x 1.2 Pyridine <0.004 1x (.004) 1.0 Table $5$: Chymotrypsin activity in organic solvents Consider the following questions: • How much water do the enzymes need? 1 molecule of chymotrypsin in octane has less than 50 molecules of water associated and can demonstrate activity. To form a monolayer requires about 500 water molecules. Water can be added which presumably leads to more bound water and higher activity. • How stable are the enzymes? Denaturation requires conformational flexibility, which apparently requires water. The half-life of chymotrypsin in water at 60 oC is minutes, but in octane at 100 oC it is hours. At 20 oC, the half-life in water is a few days, but in octane it is greater than 6 months. Remember two factors contribute to stability: 1. The protein can denature at high temperatures. 2. Chymotrypsin is a protease, it can cleave itself in an autoproteolytic reaction. Table $6$ below shows the half-life of chymotrypsin activity in water and octane Solvent 60oC 100oC 20oC water minutes - few days octane - hours > 6 months Table $6$: Half-life of chymotrypsin activity in water and octane at different temperatures • Is the enzyme specificity changed? The net binding energy is a function of the binding energy of the substrate - the binding energy of the water, since water must be displaced from the active site on binding. In an anhydrous solvent, specificity changes must be expected. For chymotrypsin, the driving force for binding of substrates in water is mostly hydrophobic. In water, the kcat/KM for the reaction of N-acetyl-L-Ser-esters is reduced 50,000 times compared to the Phe ester. However, in octane, chymotrypsin is three times more active toward Ser esters than Phe esters. Table $7$ shows specificity changes in chymotrypsin in water and octane Substrate kcat/Km solvent: H2O solvent: Octane N-acetyl-L-Ser-ester 1x 3x N-acetyl-L-Phe-ester 50,000x 1x Table $7$: Specificity changes in chymotrypsin in water and octane Now, consider competitive inhibitors. Naphthalene binds 18 times more tightly than 1-naphthoic acid, but in octane, chymotrypsin binds naphthoic acid 310 times as tightly. Likewise the ratio of [kcat/Km (L isomer)]/[kcat/Km (D isomer)] of N-acetyl-D- or N-acetyl-L-Ala-chloroethyl esters is 1000-10,000 in water, but less than 10 in octane. Table $8$ shows chymotrypsin inhibition constants in water and octane. Inhibitor Inhibition Constant Ki (nM) In water In Octane Benzene 21 1000 Benzoic acid 140 40 Toluene 12 1200 Phenylacetic acid 160 25 Naphthalene 0.4 1100 1-Naphthoic acid 7.2 3 Table $8$: Chymotrypsin inhibition constants in water and octane Can new reactions be carried out in nonpolar solvents? The quick answers is yes, since reactions in aqueous solutions can be unfavorable due to low Keq values, side reactions, or insolubility of reactants. Consider lipases, which cleave fatty acid esters by hydrolysis in aqueous solutions. In nonaqueous solutions, reactions such as transesterification or ammonolysis can be performed. Enzymes are clearly active in organic solvents which appears to contradict our central concepts of protein stability. Two reasons could could explain this stability: 1. It is possible that from a thermodynamic view, the enzyme is stable in organic solvents. However, as was discussed above, this is inconceivable given the delicate balance of noncovalent and hydrophobic interactions required for protein stability. 2. The second reason must win the day: the protein is unable to unfold from a kinetic point of view. Conformational flexibility is required for denaturation. This must require water as the solvent. Denaturation in organic solvents is kinetically, not thermodynamically controlled. A specific example helps illustrate the effects of different solvents on chymotrypsin activity. Dry chymotrypsin can be dissolved in DMSO, a water miscible solvent. In this solvent it is completely and irreversibly denatured. If it is now diluted 50X with acetone with 3% water, no activity is observed. (In the final dilution, the concentrations of solvents are 98% acetone, 2.9% water, and 2% DMSO.) However, if dry chymotrypsin was added to a mixture of 98% acetone, 2.9% water, and 2% DMSO, the enzyme is very active. We end up with the same final solvent state, but in the first case the enzyme has no activity while in the second case it retains activity. These ideas are illustrated in Figure $32$. Dry enzymes added to a concentrated water-miscible organic solvent (like DMSO) will dissolve and surely denature, but will retain activity when added to a concentrated water-immiscible solvent (like octane), in which the enzyme will not dissolve but stay in suspension. It appears the enzymes have very restricted conformational mobility in nonpolar solvents. By lyophilizing (freeze-drying) the enzyme against a specific ligand, a given conformation of a protein can be trapped or literally imprinted onto the enzyme. For example, if the enzyme is dialyzed against a competitive inhibitor (which can be extracted by the organic solvent), freeze-dried to remove water, and then added to a nonpolar solvent, the enzyme activity of the "imprinted" enzyme in nonpolar solvents is as much as 100x as great as when no inhibitor was present during the dialysis. If chymotrypsin is lyophilized from solutions of different pHs, the resulting curve of V/Km for ester hydrolysis in octane is bell-shaped with the initial rise in activity reaching half-maximum activity at a pH of around 6.0 and a fall in activity reaching half-maximum at pH of approximately 9. Use of enzymes in organic solvent allows new routes to organic synthesis. Enzymes, which are so useful in synthetic reactions, are: • stereoselective - can differentiate between enantiomers and between prochiral substrates • regioselective - can differentiate between identical functional groups in a single substrate • chemoselective - can differentiate between different functional groups in a substrate (such as between a hydroxyl group and an amine for an acylation reaction) Enzyme in anhydrous organic solvents are useful (from a synthetic point) not only since new types of reactions can be catalyzed (such as transesterification, ammonolysis, thiolysis) but also because the stereoselectivity, regioselectivity, and chemoselectivity of the enzyme often changes from activities of the enzyme in water. Organic reactions are usually conducted in organic solvents, since many organic molecules react with water, and the reagents and products are usually not soluble in water. In a manner analogous to using an enzyme as a heterogeneous catalyst in nonpolar solvent, Sharpless is pioneering a technique to conduct organic reactions in water. They (Narayan et al.) have shown that many unimolecular and bimolecular reactions occur faster in water than in organic solvents. As in enzyme catalysis in nonpolar solvent, the reactions must be mixed vigorously to disperse reactants in micro-drops (a suspension) in water, greatly increasing the surface area that might allow water to act on transition states or intermediates to stabilize them through hydrogen bonding. They called these reactions "on water" reactions since reactants usually float on water. They have performed cycloadditions, alkene reactions, Claisen rearrangments, and nucleophilic substitution reactions using this process. One cycloaddition reaction went to completion in ten minutes at room temperature, compared to 18 hours in methanol and 120 in toluene. Adding nonpolar solvent at certain times greatly increased the rate of the reaction. Carboxypeptidase A This enzyme (EC 3.4.17.1) cleaves the C-terminal amino acid from a protein through a hydrolysis reaction. As such it is an exoprotease (not an endoprotease which cleaves proteins internally within the sequence). In terms of selectivity toward C-terminal amino acids, its activity is increased if the C-terminal side chain group is aromatic or branched aliphatic (Phe, Tyr, Trp, Leu or Ile). X-Ray structures of the enzyme with and without a competitive inhibitor show a large conformational change at the active site when inhibitor or substrate is bound. Without inhibitor, several waters occupy the active site. When an inhibitor (and presumably, by extension, a substrate) is bound, the water leaves (which is entropically favored), and Tyr 248 swings around from near the surface of the protein into the active site to interact with the carboxyl group of the bound molecule, a distance of motion equal to about 1/4 the diameter of the protein. This effectively closes off the active site and expels the water. A Zn2+ ion is present at the active site. It is bound by His 69, His 196, Glu 72, and finally a water molecule as the fourth ligand. A hydrophobic pocket that interacts with the phenolic group of the substrate accounts for the specificity of the protein. In the catalytic mechanism, Zn2+ might have several roles. In one, it may help a coordinated water to be more nucleophilic by either polarizing the water or converting it to a more potent nucleophile OH-. It might also stabilize developing negative charges in the transition state and in an intermediate. Two possible mechanisms have been offered. The Water Pathway. In this proposed mechanism, water acts as a nucleophile, and is deprotonated by Glu 270, acting as a general base. Glu 270, along with Zn2+, helps to promote dissociation of a proton from the bound water, making it to a better nucleophile. Water attacks the electrophilic carbon of the sessile bond, forming a tetrahedral intermediate. The tetrahedral intermediate then collapses, expelling the alkoxy leaving group, which picks up a proton from Glu 270, now acting as a general acid catalyst. People used to believe that Tyr 248 acted as a general acid, but mutagenesis showed that Tyr 248 can be replaced with Phe 248 without significant effect on the rate of the reaction. A simplified reaction reaction is shown in Figure $33$. Figure $33$: Water pathway mechanism for carboxypeptide A. After Wu et al. J Phys Chem B. 2010 July 22; 114(28): 9259–9267. doi:10.1021/jp101448j Nucleophilic Pathway. In this pathway, Glu 270 is the primary initial nucleophile in the formation of the initial tetrahedral intermediate. The role of Zn2+ is in charge stabilization. This pathway is illustrated in Figure $34$. Figure $35$ shows an interactive iCn3D model of the active site of bovine carboxypeptidase in the absence of a substrate or inhibitor (1M4L). The Zn2+ ion is shown as a red sphere. Figure $35$: Bovine carboxypeptidase A (1M4L) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...sX1NYbGav1CFY6 Note how far Tyr248 is away from the active site in the model. Glu72 and Glu270 are negatively charged in the resting state of the enzyme at pH 7.5. The values are much higher (weaker acid) than solution pKa of the side chain of glutamic acid. Also the water bound to the Zn2+ is long enough to suggest that the water is neutral and not in the form of OH- in this form of the enzyme. If OH- were present, the distance between it and the Zn2+ would be shorter due to the great electrostatic force. Figure $36$ shows an interactive iCn3D model of the active site of bovine carboxypeptidase bound to the inhibitor aminocarbonylphenylalanine (1HDU). The Zn2+ ion is shown as a red sphere. Figure $36$: Bovine carboxypeptidase A bound to bound to the inhibitor aminocarbonylphenylalanine (1HDU). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...vWt2qeZtUrwj78 Note the closer proximity of tyrosine 248 to the active site. Lysozyme Lysozyme (EC 3.2.1.17), found in cells and secretions of vertebrates but also in viruses which infect bacteria, cleaves peptidoglycan GlcNAc (β-1,4) MurNAc repeat linkages (NAG-NAM) in the cell walls of bacteria and the GlcNAc(β-1,4) GlcNAc (poly-NAG) in chitin, found in the cells walls of certain fungi. Since these polymers are hydrophilic, the active site of the enzyme would be expected to contain a solvent-accessible channel into which the polymer could bind. The crystal structures of lysozyme and complexes of lysozyme and NAG have been solved to high resolution. The inhibitors and substrates form strong H bonds and some hydrophobic interactions with the enzyme cleft. Kinetic studies using (NAG)n polymers show a sharp increase in kcat as n increases from 4 to 5. The kcat for (NAG)6 and (NAG-NAM)3 are similar. Models studies have shown that for catalysis to occur, (NAG-NAM)3 binds to the active site with each sugar in the chair conformation, except the fourth which is distorted to a half chair form. This labilizes the glycosidic link between the 4th and 5th sugars. Additional studies show that if the sugars that fit into the binding site are labeled A-F, then because of the bulky lactyl substituent on the NAM, residues C and E cannot be NAM, which suggests that B, D and F must be NAM residues. Cleavage occurs between residues D and E. A review of the chemistry of glycosidic bond (an acetal) formation and cleavage shows the acetal cleavage is catalyzed by acids and proceeds by way of an oxonium ion which exists in resonance form as a carbocation. A reaction mechanism of hemiacetal/acetal formation and cleavage is illustrated in Figure $37$. Catalysis by the enzyme involves Glu 35 and Asp 52 which are in the active site. Asp 52 is surrounded by polar groups but Glu 35 is in a hydrophobic environment. This should increase the apparent pKa of Glu 35, making it less likely to donate a proton and acquire a negative charge at low pH values, making it a better general acid at higher pH values. Here is a possible general mechanism: • binding of a hexasaccharide unit of the peptidoglycan with concomitant distortion of the NAM. • protonation of the sessile acetal O by the general acid Glu 35 (with the elevated pKa), which facilitates cleavage of the glycosidic link and formation of the resonance stabilized oxonium ion. • Asp 52 stabilizes the positive oxonium through electrostatic catalysis. The distorted half-chair form of the NAM stabilizes the oxonium which requires co-planarity of the substituents attached to the sp2 hybridized carbon of the carbocation resonant form (much like we saw with the planar peptide bond). • water attacks the stabilized carbocation, forming the hemiacetal with release of the extra proton from water to the deprotonated Glu 35 reforming the general acid catalysis. Part of a mechanism illustrating the roles of Glu 35 and Asp 52 is shown below in Figure $38$. Binding and distortion of the D substituent of the substrate (to the half chair form as shown above) occurs before catalysis. Since this distortion helps stabilize the oxonium ion intermediate, it presumably stabilizes the transition state as well. Hence this enzyme appears to bind the transition state more tightly than the free, undistorted substrate, which is yet another method of catalysis. pH studies show that side chains with pKa's of 3.5 and 6.3 are required for activity. These presumably correspond to Asp 52 and Glu 35, respectively. If the carboxy groups of lysozyme are chemically modified in the presence of a competitive inhibitor of the enzyme, the only protected carboxy groups are Asp 52 and Glu 35. In an alternative mechanism, Asp 52 acts as a nucleophilic catalyst and forms a covalent bond with NAM, expelling a NAG leaving group with Glu 35 acting as a general acid as shown in Figure $39$. This alternative mechanism also is consistent with other β-glycosidic bond cleavage enzyme. Substrate distortion is also important in this alternative mechanism. Recent structural work shows that Asp 52 is involved in a strong hydrogen bond network that might preclude its ability to form a covalent bond with the glycan substrate. An earlier structure (1H6M) did show a covalent bond. Figure $40$ shows an interactive iCn3D model of the active site of hen egg white lysozyme bound to a (NAG)4 glycan (7BR5). Note the positions of E35 and D52. Figure $40$: Interactions of hen egg white lysozyme with bound (NAG)4 glycan (7BR5) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/icn3d/share.html?mnZuP4W4pfTxKRmX7 Summary In this section, we explored some of the biochemical arrow pushing conventions for SN2 reactions, including methyltransferase enzymes that are dependent on the coenzyme SAM, and kinases, which transfer a phosphate group. We saw that the nucleophilic acyl substitution mechanisms for chymotrypsin and carboxypeptidase can be quite abbreviated such that it takes some biochemical intuition to recognize that a tetrahedral intermediate is operative. There are two major proposed mechanisms for carboxypeptidase, which propose distinct roles for the metal ion in the reaction. The investigation of chymotrypsin's mechanism by substrate specificity experiments (changing the substrate), altering the pH, mutagenesis and reaction with irreversible inhibitors (changing the protein) gives rich information that can be used to deduce enzyme mechanisms. Coupled with structural data, these investigations reveal key details about enzyme structure-function, and allow biochemists to propose reasonable enzyme mechanisms.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/06%3A_Enzyme_Activity/6.05%3A_Enzymatic_Reaction_Mechanisms.txt

Search Fundamentals of Biochemistry Many different mechanisms control protein activity within a cell. The primary sequence of a protein is a main determinant of protein folding and final conformation as well as biochemical activity, stability, and half-life. However, at any given moment, the proteome, the full complement of proteins within a cell, is up to two or three orders of magnitude more complex than the encoding genomes would predict. This chapter will give an overview of the major mechanisms utilized by biological systems to regulate protein functions after the protein has been synthesized. Note that these mechanisms seldom work in isolation. There are multiple levels of protein control that function at any given time and in response to many different environmental cues and signals. We will discuss many ways to regulate protein activity. At the end, we will discuss regulation through the use of different isozymes of an enzyme, which are variants of an enzyme arising from differential splicing of a gene or arising from slightly different genes that arose from a common precursor gene. We will focus on cyclooxygenases, the target of so many medicinal drugs. Post-Translational Modifications (PTMs). The human genome contains approximately 20,000 genes. When analyzing the transcriptome, it becomes apparent that the genome becomes amplified by the wide array of splice variants that can occur when primary RNA transcripts are spliced to form mature messenger RNAs. There are about 200,000 expressed transcripts of which about 146,00 encode proteins, with about 7.4 transcripts per gene. A summary of human gene transcripts is shown in Table $1$ below. Type GTEx dataset Expressed all genes Expressed protein-coding genes Transcripts per Gene 3.42 3.63 7.43 Gene counts 58,219 53,539 19,591 Transcript counts 199,324 194,146 145,571 Table $1$: Numbers of transcript and gene in the GTEx dataset. Tung, KF., Pan, CY., Chen, CH. et al. Top-ranked expressed gene transcripts of human protein-coding genes investigated with GTEx dataset. Sci Rep 10, 16245 (2020). https://doi.org/10.1038/s41598-020-73081-5. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. The number of distinct primary structures of proteins (proteoforms) from 20,000 human genes is amplified again through posttranslational modifications (PTMs) of proteins, which produce up to a million different variants. PTMs are present in both eukaryotes and prokaryotes, but are more common in eukaryotic cells, in which about 5% of the genome is dedicated to enzymes that carry out posttranslational modifications of proteins. We discussed chemical modification of specific amino acids in Chapter 3.1: Amino Acids and Peptides. Protein PTM results from the enzymatic or nonenzymatic attachment of specific chemical groups to amino acid side chains. Such modifications occur either following protein translation or concomitant with translation. PTM influences both protein structure and physiological and cellular functions. Examples of enzymatic PTMs include phosphorylation, glycosylation, acetylation, methylation, sumoylation, palmitoylation, biotinylation, ubiquitinylation, nitration, chlorination, and oxidation/reduction. Nonenzymatic PTMs include glycation, nitrosylation, oxidation/reduction, acetylation, and succinylation. Some rare and unconventional PTMs, such as glypiation, neddylation, siderophorylation, AMPylation, and cholesteroylation, are also known to influence protein structure and function. Note that many of these modifications are not made in isolation. It is common for proteins to have several different types of modifications and that these modifications can differ depending on the tissue type and environmental circumstances present. The major PTMs in eukaryotes, their target amino acid residue(s), and the types of enzyme(s) or protein(s) involved are shown in Table $2$ below. Table $2$: Common Protein Post-Translational Modifications, Their Target Amino Acid Residues, and the Enzyme(s) or Proteins Involved. Santos, A.L, and Lindner, A.B. (2017) Oxidatve Medicine and Longevity, Article ID: 5716409 PTMs can exert their effect through many different structural changes, including opening and closing the active site, changing the conformation and electrostatic properties of binding sites, changing the flexibility of the chain, increasing or decreasing intrinsically disordered regions, altering protein-protein interactions, etc. These effects can also arise from the binding of small molecules. Methods to Detect Protein Posttranslational Modifications Specific amino acid residues are subjected to PTMs depending on the chemistry of the reaction and the sequence specificity of the enzyme involved. Initially, the detection of PTMs was carried out by various analytical methods, such as radiolabeling of the proteins, thin-layer chromatography, column chromatography, and/or polyacrylamide gel electrophoresis. Other methods, such as protein sequencing by Edman degradation and Western blotting using protein-specific antibodies, have since been developed. Currently, antibody-based detection methods and mass spectrometry-based proteomic analysis are the predominant methods used to detect and analyze PTMs. However, mass spectrometric methods are the only available tool to perform global or large-scale PTM analysis. Antibody-based methods mostly rely on the availability of antibodies that can specifically recognize a modified amino acid residue within a protein or peptide. Such antibodies can be polyclonal or monoclonal and are developed against either the modified peptide/protein or against the modified amino acid. Moreover, antibody-based detection and quantification of PTMs on protein/peptide samples can be performed by two methods: chemiluminescence-based Western blotting and absorbance/fluorescence-based ELISA. However, the detection of PTMs depends entirely on the recognition site of the antibody used. If the antibody detects only the modified amino acid, additional analysis—for instance, protein/peptide isolation and sequencing—should be performed to detect the sequence context of the modification. However, if the antibody detects the PTM within a specific sequence context, the presence of PTM at other sites will remain undetected (ie the antibody will be specific for only that single modification). Mass spectrometric detection of specific PTMs is based on mass changes. Depending on the type of modification, a specific change in the mass of the modified amino acid or peptide occurs. Subsequently, the change in mass is detected by the mass spectrometer to identify the presence of a PTM in a peptide sample. Using tandem mass spectrometric methods, identification of the specific site of PTM can be achieved by subsequent fragmentation and sequencing of the relevant peptide. Yet, technical challenges hamper MS-based investigation of biologically important PTMs, such as ADP-ribosylation, one of the key signaling molecules that regulate DNA repair, a critical process in maintaining genome stability that is compromised in cancer and aging. Data increasingly implicate PTMs not only during aging and/or under pathological conditions but in the normal functioning of the cell. In turn, PTMs are increasingly studied for their role in health and disease. For example, the precise and accurate measurement of distinct PTM-containing moieties offers potential biomarker utility to aid early diagnosis, prognosis, monitoring response to therapy and decisions regarding inclusion in clinical trials as new medicines are developed. However, technical difficulties limit these studies, leaving many unanswered questions. The identification of unknown/unexpected PTMs by proteomic data reanalysis is an emerging subfield of proteomics recently boosted by the increased availability of raw data shared in public repositories. Notably, though, a sampling of the proteome in a given organism or cell provides only a snapshot of a highly dynamic process, confounding the analytical problem and ultimately arguing for time-resolved inventories. Thus, while many tools are currently available for the study of PTMs, new methods are needed to further advance the study of these modifications. Examples of PTMs Protein PTMs involve the covalent addition of some chemical group by enzymatic catalysis. Typically, an electrophilic fragment of a co-substrate is added to an electron-rich protein side chain, which acts as a nucleophile in the transfer. Common covalent protein PTMs include phosphorylation, acylation, methylation, sumoylation, ubiquitination, glycosylation, lipidation, oxidation and disulfide bond formation (either internal within a single protein or linking two protein/peptide chains together). Examples of common PTMs are provided in Figure $1\ below. Protein Phosphorylation One of the most common posttranslational modifications, protein phosphorylation, is the reversible addition of a phosphoryl group from adenosine triphosphate (ATP) to amino acid side chains such as serine, threonine, and tyrosine residues as shown in Figure \(2$. This modification causes conformational changes that either (1) affect the catalytic activity to activate or inactivate the protein and/or cause the tendency of a protein to misfold and aggregate or (2) recruit other proteins to bind; both responses result in altered protein function and cell signaling. Phosphorylated proteins have critical and well-known functions in diverse cellular processes across eukaryotes, but phosphorylation also occurs in prokaryotic cells. In humans, about one-third of proteins are estimated to be substrates for phosphorylation. Indeed, phosphorylated proteins are now identified and characterized by high-throughput phosphoproteomics studies. The reversibility of protein phosphorylation is attributed to the actions of kinases and phosphatases, which phosphorylate and dephosphorylate substrates, respectively. The temporal and spatial balance of kinase and phosphatase concentrations within a cell mediates the size of its phosphoproteome. Protein Acylation The simplest form of acylation is protein N-Acetylation. This occurs at the amino terminus amine and the ε-amino group of the lysine side chains through the action of acetylases. The acetylation of lysine side chains can be reversed through the actions of deacetylases (similar to the combined actions of protein kinases and phosphatases). The general reactions of protein N-acetylation are shown in Figure $3$. Interestingly, 80–90% of eukaryotic proteins are acetylated, yet the underlying biological significance remains unclear. The acetyl donor is acetyl-CoA. The N-terminus (reaction A) is acetylated by N-terminal acetyltransferases (NATs). The ε-amino group of lysine (reaction B) is acetylated by lysine acetyltransferases (KATs). The latter modification is reversed through two different enzymes. One is a simple hydrolysis (reaction C) catalyzed by lysine deacetylases (KDACs). The other (reaction D) requires NAD+ and is catalyzed by enzymes called sirtuins (silent information regulator). Since sirtuins use NAD+, one of the main oxidizing agents in the biological world, they link the protein acetylome with metabolic status and promote metabolic health. Histones, positively charged proteins found in eukaryotic cell nuclei, pack and order the DNA into structural units called nucleosomes as shown in Figure $4$. The histones have multiple lysine and arginine residues that interact with the negatively charged phosphate groups of the DNA backbone. The nucleosome core is formed of two H2A-H2B dimers and an H3-H4 tetramer, forming two nearly symmetrical halves by tertiary structure (C2 symmetry; one macromolecule is the mirror image of the other). The 4 'core' histones (H2A, H2B, H3 and H4) are relatively similar in structure and highly conserved through evolution, all featuring a 'helix-turn-helix' motif (a DNA-binding protein motif that recognizes specific DNA sequence). They also share the feature of long 'tails' on one end of the amino acid structure, which is the location of post-translational modification, specifically N-acetylation. Histone acetylation typically results in transcriptional activation; deacetylation typically results in transcriptional suppression. Acetylation occurs via histone acetyltransferases (HATs) and is reversible via the action of histone deacetylases (HDACs). One group of histone deacetylases are the sirtuins (silent information regulator), which maintain gene silencing via hypoacetylation. Sirtuins have been reported to aid in maintaining genomic stability. Figure $5$ shows the effect of acetylation on histone:DNA packing. Although first described in histones, acetylation is also observed in cytoplasmic proteins. Acetylated proteins can also be modulated by cross-talk with other posttranslational modifications, including phosphorylation, ubiquitination, and methylation. Therefore, acetylation may contribute to cell biology beyond transcriptional regulation. Protein Glycosylation Protein glycosylation involves the addition of a diverse set of sugar moieties to the protein core. Glycosylation has significant implications for protein folding, conformation, distribution, stability, and activity. Glycosylated proteins can have additions of simple monosaccharides. For example, many nuclear transcription factors are modified in this way. Alternatively, some proteins are modified with highly complex branched polysaccharides, such as those seen on cell surface protein receptors. More than half of all mammalian proteins are believed to be glycosylated. While proteins exhibit improved stability and trafficking after glycosylation in vivo, glycan structures can alter protein functions or activities. Glycan structures often are modified by glycan-processing enzymes working within a cell at any given time. However, the structures are sometimes protein-specific, depending on protein trafficking properties and interactions with other cellular factors. There are three types of protein glycosylation in higher eukaryotes: N-linked, O-linked, and C-linked. These types reflect their glycosidic linkages to amino acid side chains. In N-linked glycosylation, β-N-acetylglucosamine (GlcNAc) is attached through an amide linkage to the side chain of Asn within a consensus sequence of AsnXaaSer/Thr (Figure 8.8). N-linked glycans have multiple functions. While they act as ligands for glycan-binding proteins in cell-cell communication, they also can regulate glycoprotein aggregation in the plasma membrane and affect the half-life of antibodies, cytokines, and hormones in serum. O-linked glycosylation in higher eukaryotes occurs through several different mechanisms. The most abundant type of O-linked glycosylation is the mucin-type, which involve the attachment of an α-N-acetylgalactosamine (GalNAc) to the hydroxyl group of Ser/Thr side chains. Mucins are a family of high molecular weight, heavily glycosylated proteins (glycoconjugates) produced by epithelial tissues in most animals. A key characteristic of mucin proteins is their ability to form gels; therefore they are a key component in most gel-like secretions, serving functions from lubrication to cell signaling to forming chemical barriers. Aberrant expression of mucin-type O-linked glycans occurs in cancer cells and may provide targets for anticancer vaccines. O-linked glycosylation occurring with the addition of α-O-mannose is the only form of O-linked glycosylation in yeast, but also occurs in the brains of higher eukaryotes. Higher eukaryotes also have an α-O-fucose modification of Ser/Thr residues. This type of glycosylation modulates signaling pathways during eukaryotic development. Another modification, β-O-galactosylation, may contribute to rheumatoid arthritis. Finally, C-linked glycosylation involves the addition of α-mannose (Man) to the 2-position of the indole side chain of tryptophan residues. While first identified on ribonuclease 2, it also occurs on other proteins, including some of the mucin proteins, thrombospondin, and the Ebola virus soluble glycoprotein. Figure $6$ shows examples of glycan adducts. We will discuss glycoprotein in great detail in the next chapter. Protein Ubiquitination and Sumoylation Ubiquitination (or ubiquinitylation) is the addition of an 8 kDa polypeptide, called ubiquitin, to lysine residues of target proteins via the C-terminal glycine residues of ubiquitin. The addition of one ubiquitin can lead to the addition of more to form ubiquitin chains on the target protein. The activity of the target protein can be modified by the addition of one ubiquitin. This can regulate the activity of a protein, its location within the cell, and its interaction with other proteins. The addition of a single ubiquitin to lysine 120 (K120) in humans and to K123 in yeast alters gene transcription through remodeling of chromatin. However, if multiple ubiquitins are added to the target protein forming a polyubiquitinated protein, the protein is tagged for degradation by the 26S proteasome. Figure $7$ shows different patterns of ubiquitylation for proteins. An elaborate system of enzymes is involved in attaching a ubiquitin through its C-terminal Gly 76, to an internal lysine (or cysteine) in a target protein, and then adding more ubiquitins to form the structures shown above. Figure $8$ shows the first step in the process which requires three enzymes, E1, E2, and E3. Once the first (or proximal) ubiquitin is added, more can be added to form chains. The steps involved in adding a second ubiquitin to the proximal one are shown in Figure $9$. Similarly, protein sumoylation is a reversible posttranslational modification whereby a small ubiquitin-like modifier (SUMO) protein is covalently attached to a target. Like ubiquitin, SUMO is linked to a lysine side chain of target proteins and is removed by SUMO-specific isopeptidases. Sumoylation controls many aspects of nuclear function. Protein sumoylation is involved in many extranuclear neuronal processes and potentially in a wide range of neuropathological conditions. Figure $10$ shows ubiquitin (1UBQ, magenta) and SUMO-1 (1A5R, cyan) superimposed. Note the similarity in their structures even though they share only 18% sequence identity. Key amino acids are shown in spacefill. SUMO-1 doesn't target proteins for degradation. One of its roles is in the transport of proteins between the nucleus and cytoplasm, and it is involved in the modulation of protein-protein interactions. Both ubiquitin and SUMO-1 have C-terminal glycine involved in isopeptide bond formation between ubiquitin or SUMO and the target protein. A major difference between ubiquitin and SUMO-1 is along disordered N-terminal end in SUMO. Also lysines 48 and 63, found in ubiquitin, are replaced by other amino acids. This may account for the observation that SUMO-1 does not form polymers. The interplay between ubiquitylation and SUMOlaytion is complex. If the ubiquitylation of proteins in inhibited, SUMOylation of newly synthesized proteins increases. These can end up in phase-separated condensates called Promyelocytic Leukemia Nuclear Bodies (PML nuclear bodies), 0.1–1 μm condensates without a membrane found in most mammalian cell nuclei. These are somehow involved in gene expression in the nucleus. Protein Oxidation The reaction of proteins with a variety of free radicals and reactive oxygen species (ROS) leads to oxidative protein modifications such as the formation of protein hydroperoxides, hydroxylation of aromatic groups and aliphatic amino acid side chains, oxidation of sulfhydryl groups, oxidation of methionine residues, conversion of some amino acid residues into carbonyl groups, cleavage of the polypeptide chain, and formation of cross-linking bonds. Aromatic and sulfur-containing amino acid residues are particularly susceptible to oxidative modification. Unless repaired or removed from cells, oxidized proteins are often toxic and can impair cellular viability, since oxidatively modified proteins can form large aggregates. Oxidatively damaged proteins undergo selective proteolysis, primarily by the 26S proteasome in a ubiquitin- and ATP-independent way. Upon extensive protein oxidation, these aggregates can become progressively resistant to proteolytic digestion and actually bind the 20S proteasome and irreversibly inhibit its activity. Protein carbonylation is defined as an irreversible posttranslational modification (PTM) whereby a reactive carbonyl moiety, such as an aldehyde, ketone, or lactam, is introduced into a protein. The first identified source of protein-bound carbonyls was metal-catalyzed oxidation (MCO). MCO results from the Fenton reaction when transition metal ions are reduced in the presence of hydrogen peroxide, generating highly reactive hydroxyl radicals in the process. These hydroxyl radicals can oxidize amino acid side chains or cleave the protein backbone, leading to numerous modifications including reactive carbonyls. For example, oxidation of proline and arginine results in the production of glutamic semialdehyde, while lysine is oxidized to aminoadipic semialdehyde and threonine to 2-amino-3-ketobutyric acid. Direct oxidation of other amino acid residues can also lead to protein-bound carbonyls. Tryptophan oxidation by ROS produces at least seven oxidation products. Among them are kynurenine and N-formyl kynurenine, as well as their hydroxylated analogs, which contain aldehyde or keto groups formed by oxidative cleavage of the indole ring. Another important source of protein-bound carbonyls is reactive lipid peroxidation products, which are produced during oxidation of polyunsaturated fatty acids. Protein carbonylation can also occur via glycoxidation. Reactive α-carbonyls formed during glycoxidation, such as glyoxal, methylglyoxal, and 3-deoxyglucosone, can then modify the basic residues Lys and Arg to generate, for example, pyrralines and imidazolones. Glycation (i.e., the reaction of reducing sugars such as glucose or fructose with the side chains of lysine and arginine residues) forms Amadori and/or Hynes products. These glycated residues can be further changed by ROS into advanced glycation end products (AGE) carrying carbonylated moieties. Some oxidative modifications are made enzymatically and have key regulatory or structural functions within the modified proteins. For example, proline can be converted to hydroxyproline and lysine to hydroxylysine, as shown in Figure $11$. 4-Hydroxyproline makes up about 13.5% of the residues within the mammalian collagen family of proteins. Recall that collagen is the main protein of the connective tissue and represents about one-fourth of the total protein content in many animals. Hydroxyproline contributes to the stability of the triple helix and also aids in cross-linking collagen fibers to form larger macromolecular complexes. Protein Methylation Alkyl substituents are also a common posttranslational modification. The introduction of such alkyl groups results in the alteration of the hydrophobicity of the modified protein. The most common type of protein alkylation is protein methylation, which is mediated by methyltransferase enzymes. One-carbon methyl groups are added to nitrogen or oxygen (N- and O-methylation, resp.) on amino acid side chains, increasing protein hydrophobicity or neutralizing a negative charge when bound to carboxylic acids. While N-methylation is typically irreversible, O-methylation is potentially reversible. Methylation occurs so often that its primary methyl donor, S-adenosyl methionine (SAM), is one of the most-used enzymatic substrates after ATP. The methylation of a histone protein is shown in Figure $12$. This modification (not unlike phosphorylation reactions) plays a role in regulation of protein-protein interactions. For instance, the arginine methylation of proteins can either inhibit or promote protein-protein interactions depending on the type of methylation. Protein methylation is also a common modification found in histone proteins. The transfer of methyl groups from S-adenosyl methionine to histones is catalyzed by enzymes known as histone methyltransferases. The N-terminal tails of histones H3 and H4 receive methyl groups on specific lysines. Methylation then determines if gene transcription is activated or repressed, thus leading to different biological outcomes. Histone methylation was traditionally thought to be irreversible. However, histone demethylases demonstrate the reversibility of this PTM. The simultaneous removal of one histone methylation and the addition of another can enable transcriptional fine-tuning. In addition to methylation, histone acetylation, deacetylation, and mono-ubiquitination are also essential parts of gene regulation, as shown in Figure $\PageIndex{13$ below. Acetylation removes the positive charge on the histones, thereby decreasing the interaction of the N termini of histones with the negatively charged phosphate groups of DNA. As a consequence, condensed chromatin is transformed into a more relaxed structure associated with greater levels of gene transcription. Nonhistone proteins also exhibit methylation as a common PTM which regulates signal transduction pathways. Furthermore, methylation works in concert with other types of PTMs, to exert influence on not only chromatin remodeling but also gene transcription, protein synthesis, and DNA repair. Allosteric Regulation Allosteric regulation fine-tunes most biological processes, including signal transduction, enzyme activity, metabolism and transport. Allostery, an intrinsic property of a protein, is referred to as the regulation of activity at one site (also known as an orthosteric site) in a protein by a topographically and spatially distant site; the latter is designated as an allosteric site. Allosteric regulation occurs through the binding of a modulator (e.g., small molecule or protein) at an allosteric site to engender a conformational change that affects function at the orthosteric site. This effect may cause the re-distribution of the conformational ensemble by either stabilizing an active conformation (allosteric activation) or destabilizing an inactive conformation (allosteric inhibition) in response to allosteric perturbations (Figure 8.13). Traditionally, the repertoire of allostery was primarily confined to determining the allosteric effects or mechanisms in individual multi-subunit or monomer proteins by conformational transitions. Recently, increasing evidence has indicated that allosteric signals can propagate across several or numerous proteins to sculpt allosteric networks. Allosteric regulation is particularly important in the cell's ability to adjust enzyme activity based on the surrounding environmental conditions. Feedback control loops, such as feedback inhibition from downstream products or feedforward from upstream substrates are common allosteric regulatory mechanisms found in nature. Another example of allostery includes oxygen binding to one of the subunits of hemoglobin that prompts cooperative binding to other subunits. We mentioned before that PTMs can lead to conformational changes in proteins. Other methods to induce such allosteric changes include the binding of small molecules, the binding of proteins, changes in the redox environment of a protein, which affects disulfide bonds, and general changes in protein flexibility and dynamics. Let's look at some examples of allostery brought about by these changes. Many of these examples are presented by Laskowski et al ( https://doi.org/10.1016/j.febslet.2009.03.019). Allosterism by small molecules: opening and closing active sites Small molecule binding at an allosteric site can lead to small and large conformation changes in a distal active site. An often observed motion is a partial or full hinge-clamping conformational change that brings critical catalytic groups into a more organized and effective active site, which excludes water as a competing hydrolytic substrate (for example). One example of a small hinge-clamping change is seen in phosphoglycerate dehydrogenase (PGDH, which catalyzes the first step (oxidation by NAD+) in the synthesis of serine. It converts 3-phosphoglycerate into 3-phosphohydropyruvate. The end product of the pathway, serine, binds to an allosteric site on PGDH and inhibits it, a classic example of feedback inhibition of the first reaction of a pathway by the end product. PGDH has a regulatory binding domain (RBD) that binds serine, a substrate binding domain (SBD) and an NAD+/NADH binding domain (NBD) which is where the allosteric inhibitor binds, plus binding domains for substrate (SBD) and the NAD nucleotide (NBD). Figure $14$ shows the small hinge-bending conformational change on bind conversion of monomeric apo-PGDH (1psd) to holo-PGDH with serine (not shown) bound in the RBD allosteric domain. The RBD-SBD domains, relative to the NBD domain, undergoes a 15° rotation. The functional enzyme is a tetramer (only the monomer is shown in the above figure). The rotation in the full tetramer aligns catalytic residues and closes off the active site. Conformational changes are ubiquitous on the binding of ligands to protein. These changes also occur on substrate binding at the active site. It is important to recognize substrate-induced changes in the active site are not allosteric changes since the conformational changes, no matter how big, are caused by binding at the active site, not at an allosteric site. A classic example of a huge conformational change on binding of substrate is elicited by the binding of glucose or glucose analogs to the active site of hexokinase, the first enzyme in the glycolytic pathway. Figure $15$ shows the large conformational change on binding a glucose analog to hexokinase (1hkg, no glucose; 2yhx, with glucose analog). The background is shown in black to remind readers that this is NOT an example of allosterism, although the changes facilitate catalysis by excluding water, a competing substrate) from the active site after glucose and ATP (not shown) bind. On binding the glucose analog in the active, the active site becomes sequestered ("jaw" clamping down on binding). Allosterism by small molecules: Subtile electrostatic effects Often no large conformation change is evident in the protein. In those cases, subtle rearrangements of key residues in the active site or near it (which might promote access) may be the result of allosteric effector binding. A simple change in the electrostatic environment might account for the effect. Such appears to be the case with chorismate mutase, an enzyme in the bacterial, fungal, and plant pathways for the synthesis of aromatic amino acids tyrosine and phenylalanine. The enzyme catalyzes the conversion of chorismate to prephenate, which proceeds on to the aromatic amino acids. An offshoot pathway takes chorismate to the last aromatic amino acid tryptophan. The enzyme chorismate mutase then is key in the metabolic decision pathway to make tyrosine/phenylalanine or tryptophan. The enzyme is activated by tryptophan and inhibited by tyrosine. The aligned sequences of two crystal structures of the enzyme with the bound allosteric activator tryptophan (Trp 502 in pdb structure 1csm showing a magenta glutamic acid 23) and with bound inhibitor (Tyr 300 in pdb structure 2csmA showing cyan Glu 23) are shown in Figure $16$: There are no significant changes in the overall structure of the protein or in its active site. However, the alignment of glutamic acid 23 is different, and this may be the basis for the observed allosteric effects. When active, glutamic acid 23 is buried in the active site pocket, but in the inhibited site, it swings into the binding site. Since the chorismate is a charged dicarboxylic acid, Glu 23 probably repels its binding, inhibiting the enzyme's activity. Allosterism by phosphorylation We'll focus on one example of a huge conformational change on phosphorylation of a serine side chain in glycogen phosphorylase (GP), an enzyme that catalyzes the phosphorolysis of an acetal link between a terminal glucose on a glucose polymer, glycogen, and the next glucose in the chain. This reaction is not a hydrolysis, in which water acts as a nucleophile to cleave acetal bonds. This is a key reaction in metabolism since it cleaves a major energy storage molecule, glycogen. The enzyme is a multimer that exists in two major states, a T-state and R-state glycogen phosphorylase (GP). We've explored T and R states and allosterism in their interconversion when we discussed the binding of oxygen to hemoglobin in Chapter 5.3. Crystal structures of the T and R-state bound to allosteric activators IMP and AMP are known. High AMP concentrations imply low ATP concentrations and the need to mobilize glycogen reserves. Conversion to the R state is also promoted by the phosphorylation of Seriner14, which activates the enzyme. The allosteric activators AMP and IMP bind to a disordered loop (313-326) and change it to a form promoted by nucleotide binding. which is disordered in the free structure, and adopts a conformation dictated mainly by the type of nucleotide that binds at this site. Figure $17$ shows a monomer of the European rabbit glycogen phosphorylase in the R (active) state bound to AMP (3e3n). Only one chain of the active homodimer is shown. The helix containing the phospho-Ser 14 swings away from the rest of the protein. Figure $18$ shows the difference between the T (inactive) state of rabbit muscle glycogen phosphorylase (7P7D) and the R (active) state (3e3n, neither phospho-Ser 14 or AMP shown). Note the huge conformational state elicited on phosphorylation of Ser 13. Allosterism by small molecules: control of quaternary structure The binding of small molecules at allosteric sites may promote or inhibit the formation of the correct functional multimeric structure of a protein. One example is ATP phosphoribosyltransferase (APRT), which catalyzes the first step in the synthesis of histidine. As we saw with phosphoglycerate dehydrogenase (PGDH), APRT has three domains. I and II comprise the active site that is located between them, and a regulatory domain, III (the functional protein is a dimer). As with PGDH, APRT is allosterically inhibited by the end product of the pathway, histidine. On histidine binding to the inactive form (1nh7), a large conformational change results (1nh8), as shown in Figure $19$. This change drives the formation of a hexamer - (dimer)3, which closes off the active site and inactivates the enzyme. The enzyme ribonuclease reductase behaves similarly. Allosteric by protein binding Another protein can bind to an enzyme and activate it by promoting an allosteric change. An example is the binding of a regulatory protein CyclinA to cyclin-dependent kinase 2 (CDK2). On binding of cyclin to one side of CDK2's catalytic cleft, a large conformational change occurs, which activates the enzyme by altering active site geometry and making the active site more accessible. CDK enzymes are involved in cell-cycle progression. For more control of the cell cycle, both the binding of cyclin and the phosphorylation of CDK2 are required for activity. On binding cyclin, the active site is more available for substrate (ATP) binding. Next, the amino acid that needs to be phosphorylated for activation, Thr 16, is made accessible. Figure $20$ shows the conformational change in apo-CDK2 (1hc1) on cyclin binding, which allows ATP binding (1fin). Cyclin (the regulatory protein) is shown in gray. ATP is shown in spacefill. CDK2 is shown as a colored cartoon. Allosterism by disulfide bonds It should make sense that cleaving an intrachain or interchain disulfide, whose presence constraint a specific protein conformation, should significantly alter protein structure and function. One example is the protein botulinum neurotoxin type A. It is neurotoxin and Zn protease that cleaves proteins like synaptobrevins, syntaxin and SNAP-25 in the neurosynapase that are required for neurotransmitter release. It has a catalytic (Zn peptidase/protease) and a translocation domain in a single protein chain that contains two disulfide bonds when synthesized protein (PDB code 3bta). On cleavage at a select peptide bond, it is split into A and B chains, which remain connected by a single disulfide bond, with the A chain containing the catalytic domain. The B chain contains the translocation domain, which effectively still blocks the active site of the peptidase/protease domain. The disulfides are cleaved when the protein enters an endosome, which contains a more reducing environment. The separate A chain now separates for the inhibiting B domain and expresses protease activity. The active protein then enters the cytoplasm. Figure $21$ shows the domain structure and disulfide bonds of the unprocessed form. The N-terminal Zn proteinase domain (Peptidase_M27, amino acids 1-409) is followed by an intrachain disulfide between C429 and C 453. The mature form is proteolyzed between amino acids 447 and 448 (within the sequences connected by the disulfide (429-453). The protein remains inactive until the disulfide bond is cleaved. Figure $22$ shows an interactive iCn3D model of botulinum neurotoxin type A (3bta). Figure $22$: Botulinum neurotoxin type A (3bta) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...CfSsBH94jcPyb8 The N-terminal Zn peptidase/protease domain is shown in color and the other regions of the protein are shown in gray. The two disulfide bonds are shown in spacefill, with CPK color. On cleavage of the disulfide bond between C429 and C453, the Zn protease domain, which is inhibited in the presence of the constraining disulfides, is released and activated. Allosterism by change in protein flexibility and population shifts Sometimes crystal structure show little difference between the apo- and holo-forms of the protein containing a separate allosteric binding site. One example is the enzyme dihydrodipicolinate synthase (DHDPS) which catalyzes the first step in the bacterial and plant pathways for lysine synthesis. DHDPS catalyzes the synthesis of 4-hydroxy-2,3,4,5-tetrahydro-2-dipicolinic acid from aspartate-β-semialdehyde and pyruvate. One explanation for allosterism in the protein is that it occurs on time scales too short to be seen in crystal or NMR structures. Rather some transient conformation might cause allosterism. Such changes are detectable in molecular dynamics simulation of the protein in the presence and absence of the allosteric effector molecule. This possibility suggests that the allosteric effector might change the distribution of possible transient conformational states within a population of such states. If an allosteric effector bound one of the transient states in the population, that state would be "removed" from the ensemble of states, shifting the dynamic equilibrium among the populated states to produce more of the specific conformation that binds the allosteric effector. This sounds remarkably like the T to R state conversion in the MWC model oxygen binding to hemoglobin and the allosterism discussed above with the enzyme glycogen phosphorylase only a much shorter time scale. This model is consistent with a conformational selection model in which the allosteric molecule binds preferentially to a different preexisting conformational state that exists transiently. Zymogen Activation A zymogen also called a proenzyme, is an inactive precursor of an enzyme. A zymogen requires a biochemical change (such as a hydrolysis reaction revealing the active site, or changing the configuration to reveal the active site) for it to become an active enzyme. Protease enzymes secreted by the pancreas are initially synthesized as zymogens. The pancreas secretes zymogens to help prevent the enzymes from inappropriately digesting proteins in the pancreatic cells in which they are synthesized. Enzymes like Trypsin are synthesized as proenzymes. For trypsin, trypsinogen is an inactive precursor that is translated in the rough endoplasmic reticulum and transported to the Golgi apparatus for sorting. Trypsinogen is always co-synthesized and packed with a pancreatic secretory trypsin inhibitor (PSTI) that inhibits its premature activation. Thus, there are two mechanisms in place to maintain the inactivity of the protease within the pancreas: (1) synthesis of the zymogen or proenzyme form, and (2) co-expression of a trypsin inhibitor protein that will bind and inhibit any prematurely cleaved trypsin until it has reached the small intestine. An animation showing structural differences between bovine trypsinogen (magenta) with just amino acids 10-15 of the presequence (spacefill orange) and mature trypsin (cyan) is shown in Figure $23$. During packaging within the Golgi system, the trypsinogen and other digestive enzymes condense into core particles and are packed in zymogen granules. The condensed enzymes are stable and minimal activation happens within the zymogen granules. Once the pancreatic cells receive secretory stimuli, these zymogen granules are released in to the lumen of the pancreatic duct, which carries the digestive enzymes into the duodenum. Once in the duodenum, enteropeptidase activates trypsinogen by removing the 7-10 amino acids trypsinogen activation peptide (TAP) from the N-terminal region (see above). Removal of TAP induces conformational change resulting in active trypsin. TAP is immunologically distinct from the same sequence within trypsinogen, thereby allowing the detection of trypsinogen activation in situ. Once activated, trypsin will cleave and activate other zymogen proteases and lipases present in the duodenum. These include the activation of elastase, chymotrypsin, carboxypeptidase, and lipase as shown in Figure $24$. Zymogens are also found in other cellular processes as well. For example, intracellular proteases known as caspases, are activated in a similar manner during the process of cellular apoptosis or programmed cell death. The process of blood clotting also involves the activation of zymogens. Figure $24$: Activation of proenzymes Isozymes Isozymes (also known as isoenzymes) are enzymes that differ in amino acid sequence but catalyze the same chemical reaction. These enzymes usually display different kinetic parameters (e.g. different KM or Kcat values), or different regulatory properties. The existence of isozymes permits the fine-tuning of metabolism to meet the particular needs of a given tissue or developmental stage. In many cases, isozymes are coded for by homologous genes that have been duplicated within the genome and then diverged over time. Isozymes should not be confused with allozymes, which are allelic variants of the same gene locus that are found within a population. Allozymes represent enzymes from different alleles of the same gene. Isozymes represent enzymes from different genes that process or catalyze the same reaction, or from the same gene that produces, through differential splicing of primary RNA transcripts, sequence of similar but different sequences. We will focus on isozymes within this section. Part of the regulation derived from the production of different isozymes can arise from the differential expression of isozymes. Isozymes are usually the result of gene duplication. Over evolutionary time, if the function of the new variant remains identical to the original, then it is likely that one or the other will be lost as mutations accumulate, resulting in a pseudogene. However, if the mutations do not immediately prevent the enzyme from functioning, but instead modify either its function or its pattern of expression, then the two variants may both be favored by natural selection and become specialized for different functions. For example, they may be expressed at different stages of development or in different tissues. Some isozymes may also arise from convergent evolution and may not share a high degree of sequence homology or common ancestry. The Cyclooxygenase I and II (Cox-1 and Cox-2) Isozymes As an example of isozymes, we will discuss cyclooxygenases COX-1 and COX-2, which are also called Prostaglandin Synthases. They regulate a key step in prostaglandin and thromboxane synthesis and are the targets of nonsteroidal antiinflammatory drugs (NSAIDs), such as aspirin, ibuprofen, naproxen and celebrix (Figure $25$). The prostaglandins (PG) are a group of physiologically active lipid compounds called eicosanoids having diverse hormone-like effects in animals. Prostaglandins have been found in almost every tissue in humans and other animals. They are derived enzymatically from the fatty acid arachidonic acid. Every prostaglandin contains 20 carbon atoms, including a 5-carbon ring. They are a subclass of eicosanoids and of the prostanoid class of fatty acid derivatives. Cycloxygenases are in a class of enzymes called dioxygenases, that incorporate both atoms of O2 into a substrate. We will explore the mechanism of cyclooxygenase in more detail in Chapter 13.5: Biological Oxidation and Reduction Reactions. The structural differences between prostaglandins account for their different biological activities. A given prostaglandin may have different and even opposite effects in different tissues. The ability of the same prostaglandin to stimulate a reaction in one tissue and inhibit the same reaction in another tissue is determined by the type of receptor to which the prostaglandin binds. They act as autocrine or paracrine factors with their target cells present in the immediate vicinity of the site of their secretion. Prostaglandins differ from endocrine hormones in that they are not produced at a specific site but in many places throughout the human body and tend to act locally once secreted. Prostaglandins are implicated in various physiological processes such as gastrointestinal cytoprotection, hemostasis and thrombosis, as well as renal hemodynamics. Through their role in vasodilation, prostaglandins are also involved in inflammation and can trigger the onset of a fever or the sensation of pain. They are synthesized in the walls of blood vessels. They prevent needless clot formation and regulate the contraction of smooth muscle tissue. The prostacyclins, a special class of prostaglandins, are powerful, locally-acting vasodilators and inhibit the aggregation of blood platelets. Conversely, thromboxanes (produced by platelet cells) are vasoconstrictors and facilitate platelet aggregation. Their name comes from their role in clot formation or thrombosis. The cyclooxygenases COX-1 and COX-2 regulate the first two steps in prostaglandin and are bifunctional enzymes containing two active sites. The first active site performs the bis-oxygenation and cyclization of arachidonic acid, whereas the second active site mediates a peroxidase (reduction) reaction to form PGH2. The enzyme is an example of a dioxygenase, which uses O2 as a substrate. The enzyme contains a heme cofactor. The functional enzyme of both COX-1 and COX-2 are homodimers of 70 kDa subunits, each having an N-terminal epidermal growth factor domain, a membrane binding domain and a C-terminal catalytic domain. The cyclooxygenase active site in on the opposite side of the peroxidase active site in the catalytic domain. Figure $26$ shows an interactive iCn3D model of arachidonic acid bound to V349I murine COX-2 (6OFY). Just one chain of the dimer is shown. Figure $26$: Arachidonic acid and heme bound to V349I murine COX-2 (6OFY) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/icn3d/share.html?gUtX3uAtwoozBvJUA The heme is shown in stick while the arachidonic acid is shown in spacefill. Note that arachidonic acid is very kinked and not extended due to its four cis double bonds. Three additional amino acids are highlighted that play key roles in NSAID inhibition of Cox. These include Ser 530 (in model 531), which is covalently acetylated by aspirin, and Arg 120 (in model 121) and Tyr 355 (in model 356). These play key role in binding of NSAIDs and access to the arachidonic acid binding site. The COX-1 enzyme is widely distributed in many tissues where it is constitutively expressed. Expression of the COX-2 isoform (shown in Figure $2$), on the other hand, is normally undetectable in most tissues (except for the central nervous system, kidneys, and seminal vesicles). COX-2 is induced by various inflammatory and mitogenic stimuli. More recently, a third isoform named COX-3 was identified as a COX-1 splicing variant. This new variant may play a role in processes such as fever and pain. Additionally, a high level of COX-2 expression is found in several forms of cancer. For example, COX-2 overexpression is related to poor prognosis in certain breast cancers and endometrial adenocarcinomas. COX-2, unlike COX-1, is induced in inflammatory cells and activated by various inflammatory and mitogenic stimuli. Under these conditions, COX-2 activity leads to the production of prostanoid mediators that trigger important inflammatory processes. Although inflammation is initially a necessary process to fight infection, if it is maintained or remains uncontrolled, it can provoke chronic pathologies and tissue damage. This is why the inhibition of COX proteins are key targets for anti-inflammatory and pain-management. Their actions are illustrated in Figure $27$. Figure $27$: NSAID Inhibition of COX-1 and COX-2 Enzymes. The schematic representation of the COX-1 (large green figure) active site being inhibited by a nonselective NSAID (central blue figure). The entrance channel to COX-1 is blocked by the NSAID. Binding and transformation of arachidonic acid (bottom yellow figure) within COX-1 is prevented. Middle Lower Panel shows the inhibition of COX-2 by a nonselective NSAID (central blue figure). Nonselective binding uses an amino acid residue, Arg120, that is conserved in both enzymes. The right panel shows the inhibition of COX-2 by COX-2 selective NSAIDs (central red figure). The COX-2 side pocket allows specific binding of the COX-2 selective NSAID’s. The entrance channel to COX-2 is blocked. The bulkier COX-2-selective NSAID will not fit into the narrower COX-1 entrance channel, allowing COX-1 to remain active. Upper Figure by Saiz, M., Gonzalez, R., & Garcia, E., (2019) Protopedia and Lower Figure by Meek, I.L., et al. (2010) Pharmaceuticals 3(7):2146-2162. Nonsteroidal Antiinflammatory Drugs (NSAIDs) Clinically available NSAIDs can be separated into 3 different classes based upon their mechanism of action: • ASPIRIN: - Acts to irreversibly inhibit COX 1 & COX-2 by covalent acetylation of Ser 530 in the active site. Most notably, low doses of aspirin can suppress platelet COX-1 activity by 95% or more, an effect that is permanent for the lifetime of the platelet, since platelets lack DNA and cannot synthesize new enzymes. Due to aspirin's antithrombotic properties at low doses, this treatment has been found to have cardioprotective effects and is often prescribed for patients at high risk of myocardial infarction. All other NSAIDs interact with COX isoforms reversibly and produce variable COX inhibition (ranging from 50% to 95%) in a time-dependent fashion based on how quickly they are metabolized in the body. • NON-SELECTIVE COX INHIBITORS: Different non-selective NSAIDs have varying inhibitory effects against COX-1 & COX-2 (Figure 8.3). The two most commonly used over-the-counter drugs in this group (ibuprofen & naproxen) produce reversible platelet inhibition ranging from 50 to 95% in a reversible, time-dependent manner (more on this below). These NSAIDs may be insufficient to provide cardio-protection throughout a commonly used dosing interval and are not commonly used for this purpose. Ketorolac (Toradol ®), an NSAID most commonly used in a hospital setting to treat moderately severe pain, is classified as a non-selective NSAID, although it is arguably, a very selective for COX-1 inhibitor (Figure 8.3). Inhibition of COX-1 can result in unwanted side effects, such as gastrointestinal discomfort and in severe cases, ulceration. • COXIBS: Selective COX-2 inhibitors were designed and marketed to treat pain and inflammation, while avoiding the GI side effects. However soon after they were introduced into the market, their use led to the first reported incidence of increased cardiovascular events (myocardial infarction and stroke) in 2004. Rofecoxib (Vioxx ®), one of the most selective COX-2 inhibitors was removed from the market because of mounting evidence for significant cardiovascular toxicity (Drazen, 2005). Celecoxib (Celebrex ®) is currently the only FDA approved coxib available in the US, and it has been given a black box warning indicating the potential risk of cardiovascular toxicity. It has a 10-20 fold selectivity for COX-2 over COX-1. Etoricoxib (Arcoxia ®) is a second coxib with ~106 fold selectivity for COX-2 over COX-1 that is available outside of the United States. Figure $28$ shows Selectivity and Treatment Efficacy of Nonsteroidal Anti-inflammatory Drugs (NSAIDs). The structures of ibuprofen (Advil), naproxen, aspirin, celebrix and acetaminophen (Tylenol, which reduces fever and pain but not inflammation so its not a NSAID) are shown in Figure $29$. Acetominophen - Tylenol Although this extremely popular drug relieves pain and reduces fever, it does not reduce peripheral inflammation and is hence not classified as an NSAID. It does have effects central nervous system affects. It does not appear to bind to the active site of COX-1 or COX-2 (no PDB structures available), but can reduce their activities probably either upstream or downstream of the pathways that these enzymes are part of. It does decrease prostaglandin production in the CNS, which reduces CNS pain and fever. It might act as a reducing substrate in the peroxidase site. Many proteins bind this drug. which is understandable given its relatively simple structure shown in Figure $5$. A metabolite of it, p-aminophenol, can be esterified to arachidonic acid to produce the fatty acid amide, which can work through cannabinoid receptors to reduce pain. Figure $30$ shows an interactive iCn3D model of the NSAID ibuprofen bound to cyclooxygenase-2 (4PH9). Just one chain of the functional dimer is shown. Figure $30$: Ibuprofen bound to cyclooxygenase-2 (4PH9). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...syYYjSoeZRGNU8 Ibuprofen is sold as the racemic mixture of R and S enantiomers but it was thought that only the S isomer's inhibition of Cox II leads to its antinflammatory effects while the other isomer had no effects and no side effects. The crystal structures of IBP bound to both Cox I and II are known. At least in the mouse Cox2, the S-isomer binds more tightly than the R-isomer. Arg-120 and Tyr-355 at the entrance of the cyclooxygenase channel are important in the action of IBP in Cox2 (which are labeled in in Figure $6$. As mentioned above, aspirin inhibits COXs through covalent acetylation of a key serine side chain. The other NSAIDs appear to inhibit prostaglandin H(2) synthase through blockage of the channel for arachidonic acid binding. This should make you think that they act as competitive inhibitors, which is true for ibuprofen and a derivative, methyl flurbiprofen. However, the effect of two others NSAIDs, aclofenac and flurbiprofen, seems to have an added time component not found in simple competitive inhibition model. They are called slow tight-binding inhibitors and a simple model describing it is shown in Figure $31$. A time step suggests a conformational change after initial binding. Yet crystal studies of the four inhibitors mentioned above show that the NSAID-occupied active sites are essentially the same, suggesting no major global conformational change. It may be that the rate involves slow hydrogen bonding around two key residues, Arg 120 and Tyr 355. Slow binding and slow tight-binding inhibition All the enzyme inhibition models we discussed in Chapter 6.4 are based on reversible rapid equilibrium binding of an inhibitor to E (competitive), ES (uncompetitive), and E and ES (mixed/noncompetitive). Equations can be derived for slow binding inhibition and for slow tight-binding. In these models, the degree of inhibition at a fixed concentration of the inhibitor changes with time to establish an "equilibrium" among all species. The model above works for both. Both types of inhibitors (slow and slow-tight) inhibit enzymes in a time-depended fashion. In slow binding, it takes longer to establish equilibrium among the three species, E, EI and EI*. In slow binding, the steps determining the concentration of EI (k3I and k-3) are assumed to be fast compared to those involved in determining the concentration of EI* (k4 and k-4). When [I] >> [E], the following dissociation constants for pure competitive (KIS = KC) and for the slow binding inhibition (KI), which accounts for mass balance, hold. \mathrm{K}_{\mathrm{IS}}=\mathrm{K}_{\mathrm{C}}=\frac{[\mathrm{E}][\mathrm{I}]}{[\mathrm{EI}]} \mathrm{K}_{\mathrm{I}}^{*}=\frac{[\mathrm{E}][\mathrm{I}]}{[\mathrm{EI}]+\left[\mathrm{EI}^{*}\right]} In initial rate Michaelis-Menten kinetics, v0 is measured as a function of [S]. The initial rate v0 is determined by measuring [P] vs t in the beginning of the reaction when the substrate is not depleted. Valid v0 values require that [P] vs t curves are linear. The slope of that line is the initial velocity, v0. This is true for uninhibited and reversibly inhibited (competitive, uncompetitive, and mixed) reactions. However, with slow binding inhibition, the [P] vs t curves bend with time as the slower formation of EI* occurs. It's a bit like the pre-steady state assumption in enzyme kinetics. At very short times (msec), the P vs t curves bend as a steady state emerges. It's the same with slow binding inhibition. This is illustrated in Figure $32$. A progress curve equation showing [P] vs t for slow binding in inhibition is shown below without inhibition. [\mathrm{P}]=\mathrm{v}_{\mathrm{s}} \mathrm{t}+\frac{\left(\mathrm{v}_{\mathrm{o}}-\mathrm{v}_{\mathrm{s}}\right)\left(1-\mathrm{e}^{-\mathrm{k}_{\mathrm{a}} \mathrm{t}}\right)}{\mathrm{k}_{\mathrm{a}}}+\mathrm{C} In this equation, ka is the apparent first-order rate constant to the development of the steady state at a given substrate and inhibitor concentration, v0 is the very first initial rate and vs is the steady-state rate. Slow tight-binding inhibition occurs when the rate constants for net production of EI* are fast compared to the step for the formation of EI. In this case, even small amounts of I will produce EI* as the reaction is pulled to EI*, and more complicated equations must be used to determine product vs time equations. Other chemical models could also account for slow binding inhibition. These include a slow binding rate constant k4, a slow isomerization of EI after fast binding of I, or a slow binding to a specific, low population conformation of enzyme in a process called conformational selection. Slow-binding inhibitors of enzymes (such as neural acetylcholine esterase) are known. Here is a Vcell simulation showing progress curves for an uninhibited reactions, one in the presence of a competitive inhibitor and one in the presence of a slow (competitive) inhibitor. MODEL Comparison No Inhibition, Competitive Inhibition, and Slow (Competitive) Inhibition Vcell reaction diagram with all equations based on mass action (not Michael-Menten kinetics) Concentrations • A, B and C (substrates), t0 = 1 uM • I1 and I(inhibitors) = 1 uM • E, E1 and E2 (enzymes) at t0 = 0.1 uM • P, Q and R (products) at t0 = 0 uM Rate Constants All forward (kf) and reverse (kr) rate constants are set initially to 1 except for • kr2 for all reactions = 0 and kf4 = 1. Select Load [model name] below Select Start to begin the simulation. Interactive Element Only plots of P, Q and R (products) are initially shown.  Select Plot to change Y axis min/max, then Reset and Play  |  Select Slider to change which constants are displayed |  Select About  for software information. Move the sliders to change the constants and see changes in the displayed graph in real-time. Time course model made using Virtual Cell (Vcell), The Center for Cell Analysis & Modeling, at UConn Health.  Funded by NIH/NIGMS (R24 GM137787); Web simulation software (miniSidewinder) from Bartholomew Jardine and Herbert M. Sauro, University of Washington.  Funded by NIH/NIGMS (RO1-GM123032-04) Coxibs and the Thromboxane/Prostacyclin Imbalance Hypothesis Previous research indicates that in the cardiovascular system, a greater inhibition of COX-2 vs COX-1 (as produced by COX-2 selective “coxibs”) can tip the normal balance between the effects produced by prostacyclin & thromboxane, resulting in an increased likelihood for platelet aggregation and vasoconstriction. These effects can help to explain the higher incidence of myocardial infarction and stroke observed when these drugs have been used clinically. The mechanisms involved are illustrated in Figure $33$. The left graphic illustrates the normal balanced effect between prostacyclin (PGI2) and Thromboxane (TXA2). PGI2 is produced primarily by COX-2 activity in the endothelial cell wall of blood vessels. PGI2 produces vasodilation, and inhibits platelet activation. In contrast, TXA2 is produced primarily by COX-1 activity inside platelets, and produces vasoconstriction and enhanced platelet aggregation. When there is a balanced effect of both PGI2 & TXA2, normal vascular homeostasis is maintained. However, when the balance is tipped in favor of TXA2 formation after selective inhibition of COX-2 (right graphic), vasoconstriction and platelet clumping are more likely to occur, potentially causing an increased risk for cardiovascular events such as myocardial infarction and stroke. Overall, the COX-1/COX-2 isozyme example sheds light on the complexity of biological systems and the ability for slight adjustments in gene expression to create varied and tissue-specific responses.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/06%3A_Enzyme_Activity/6.06%3A_Enzyme_and_Protein_Regulation.txt

Search Fundamentals of Biochemistry Ribozymes Any molecule that displays any of the catalytic motifs seen in the earlier chapters (general acid/base catalysis, electrostatic catalysis, nucleophilic catalysis, intramolecular catalysis, and transition state stabilization) can be a catalyst. So far we have examined only protein catalysts. These can fold to form unique 3D structures, which can have active sites with appropriate functional groups or nonprotein "cofactors" (metal ions, vitamin derivatives) that participate in catalysis. There is nothing special about the ability of proteins to do this. RNA also can form secondary and tertiary structures as we will see in Chapter 8. RNA molecules that act as enzymes are called ribozymes. We are presenting the section before Chapter 8 for a few reasons. Most readers have encountered the structures of RNA and DNA before. They most likely know about three different types of RNA, ribosomal RNA (rRNA), transfer RNA (tRNA) and messenger RNA (mRNA). Likewise, they know from introductory biology classes the essential dogma of biology: (DNA, the holder of the genetic code) is transcribed into RNA which is translated into a protein sequence. Finally, most have studied (even at the high school level) that DNA of many species has exons and introns (intervening sequences), the latter that are spliced out of RNA transcripts to form mature RNA. In this section, we will discuss the catalytic properties of ribozymes, so the introductory background we just mentioned, although important, takes a "second" seat to the chemistry of catalysis, which is the main topic of Chapter 6. There are 12 classes of ribozymes • small self-cleaving RNAs (9 classes) • Group I introns • Group II introns • Ribonuclease P The large ribonucleoprotein nanoparticles, the spliceosome and ribosome, are also functionally ribozymes as well. The term ribozyme is used for RNA that can act as an enzyme. Ribozymes are mainly found in selected viruses, bacteria, plant organelles, and lower eukaryotes. Ribozymes were first discovered in 1982 when Tom Cech’s laboratory observed Group I introns acting as enzymes. This was shortly followed by the discovery of another ribozyme, Ribonuclease P, by Sid Altman’s laboratory. Both Cech and Altman received the Nobel Prize in chemistry in 1989 for their work on ribozymes. Ribozymes can be categorized based on size. Small ones, which usually don't require metal ions for activity, vary from 30-150 nucleotides while large ones can be a few thousand nucleotides in length. This translates into approximate molecule weights (using this formula for single-stranded RNA: (# nucleotides x 320.5) + 159.0) into 9800 for a 30-mer and 640,000 for a 2000 mer, typical of small and very large proteins/protein complexes, respectively. into two groups depending upon their size – small and large. Large ribozymes, which required metal ions for activity, can vary in size from a few hundred to several thousand nucleotides. Examples of small ones include hammerhead, viroid, hairpin, and riboswitch ribozymes. Examples of large ones include type I and II self-spicing introns, bacterial ribonuclease P, as well as the RNA in spliceosomes and ribosomes. Many also are not really true enzymes since they catalyze their own cleavage, although some can cleave presented RNA substrates. Large ones act as true catalysts. Since RNAs can carry genetic information and act as enzymes, they probably evolved before proteins, which require nucleic acids for their synthesis. In addition, DNA required a special enzyme (ribonucleotide reductase) encoded by DNA to reduce the 2'OH to a 2'H. Artificial ribozymes have been made to catalyze many reactions that require protein enzymes. We will explore some ribozymes in several classes. Small self-cleaving RNAs We'll consider four small self-cleaving RNAs- hammerhead, viroid and hairpin ribozymes as well as the glucosamine-6-phosphate riboswitch (glmS). All catalyze the cleavage of an internal phosphodiester bond (cis catalysis) or in a presented substrate (trans catalysis) by a transesterification reaction. Internal cis catalysis reactions cleave the ribozyme into two fragments, which inactivates their catalytic activity. In that sense, they don't act as true catalysts since they engage in only one cycle of cleavage. Trans catalysis in which a substrate RNA binds to and is cleaved by the ribosome would be considered true catalysis. The cleavage reaction in an internal cis cleavage is an SN2 trans-esterification reaction as shown in Figure \(1\). In the reaction, an adjacent general base (:B) abstracts a proton of the C'2 OH of nucleotide N1. The resulting 2' O- acts as a nucleophile in a SN2 reaction and attacks the δ+ phosphorous in the phosphodiesterase bond, forming a pentavalent, trigonal pyramidal sp3d hybridized intermediate/transition state, which collapses breaking the phosphodiesterase bond between nucleotide N1 and N2. This is an inline mechanism in that the incoming nucleophilic O in C2' and the exiting one on the O of 5' CH2OH of nucleotide 2 are axial to each other separated by 1800. A general acid, BH, facilitates the departure of the exiting nucleophile by its protonation. Bound metal ions may facilitate the reaction and can be considered cofactors but might be more involved in maintaining a catalytically-active structure. Self-cleaving small RNAs are also found in humans and may be part of long noncoding RNAs. a. Hammerhead RNA The hammerhead ribozyme is a small RNA ribozyme with a conserved core with three helical stems. It has a structure similar to the head of a hammerhead shark. One predicted secondary structure of a hammerhead ribozyme is shown below in Figure \(2\) A possible trigonal pyramidal intermediate/transition state in the Hammerhead ribosome is shown in Figure \(3\). A deprotonated G-12 in the ribozyme probably acts as a general base that activates the 2'-OH to form the incoming nucleophile that attacks the trans-substrate RNA. The 2′-OH of G-8 in the ribozyme appears to hydrogen bond to the 5′-O of the departing nucleophile in the substrate where bond scission occurs. The ribozyme increases the rate by 1000-fold. Figure \(4\) shows an interactive iCn3D model of the full-length Schistosoma manson catalytically active hammerhead ribozyme (3dz5). This is an example of a ribozyme that acts in trans as the cleaved phosphodiester bond is a bound RNA single-stranded substrate. Figure \(4\): Full-length Schistosoma manson catalytically active hammerhead ribozyme (3dz5). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/icn3d/share.html?qNBw2MY1RZ7xKogH7 The numbering system is a bit different in the iCn3D model above. PDB requires sequential numbering whereas ribozymes sequences are numbered in discontinuous ways. Core residues that are conserved are given common numbers. However, the different projecting double-stranded RNA regions, which vary among ribozymes, are numbered differently. The G8 and G12 in Figure \(3\) are numbered G20 and G36, respectively. b. Viroids Viroids are small single-stranded circular RNAs that infect plant cells. They are not packaged with viral capsid protein. Some enter cells along with viruses and are called virusoids or viroidlike satellite RNAs. Intrastrand pairing occurs and they are synthesized as tandem repeats containing multiple adjacent copies of the viroid. These repeats are cut and ligated to form the individual mature viroid by internal ribozymes sequences. One example is the hepatitis delta virus (HDV), a satellite of the hepatitis B virus. A possible mechanism of catalysis of the hepatitis delta virus ribozyme involving general acid/base catalysis is shown in Figure \(5\). c. Hairpin ribozyme Hairpin ribozymes are encoded by the satellite RNA of plant viruses. They are about 50 nucleotides long, and can cleave itself internally, or, in a truncated form, can cleave other RNA strands in a transesterification reaction. The structure consists of two domains, stem A required for binding (self or other RNA molecules) and stem B, required for catalysis. Self-cleavage in the hairpin ribozyme occurs in stem A between an A and G bases (which are splayed apart) when the 2' OH on the A attacks the phosphorous in the phosphodiester bond connecting A and G to form a pentavalent intermediate. Rupert et al solved the crystal structure of a hairpin ribozyme with a non-cleavable substrate analog containing a 2'-O-CH3 group on the ribose. This acts as a nucleophile in the transesterification cleavage of RNA. A38 in Stem B appears to be able to interact with the products (the cleaved A now in the form of a cyclic phosphodiester with itself) and the departing G, and with a transition state pentavalent analog of the sessile A-G bond in which the phosphodiester linking A and G in the substrate is replaced with a pentavalent vanadate bridge between A and G. This is Illustrated in Figure \(6\). However, A 38 does not appear to react with the sessile A -G groups in the normal substrate, indicating that the main mechanism used by this ribozyme is transition state binding. Since RNA molecules have fewer groups available for acid/base and electrostatic catalysis (compared to protein enzymes), ribozymes, presumably the earliest type of biological catalyst, probably make more use of transition state binding as their predominant mode of catalytic activity. Figure \(7\) shows an interactive iCn3D model of the hairpin ribozyme in the catalytically-active conformation (1M5K). The hairpin ribozyme is shown in cyan sticks and the inhibitor substrate in brown sticks. The inhibitor contains a 2'-O-methyl adenosine (A2M12), so it can not be cleaved and instead acts as an inhibitor. The A38 shown in the catalytic mechanism is labeled A57 in the iCn3D. d. Glucosamine-6-phosphate riboswitch (glmS): A novel use of ribozymes was recently reported by Winkler et. al. They discovered that the 5' end of the mRNA of the gene glmS (from Gram-positive bacteria) is a ribozyme. The GlmS gene encodes glucosamine-6-phosphate synthetase (GlmS), which catalyzes the reaction of fructose-6-phosphate and glutamine to glucosamine-6-phosphate (GlcN6P) and glutamate. This is the first committed step in bacterial cell wall synthesis. Glucosamine-6-phosphate binds to the ribozyme (3' end of the mRNA) and acts as a cofactor leading to self-cleavage of the ribozyme. What an amazing mechanism for pathway inhibition. At high GlcN6P concentrations, it binds to the ribozyme, inhibiting its own synthesis. G40 in the active site appears to act as a general base. Figure \(8\) shows an interactive iCn3D model of GlmS Ribozyme Bound to Its Catalytic Cofactor, glucosamine 6 phosphate (GlcN6P) (2NZ4). Riboswitches are discussed in greater detail in Chapter 28.1: Regulation of Gene Expression in Bacteria. Group I and Group II Introns Introns present in RNA molecules must be removed to form mature RNA. In humans, about 80% of introns are less than 200 nucleotides long, but some can be 10,000 nucleotides or longer in length. Before we discuss introns, we'll provide a quick background on RNA splicing. There are two major types of self-splicing introns, Groups I and II. Other introns are removed by a ribonucleoprotein called the spliceosome. Some call these Group III introns. A simple two-step mechanism for the self-splicing Group I and II introns is shown in Figure \(9\). Both required first a scission of the RNA strand followed by ligation of the two exons to form the mature RNA. Note that Group I introns require an external guanosine nucleophile and the removed intron forms a circular RNA when removed. In contrast, in Group II introns, an internal A residue acts as the first nucleophile in the scission reaction and the intron on removal forms a branched lariat structure. A simple two-step mechanism for Group II introns which are spliced out from pre-mRNA in the nucleus by a ribonucleoprotein complex called the splicesome is shown in Figure \(10\). Figure \(10\): Two steps of canonical RNA processing, from pre-mRNA to spliced RNA and the branched lariat intron. https://commons.wikimedia.org/wiki/F...g_reaction.svg. Creative Commons Attribution-Share Alike 3.0 Unported Note that the mechanism is extremely similar to the auto-removal of Group II introns, suggesting an evolutionary relationship between the two. Figure \(11\) below shows a more detailed view of the catalytic cycle of the spliceosome. Five small ribonucleoproteins (U1, U2, U4/U6 and U5 snRNPs) assemble on the nuclear pre-mRNA and facilitate the removal of the intro,n but the main mechanism involves ribozyme activity. Group I introns These are found in bacteria, lower eukaryotes (including mitochondrial and chloroplast RNA) and higher plants and are in ribosomal RNA (rRNA), mRNA and tRNA. They are also found in Gram-positive bacteria bacteriophages (viruses that attack bacteria). As shown in Figure \(9\), they require guanosine as a cofactor and have a single active site for both scission and ligation to produce the mature mRNA, tRNA or rRNA. Mg2 is required not for catalysis per se but to maintain the correct tertiary structure of the ribozyme with the correct secondary structure. In Group I introns, the splicing reaction is initiated by a guanosine cofactor. They have one active site that catalyzes the initial cleavage of the phosphodiester bond and the final religation after cleavage. The Group I catalytic core from Tetrahymena thermophila has two domains. A cleft is formed between them when they pack which can bind the short helix with the 5' splice site. and the guanosine cofactor. This "active" site is preformed without substrates similar to the active sites of protein enzymes. Figure \(12\) shows the secondary structure and the reaction of the group I intron ribozyme from Tetrahymena. The intron runs between the two triangles, which show the 5' (filled triangle) and 3' (open triangle) splice sites. The orange P1 helices contain the 5′-splice site (G:U). The P10 helix (grey), P9.0 helix (green), and the P9.2 helix (blue) are structured to present the appropriate 3′-splice site. During splicing (from (A) to (B)), the P1 helix extension (orange) is opened to expose the 3′-hydroxyl group of the terminal uridine at the 5′-splice site. The P10 helix then facilitates a conformational change, in which the 3′-exon (upper dashed line) is positioned adjacent to the 5′-exon (lower dashed line), allowing the nucleophilic attack of the 3′-uridine the 3′-splice site, joining 5′-exon and 3′-exon. Figure \(13\) shows an interactive iCn3D model from cryoEM of the full-length holo L-16 ScaI Tetrahymena ribozyme (7EZ2). Figure \(13\): Holo L-16 ScaI Tetrahymena ribozyme (7EZ2) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...PhK3BSbFLDnDt8 Three sets of coplanar bases are found in the active site. These include C262, A263 and G312 (top layer, brown), G264, C311 and the ωG - labeled G3 in the iCn3D model- (cyan layer) and A261, A265 and U310 (bottom layer red). The ωG - labeled G3 - is the nucleophile. The structure is nearly identical to the apo-form of the ribozyme with just an internal guide RNA sequence undergoing a large change and the guanosine binding site undergoing a small shift on binding RNA substrates. Group 2 Introns Group II introns are found in mRNA of bacteria and some Archaea, in rRNA, tRNA, and mRNA of chloroplasts and mitochondria, and in fungi, plants, and protists. No Class 2 introns appear to be found in eukaryotic genomes. Some of these introns are in gene-encoding proteins, but most are in bacterial noncoding sequences. In Group II introns, the splicing reaction is initiated by an adenosine cofactor, as shown in Figure \(9\). What's especially interesting about group II introns is that they can reinsert in DNA so that can be considered to be mobile genetic elements. A maturase/reverse transcriptase enzyme (562 amino acids) is associated with the intron, which helps stabilize the active site of the intron for the reversible session and ligation of the intron. Reintegration of the excised branched lariat intron into DNA is called retrotransposition (copy/paste). Mitochondrial and chloroplast Group 2 intron have lost their mobility and act as classic introns. The structures of a group II intron from Thermosynechococcus vestitus (a cyanobacteria) before and after integration have been determined. A branch-site domain VI helix swings 90°, enabling DNA integration. The maturase/revere transcriptase protein assists excision of the intron through the interaction of domain VI of that intron that positions the key adenosine for branched lariat formation during forward splicing, as shown in Figure \(9\). The changes in the structure of the group II intron retroelement before (6ME0) and after DNA integration (6MEC) are shown in Figure \(14\). The target DNA before integration is shown in cyan spacefill and in orange spacefill after integration. The protein is shown as a colored cartoon. The Group II intron is 867 nucleotides and the sense target DNA is 47 nucleotides in length. Spliceosomal Introns A comparison of Figure \(9\) through Figure \(11\) show the similarities between spliceosomal introns and group II self-splicing introns. Spliceosomal and group II self-splicing introns are structurally and mechanistically homologous, right down to the stereochemistry of the splicing reaction. In eukaryotes, introns in pre-mRNA are removed by splicing and subsequent exon ligation, releasing the intron as a branched lariat molecule. This reaction is performed by the spliceosome, a large nuclear ribonucleoprotein (RNP) complex. Spliceosomes remove introns and splice the exons of most nuclear genes. They are composed of 5 kinds of small nuclear RNA (snRNA) molecules and over 100 different protein molecules. It is the RNA — not the protein — that catalyzes the splicing reactions. The molecular details of the reactions are similar to those of Group II introns, and this has led to speculation that this splicing machinery evolved from them. Figure \(15\) shows two views of the cryo-EM structure of the human-activated spliceosome (the Bact complex) C Figure \(15\): Cryo-EM structure of the human-activated spliceosome (the Bact complex). Zhang et al. https://www.nature.com/articles/cr201814.pdf. Creative Commons Attribution 4.0 Unported License. http:// creativecommons.org/licenses/by/4.0/ There are 52 proteins, 3 small nuclear RNAs (snRNA), and one pre-mRNA. The total molecular mass is 1.8M. U2, U5, and U6 snRNAs are colored marine, orange, and green, respectively. Pre-mRNA is colored red. Figure \(16\) shows just the structural changes in RNA and protein components that occur between the early Bact complex (left panel) and the mature Bact complex (right panel). Ribonuclease P This enzyme is a ribonucleoprotein that cleaves RNA through the catalytic action of one essential RNA subunit that displays ribozyme activity. It's found in most organisms. As with many ribozymes, the activity is increased 2-3 fold with bound proteins that stabilize the folded ribozyme and help bind the preferred substrate, which is pre-tRNA. Figure \(17\) shows bacterial RNase P ribozyme in complex with tRNA (3q1r). The tRNA is shown as a magenta cartoon/surface and the protein in cartoon form. The enzyme cleaves the 5' head end of the precursors of transfer RNA (tRNA) molecules. In bacteria, the enzyme is a heterodimer with one RNA and protein subunit. Figure \(18\) shows a possible transition state with key residues involve in binding two Mg2+ ions in the active site. These ions are essential for catalysis as they stabilize the pentavalent intermediate/transition state. Figure \(19\) shows an interactive iCn3D model of the active site with key residues labeled of bacterial RNase P ribozyme in complex with tRNA (3q1r). Figure \(19\): Bacterial RNase P holoenzyme in complex with tRNA (3q1r) (Copyright; author via source). Click the image for a popup or use this external link:https://structure.ncbi.nlm.nih.gov/i...6SjVMa8g6K47x5 RNA Polymerase Ribozyme If primordial RNA acted as both a metabolic enzyme catalyst as well as the holder of the genetic information, it would need to have RNA polymerase activity. Artificial ribozymes with class I RNA ligase activity have been made. Figure \(20\) shows an interactive iCn3D model of the active site region of the Class I ligase ribozyme-substrate preligation complex, C47U mutant, Mg2+ bound ( 3R1L). The blue stick represents just the 3' terminal adenosine end of the target substrate (5'UCCAGUA3') to which a new nucleoside would be added. The brown represents the active site region of the ribozyme. Three catalytic residues A29, C30 and C47 have been identified in the actual ribozyme. In iCn3D model is of a mutant, C47U, which has no catalytic activity. The green sphere represents Mg2+ ions. Figure \(21\) show the active site residues and how they might facilitate stabilization of the pentavalent intermediate/transition state and the similarity of the active site to a protein RNA polymerase. A divalent Mg2+ in the active site of the ribozyme enhances the nucleophilicity of the 3-OH on the primer, which attached the terminal phosphate of the G(1)TP substrate to form a pentavalent intermediate. The Mg cation is stabilized by oxygens on P 29 and 30 of the ribozyme. The Mg ion also stabilizes the developing charge in the transition state and in the charge in the intermediate. Stabilization of analogous divalent cations in the protein polymerase occurs through Asp side changes in the protein. Ribosome Protein synthesis from mRNA templates occurs on a ribosome, a nanomachine composed of proteins and ribosomal RNAs (rRNA). The ribosome is composed of two very large structural units. The smaller unit (termed 30S and 40S in bacteria and eukaryotes, respectively) coordinates the correct base pairing of the triplet codon on the mRNA with another small adapter RNA, transfer or tRNA, that brings a covalently connected amino acid to the site. Peptide bond formation occurs when another tRNA-amino acid molecule binds to an adjacent codon on mRNA. The tRNA has a cloverleaf tertiary structure with some intrastrand H-bonded secondary structure. The last three nucleotides at the 3' end of the tRNA are CpCpA. The amino acid is esterified to the terminal 3'OH of the terminal A by a protein enzyme, aminoacyl-tRNA synthetase. Covalent amide bond formation between the second amino acid to the first, forming a dipeptide, occurs at the peptidyl transferase center, located on the larger ribosomal subunit (50S and 60S in bacteria and eukaryotes, respectively). The ribosome ratchets down the mRNA so the dipeptide-tRNA is now at the P or Peptide site, awaiting a new tRNA-amino acid at the A or Amino site. The figure below shows a schematic of the ribosome with bound mRNA on the 30S subunit and tRNAs covalently attached to amino acid (or the growing peptide) at the A and P site, respectively. Figure \(22\) shows a cartoon model of the prokaryotic ribosome with bound mRNA, tRNAs and the P and A sites. We present another more detailed model of the ribosome complex illustrating protein synthesis in Figure \(23\). A likely mechanism (derived from crystal structures with bound substrates and transition state analogs) for the formation of the amide bond between a growing peptide on the P-site tRNA and the amino acid on the A-site tRNA is shown below. Catalysis does not involve any of the ribosomal proteins (not shown) since none is close enough to the peptidyl transferase center to provide amino acids that could participate in general acid/base catalysis, for example. Hence the rRNA must act as the enzyme (i.e. it is a ribozyme). Initially, it was thought that a proximal adenosine with a perturbed pKa could, at physiological pH, be protonated/deprotonated and hence act as a general acid/base in the reaction. However, none was found. The most likely mechanism to stabilize the oxyanion transition state at the electrophilic carbon attack site is precisely located water, which is positioned at the oxyanion hole by H-bonds to uracil 2584 on the rRNA. The cleavage mechanism involves the concerted proton shuffle shown below. In this mechanism, the substrate (Peptide-tRNA) assists its own cleavage in that the 2'OH is in position to initiate the protein shuttle mechanism. (A similar mechanism might occur to facilitate hydrolysis of the fully elongated protein from the P-site tRNA.) Of course, all of this requires perfect positioning of the substrates and isn't that what enzymes do best? The main mechanisms for catalysis of peptide bond formation by the ribosome (as a ribozyme) are intramolecular catalysis and transition state stabilization by the appropriately positioned water molecule. These processes are illustrated in Figure \(24\). The crystal structure of the eukaryotic ribosome has recently been published (Ben-Shem et al). It is significantly larger (40%) with a mass of around 3x106 Daltons. The 40S subunit has one rRNA chain (18) and 33 associated proteins, while the larger 60S subunit has 3 rRNA chains (25S, 5.8S and 5S) and 46 associated proteins. The larger size of the eukaryotic ribosome facilitates more interactions with cellular proteins and greater regulation of cellular events. Figure \(25\) shows the two copies of the 80S yeast ribosome (4v88), presented to humble readers and authors alike.Figure \(25\): The 80S yeast ribosome (4v88). Each subunit is given a different color. Ribozyme methyltransferase The ribozymes described above and generally found in nature catalyze phosphoryl transfer reactions and with the ribosome, peptide bond formation. In vitro evolution can be used to drive new enzymatic functionalities, which would have been required in a RNA-only world that preceded the use of proteins as catalysts. RNA ribozymes are limited in having only 4 bases that can be employed in binding and catalytic steps, compared to 20 amino acids which can serve the same function in proteins. However, as in the case of protein, small molecule cofactors that bind to a potential ribozyme might facilitate greater catalytic efficiency and an expanded repertoire of reaction types. Indeed, we have seen above how small molecules can bind to riboswitches. Figure \(26\) shows the reaction and structure of a methyltransferase 1 ribozyme (MTR1) that acts as a methyltransferase. The small ligand, O6-methylguanine, binds to the ribozymes and acts as a cofactor in the methylation of adenine 63 in the RNA. Figure \(26\): Deng, J., Wilson, T.J., Wang, J. et al. Structure and mechanism of a methyltransferase ribozyme. Nat Chem Biol 18, 556–564 (2022). https://doi.org/10.1038/s41589-022-00982-z. Creative Commons Attribution 4.0 International License. Creative Commons Attribution 4.0 International License, http://creativecommons.org/licenses/by/4.0/ Panel a shows the chemical reaction in which the methyl group of the small ligand O6-methylguanine is transferred to N1 of Adenine 63 in the RNA. Panel b shows the sequence of the MTR1 ribozyme as crystallized for the experiments. The RNA is a three-way junction composed of three arms P1, P2 and P3. A GNRA tetraloop has been added to the end of the P3 helix so that the entire ribozyme comprises a single RNA strand. Subsections of the strands are named J12 (colored green), J23 (colored blue) and J31 (colored red). X-ray crystal structures of the ribozyme in the presence of the cofactor O6-methylguanine were determined. The final structure contained guanine and an A63 methylated adenosine (1MA) implying the methyl group of the O6-methylguanine had transferred to A63, leaving guanine bound in the active site. The structure of the guanine bound to the ribozyme is shown in Figure \(27\). Figure \(27\): The four planes of nucleobase interactions in the core of the ribozyme. The four planes are composed of (from bottom to top) G12•C38, C11•G41, exogenous guanine hydrogen bonded to C10, U45 and A63 and the triple interaction A9•A46•A40. Deng, J et al, ibid. A hypothetical reaction mechanism is shown in Figure \(28\). Figure \(28\): Proposed catalytic mechanism for the ribozyme methyltransferase. Deng, J et al, ibid. In step 1, the nucleobase of C10 becomes protonated, and in step 2, the O6-methylguanine becomes bound. However, it is likely that these two steps are coordinated, as the binding will raise the pKa of the cytosine. The methyl transfer reaction occurs in step 3 by the nucleophilic attack of A63 N1 on the methyl group of O6-methylguanine and the coordinated breakage of the guanine O6–C bond. This involves a train of electron transfers, the movement of the proton from C10 N3 to guanine N1 and a concomitant shift of the positive charge from C10 to the N1-methyladenine at position 63. In principle, the guanine can now be released as product, although there is no evidence that this occurs with the present form of the ribozyme. Regeneration of active ribozyme would also require an exchange of the substrate strand to place unmethylated adenine at position 63. The proposed mechanism is fully consistent with the structure of the MTR1 riboswitch and the effect of the substitutions at C10 and U45 on activity. The complete loss of methylation activity of the C10U variant is fully consistent with the proposed role as a general acid in addition to ligand binding. Figure \(29\) shows an interactive iCn3D model of a methyltransferase ribozyme (7V9E). Figure \(19\): A methyltransferase ribozyme (7V9E). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...LtyVP4DBnkEgH6 The parts of the ribozyme are named J12 (light green), J23 (cyan) and J31 (magenta). The active site residues, C10, U45 and 1MA63 (1-methyladenosine) are shown in CPK-colored sticks and labeled (disregard small separated spheres). The Ba2+ ion in the crystal structure is not displayed. The guanine is shown in spacefill.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/06%3A_Enzyme_Activity/6.07%3A__Ribozymes_-_RNA_Enzymes.txt

Search Fundamentals of Biochemistry Cofactors and Electron Pushing: Sources and Sinks To make and break bonds, electrons have to be moved. In drawing reaction mechanisms, we showed how electrons move from "sources" to "sinks". In many enzyme-catalyzed reactions, vitamin derivatives are used as substrates or "cofactors" or "coenzymes" to facilitate the flow of electrons in bond-making and breaking. The section focuses on cofactors, which facilitate the flow of electrons from the substrate to product. We will see these enzymes in more detail in specific chapter sections. Cofactors are molecules that bind to enzymes and are required for catalytic activity. They can be divided into two major categories: metals and coenzymes. Metal cofactors commonly found in human enzymes include iron, magnesium, manganese, cobalt, copper, zinc, and molybdenum. Coenzymes are small organic molecules that are often derived from vitamins. Coenzymes can bind loosely with the enzyme and release from the active site. As such, they are also considered substrates for the reaction. Alternatively, they may be tight binding and cannot dissociate easily from the enzyme. In this case, after their initial participation in an enzyme-catalyzed reaction, the enzyme would no longer be able to use the cofactor in another round of catalysis until the initial state of the cofactor is reformed, which takes another chemical reaction and often an additional substrate. Tight-binding coenzymes are referred to as prosthetic groups. Enzymes not yet associated with a required cofactor are called apoenzymes, whereas enzymes bound with their required cofactors are called holoenzymes. Sometimes organic molecules and metals combine to form coenzymes, such as in the case of the heme cofactor (Figure 7.15). Coordination of heme cofactors with their enzyme counterparts often involves interactions with histidine residues, as shown in the succinate dehydrogenase enzyme shown in Figure \(1\). Many biological cofactors are vitamin B derivatives, as shown in Table \(1\) below. Many vitamin deficiencies cause disease states due to the inactivity of apoenzymes that can not function without the correctly bound coenzyme. Table \(1\): Essential B-Vitamins and their Modified Enzyme Cofactors Cofactors can help to mediate enzymatic reactions through the use of any of the different catalytic strategies listed above. They can serve as nucleophiles, mediate covalent catalysis, form electrostatic interactions with the substrate, and stabilize the transition state. They can also cause strain distortion or facilitate acid-base catalysis. Metal-aided catalysis can often use homolytic reaction mechanisms that involve radical intermediates. This can be important in reactions such as those occurring in the electron transport chain that requires the safe movement of single electrons. We present plausible mechanisms for prototypical reactions using some of the cofactors shown in Table \(1\) above. Each shows the flow of electrons from a source to a sink. The source is often a pair of electrons on an anion, formed by the prior removal of a proton from the atom by a general base. A sink could be a carbonyl O, which receives a pair of electrons from one of the C=O bonds of the carbonyl. As a bond is made to the carbonyl, one of the double bonds must break with the electrons going (temporarily if the reaction is a nucleophilic substitution reaction) to the carbonyl O, an excellent sink since it is so electronegative. An even better sink is a positive N of an iminium ion; examples are shown below. Just the "business parts" of the cofactors are shown below. To appreciate the mechanism used by cofactors, and show a clear example of an electron source/sink, let's look at a reaction that doesn't require a cofactor, the spontaneous decarboxylation of a β-keto acid as shown in Figure \(1\). Even though no cofactor is required, nucleophilic catalysis by an amine through Schiff Base formation would speed up the reaction (as we will see below). Now let's look at how some of the cofactors listed in Table \(1\) above facilitate electron flow in reactions. Thiamine pyrophosphate - decarboxylation of α-keto acids Thiamine pyrophosphate (TPP) facilitates the decarboxylation of α-keto acids. TPP is a derivative of thiamine, vitamin B1, whose deficiency causes beriberi. TPP is covalently attached to the enzyme, such as in pyruvate dehydrogenase and alpha-ketoglutarate dehydrogenase, two enzymes that catalyze the decarboxylation of α-keto acids. The structure and "business" end of TPP and its catalytic activity are shown in Figure \(2\). The number of arrows leading to the product does not reflect the actual number of steps. Figure \(3\) shows an interactive iCn3D model of the thiamin diphosphate-dependent enzyme pyruvate decarboxylase from the yeast Saccharomyces cerevisiae (1pvd). Flavin Adenine Dinucleotide (FAD) - hydride transfer FAD and its reduced form, FADH2, are tightly or covalently attached to an enzyme, so FAD must be regenerated in each catalytic cycle. Figure \(4\) shows an example of how this cofactor facilitates the transfer of a :H- hydride ion to the "business end" of FAD. In contrast to a transfer of protons (H+), an acid/base reaction, hydride transfer removes 2 electrons from the substrate (in this case, succinate) along with a proton in an oxidation reaction as FAD is reduced. Figure \(5\) shows an interactive iCn3D model of the FAD-binding domain of cytochrome P450 BM3 from Priestia megaterium in complex with NADP+ (4DQL) Figure \(5\): FAD binding domain of cytochrome P450 BM3 in complex with NADP+ (4DQL). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/icn3d/share.html?hoT1WDCUv1wZFMyRA FAD is shown in spacefill. NADP+, which reoxidizes the reduced FADH2 back to FAD, is shown in sticks and labeled NAP. Nicotinamide Adenine Dinucleotide (FAD) reactions NAD+ and a phosphorylated form, NADP+, are one of nature's most widely used oxidizing agents and are used as dissociable substrates/cofactors for many different types of enzyme-catalyzed oxidation reactions. Since it binds (as a substrate) and dissociates (as a product) after each catalytic cycle, the free enzyme is continually active. The biological synthesis of NAD+ requires the vitamin nicotinic acid, also called niacin (nicotinic acid), an absence of which causes pellagra. Oxidation of an alcohol to an aldehyde: The oxidation of ethanol to acetaldehyde by NAD+, catalyzed by the enzyme alcohol dehydrogenase, is shown in Figure \(6\). The product acetaldehyde contributes to hangovers after ethanol consumption. Note that this reaction is a hydride transfer, which would not be expected to occur in the aqueous environment of a cell, given the extreme reactivity and basicity of a :H- hydride ion. This transfer happens in the active site of the enzyme, which is anhydrous after binding substrates. Oxidative decarboxylation of an alcohol: A two-step mechanism for this reaction is shown in Figure \(7\) After the first step, an electron sink (the oxygen of the carbonyl) is present at the β-carbon, facilitating the decarboxylation step. Oxidative deamination of an amine: A two-step reaction, a hydride transfer to form a Schiff base, followed by hydrolysis of the Schiff base, is shown in Figure \(8\). We will discuss Schiff base chemistry in more detail below. Pyridoxal Phosphate Enzymes Pyridoxal phosphate (PLP) is a derivative of vitamin B6 or pyridoxal. Deficiencies cause convulsions, chronic anemia, and neuropathy. It assists in many reactions (catalyzed by PLP-dependent enzymes). The PLP is bound covalently to lysine residues in a Schiff base linkage (aldimine). This form reacts with many free amino acids (as substrates) to replace the Schiff base to Lys of the enzyme with a Schiff base to the amino acid substrate. First, we will review of Schiff base (an imine) formation by the reaction of an aldehyde or ketone with an amine as shown in Figure \(9\). The reaction is essentially a nucleophilic attack of a carbonyl carbon of an aldehyde or ketone by an amine, followed by a dehydration step. Note that the net effect is to replace one electron sink, a carbonyl (C=O), with an imine (C=NH ↔ C=NH2+), with pKa around 7.0. Hence at neutral pH, 50% of the imine is protonated to form the iminium cation, a much better electron sink than the starting carbonyl! The structure of pyridoxal phosphate, which contains a reactive aldehyde, is converted to an imine by reaction with the ε-amino side chain of a lysine in the active site of a PLP-dependent enzyme, is shown in Figure \(10\). The figure also shows the replacement of the enzymes' lysine ε-NH2-PLP bond to that of a free amino as an incoming substrate, a process which should proceed with a ΔG0 of approximately 0. This occurs in PLP-dependent enzymes with free amino acids as substrates (we will discuss several examples below). Figure \(11\) shows an interactive iCn3D model of the E. Coli Aspartate aminotransferase, W140H mutant, maleate complex (1ARI). Figure \(11\): Aspartate aminotransferase, W140H mutant, maleate complex (1ARI). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...ZQixXhCwX5EEQ9 Note that the PLP is in Schiff base linkage with the ε-NH2 group of a lysine in the enzyme's active site. PLP is quite impressive! From a chemistry perspective, PLP is an ideal molecule to facilitate electron flow in biochemical reactions. William Jencks noted this in his classic text, Catalysis in Chemistry, in which he wrote this elegant description of its properties: "It has been said that God created an organism especially adapted to help the biologist find an answer to every question about the physiology of living systems; if this is so, it must be concluded that pyridoxal phosphate was created to provide satisfaction and enlightenment to those enzymologists and chemists who enjoy pushing electrons, for no other coenzyme is involved in such a wide variety of reactions, in both enzyme and model systems, which can be reasonably interpreted in terms of the chemical properties of the coenzyme. Most of these reactions are made possible by a common structural feature. That is, electron withdrawal toward the cationic nitrogen atom of the imine and into the electron sink of the pyridoxal ring from the alpha carbon atom of the attached amino acid activates all three of the substituents of this carbon for reactions which require electron withdrawal from this atom." We'll present three examples of the reaction of an amino acid with a PLP-dependent enzyme. In each case, a different bond to the α-carbon of the amino acid substrate is broken. α-decarboxylation of an amino acid: Figure \(12\) shows a plausible reaction mechanism. β-elimination from serine: The enzyme serine dehydratase catalyzes the reaction shown in Figure \(13\). Racemization of amino acids: Amino acid racemases use PLP as a cofactor using a mechanism shown in Figure \(14\). Why do racemases exist since the biological world consists of only L-amino acids? There are two possible reasons. Some D-amino acids are found, such as in bacterial cell walls. In addition, amino acids spontaneously racemize on their own, albeit at a slow rate. Racemases that have oxygen atoms in the beta-carbon racemize at a much higher rate since they can stabilize the carbanion intermediate formed when the alpha proton is removed in the process of racemization. The concentration of D-Asp and D-Asn can be used in dating biological material as well. Transamination reactions: PLP enzymes also catalyze the transamination reaction, which is shown in Figure \(15\) Amino Acid 1 + α-keto acid 1 ↔ α-keto acid 2 + Amino Acid 2 For example: Figure \(\PageIndex{x}\) First, Asp, bound to PLP through a Schiff base link, loses the α-H and forms a ketimine through a tautomerization reaction, which ultimately hydrolyzes to form the released oxaloacetate and pyridoxamine. The pyridoxamine reacts with α-ketoglutarate in the reverse of the first three reactions to form Glu. We will explore other cofactors in future chapters.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/06%3A_Enzyme_Activity/6.08%3A__Cofactors_and_Catalysis__-_A_Little_Help_From_My_Friends.txt

Thumbnail: Cellulose molecular structure (CC BY-SA 3.0 Unported; Pintor4257 via Wikipedia) 07: Carbohydrates and Glycobiology Search Fundamentals of Biochemistry Introduction Carbohydrate or glycan biochemistry is very complex and challenging owing to the stereochemical complexity of simple sugars, a large number of positions on the sugars used to form linkages between other sugars to create polymers, the large number of chemical modifications to base sugars, and the lack of a genetic template to instruct glycan polymer formation. It is no wonder that our understanding of complex glycans has developed after that of the chemically simpler polymers like nucleic acids and proteins. In addition, the terminology used to describe them varies as well. We use these general descriptions of them: Sugar: usually refers to low molecular weight carbohydrates like glucose, lactose, and sucrose, but it can also refer broadly to any carbohydrate. Carbohydrate: a general term that applies to simple sugars to complex sugar polymers like glycogen, starch, and cellulose. The name derives from the formula for simple sugars like glucose (C6H12O6), which can be written as C6(H2O)6 - a carbo (C) - hydrate (H2O). Glycan: A general term for molecules containing simple sugars and sugar derivatives linked in a polymer, either standalone molecules or attached to other molecules like proteins. Monosaccharides Structures The above definition of sugar needs some further nuance. From a chemical perspective, sugars can be defined as polyhydroxy-aldehydes or ketones. The simplest sugars contain at least three carbon atoms, and the most common are the aldo- and keto-trioses, tetroses, pentoses, and hexoses. The 3C sugars are glyceraldehyde and dihydroxyacetone, as shown in Figure \(1\). Glucose, an aldohexose, is a central sugar in metabolism. It and other 5C and 6C sugars can cyclize through intramolecular nucleophilic attack of one of the free hydroxyl groups on the carbonyl carbon of the aldehyde or ketone. Such intramolecular reactions occur if stable 5- or 6-member rings can form. The resulting rings are labeled furanose (5-member) or pyranose (6-member) based on their similarity to furan and pyran. On nucleophilic attack to form the ring, the carbonyl O becomes an OH that points either below (α anomer) or above (β anomer) the ring. Figure \(2\) shows different representations of the linear and cyclic forms of the sugars D-glucose, D-ribose, and D-fructose Monosaccharides in solution exist as equilibrium mixtures of the straight and cyclic forms. In solution, glucose (Glc) is mostly in the pyranose form, fructose is 67% pyranose and 33% furanose, and ribose is 75% furanose and 25% pyranose. However, in polysaccharides, Glc is exclusively pyranose and fructose and ribose are furanoses. Sugars can be drawn in the straight chain form as either Fischer projections or perspective structural formulas. In the Fisher projection, the vertical bonds point down into the plane of the paper. That's easy to visualize for 3C sugars but more complicated for larger ones. For those, draw a wedge and dash line drawing of the molecule. When determining the orientation of the OHs on each C, orient the wedge and dash drawing in your mind so that the C atoms adjacent to the one of interest are pointing down. Sighting towards the carbonyl C, if the OH is pointing to the right in the Fisher project, it should be pointing to the right in the wedge and dash drawing, as shown below for D-erythrose and D-glucose. Figure \(3\) shows how to convert Fisher projections to wedge dash representations. Figure \(4\) shows an interactive iCn3D model of D-glucose in a linear form. Orient the molecule as shown in Figure \(5\) below, with the carbonyl oxygen pointed to the far right, and compare it to the orientation shown in Figure \(5\) to reinforce your understanding of Fisher and wedge/dash projections. Figure \(5\): Cyclic forms can be drawn either as the Haworth projections, which show the molecule as cyclic and planar with substituents above or below the ring) or the more plausible bent forms (showing glucose in the chair or boat conformations, for example). β-D-glucopyranose is the only aldohexose that can be drawn with all its bulky substituents (OH and CH2OH) in equatorial positions, which probably accounts for its widespread prevalence in nature. Figure \(6\) shows four different representations of glucose. Haworth projections are more realistic than the Fisher projections, but you should be able to draw both structures. Generally, if a substituent points to the right in the Fisher structure, it points down in the Haworth. If it points left, it points up. Generally, the OH on the α-anomer points down (αnts down) while on the β-anomer, it points up (βutterflies up) as illustrated in Figure \(7\) In the Haworth projections, the bulky R group of the next carbon after the carbon whose OH group was the nucleophile for ring formation is pointed up if the OH engaged in the attach was on the right-hand side in the straight chain Fisher diagram (as in α-D-glucopyranose above when the CH2OH group is up). It is pointed down if the OH engaged in the attack was on the left-hand side in the straight chain Fisher diagram (as in α-D-galactofuranose above when the (CHOH)CH2OH group is down). The rest of the OH groups still follow the simple rule that if they point to the right in the Fisher straight chain form, they point down in the Haworth form. The Fisher structures of most common monosaccharides (other than glyceraldehyde and dihydroxyacetone), which you will encounter most frequently, are shown in Figure \(8\). The mirror image of D-Glc is L-Glc. The D- and L- designations refer to the center of asymmetry most remote from the aldehyde or ketone. By convention, all chiral centers are related to D-glyceraldehyde, so sugar isomers related to D-glyceraldehyde at their last asymmetric center are D sugars. Figure \(9\) shows multiple renderings of common hexoses. Isomers Sugars can be configurational (interconverted only by breaking covalent bonds) or conformational isomers. Figure \(10\) reviews different configurational isomers. The configurational isomers include enantiomers (stereoisomers that are mirror images of each other), diastereomers (stereoisomers that are not mirror images), epimers (diastereomers that differ at one stereocenter), and anomers (a particular form of stereoisomer, diastereomer, and epimer) shows enantiomers, diastereomers, epimers and anomers of 6 carbon sugars. Sugars can also exist as conformational isomers, which interchange without breaking covalent bonds. These include chair and boat conformations of the cyclic sugars as shown in Figure \(12\). Monosaccharide Derivatives Many derivatives of monosaccharides are found in nature. These include • oxidized forms in which the aldehyde and/or alcohol functional groups are oxidized to carboxylic acids • phosphorylated forms in which phosphates are transferred from ATP to form phosphoester derivatives • amine derivatives such as glucosamine or galactosamine • acetylated amine derivatives such as N-Acetyl-GlcNAc (GlcNAc) or GalNAc • lactone forms (intramolecular esters) in which an OH group attacks a carbonyl C that was previously oxidized to a carboxylic acid • condensation products of sugar derivatives with lactate (CH3CHOHCO2-) and pyruvate (CH3COCO2- ), both from the glycolytic pathway, to form muramic acid and neuraminic acids (also called sialic acids), respectively. Figure \(13\) some simple monosaccharide derivatives. Figure \(14\) shows some additional oxidative derivatives of glucose shown in Fischer projections. Other important derivatives of monosaccharides are sialic acids. N-acetylmuramic acid, found in bacterial cell walls, consists of GlcNAc in ether link at C3 with lactate, while N-acetylneuraminic acid results from an intramolecular cyclization of a condensation product of ManNAc and pyruvate. These sialic acids are shown in Figure \(15\). Sugars are very complicated as the linkages and substituents are so diverse. Figure \(16\) show differences in sialic acids between humans and chimps. What happens when non-vegan humans eat animal products (meat, milk) with N-glycoyl neuraminic acids (Neu5Gc)? Some get incorporated into human membrane glycans. Sialic acids on surface proteins can serve as "receptors" that allow binding of self-cells as well as foreign cells or proteins that have evolved to bind them. A toxin, SubAB, secreted by E. Coli 0157, can bind Neu5Gc. Hence eating meat products can make us more susceptible to bacteria that recognize Neu5Gc. Formation of Hemiacetals, Acetals, and Disaccharides Monosaccharides that contain aldehydes can cyclize through an intramolecular nucleophilic attack of an OH at the carbonyl carbon in an addition reaction to form a hemiacetal.  In the past the group was called a hemiketal if the attack was on a ketone but now they are also called hemiacetals. On the addition of acid (which protonates the anomeric OH, forming water as a potential leaving group), another alcohol can add, forming an acetal with water leaving. These reactions are shown in Figure \(17\). If the other alcohol is a second monosaccharide, a disaccharide results. The acetal link bonding to the two monosaccharides is called a glycosidic link. If the link between the two sugars involves an anomeric carbon, the newly formed OH group at the link can be designated either as α (if the O on the carbon involved in the glycosidic link is pointing down) or β (if the O is pointing up). For a 2-2 link between hexoses (i.e. between two non-anomeric carbons, the α/β designation is not used.  Since sugars contain so many OH groups, which can act as the "second" alcohol in acetal formation, links between sugars can be quite diverse. These include α and β forms of 1-2, 1-3, 1-4, 1-5, 1-6, etc. links. Here are examples of disaccharides: • maltose: Glc(α 1,4)Glc, which can be considered a disaccharide hydrolysis product of the polysaccharide glycogen or starch (discussed in the section) • cellobiose: Glc(Glc(α 1,4)Glc 1,4)Glc, which can be considered a disaccharide hydrolysis product of cellulose. • lactose: Gal(β 1,4)Glc, also known as milk sugar. • sucrose: Glc(α 1,2)Fru. Since fructose is attached through the anomeric OH of this ketose, the fructose is not in equilibrium with its straight-chain keto form; hence, sucrose is a nonreducing sugar.  Note also since the anomeric C-OH or each sugar is used, the α/β designation in the dissacharide is used.  Hence sucrose would be abbreviated as Glc(α1,2β)Fru The differences between lactose and sucrose are illustrated in Figure \(18\). Note that the β-D-fructofuranose ring is flipped (left to right as in turning one of your hands over) compared to Figure \(16\). Acetal links between sugars in glycans can be hydrolyzed by water (catalyzed by H+), just as with the other key biological polymers, proteins and nucleic acids. The disaccharides described above that are linked through a 1,4 linkage are called reducing sugars since they can act as reducing agents in reactions in which they get oxidized. For example, in lactose, since galactose is attached to glucose through the OH on C4, the anomeric glucose carbon, C1, could revert to the noncyclic aldehyde form. This aldehyde is susceptible to oxidation by reagents (Benedict's Solution - with citrate, Fehling's reagent - with tartrate) as these reagents are subsequently reduced. In both reagents, reducing sugars reduce a basic blue solution of CuSO4 (Cu2+) to a brick-red precipitate of \(\ce{Cu2O}\) (Cu+). Sugars (monosaccharides, disaccharides and polysaccharides) that have a potentially open aldehyde at C1 or have an α-hydroxymethyl ketone group which can isomerize to an aldehyde under basic conditions (such as fructose) are called reducing sugars. These oxidizing agents are mild and react with aldehydes and not ketones. If a monosaccharide, disaccharide, or even polysaccharide has a least one hemiacetal link (for instance the second sugar in lactose), it is a reducing sugar, as the monomer with the cyclic hemiacetal can reversibly open to form an aldehyde. However, if the only links in sugars are full acetals (such as in sucrose when the link is between the two anomeric carbons), the sugar is not reducing. Alpha-gal syndrome Alpha-gal syndrome (AGS) is a relatively newly discovered disease caused by the bite of a tick. Tick saliva contains the disaccharide galactose-α-1,3-galactose (alpha-gal).  After a tick bite, people develop an immune response to the disaccharide in the form of IgE antibodies.  Further bites could cause a mild rash up to an anaphylactic response. What makes AGS worse is that red meat also contains the disaccharide but is not found in fish, birds, or people. Hence people who mount a strong IgE response to the disaccharide can also elicit the same response when they eat red meat or even if they drink cow's milk, for example.  Estimates that up to 450,000 people in the US may develop serious and even life-threatening symptoms after eating red meat. The structure of Gal(α1,3)Gal is shown in Figure \(19\) below. Figure \(19\):  The structure of the disaccharide Gal(α1,3)Gal

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/07%3A_Carbohydrates_and_Glycobiology/7.01%3A_Monosaccharides_and_Disaccharides.txt

Search Fundamentals of Biochemistry Polysaccharides contain many monosaccharides in glycosidic links and may have many branches. They serve as either structural components or energy storage molecules. Polysaccharides consisting of single monosaccharides are homopolymers. The most common are starch, glycogen, dextran, cellulose, and chitin. We'll discuss based on whether the acetal link is alpha or beta. α 1,4 main chain links Starch and Glycogen: These polysaccharides are polymers of glucose linked in α 1,4 links with α 1,6 branches. Starch, found in plants, is subdivided into amylose, which has no branches, and amylopectin, which does. Starch granules consist of about 20% amylose and 80% amylopectin. Glycogen, the main CHO storage in animals, is found in muscle and liver, and consists of glucose residues in α 1,4 links with lots of α 1,6 branches (many more branches than in starch). Here are various ways to render in 2D the chemical structure of a branched glycogen and starch fragment, as shown in Figure \(1\). The top part of the figure shows the Haworth structure. The bottom part shows two glucose units in red and blue in the more structurally clear chair and wedge/dash representations. Figure \(2\) shows an interactive iCn3D model of 10 glucose monosaccharides in an α-(1,4) linkage with five glucose units with α-(1,4) linkages attached to the main chain through α-(1,6) branch at glucose 6 of the main chain. The type of substructure would be found in starch (amylopectin) and glycogen. Figure \(3\) the structure in the iCn3D model in a diagrammatic fashion in which glucose is represented as a blue circle with the acetal/glycosidic/glucosidic linkages between the monosaccharides written between the circles. The 14A label shows that the acetal linkage is an α-(1,4) link with a single α-(1,6) branch. The linkages are written in a variety of conventions. These include 14A, 14α, 4A and 4α. The between many sugars is often a 1,x link where x is 2,3, 4, 5 or 6. In those cases the 1 can be omitted. The program used to generate images in this text uses both numbers and A or B. What makes carbohydrates so complex is their 3D structures. Like proteins and nucleic acids, they can adopt a myriad of conformations. As the monomeric units are so homogeneous, especially in homopolymers, it isn't easy to get crystal structures for them so computer models are often used. Studies have shown that the simple starch fraction amylose α 1,4 polymer of glucose, often envisioned as a straight chain, can adopt three main conformations. They are double-helical A- (found chiefly in cereals), double helical B-(found primarily on tubers) amyloses and single-helical V-amylose (or simply A, B and V structures). The A and B do NOT represent alpha or beta in this classification system. The A and B forms consist of double helices aligned in a parallel fashion with about 6 glucoses per turn. The helices appear to be left or right-handed, and this ambiguity might arise from a lack of crystal structures. In contrast, a well-defined structure of the V helix is known. It folds into a left-handed helix with 6 glucoses per turn and a pitch of about 8Å. Unlike alpha helices of proteins and the double-stranded helix of DNA, the center of the helix is NOT packed tightly and can accommodate small molecules. One is iodine (actually triiodide, I3-), which, when bound in amylose with a sample of starch, exhibits a dark blue color. This is the basis for starch indicators that you may have used in titration reactions in biology and chemistry courses. In proteins, alpha helices might self-associate during folding to form a 4-helix bundle. Likewise, the helices in V-amylose can associate into bundles. Figure \(4\) shows an interactive iCn3D model of the actual structure of a V-amylose, cycloamylose 26 (1C58). It consists of a linear cycloamylose strand of 26 glucose monomers, which has collapsed to form secondary structure with 6-residue helices packed together into a tertiary structure of 4-helix bundle. The blue sphere "cartoon" color coding of each glucose residue corresponds to the blue circles in the diagrammatic representation above. Rotate the model to explore it. Trace the chains by following the blue sphere symbolic representation for glucose as you trace the main chain. Rotate it to view down the helix axes to see the 4 holes that can each accommodate a I3-. In the menu button (=), choose Style, Chemicals, Sphere to see a spacefilling model that shows the holes within each helix. Remember, NO unoccupied holes exist within either a protein alpha helix or a double-stranded DNA molecule. The well-known macrocyclic compound cyclodextrins (for example α-cyclodextrin) are structures equivalent to one turn of V-amylose. The V-amylose helix is partially stabilized by hydrogen bonds from donors and acceptors within the helix from the OH3 on the ith glucose and the OH2 on the ith+1 glucose as well as from the OH6 on ith glucose and OH2 on the ith + 6 glucose. In vivo, glycogen is synthesized by the attachment of glucose monomers to a core protein called glycogenin. Figure \(5\) shows a model of a glycogen particle with glycogenin at its core. The dimeric protein glycogenin is an enzyme that autoglucosylates itself in a stepwise fashion. The first glucose is added at Tyr 195. At some point, the active site must get buried and the protein can no longer add more monomers. It makes chemical sense to store glucose residues as either glycogen or starch, one large molecule. A review of colligative properties would inform you that if glucose was stored as the monosaccharide, a great osmotic pressure difference would be found between the outside and inside of the cell. Glycogen, with its many branchs, is a single molecule. When glucose is needed, it is cleaved one residue at a time from all the branches (at the nonreducing ends) of glycogen, producing a large amount of free glucose quickly. Phi/Psi angles can also be described for the starch/glycogen main chain (around the acetal O) in a fashion comparable to that for proteins (around the alpha carbon). The phi torsion angle describes rotation around the C1-O bond of the acetal link, and the psi angle describes rotation around the O-C4 bond of the same acetal link, with the glucopyranose ring considered as a rigid rotator (just as the 6 atoms in the planar peptide bond unit). The most extended form of a glucose polymer occurs when the glycosidic link is β 1,4 (as in cellulose), which forms linear chains. This would be analogous to the more extended parallel beta strand (phi/psi angles of -1190, -1130) and antiparallel beta strands (phi/psi angles of -1390, +1350) of proteins. The α 1,4 linked main chain of glycogen and starch causes the chain to turn and form a large helix. Iodine (or I3-) can fit into the helix, which turns a solution/suspension of starch blue, which turns starch purple. The less extended structure is analogous to the less extended protein alpha helix, which has phis/psi angles of -570,-470. Figure \(6\) shows phi/psi angles for acetal/glycosidic linkage in maltose, a dissacharide of glucose, is shown below. α 1,6 main chain links Dextran is a branched polymer of glucose in α 1,6 links with α 1,2, α 1,3, or α 1,4 linked side chain. This polymer is used in some chromatography resins. Figure \(7\) shows chair structures (A) and wedge/dash structures (B) for dextran showing the main chain α 1,6 link with one α 1,3 branch. Depending on its molecular weight, it is soluble in water (forming viscous solutions) and organic solvents. It is also used as a food thickener and stabilizer. It is synthesized by lactic acid-forming bacteria using sucrose as an energy source. Most uses are commercial. β 1,4 links Cellulose, a structural homopolymer of glucose in plants, has of β 1,4 main chain links without branching. Multiple chains are held together by intra and inter-chain H-bonds. It is the most abundant biological molecule in nature. Various rendering of 4 glucose residues in cellulose are shown in Figure \(8\). Haworth structures are not shown. Instead, more chemically informative chair and wedge/dash structures are used. It's important to see the structures displayed in many ways, since different representations of carbohydrate structurse can be found in different sources. In A, the most common chair representation, the 2nd and 4th residues from the right-hand end are flipped versions of residues 1 and 3. Residues 1 and 2 are colored red and blue for clarity. This unit is repeated to generate the full chain. The top part of A show a simplified version of the flip of the red ring to produce the blue ring to help you see that they are indeed identical structures. The same structure as in A is shown in the left part of B in wedge/dash from (looking down on the ring). The right-hand side of B shows a variant of the left-hand side of B that is generated by simple 1800 rotation around the bond indicated in the left of B. In C, the simple repeat is shown without the chain flips in A and B. The acetal/glycosidic/glucosidic bond seems to be shown in a straight line in the chair structures (a bit confusing and structurally deceptive) but is shown more clearly in the adjacent wedge/dash structure. All of the structures are correct, but the one shown in A is most often used. One long chain of starch can interact with other chains in a structure stabilized by intrachain and interchain hydrogen bonds. Different sources display different hydrogen bonds. Some common ones are shown below. These chains align in parallel and twist to form larger cellulose fibers. Figure \(9\) shows an interactive iCn3D model of cellulose chains. Chitin The glycan is the major component in the exoskeletons of anthropoids and mollusks. It is a β 1,4 linked polymer of N-acetylglucose (GlcNAc). Compare this to cellulose which is a β 1,4 linked polymer of glucose. What a difference an N-acetyl substituent makes! The basic chemical structure of chitin is shown in chair form in Figure \(10\) along with the symbolic nomenclature for glycans (SNFG). Symbolic nomenclature for glycans (SNFG) - Before we go further into the complexities of glycan structure, let's explore the symbolic nomenclature for glycan structures. The Consortium for Functional Glycomics (2005) proposed a scheme based on specific colored geometric shapes for each, as shown for the example glycan shown in Figure \(11\) for a complex glycan. This nomenclature has recently been updated in Appendix 1B of Essentials of Glycobiology, 3rd Edition (Glycobiology 25(12): 1323-1324, 2015. doi: 10.1093/glycob/cwv091 (PMID 26543186) and is summarized in the Figure \(12\). Glycosaminoglycans - Heteropolysaccharides with dissacharide repeating units Many polysaccharides consist of repeating disaccharide units. A major class of polysaccharides with disaccharide repeats include the glycosaminoglycans (GAGs), all which contain one amino sugar in the repeat and in which one or both of the sugars contain negatively charged sulfate and/or carboxyl groups. The extent and position of sulfation vary widely between and within GAGs. GAGs are found in the vitreous humor of the eye and synovial fluid of joints, and in connective tissue like tendons, cartilage, etc, as well as skin. They are found in the extracellular matrix and are often covalently attached to proteins to form proteoglycans. From a bird's eye view, they are all elongated polyanions. They and their structures are very complicated and exceedingly diverse. This makes them difficult to understand for those who want clear and unambiguous structures. From a biological perspective, they present in their local environment an incredibly diverse array of potential binding sites for ligand (both small and large). Because of these they also have functions in cell signaling. In addition, some GAGs are free-standing, others are covalently attached to proteins (a bit like glycogen is attached to glycogenin). These large molecules are called proteoglycans. We will discuss this later in Chapter 7.4 when we discuss the "carbohydrate code" Here are the ring structures and descriptions of important GAGs. The common disaccharide repeat unit is shown twice for each structure, with the knowledge that sulfation patterns may differ for the disaccharide repeats in the actual chains. Note also that the first member of each disaccharide repeat shows the ring flipped vertically (top to bottom) as was shown in the structures for other beta-linked glycans (cellulose, chitin) above. In a long chain, selecting which is the repeating disaccharide unit is a bit relative, as shown in Figure \(13\) for the repeating disaccharide sequence of N-acetylglucosamine (blue square) and N-acetylgalactosamine (yellow square). In the top, the repeating units (blue-yellow) are connected to each other through beta 1,4 links while in the bottom, the connection of the repeating unit (yellow-blue) is beta 1,3. Without knowing the full chain, the best choice of annotating the repeating unit is illusive. What's most important however is to note the alternating acetal/glycosidic links throughout the whole sequence. In the figures below different disaccharide repeats are highlighted. Hyaluronic acid This is a polymer of glucuronate (β 1,3) GlcNAc. It offers a backbone for theattachment of protein and other GAGs. It's the only GAG without sulfate. Figure \(14\) shows a tetrasaccharide fragment with two disaccharide repeats. The internal acetal/glycosidic link of the illustrated disaccharide repeat is β 1,3 while the connection between the disaccharides is β 1,4. For one last time, the vertical flip of the glucuronic acid is shown to allow a better understanding of its flipped presentation in the actual GAG. Hyaluronic acids are found in a variety of locations including synovial fluid, the extracellular matrix and skin, where it helps control skin moisture. It is water soluble and displays twin antiparallel left-oriented helices. Covalent conjugates of the chemotherapeutic drugs doxorubicin and camptothecin linked to hyaluronic acid, whose overall structure is similar to "worm-like micelles", have been used successfully to treat skin cancers. Keratan sulfate This GAG contains repeats of N-acetyl-D-glucosamine-6-phosphate in β 1,3 link to D-galactose or D-galactose-6-sulfate. The link between Gal and the modified glucosamine is β 1,4. Keratin sulfate is highest abundant in the cornea of the eye but is also found in other connective tissues such as bone, cartilage and tendon, as well as in the central and peripheral nervous system. Figure \(15\) shows a tetrasaccharide containing two repeating disaccharides. Chondrotin sulfate Th repeat dissacharide unit is D-glucuronate β(1,4) GalNAc-4 or 6-sulfate. It's found in connective tissue matrix as well as the cell surface (in the form of proteoglycans), in basement membranes, as well as intracellular granules. A tetrasacccharide showing two disaccharide repeats is shown in Figure \(16\). Dermatan sulfate This glycosoaminoglycan is similar to chondroitin sulfate. It is first made as a polymer of the disaccharide unit of D-gluconic and N-acetyl-D-galactosamine. The gluconic acid is epimerized to L-iduronic acid, followed by sulfation. Its structure is shown in Figure \(17\). Heparin This GAG contains a highly trisulfated disaccharide repeat as shown in Figure \(18\). Note that the molecule can contain glucuronate or iduronate, and the degree of sulfation of the chains varies. (Remember, there is no genetic code that specifies the actual sequence or sulfation pattern in these polymers.) Most people are familiar with the anti-clotting properties of heparin administered as a drug. Heparin acts as a "catalyst" to accelerate the inhibition of the enzyme thrombin, which cleaves fibrinogen and activates platelets to form clots, by the blood protein antithrombin III. Heparin works in two ways to facilitate thrombin inactivation. It has a specific binding site for antithrombin III which causes a conformation in the protein, making it a more effective inhibitor. Thrombin, a positively-charged serine protease, can bind the heparin, a polyanion, nonspecifically. When it does, it diffuses along the heparin chain, where it can find bound antithrombin III much more quickly than if the inhibitor was free in the blood. Heparin effectively changes the search path of thrombin from a 3D to a 1D search. Figure \(19\) shows an interactive iCn3D model of the amino acids in antithrombin III within seven angstroms of a bound heparin 5mer (1NQ9). Dotted lines represent hydrogen bonds and salt bridges between the two. Heparin is highlighted in yellow Agarose Agarose is the main polysaccharide component derived from red algae. Agarose is a polymer of a disaccharide repeat of (1,3)-β-D-galactopyranose-(1,4)-3,6-anhydro-α-L-galactopyranose, is often used for a gelable solid phase for electrophoresis of nucleic acid and as a component of chromatography beads. As with starch, which is present as mixtures of amylose and amylopectin, agarose is often found with agaropectin, which is a sulfated galactan. A tetrasaccharide fragment with two dissacharide repeats is shown in Figure \(20\).

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/07%3A_Carbohydrates_and_Glycobiology/7.02%3A_Polysaccharides.txt

Search Fundamentals of Biochemistry Many proteins, especially those destined for secretion or insertion into membranes, are post-translationally modified by the attachment of carbohydrates. They are usually attached through either Asn or Ser side chains. Carbohydrate modifications on the protein appear to be involved in the recognition of other binding molecules, prevention of aggregation during protein folding, protection from proteolysis, and increases the half-life of the proteins. In contrast to a protein sequence determined by a DNA template, sugars are attached to proteins by enzymes that recognize appropriate sites on proteins and attach the sugars. Since there are many sugars with many functional groups that can serve as potential attachment sites, the structures of the oligosaccharides attached to proteins are enormously varied, complex, and hence "information rich" compared to linear or folded polymers like DNA and proteins. N-linked Glycoproteins These contain carbohydrates attached through either a GlcNAc or GalNAc to an Asn in a X-Asn-X-Thr sequence of the protein. There are three types of N-linked glycoproteins, high mannose, complex, and hybrid. They all contain the same core oligosaccharide - (Man)3(GlcNAc)2 attached to Asn as shown in Figure \(1\). Table \(1\) below shows the SNFG representation for the main core and variant glycans in N-linked glycoproteins. Note that the designation of α2 implies an α(1→2) linkage. Unless otherwise stated the linkage is presumed to start from carbon 1. Core High mannose Complex Mixed hybrid Table \(1\): SNFG representation for the main core and variant glycans in N-linked glycoproteins Complex N-linked glycans don't contain mannose outside of the core glycan and have GlcNAc attached to the branching mannoses in the core structure. The complex glycan shown above has a Gal(β1,4)GlcNAc sequence which could be named as the disaccharide lactosamine. Often, lactosamine repeats in the sequence. Hybrid glycans have both unsubstituted terminal mannoses (as in the high-mannose type) and substituted mannoses with an N-acetylglucosamine attached (as in the complex type. GlcNAc residues added to the core in the hybrid and complex N-glycoproteins are called antennae. Figure \(2\) shows an example of a biantennary N-linked glycan with two GlcNAc branches linked to the core. The core is outlined in red and the two GlcNAcs are labeled 1 and 2. Complex glycans also have bi-, tri- and tetraantennary forms and comprise most of N-glycans. As shown in Table \(1\) above, complex N-linked glycans usually end with sialic acid residues. About 50% of the surface area of the COVID Sars-2 spike protein is covered with glycans as shown in the model structure in Figure \(3\). The protein surface is gray and the glycans (biantennary LacNAc N-glycans) in spacefill CPK with carbon in cyan. In the hybrid oligosaccharide shown above, one terminus contains Gal(β1,4)GlcNAc. However, in all other mammals except man, apes, and old-world monkeys, an additional Gal is often connected in an α1,3 link to the Gal to give a terminus of Gal(α1,3)Gal(β1,4)GlcNAc. These animals have an additional enzyme, an α1,3 Gal transferase. Bacteria also have this enzyme, and since we have been exposed to this link through bacterial infection, we mount an immune response against it. Why is this important? Pig hearts turn out to be similar to human hearts, so they might be good candidates for transplantation into humans (xenotransplants). However, the Gal-α1,3-Gal link is recognized as foreign, and we mount a significant immune response against it. Several biotech firms are trying to delete the pig α1,3 Gal transferase which would prevent the addition of the terminal Gal, and make them good donors for transplanted hearts. Figure \(4\) shows an interactive iCn3D model of an N-linked glycoprotein, human beta-2-glycoprotein-I (Apolipoprotein-H) (1C1Z). Exercise \(1\) The glycan structures for the beta-2-glycoprotein-I are shown below. Identify the monosaccharides in each and specific to which asparagine they are linked. Answer add answer here. Figure \(5\) shows an interactive iCn3D model of the GP120 HIV protein that contains a high mannose, complex and hybrid N-linked glycans. Most glycoproteins in the Protein Data Bank do not contain attached glycans. The glycans here were added with the program GlyProt at 3 of 17 possible Asn residues that would presumably have attached glycans. Use your mouse or key paid to hover over the monomers in the attached glycans. Abbreviations for the given residues in the model are as follows: adm = alpha-D-Man, bdg= beta-D-Glc or Gal, adn = alpha-D-neuramindase. Exercise \(1\) Which Asn chains contain the high Man, complex, and hybrid glycans? Answer Add answer here The coronavirus pandemic of 2020-23 has been deadly (over 1.1 million deaths in the USA alone as of February 8, 2023 and 6.8 million around the world). However, the 1918 influenza pandemic was far worse with an estimated 650,000 deaths in the USA and 50 million around the world in a population since the population was less than 1/3 of the present. However, the number of USA deaths during the Covid-19 pandemic is greater than during the 1918 Flu pandemic. An additional 3 million deaths in the USA were probably prevented by vaccination as of December 2022. A large number of deaths in the developing world would have been prevented if wealthy nations allocated more resources to produce and distribute the vaccines. The evolution of the virus in nonvaccinated areas might come back to haunt wealthy countries if present vaccines become ineffective against the mutants. A worse pandemic might await us. An avian version of the influenza virus (H5N1), presently endemic in wild birds and now found in mink populations, has infected 240 people as of January 2023 and killed 56% of them. A quick note: the 1918 pandemic affected youth the most. Influenza and the Avian Flu The influenza virus, a simple yet deadly virus, is shown below in Figure \(6\). It interacts with human cells through a surface protein, hemagglutinin (HA). The virus binds to host cells through the interaction of HA with cell surface carbohydrates. Once bound, the virus internalizes, ultimately leading to the release of the RNA genome of the virus into the host cell. The hemagglutinin protein is the most abundant protein on the viral surface. 15 avian and mammalian variants have been identified (based on antibody studies). Only 3 adapted to humans in the last 100 yr, giving pandemic strains H1 (1918), H2 (957) and H3 (1968). Three recent avian variants (H5, H7, and H9) jump directly to humans recently but have low human-to-human transmissibility. The influenza hemagglutinin protein has the following characteristics: • the mature form is a homotrimer (3 identical protein subunits), MW 220,000 with multiple sites for covalent attachment of sugars. Hemagglutinin is a glycoprotein. • each monomer is synthesized as a single polypeptide chain precursor (HA0), which is cleaved into HA1 and HA2 subunits by the protease trypsin in epithelial cells of the lung. • structure known for human (H3), swine (H9), avian (H5) subtypes. Hemagglutinin binds to sialic acid (Sia) covalently attached to many cell membrane glycoproteins. The sialic acid is usually connected through an α(2,3) or α(2,6) link to galactose on N-linked glycoproteins. The subtypes found in avian (and equine) influenza isolates bind preferentially to Sia (α2,3) Gal which predominates in the avian GI tract where viruses replicate. Human influenza isolates prefer Sia α(2,6) Sia (α2,3) Gal which predominates in the avian GI tract where viruses replicate. Human influenza isolates prefer Sia α(2,6) Gal. The human virus of H1, H2, and H3 subtypes (causes of the 1918, 1957, and 1968 pandemics) recognize Sia α(2,6) Gal, the major form in the human respiratory tract. The swine influenza HA binds to Sia α(2,6) Gal and some Sia (α2,3) Gal both of which are found in swine. The structures of the Sia-Gal disaccharide are shown in Table \(2\) below. Sia α(2,6) Gal (Human) Sia α(2,3) Gal (Avian and some Swine) (made with Sweet, with an OH, not AcNH on sialic acid on C5) (made with Sweet, with an OH, not AcNH on sialic acid on C5) Table \(2\): Structures of Sia α(2,6) Gal (human) and Sia α(2,3) Gal (avian/swine) The H5N1 avian flu H5N1) virus is deadly but presently lacks human-to-human transmissibility. Why? One reason is that it appears to bind deep in the lungs and is not released easily on coughing or sneezing. It appears that cell surface glycoproteins deeper in the respiratory tract have Sia (α2,3) Gal which accounts for this pathology. Before it leaves the cell, the virus forms a bud on the intracellular side of the cell with the HA and NA in the cell membrane of the host cell. The virus in this state would not leave the cell since its HA molecules would interact with sialic acid residues in the host cell membrane, holding the virus in the membrane. Neuraminidase hydrolyzes sialic acid from cell surface glycoproteins, allowing the virus to complete the budding process and be released from the cell as new viruses. The drugs Oseltamivir (Tamiflu) and zanamivir (Relenza) bind to and inhibit neuraminidase, whose activity is necessary for viral release from infected cells. Tamiflu appears to work against N1 of the present H5N1 avian influenza viruses. Governments across the world are hopefully stockpiling this drug in case of a pandemic caused by the avian virus jumping directly to humans and becoming transmissible from human to human. O-linked Glycoproteins The CHOs are usually attached from a Gal (β 1,3) GalNAc to a Ser or Thr of a protein as shown in Figure \(7\). Figure: O-linked Glycoproteins The blood group antigens (CHOs on cells attached to either proteins or lipids) are examples. The sugars shown as chairs (in contrast to structures found in many texts) in Figure \(8\) are the blood group antigens. They are attached to a core heterosaccharide (shown as a red ellipse below), which is connected to either a membrane glycoprotein or glycolipid. Figure \(9\) shows the SNFG representation for the A antigen in the glycolipid form. The trimeric branched residues on the left-hand side represent the A antigen shown above. The red triangle is L-fucose. Yellow represents galactose or GalNac, while blue is glucose or GlcNAc. Proteoglycans Some proteins are so modified with CHOs that they contain more CHOs than amino acids. Proteins linked to glycosaminoglycans are together called proteoglycans (PGs). The consists of a core protein linked to one or more glycosaminoglycans. GAGs are linear sulfated glycans which we described earlier. The structures of a few proteoglycans are known. The GAGs are O-linked to the protein, typically to a Ser of a Ser-Gly dipeptide often repeated in the protein. Some of the proteoglycans also contained N-linked oligosaccharide groups. Figure \(10\) shows a representation of proteoglycan structure. PGs can be soluble and are found in the extracellular matrix, or as integral membrane proteins. There are about 43 genes for proteoglycans. Differential splicing of the RNA transcripts give rise to soluble and transmembrane forms. Given the diversity of sugars and the varying extent of sulfation, the CHO part of PGs provides an incredible variety of binding structures at or near the cell surface. Figure \(11\) shows the variety of proteoglycans found in mammalian cells. PGs help form the extracellular matrix, which provides a rich binding environment between cells. One PG, syndecan, binds through its intracellular domain to the internal cytoskeleton of the cell, while interacting with another protein - fibronectin - in the extracellular matrix. Fibronectin also binds other molecules which can regulate cellular growth and other interactions. PGs act like glue in connecting the extracellular and intracellular functions of the cell. There are four different core syndecan proteins (SDCs 1–4), with SDC4 lacking the cytoplasmic and transmembranes and thus is a soluble form in the intracellular matrix. The glycan components of syndecans are mostly heparan sulfate while SDC 1 and 3 also have two chondroitin sulfate chains. Most proteins bind PGs through a PG binding motif of BBXB or BBBXXB where B is a basic amino acid. Some proteins bind to specific sequences in specific GAGs. For instance, antithrombin 3, an inhibitor of blood clotting, binds specifically to heparin. This enhances its interaction with clotting proteins such as thrombin and Factor Xa. Figure \(12\) shows an interactive iCn3D model of a 5 residue fragment of heparin interacting with the key amino acids side chains of Factor Xa (2gd4). For those more chemically oriented, the extracellular matrix (ECM) might appear to be a nondescript mess, since chemists are used to well-defined structures. Figure \(13\) shows a cartoon of the ECM and may clarify the components. Few structure files exist for them given the inherent flexibility of the glycan components. Cell Walls and Glycolipids In contrast to eukaryotic cells, bacteria and plant cells have a cell wall in addition to a lipid bilayer membrane. These are essentially carbohydrate polymers that determine cell shape, affording protection from exterior pathogens, hypotonic conditions and high internal osmotic pressures, preventing swelling and bursting of the cells. This is especially important in plants, which need strength and rigidity against the "turgor" pressure of the aqueous cytoplasm against the cell membrane. This prevents wilting in plants. The cell wall in plants and probably bacteria are involved in cell signaling across the cell membrane. Bacteria Cell Walls Two types of cell walls occur in Nature. a. Gram positive bacteria- These bacteria can be stained with Gram stain. The wall consists of a GlcNAc (β 1,4) MurNAc repeat. (GlcNAc is often abbreviated as NAG while MurNAc is abbreviated as NAM.) This is similar to the GlcNAc (β 1,4) GlcNAc homopolymer chitin, except that every other GlcNAc contains a lactate molecule covalently attached in an ether-linkage to the C3 hydroxyl to form the monomer N-Acetylmuramic acid. A pentapeptide (Ala-D-isoGlu-Lys-D-Ala-D-Ala) is attached through an amide link to the carboxyl group of the lactate in MurNAc. The GlcNAc (β 1,4) MurNAc strands are covalently connected by a pentaglycine bridge through the epsilon amino group of the pentapeptide Lys on one strand and the terminal D-Ala of a pentapeptide on another strand. A small part of the structure of a gram-positive bacterial cell wall is shown in Figure \(14\). It shows one repeating GlcNAc-MurNAc disaccharide unit in front (darker) and one in the back (lighter) connected through the peptides shown. The SNFG representation of a larger section of the gram-positive cell well is shown in Figure \(15\). One final structure is found in Gram + peptidoglycan cell walls. Techioic acids are often attached to the carbon 6 of MurNAc. Teichoic acid is a polymer of glycerol or ribitol to which alternative GlcNAc and D-Ala are linked to the middle C of the glycerol. Multiple glycerols are linked through phosphodiester bonds. These teichoic acids often make up 50% of the dry weight of the cell wall and present a foreign (or antigenic) surface to infected hosts. These often serve as receptors for viruses that infect bacteria (called bacteriophages). Its structure is illustrated in Figure \(16\). Notice that all monomeric units of peptidoglycan and attached teichoic acid derivatives are covalently attached on form one large molecule comprising the entire cell wall! This structure, along with the Gram-negative cell wall structures, is the largest single macromolecule in nature. b. Gram-negative bacteria These bacteria can NOT be stained with Gram stain. The wall consists of the same structure as in Gram-positive bacteria but the GlcNAc (β 1,4) MurNAc strands are covalently connected through a direct amide bond between a derivative of Lys, meso-diaminopimelic acid (m-A2pm), on one peptide strand and to the last D-Ala of a pentapeptide on another strand. (i.e. there is no pentaGly spacer). The connector peptide is Ala-D-isoGlu-m-A2pm-D-Ala-D-Ala m-A2pm replaces Lys 3 of the peptide in most Gram-negative species and in Gram-positive bacteria of the genus Bacillus and mycobacteria. The stereochemistries at each chiral center are different (R and S), but because the molecule has a plane of symmetry, it is an example of a meso-compound, a diastereoisomer of a molecule, which does not have a different enantiomeric version. The structure is shown in Figure \(17\). A small part of the structure of a Gram-negative bacterial cell wall is shown in Figure \(18\). Figure \(19\) shows an interactive iCn3D model (actual computed model, not a crystal or NMR structure) of the Gram-negative peptidoglycan of E. Coli. The PDB coordinates were kindly provided by Jame Gumbart. The peptide part of the peptidoglycan is represented in spacefill. The repeating (GlcNac-MurNac)n and pentapeptide In addition, Gram-negative bacterial don't have teichoic acid polymers. Rather they have a second, outer lipid bilayer. The cell wall peptidoglycan (PG) is sandwiched between the inner and outer bilayers. The space between the lipid bilayers is called the periplasm. The outer membrane is coated with a lipopolysaccharide (LPS) of varying composition. The LPS determines the antigenicity of the bacteria. The different LPS are called the O-antigens. Figure \(20\) shows the structure of the Gram-negative bacterial membrane organization. (PS is LPS, PG is peptidoglycan) A detailed view of the structure of the lipopolysaccharide (LPS) from Salmonella tryphimurium is shown in Figure \(21\) below. Exercise \(1\) questions on this:diff btw g+ and g- cells Answer Add texts here. Do not delete this text first. c. Archaeal Cell Membranes and Walls We have already discussed that the lipids in Archaeal cell membranes contain L (instead of D) glycerol derivatives and that ether links (more stable in reactive environments) replace ester links with isoprenoid (sometimes branched) chains replacing fatty acid chains. The cell wall is quite different as well, and some don't have one. The type of cell wall is depending on the environmental need for stability. They don't contain peptidoglycans. Figure \(22\) shows four different types. Some differences include the presence of • pseudomurein - This is the closest to the peptidoglycans presented above. Instead of repeating disaccharide units of (NAM-NAG)n, they have a repeating disaccharide unit of N-acetylalosaminuronic acid (NAT)-NAG. The structure of NAT is shown in Figure \(23\). • methanochondroitin - This is similar to the glycosaminoglycan chondrotin sulfate • S-Layer • Sheath/S-Layer d. Plant Cell Wall If you thought bacterial cell walls were complicated, wait until you see plant cell walls! There are about 35 different types of plant cells, and each may have a different cell wall depending on the local needs of a given cell. Cells synthesize thin cell wall that extends and stay thin as the cell grows. Figure \(24\) shows the primary cell wall of plants. The primary cell wall contains cellulose microfibrils (no surprise) and two other polymers, pectin and hemicellulose. The middle lamella consisting of pectins is somewhat analogous to the extracellular matrix discussed above. After cell growth, the cell often synthesizes a secondary cell wall thicker than the first for extra rigidity. The enzymatic machinery for its synthesis is in the cytoplasm and the cell membrane. It is deposited between the cell membrane and the primary cell wall, as shown in the animated image shown in Figure \(25\). Figure \(26\) shows a structural representation of both the primary and secondary cell wall. The middle lamella, which contains pectins, lignins and some proteins, helps "glue together" the primary cell walls of surrounding plants. Primary Cell Wall: The main component of the primary plant wall is the homopolymer cellulose (40% -60% mass) in which the glucose monomers are linked β(1→4)-linked into strands that collect into microfibrils through hydrogen bond interactions. Two other groups of polymers, hemicellulose and pectin make up the plant cell wall. Hemicellulose can make up to 20-40% by the mass These polymers have β(1,4) backbones of glucose, mannose, or xylose (called xyloglucans, xylans, mannans, galactomannans, glucomannans, and galactoglucomanannans along with some β(1,3 and 1,4)-glucans. The most abundant hemicellulose in higher plants are the xyloglucans which have a cellulose backbone linked at O6 to α-D-xylose. Pectin consists of linked galacturonic acids forming homogalacturonans, rhamnogalacturonans, and rhamnogalacturonans II (RGII) [12] [13]. Homogalacturonans (α1→4) linked D-GalA making up more than 50% of the pectin Figure \(27\) shows some variants of the cell wall components of a plant. Secondary Cell Wall The structure of the secondary cell wall depends on the function and environment of the cell. It contains cellulose fibers, hemicellulose and in addition a new polymer, lignin. It is abundant in xylem vessels and fiber cells of woody plants. It gives the plant extra stability and new functions, including the transport of fluids within the plant through channels. Lignins, which can make up to 25% of the biomass weight, are made from derivatives of phenylalanine, but more directly from cinnamic acid. This derives from is made from phenylalanine which is hydroxylated and converted through other steps to hydroxycinnamyl alcohols called monolignols as shown in Figure \(28\). Three common monomer (M) derivatives, p-coumaryl, coniferyl, and sinapyl alcohols can polymerize into lignins, with the units in the polymer (P) names hydroxyphenyl, guaiacyl and syringly, respectively. Lignols are activated phenolic compounds, which form phenoxide free radicals (catalyzed by enzymes called peroxidases), which can attack other lignols to form covalent dimers. Reaction mechanisms for the dimerization of the MS sinapyl alcohol free radical are shown as an example in Figure \(29\). Now imagine this polymerization continuing through the formation of additional phenolic free radicals and coupling at a myriad of sites to form a huge covalent lignin polymer. Figure \(30\) shows one example of a larger lignin. Finally, Figure \(31\) shows an image of a poplar tree cell wall, made using surface Raman scattering, showing lignin, cellulose, and lipids in secondary xylem cell walls. The Extracellular Matrix (ECM) and Basement Membranes We won't formally discuss cell membranes until Chapter 11, but since anyone reading this book has previously seen biological membranes (including the Gram-negative and positive bilayers discussed above), let's explore a term that most chemistry students, but perhaps not biology students, will find very confusing. That topic is the basement membrane. The basement membrane is encountered often so often, that we will explore its overall structure here even though it is not a lipid bilayer. It fits well here since it is a complex structure consisting of proteins and proteoglycans. It's very amorphous which makes its structure difficult to those hoping for crystal structures or even complex bilayers. It is somewhat similar to the cell wall in functionality. We will offer a cursory explanation. For a great overall introduction, please visit Introduction to Extracellular Matrix and Cell Adhesion in BioLibre texts. Some of the images (when noted) below come from that Cell Biology book chapter. The extracellular matrix (ECM) is a general term for the large protein and polysaccharide network formed on secretion by some cells in a multicellular organism. They act as connective material to hold cells in a defined space. Cell density can vary greatly between different tissues of an animal, from tightly-packed muscle cells with many direct cell-to-cell contacts to liver tissue, in which some of the cells are only loosely organized, suspended in a web of extracellular matrix, shown in Figure \(32\). The ECM is a generic term encompassing mixtures of polysaccharides and proteins, including collagens, bronectins, laminins, and proteoglycans, all secreted by the cell. The proportions of these components can vary greatly depending on tissue type. Two, quite different, examples of ECM are the basement membrane underlying the epidermis of the skin, a thin, almost two-dimensional layer that helps to organize the skin cells into a nearly-impenetrable barrier to most simple biological insults, and the massive three-dimensional matrix surrounding each chondrocyte in cartilaginous tissue. The ability of the cartilage in your knee to withstand the repeated shock of your footsteps is due to the ECM proteins in which the cells are embedded, not to the cells that are actually rather few in number and sparsely distributed. Although both types of ECM share some components in common, they are distinguishable not just in function or appearance, but in the proportions and identity of the constituent molecules Figure \(33\) shows a general structure of the basement membrane. Think of it as an amorphous polymer mixture (somewhat similar to a polyacrylamide gel).

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/07%3A_Carbohydrates_and_Glycobiology/7.03%3A_Glycoconjugates_-_Proteoglycans_Glycoproteins_Glycolipids_and_Cell_Walls.txt

Search Fundamentals of Biochemistry Introduction By now, you should be convinced that the structures of glycans are extraordinarily complex and, in many ways, much more complicated than proteins and nucleic acids. Their structure diversity is staggering, given the number of different sugar monomers, stereocenters, linkages, lengths, conformers, dynamic flexibility and chemical modifications. Yet evolution has allowed this astronomical diversity, which must serve more than just simple functions such as protection of proteins from degradation, to pick one example. Much of the diversity derives from the lack of an equivalent genetic code for glycan synthesis. Since all events in biology start with a binding interaction, let's ponder the binding interaction of glycans with partner "ligands" such as proteins, lipids, nucleic acids, etc. A binding site on a glycan could be a single monosaccharide to a much larger and more complex interface. Figure \(1\) shows an interactive iCn3D model of one of the few glycoproteins with pdb coordinates, the unliganded simian immunodeficiency virus (SIV) gp120 core glycoprotein (3fus). The protein surface is shown in ivory and the glycans are shown in color sticks with the correct symbolic color-coded spheres or cubes around them. Now let's convert in our imagination an image file showing one face of the protein to a black and white QR code as shown in Figure \(2\). Computers can recognize information encoded in the QR codes and decode them into another form of information, such as a menu at a restaurant. Likewise, organisms have evolved "readers" to decode the glycan code written by enzymes (glycan synthases, hydrolases, and modifying enzymes). The glycan code is written onto the 3D-surfaces of polysaccharides, glycoproteins, glycolipids, and proteoglycans. It should be no surprise that the biological readers of the glycan code are mostly proteins, which locate and bind to the correct "QR" code displayed on the surface of glycan. Luckily, the QR code metaphor for the glycan code is a bit exaggerated since the readers of the glycan code, glycan-binding proteins, seem to recognize just small sections of a glycan. They can be compared to protein antibodies that bind to foreign molecules such as proteins. The binding site on a foreign protein recognized by an antibody is called an epitope. Epitopes can be continuous (linear) stretches of the foreign protein sequence, or discontinuous (conformational), made of some continuous stretches of amino acid and some further away in the sequence but close in the 3D folded protein. The average continuous epitope is often 5-6 amino acids long. Yet that might be an underestimation since an analysis of all contact residues (within a conservative 4 A° distance) for target proteins and their bound antibodies found in the Protein Data Bank is around 18-19 amino each (Stave and Lindpaintner). Glycan-binding proteins presumably also bind a mixture of continuous and discontinuous glycan sequences. Linear one would be much easier to determine and study. Now, let's explore the family of these glycan-binding proteins (GBP), the readers of the glycan code. Glycan-Binding Proteins (GBPs) There appear to be nine types of glycan-binding proteins (GBPs). These include nonenveloped capsid virus GBPs,enveloped-virus GBPs (ex. influenza and coronaviruses), eukaryotic microbial GBPs (ex yeast), and bacterial toxin GBPs (ex botulinum toxin). Bacterial adhesins (parts of organelles like flagella), lectins (soluble proteins) and lectin domain-containing proteins are also examples of glycan-binding proteins (GBP). We will discuss in more detail three other types, C-type lectins, galectins, and siglecs. In the broadest sense, if a lectin is a protein that binds a specific carbohydrate motif (i.e a glycan code) without modifying the motif, then any glycan-binding protein could be called a lectin. This would exclude enzymes that synthesize, degrade or modify glycans, as well as antibodies that would recognize foreign or self glycan sequences. Table \(1\) below shows some lectins and their target glycan ligand from plants, animals, viruses and bacteria. Lectin Family/Lectin Abbreviation Ligand(s) Plants Concanavalin A ConA Man α1- OCH3 Griffonia simplicifolia lectin 4 GS4 Lewis b (Leb) tetrasaccharide Wheat germ agglutinin WGA Ner5Ac(α 2,3)Gal(β 1,4)GlcGlcNAc(β1,4)GlcNAc Ricin   Gal(β 1,4)Glc Animals Galectin-1   Gal(β 1,4)Glc Mannose-binding protein MBP-A High Mannose Octasaccahride Viral Influenza Virus hemagglutinin HA Neu5Ac(α 2,6)Gal(β1,4)Glc Polyoma virus protein 1 VP1 Neu5Ac(α 2,3)Gal(β1,4)Glc Bacterial Enterotoxin LT Gal Cholera toxin CT GM1 pentasaccharide Table \(1\): Lectin families and their ligands. In animals, lectins facilitate cell-cell interactions by forming multiple, but weak interactions (also called multivalent interactions) between the protein and many sugars on the ligand to which it binds. Now let's consider the other three classes of glycan-binding proteins (or lectins), C-type lectins, galectins, and siglecs in more detail. Focus on the very different structures of the carbohydrate-binding domains. C-Type Lectins C-Type lectins comprise the largest number of glycan-binding proteins. These proteins have a glycan or carbohydrate recognition domain that depends on the Ca2+ ion. They bind self glycans as well as those on pathogens, which can target viruses to specific cells. Many are on the surface of immune cells. They have an N-terminal glycan-binding domain, also called a C-lectin (CLECT) domain or a carbohydrate recognition domain (CRD). However, some proteins with the domain do not appear to bind either Ca2+ or glycans. They serve as adhesion molecules and are also involved in cell signaling. Some residues in the lectin binding domain appear critical for binding lectins. These include an EPN motif, which interacts with Man, GlcNAc, Fuc, and Glc) and a WND motif involved in binding to Gal and GalNAc. Let's look now at one example of a C-type lectin, the selectins. P-Selectins These are involved in the interaction of immune cells in the blood with endothelial cells that line the blood vessel wall. Think of the challenges an immune cell faces as it moves from the blood into a tissue where an infection might occur! Blood flow in vessels at a rate inversely proportional to the total cross-sectional area of the blood vessel. That rate is about 5-20 cm/sec in arteries, 1.5-7 cm/sec in veins and about 1 mm/sec (1000 μm)/sec) in capillaries. Assuming an average diameter of 10 μm for a lymphocyte, the cell would move about 100 cell lengths per second. An equivalent speed for a human with an arm span of 6 feet (approximately fitting into a circle of diameter = 6 feet as drawn by Leonardo da Vinci) would be around 600 feet/second. The cell must go from its typical circulating speeds to a stop before it can move through the blood vessel wall into tissues. Nature has solved this by providing a way to slow down the moving cell until its final capture. The cells roll along the endothelial cells, making transient low-affinity interactions, which slow it down enough until high-affinity ones effectively stop it (unless it dissociates first). Also, you wouldn't want immune cells to stop and move into tissue in the absence of an infection signaled by mediator molecules. Another problem solved! P-selections are stored in the intracellular granules of platelets (the source of the name P-selectin) and endothelial cells so moving immune cells are not spuriously captured in the absence of some signal. In the presence of the right chemical signal, endothelial and platelets get active and P-selectin is transported rapidly to the cell surface where "capture" occurs before the cell can move into the underlying tissue. P-selectin mediates the first transient interactions and subsequent rolling of immune cells on activated platelets and endothelial cells. Figure \(3\) is a video animating the rolling and "capture" of a lymphocyte by endothelial cells. (See the video for the reference.) Note that cancer cells also can move through the endothelial cells of blood vessels in the process of forming metastases. Figure \(3\): Video animation of a lymphyocyte rolling and being captured by endothelial cells lining blood vessel walls P-selectins hence are receptors for molecules on immune cells. They bind Ca2+ ions, which helps create an active conformation. Their binding ligands are glycan codes and nearby sections of protein connected to the glycan. The glycan ligand on the surface of a circulating immune cell is the sialyl-Lewis X (SiaLewX) glycan or a derivative of it. One of the immune membrane proteins with SiaLewX is the P-selectin glycoprotein ligand 1 (PSGL-1, the gene name), also called SELPLG. It mediates rapid rolling of leukocyte rolling over vascular surfaces during the initial steps in inflammation through interaction with SELPLG" P-selectin is a mediator of cell adhesion (to other cells). As such it could also be classified as an adhesion protein. The three main types of selectins: 1. L-selectins: found on leukocytes ("white" blood cells that are circulating immune cells). 2. P-selectins: found on activated platelets (which can aggregate to form a type of blood clot) and activated endothelial cells. Activation occurs during the inflammatory response which can lead to the quick movement of pre-formed selectins stored within the cytoplasm to the membrane. In addition, their expression can be induced. 3. E-selectins: found on activated endothelial cells only after the cells have been induced to form them by certain immune hormones called cytokines released by immune cells during an inflammatory response. Figure \(4\) shows the domain structure of human P-selectin. It contains an N-terminal C-Lectin (CLECT) domain, which is also called the carbohydrate-recognition domain (CRDs) or the C-type lectin domain (CTLD). In addition, it has an epidermal growth factor domain (EGF), 9 complement control protein (CCP) domains and the blue transmembrane domain. Figure \(5\) shows the structure of the SLewx glycan along with its symbol nomenclature for glycans (SNFG) representation. Figure \(6\) shows an interactive iCn3D model of the crystal structure of P-selectin lectin/EGF domains complexed with SLeX (1g1r) to which P-selectin binds with weak affinity. Fucose interacts with the Ca2+ ion. The glycan interacts with the CLECT domain. SLewx is not present in isolation but rather attached to a membrane protein on an immune cell, which serves as a ligand for the P-selectin on activated endothelial cells or platelet. (The SLewx can also be part of a glycolipid.) Now let's contrast the interactions of the P-selectin LE domain with "naked" SLewx with those present between P-selectin LE with a higher affinity natural binding ligand, human P-selectin glycoprotein ligand 1 (PSGL-1), an immune cell integral membrane protein. PSGL-1 is expressed on neutrophils, monocytes and most lymphocytes. The P-selectin:PSGL-1 complex has a much lower KD (higher affinity compared to binding of the unmodified SLeX (1g1s). PSGL-1 is a disulfide-linked homodimer. When sulfated on a specific Tyr (48) the protein displays high affinity for P-selectin. In contrast, when sulfated on a different Tyr (51), it displays high affinity for L-selectin instead The SLexX type glycan O-linked to the peptide is a bit more complicated than the simple SLexX ligand as it is connected to a protein through an O-linked bond at a threonine. The SNFG is shown in Figure \(7\). The crystal structure of a trisulfated, SLewx-modified peptide from the N terminal region of PSGL-1 (1G1S) bound to P-selectin lectin and EGF domains (P-LE) has been solved. Figure \(8\) shows an interactive iCn3D model which shows some of the interactions between the PSGL-1 peptide (green backbone) and P-LE (magenta backbone). In the crystal structure, the peptide from the P-selectin ligand (which again is a membrane protein) contains 3 sulfated tyrosine residues (605, 607, and 610) which correspond to amino acids 5, 7, and 10 in the peptide). No electron density for the side chain of Tyr 605 was seen. Tyr 607 binds through multiple interactions to the P-selectin LE domain, and is most likely responsible for the high-affinity interaction of P-selectin with the P-selectin glycoprotein ligand (again represented by the green chain). In contrast, Tyr 610 interacts through an intermediary water molecule with the glycan SLexX of the peptide. Figure \(9\) shows the electrostatic surface potential map of one of the dimers of the P-select LE domains. Blue represents positive potential and red negative. The backbone of the P-selectin ligand peptide is shown in green with all of the negatively charged side chains (Tyr, Asp and Glu) shown in stick with CPK colors. Note that these amino acids are all bound in blue (positive) regions of P-selection. The glycan portion attached to the peptide (stick, CPK color) is positioned mostly over negative potential, allowing hydrogen bonding between the hydrogen bond donors of sugar OHs with the protein. You could surmise that the blue region of positive potential could also bind other strongly negatively charged ligands (such as heparin and other glycosaminoglycans) which could inhibit the function of this protein as it would prevent binding of the PSGL-1. Figure \(10\) shows an interactive iCn3D model which shows the surface electrostatic potential of the P-selectin Lectin/EGF domains and bound PSGL-1 peptide The blue represents positive potential and the red negative. The backbone of the P-selectin ligand peptide is shown in green with all of the negatively charged side chains (Tys, Asp and Glu) shown in stick with CPK colors. There are also nonpolar interactions not shown in the figure and model above. The aromatic ring of Tyr 607 (7) interacts with the nonpolar parts of a Ser (-CH2) and Lys (-CH2)4 side chains and the ring of Tys 610 (10) interacts with two leucine side chains. The selectins are also part of a class of molecules called adhesion molecules. As described for the selectins, adhesion molecules contain • an extracellular CHO binding domain (the lectin domain), which mediates binding to adjacent cells or to the extracellular matrix; • a transmembrane domain; • and a cytoplasmic domain which often interacts with the cytoskeleton within the cell. This initial binding mediated by selectin-CHO interactions activates the expression of another adhesion molecule on the leukocyte, integrin, a heterodimer with an a and b chain. These cause strong leukocyte-endothelial cell interactions, leading to the movement of the leukocytes through the vessel wall. Other classes of adhesion molecules (in addition to selectins and integrins) are cadherins (calcium-dependent adhesion molecules), and the immunoglobulin-like superfamily (ICAM1, ICAM2, VCAM). VCAM (Vascular Adhesion Molecule) binds to integrin expressed on activated lymphocytes, leading to the passage of the lymphocyte from the lumen of the vessel into the tissues. Integrins appear to bind proteins in the extracellular matrix through RGD (Arg-Gly-Asp) and also through LDV (Leu-Asp-Val) motifs on the proteins, including fibronectin (RGD), thrombospondin (RGD & LDV), fibrinogen (RGD & LDV), van Willebrand Factor (RGD), vitronectin (RGD). They also bind other matrix proteins with an "alpha domain" including collagen and laminin. Integrin/Adhesion molecule interactions involve protein/protein interactions. A fertilized egg (in the blastocyst stage which is ready for implantation in the uterine cell wall) express L-selectin which allows a low affinity (rolling-type) interaction of the fertilized egg with the uterine epithelial cells. These cells expressed the CHO ligands on their surface which bind to the L-selectin on the blastocyst. The CHO ligands are only transiently expressed on the surface of the epithelial cells of the uterus, presumably only when the uterus is primed for implantation. After the initial interaction of the blastocyst and epithelial cells, further expression of integrins on the blastocyst surface might result. Problems in any of these molecular steps could result in infertility. Figure \(11\) shows. endothelial cell/leukocyte Interactions mediated through selectins, integrins, and ICAMs. Post-translational modifications of protein modification (like glycosylation) can confer new binding and biological functions to a protein. Site-directed mutagenesis can be used to replace surface amino acids with cysteine or methionine with nonnatural amino acid analogs that contain azide or alkyne groups. These modified groups could then direct the location of chemical modifying reagents (such as sugars) to these sites. A protein completely unrelated to PSGL-1 has been selectively modified using this approach to contain covalently attached glycans and sulfated tyrosine side chain. The unrelated protein bound to P-selectin. Mannose Receptor. What do you do with a protein that no longer has the correct structure(s) to perform its designed function(s). Proteins, as with any molecule, undergo chemical changes during their biological lifetime. They must be recognized as aberrant and then removed from "service", ultimately being degraded into component amino acids for reuse. There are no repair enzymes for proteins as for DNA. One modification that changes glycoproteins and signals the need for their removal is the removal of terminal sialic acid residue, forming asialoglycoproteins, whose glycans end in galactose, as you can envision from Figure \(12\) which shows a typical structure of a N-linked glycoprotein. The asialoglycoprotein receptor, a member of the C-Type lectin family, is a transmembrane protein, which binds terminal galactose and N-acetylgalactosamine sugars on the end of circulating asialoglycoproteins, leading to their endocytosis into the cell. It is expressed on the surface of hepatocytes (liver cells). Receptors of this type are also called scavenger receptors as they remove proteins from circulation. The mannose receptor (also called CD206), also expressed in liver endothelial cells, is another C-Type lectin involved in binding and removal of glycoproteins from the circulation. It binds both sulfated and non-sulfated glycans. It also is a receptor that allows binding and phagocytosis of bacterial and fungal pathogens by a type of immune cells called macrophages and dendritic cells. Unfortunately, tumor cells can use the same process for uptake into macrophages, leading to the promotion of tumor cell growth. The protein binds and scavenges sulfated glycoprotein hormones, mannose-bearing glycoproteins released during inflammation, lysosomal enzymes released from cells on injury and fragments of collagen. Figure \(13\) shows the domain structure of the human mannose receptor. Given the large number of CLECT domains, you might surmise that this protein could bind a number of different target glycans from both self and pathogens. What is different about the domain structure compared to P-selectin is the presence of an N-terminal Ricin and a Fibronectin type 2 (FN2) domain. The FN2 domain has two cystines from the 4 conserved cysteines involved in disulfide bonds. What's so interesting about the mannose receptor is that it binds glycans both in the CLECT domains and in the FN2 domain. Glycan binding at the CLECT domain: The CLECT domain binds targets containing mannose, fucose and N-acetylglucosamine with a preference for Man(α1,2)Man or fucose. Figure \(14\) shows an interactive iCn3D model. of the CLECT 4 domain of the mannose receptor complexed with Man(α1,2)Man (7jue). Interactions of fucose lin ligands such as Lewis-a-trisaccharide strengthen the binding. The receptor can bind a variety of glycans. Both mannose and N-acetylglucosamine interact with bound Ca2+ through equatorial OHs on carbon 3 and 4 of the ring while fucose uses OHs on carbon 2 and 3, or 3 and 4. The interaction with fungal pathogens is obviously medically important. Fungi like yeast have an outer structure composed of a membrane bilayer and a mixture of glycans, which deploys an incredibly complex "glycan code" to host infected by them, as illustrated in Figure \(15\). Mannans, polymers of just mannose, differ widely in structure. Their main backbone can be DMan(α-1,6)DMan or DMan(β-1,4)DMan with many branches. Glcyan binding at the FN2 (Cysteine-Rich) Domain (1FWU): The mannose receptor can also bind non-mannose sulfated glycans, such as 3-SO4-LEWIS(X), for which the SNFG representation is shown in Figure \(16\). The mannose receptor binds this glycan, which does not even contain mannose, through the FN2 domain (which contains four disulfide bonds) and not through the CLECT calcium-dependent carbohydrate-binding domain. Hence the protein can bind both sulfated and nonsulfated glycans. Figure \(17\) shows an interactive iCn3D model of the complex of the FN2 domain of the mannose receptor with the non-mannose containing 3-SO4-LEWIS(X) glycan (1fwu). Look at the number of CLECT domains in the domain structure diagram for the mannose receptor above. Along with interactions of sulfated glycans at the FN2 domain, these would enable the binding of widely diverse glycan structures. Reported ligands for the mannose receptor include those with high mannose content released during inflammation (lysosomal hydrolases, collagen peptides, and tissue plasminogen activator), and sulfated ones (including the pituitary hormones lutropin and thyrotropin. Galectins This family of glycan-binding proteins contains a common carbohydrate recognition domain (CRD) of about 130 amino acids, which bind Galβ1,3GlcNAc or Galβ1,4GlcNAc disaccharides (hence the name galectins) as well as other glycan motifs . They are expressed in almost all cells and multicelluar organisms. There are 15 different types, grouped together in how the CRD is functionally expressed (as dimers, tandem repeats, or chimeras), as illustrated in Figure \(18\). The figure also shows their role in cancer biology. The carbohydrate-binding domain of the galactins has a jellyroll-like protein architecture with two anti-parallel β-sheets forming a β-sandwich. Galectin I This protein is secreted and is found in the extracellular matrix, as well as in the cytoplasm. It induces apoptosis in T-cells. It binds beta-galactosides as well as other glycans. The main ligand of galectin-1 has a Galβ1-4GlcNAc (or LacNAc) structure. Figure \(19\) shows an interactive iCn3D model of Human Galectin-1 in Complex with Type 1 N-acetyllactosamine (Gal(β1,3)GlcNAc), which binds less tightly than Galβ1,4GlcNAc (Type 2)(4XBL) A comparison of the crystal structures shows different phi/psi angles for bound Type I (135°) versus the more tightly bound Type 2 (-108°), which shows the nuance in binding conformations in the interactions of glycans with glycan-binding proteins. Siglecs The proteins are sialic acid-binding immunoglobulin (Ig)-like lectins found on immune cells like basophils, macrophages, mast cells and eosinophils. One type (Siglec-4)is found in myelinated structures in the central and peripheral nervous systems. They all have an N-terminal extracellular immunoglobulin domain (abbreviated as IG or V-Set) and a differing number of IG-like domains, also called C2-set Ig domains. The glycan binding epitope recognized by Siglecs are sialylated oligosaccharides on a section of the protein containing a conserved arginine. Figure \(20\) compares the domain structures of the human Siglec family. Here is one example of a Siglec. Siglec-8 This protein is expressed on immune cells like basophil, mast cells and eosinophils. When activated by infection and prolonged inflammation, they release the contents of intracellular granules which have potent physiological effects that can lead to allergic and asthmatic responses. On infection and other inflammatory states, immune cytokines are released that in a signaling process lead to the release of sialoglycans that act as ligands, binding to the Siglec-8 on the surface of the immune cells. One type of sialoglycan released is mucins, which are very large glycoproteins with many 6′S sLex glycans attached. These "multivalent" glycan epitopes can bind to Siglec-8 lead to signaling in the cells and ultimate inhibition of cell function (including by death or apoptosis). The mucins in mucus (cross-linked mucins), which cover epithelial cells or airways, also act as a first line of defense as they can bind viruses through mulitple-contact (multivalent) binding sites, effectively trapping the viruses. The glycan structure recognized by Siglec-8 is sialic acid and sulfate (NeuAcα2-3[6S]Galβ1-4G[Fucα1-3]GlcNAc-). Given their role in inhibiting and inducing apoptosis in immune cell, the family of siglecs are likely involved as checkpoints, which are important in cancer and inflammatory conditions. Figure \(21\) shows the domain structure of Siglec-8 Note that there is no CLECT domain, but rather immunoglobulin- (IG) or IG-like domains, which seems logical given their role in binding glycan "epitopes". The IG domain is also called the immunoglobulin V-set domain (V-Set). The blue rectangle represents the transmembrane domain (single helix). The cytoplasm contains a tyrosine-inhibitory motif (ITIM) involved in transducing the signal on binding 6′S sLex glycans to the IG domains. As discussed above, humans lack a hydrolase gene necessary for the hydroxylation of Neu5Ac to Neu5Gc, which is found in chimps who possess the enzyme. Chimp's immune systems seem to confer protection from acquiring simian versiosn of AIDS, cirrhosis, and other diseases which humans acquire when they are infected with the human versions of the HIV virus, hepatitis B or C, or other viruses. These diseases and others associated with overactive T cells (rheumatoid arthritis, asthma, type-I diabetes) are not common in chimps. It turns out that there is a link between the type of sialic acid and the expression of siglecs that influences the difference for our disease propensity. Varki et al have shown that chimps and gorillas show much higher levels of expression of siglecs on T cells, which are critical regulatory and effector cells in the immune system. When siglecs on T cells are activated, T-cell responses are down-regulated. Although HIV virus ultimately kills T helper cells, the virus initially activates them on infection, leading to their proliferation and production of a larger number of cells for the virus to infect. Figure \(21\) shows an interactive iCn3D model of human Siglec-8 lectin domain in complex with 6'sulfo sialyl Lewisx (2N7B)

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/07%3A_Carbohydrates_and_Glycobiology/7.04%3A__The_Sugar_Code_and_Lectin_Decoding.txt

Search Fundamentals of Biochemistry The material in this chapter is derived from the open access article referenced below and used under the following Creative Common's License. Shirakawa, A.; Manabe, Y.; Fukase, K. Recent Advances in the Chemical Biology of N-Glycans. Molecules 2021, 26, 1040. https://doi.org/10.3390/molecules26041040. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). It deals with the analysis and synthesize of N-glycans, but the same principles would be used for study of O-glycans, proteoglycans, etc. Text in boxes have been added to offer addition simplifying information when necessary for undergraduate students. References can be found in the original paper linked above. Introduction Glycosylation is the most common post-translational modification of proteins. Over 60% of proteins are linked to glycans. Asparagine-linked oligosaccharides (N-glycans) have a core pentasaccharide composed of mannose and glucosamine and are classified into three types: high-mannose, hybrid, and complex as shown in Figure \(1\) below. In the biosynthesis of N-glycan-modified proteins, the high-mannose type N-glycan consisting of 14 residues (Glc3Man9GlcNAc2) is first attached to proteins in the endoplasmic reticulum (ER). The initial high-mannose N-glycans play an important role in protein folding in the ER. Glycoproteins then migrate to the Golgi apparatus and are subsequently converted into complex-type N-glycans. Complex N-glycans have diverse structures due to differences in their associated synthesizing enzymes, resulting in different functions for each structure. For example, polylactosamine, consisting of a repeating structure of galactose and glucosamine, is involved in cancer metastasis and immune response. Sialic acids, in contrast, control immunity via recognition by Siglecs expressed in immune cells. Core fucose, which is a fucose linked to the glucosamine 6 position at the reducing end, and bisecting glucosamine, which is a glucosamine linked to the branched mannose 4 position, also play various roles and are closely related to many diseases. Hybrid N-glycans have both high-mannose and complex-type structures. Thus, N-glycans have diverse structures and are involved in a variety of biological phenomena. However, the molecular bases of their modes of action are yet to be fully elucidated. Chemical synthesis, enzymatic synthesis, and isolation of diverse, pure N-glycans have been vigorously investigated for analyzing N-glycan functions at the molecular level. Chemical synthesis is an extremely potent approach that allows the de novo construction of glycan structures. Any desired glycan structures can be constructed, including partial and artificial structures. Danishefsky et al. successfully synthesized various N-glycans with multiantennary structures. Unverzagt et al. achieved convergent synthesis of complex-type N-glycans with bisecting glucosamine and/or core fucose. We have also reported the synthesis of N-glycans. In addition, Ito et al, Boons et al., Wang et al., Wong et al., and Schmidt et al. have achieved N-glycan synthesis. Since the chemical synthesis of N-glycans with complex structures is a challenging process that requires multiple steps, the isolation of N-glycans from natural resources has also been explored. Kajihara et al. established an efficient method for the isolation of N-glycans from egg yolk, which has become a standard method for N-glycan preparation. In recent years, the preparation of N-glycans using enzymatic reactions has also been extensively investigated. Ito et al., Boons et al., Wang et al., and Wong et al. successfully constructed a wide range of N-glycan libraries via enzymatic synthesis using isolated or chemically synthesized glycans as substrates. Thus, over the years, the technical basis for a sufficient supply of various N-glycans has been established. Owing to the increased availability of pure N-glycans, their functional elucidation has advanced considerably in recent years as shown on Figure \(2\) below. N-Glycan functions have mainly been analyzed using molecular biological techniques, including knockout of biosynthetic enzymes. However, it is difficult to determine the precise structure–activity relationship using these methods. Although interaction analysis of lectins using relatively small glycan fragments, such as disaccharides and trisaccharides, has been used to study function, it is not possible to estimate the conformational effect or multivalent interactions of the complex structure of N-glycans. Recent interaction analysis of lectins using various N-glycans has elucidated the significance of such complex structures. The increased availability of N-glycans also allows one to prepare glycoproteins with homogeneous glycoforms, enabling the elucidation of N-glycan function on the distinct protein. Furthermore, N-glycans have the potential to be used in the development of novel drugs. This review provides an overview of the recent chemical biology study of N-glycans. Elucidation of the Molecular Basis of N-Glycan Recognition by Lectins Glycans control various biological phenomena through their recognition by lectins. Thus, interaction analysis between N-glycans and lectins is essential to elucidate N-glycan functions . Methods for the Glycan‒Lectin Interaction Analysis The glycan–lectin interaction analysis methods include analyses using glycan arrays, nuclear magnetic resonance (NMR), isothermal titration calorimetry (ITC), surface plasmon resonance (SPR), fluorescent polarization (FP), and X-ray crystallography. ITC gives thermodynamic parameters, whereas SPR provides kinetic parameters. FP realizes a simple and easy assay system. X-ray crystallography provides precise structural information. In this review, we focus on studies using glycan arrays and NMR, which are effective methods for elucidating the interaction between glycans and lectins. Glycan arrays are used to detect the binding of lectins to immobilized glycans. An advantage of a glycan array is that a large number (dozens and hundreds) of samples can be examined in a high-throughput manner using a small amount of glycans. Interaction analysis using various structures of glycans provides insights into precise structure–activity relationships. Glycan immobilization methods are divided into two categories: noncovalent and covalent. Noncovalent immobilization utilizes hydrophobic interactions, charge interactions, and biotin–streptavidin interactions among others. Covalent immobilization methods typically use coupling of an amino group introduced at the reducing end of a glycan to the plate surface activated with N-hydroxysuccinimide. Many other methods, including thiol-maleimide coupling and alkyne-azide click reactions, have been reported. As for the detection, fluorescence is usually used to realize high-throughput analysis. NMR can be used to analyze interactions at the atomic level. Saturation transfer difference (STD) NMR is a particularly powerful method for the analysis of glycan–lectin interactions. In this method, saturation transfer from the protein to the ligand is observed as STD signals after the saturation of protein by radio frequency as shown in Figure \(3\) below. The closer the protons are to the protein, the stronger the STD signals observed. STD-NMR was originally developed as a method for screening ligands from mixture systems but is now widely used for the analysis of protein–ligand binding modes. This method works when the affinity is not high (KD is 10−3 to 10−8 M), because STD signals are measured when the protein and ligand are in an equilibrium state of binding and dissociation. Since glycan–lectin interactions are usually weak (KD in mM-μM), STD-NMR is highly effective. NMR is also a powerful tool for the conformational analysis of glycans. Importantly, this method does not require labeled proteins. In addition, a small amount of receptor is necessary (typically micromolar range). However, an excess of the ligands is used (typically the millimolar range), thus, low solubility of the ligand causes a problem. While conformation is an important factor for glycan recognition, the flexibility of glycans makes conformational analysis difficult. In addition to analysis based on coupling constants and the nuclear Overhauser effect (NOE), an analysis using pseudocontact shift (PCS) by paramagnetic metals has recently been developed, and its efficiency has been demonstrated. Examples of the analysis of glycan‒lectin interactions using glycan arrays and NMR are introduced below. Analysis of Sugar‒Lectin Interactions Using Glycan Arrays Glycan arrays are excellent tools for the comprehensive analysis of glycan‒lectin interactions. Previous interaction analysis using small fragments, such as disaccharides and trisaccharides, revealed the minimum structure (epitope) required for recognition of individual lectins. Meanwhile, recent advances in the preparation of the whole structure of various N-glycans have allowed the full realization of structure–activity relationships, elucidating the significance of complexity of N-glycan structures as shown in Figure \(4\) below. For example, these advances have provided insights into the differences in the lectin recognition of each branch, the improvement of affinity due to the inclusion of multiple recognition units (multivalent effect), the influence of chain length on affinity, and remote (heterovalent) recognition. Wang et al. demonstrated the differences of each N-glycan branch in lectin recognition by comprehensive interaction analysis of various N-glycans with several lectins using a glycan array. Plant-derived Sambucus nigra lectin (SNA), which recognizes sialic acid, recognized the sialic acid on the α1,3-branched chain more strongly than the sialic acid on the α1,6-branched chain. Meanwhile, plant-derived Maackia amurensis lectin (MAL-I) and virus-derived lectin hemagglutinin (HA) strongly bind to sialic acid on the α1,6-branched chain. MAL-I also interacts with terminal galactose; in this case, MAL-I strongly recognizes galactose on the α1,3-branched chain, suggesting that MAL-I has two distinct glycan recognition domains. In addition, Erythrina cristagalli lectin (ECL), which recognizes the lactosamine structure, has a higher affinity to lactosamine on the α1,3-branched chain than on the α1,6-branched chain. Phaseolus vulgaris erythroagglutinin (PHA-E) prefers terminal galactose on the α1,6-branched chain and terminal glucosamine on the α1,3-branched chain, whereas wheat germ agglutinin (WGA) strongly interacts with glucosamine on the α1,3-branched chain. Branch selective binding of C-type lectins and monoclonal antibodies was also revealed by using glycan array including N-glycan positional isomers prepared by chemo-enzymatic method. DC-SIGN, C-type lectin recognizing glycan on bacteria and viruses, showed strong binding to hybrid- and complex-type glycans and N-glycans presenting Lex epitopes. DC-SIGN showed preferential binding to the biantennary glycans with terminal galactose or N-acetylgalactosamine on the α1,6-branched chain, whereas DC-SIGNR showed the opposite binding behavior. L-SECtin showed the preference to GlcNAc1,2-Man residues on the 3-arm of the complex and hybrid N-glycans. N-glycans have symmetric structures on the nonreducing end side, and these studies indicate that glycan structures on each branched chain have distinct functions. The structural redundancy of N-glycans plays an important role in enhancing their affinity to lectins because of their multivalency. Interaction analysis of Siglec-1, -2, -9, and -10 with sialic acid-containing N-glycans using a glycan array showed a higher affinity for four-branching N-glycans than for two-branching N-glycans. Multivalent effects were also confirmed in ECL, which recognizes the lactosamine structure, and Ricinus communis agglutinin (RCA120), which recognizes terminal galactose. Similarly, interactions with galectin were also enhanced as the number of recognition units increased. The structure at the remote positions of the lectin recognition unit can affect its interaction. Lens culinaris agglutinin (LCA), which binds to core fucose, only recognizes core fucosylated biantennary and triantennary N-glycans with particular branching patterns, but did not recognize triantennary N-glycans with other branching patterns or tetraantennary N-glycans. These results indicate that the branching structure away from the core fucose affected recognition by LCA, although its recognition site is core fucose. On the other hand, the affinity between HA from H3N3 and sialic acids at the nonreducing end was increased by chain elongation; the insertion of a polylactosamine repeating structure enhanced the affinity. These studies revealed that both the epitope and the whole glycan structure are important for the recognition of N-glycans. Glycan arrays, comprising N-glycans along with glycolipids and O-glycans, have been used to investigate the host–pathogen interactions in diagnostic and therapeutic applications. For example, the inhibition of human anti-N9 antibodies to influenza neuraminidases was analyzed by glycan array. The binding study of the H3N2 influenza viruses using glycan microarrays demonstrated the changes in virus hemagglutinin that affect the receptor binding properties of the viruses. Glycan arrays can also be used to explore artificial glycoligands as new drug candidates that target lectins. High-affinity ligands for Siglecs or several C-type lectins, which are involved in immune regulation, are expected to be lead compounds for drug development. However, glycan–lectin interactions are usually weak, which is a major issue in the utilization of glycans as bioactive molecules. Thus, the synthesis of glycans and derivatization of artificial molecules, followed by high-throughput screening using glycan arrays, is expected to be a powerful approach to address this limitation. Analysis Using NMR NMR analysis can provide insights into glycan–lectin interactions at the atomic level. STD-NMR can be used for high-resolution epitope mapping. Similar to the results obtained from glycan arrays, NMR analysis also reveals that not only epitopes but the whole structure of N-glycans plays an important role in glycan–lectin interactions. The conformational analysis of N-glycans using NMR is a powerful approach that provides a rational explanation for the molecular basis of the recognition of complexity of N-glycan structures. STD-NMR allows for a detailed analysis of glycan–lectin interactions. Many researchers have analyzed the interactions between sialic acid containing N-glycans and Siglecs . Silipo et al. analyzed the interaction between Siglec-2 and sialyl N-glycans using STD-NMR and molecular dynamics (MD) simulations. The Siglec-2 epitope was clearly shown by STD-NMR, and the conformation of sialyl N-glycans was predicted by NMR analysis and MD simulations. When biantennary sialyl N-glycans were recognized, Siglec-2 only interacted with the sialyl disaccharide at the nonreducing end, and the other part was expected to protrude from the protein surface. These results suggest that multiantennary N-glycans with multiple sialic acids can interact with several Siglec-2 and induce the formation of Siglec-2 oligomers on B cells. STD-NMR analysis using the whole structure of N-glycans has demonstrated that lectins not only recognize small units, such as disaccharides and trisaccharides, but also interact with N-glycans in a more complex manner. In N-glycan recognition by Pisum sativum agglutinin (PSA), a mannose-recognition lectin, core fucose was shown to alter its binding mode. When biantennary N-glycans without core fucose were used for the interaction analysis with PSA, the mannose on each branch gave comparable STD signals. While for the core fucose containing N-glycans, the STD signals of mannose on the α1,6-branched chain were weakened, and instead, an interaction with the methyl group of the core fucose was observed. STD-NMR using a fluorine derivative (2D STD-TOCSYreF) indicated that the mannose on the α1,3-branched chain was more strongly recognized by PSA than the mannose on the α1,6-branched chain. In addition, dectin-1, which recognizes fungal β-glucan, was found to recognize core fucose on immunoglobulin (IgG) . STD-NMR analysis indicated that dectin-1 interacted not only with core fucose but also with an Fmoc group attached to the amino group of asparagine introduced at the reducing end. These results suggest that dectin-1 recognizes amino acids with aromatic side chains, such as phenylalanine and tyrosine, together with core fucose. On the other hand, STD-NMR is also effective for the analysis of substrate recognition by glycosyltransferases. The STD-NMR analysis of FUT8, a fucosyltransferase that builds core fucose structure, revealed the precise interaction between FUT8 and N-glycan . FUT8 recognizes not only glucosamine at the reducing end (reaction point) but also the whole glycan structure. In particular, FUT8 strongly interacted with the α1,3-branched chain at the nonreducing end. Advanced STD-NMR methods have been developed. Saturation transfer double difference (STDD)-NMR is useful for the direct observation of ligands binding on the surfaces of living cells. Clean-STD can avoid accidental saturation to give improved detection of ligand–protein interactions at low concentration of protein. Second dimension STD-NMR, i.e., STD-TOCSY, STD-HSQC, STD-NOESY, can overcome the problems of proton overlapping typical of glycan NMR analysis. Conformation analysis of glycans using NMR provides important insights into complex glycan–lectin interactions. The N-glycan conformation can be predicted by combining PCS-based NMR analysis and MD simulations as shown in Figure \(5\) below. Kato et al. analyzed the conformation of high-mannose glycans by PCS-based NMR analysis using 13C-labeled compounds and Tm3+ as a paramagnetic metal ion tag. They elucidated the conformational change caused by mannose trimming during the N-glycan biosynthetic process. Unverzagt and Barbero et al. distinguished each branch of tetraantennary N-glycan based on the PCS method and analyzed the differences in the recognition of each branch by lectins . Datura stramonium seed lectin (DSL), which recognizes the lactosamine structure, interacts more strongly with the lactosamine on the α1,6-branched chain than with that on the α1,3-branched chain. On the other hand, no differences in the strengths of STD signals of each branch were observed with Ricinus communis agglutinin (RCA120), which recognizes terminal galactose, indicating that RCA120 recognizes all branches without distinction. In a similar analysis between sialic acid containing biantennary N-glycans and HA, STD signals from both sialic acids were observed, suggesting the contribution of two sialic acids in a multivalent effect. Furthermore, interesting results have been reported showing that the N-glycan conformation directly affects lectin recognition. N-Glycans have three back-fold conformations and two extended conformations, in which the α1,6-branched chain is folded toward the reducing end or extended, respectively. The addition of core fucose or bisecting glucosamine significantly changes their conformational equilibria and reduces the number of major conformations from five to four and five to two, respectively. Crystal structure analysis and transferred NOE (TrNOE) analysis revealed that Calystegia sepium-derived calsepa and Phaseolus vulgaris-derived phytohemagglutinin (PHA-E), which recognize bisecting glucosamine containing N-glycans, recognize N-glycans in the back-fold conformation induced by bisecting glucosamine addition Functional Analysis of N-glycans on Glycoproteins Analysis of N-glycan functions on glycoproteins needs to be considered with proteins. In recent years, improvements in the techniques for the synthesis of peptides and proteins, as well as glycans, have enabled the preparation of glycoproteins with homogeneous glycans. N-Glycans on glycoproteins can be modified by Endo-β-N-acetylglucosaminidases (ENGases). Synthesized glycoproteins with homogeneous glycan structures have helped elucidate precise glycan functions. A series of synthetic studies of glycoproteins and glycoprotein mimics by Ito and Kajihara et al. revealed the precise function of N-glycans in a quality-control mechanism for glycoproteins in the endoplasmic reticulum (ER). ER has a quality control system that promotes the correct folding of ribosome-produced proteins. In the case of N-glycosylated proteins, high-mannose N-glycans work as tags for protein folding. A common dolichol-linked oligosaccharide precursor containing terminal glucose trisaccharide is first synthesized in the ER and is transferred to proteins by the oligosaccharyltransferase (OST). The folding process then starts. The first glycosidase (GCSI) cleaves the terminal glucose and the second glycosidase (CGSII) further cleaves glucose residues to afford monoglucosylated or nonglucosylated glycoproteins. The folded nonlucosylated glycoproteins are then transferred to the glycan modification process. The UDP-glucose:glycoprotein glucosyltransferase (UGGT) complex distinguishes misfolded glycoproteins and transfers glucose to the nonreducing end of the high-mannose glycan. This monoglucosylation serves as a marker for misfolded glycoproteins and the chaperone proteins calnexin/calreticulin (CNT/CRT) promotes folding. CGSII then cleaves glucose residue to transfer the glycoproteins for the glycan modification process. This cyle is illustrated in Figure \(7\) below. The defects in this process cause congenital disorders of glycosylation (CDGs), which are severe genetic diseases. CDG is classified into Type I and Type II. In Type I, the enzymes are mutated in synthesis and transfer a common dolichol-linked oligosaccharide precursor and enzyme substrates. Type II defects the modification process of N-glycans in the ER and Golgi. Lack of GCS1 causes CDG-IIb. Unfolded proteins lead to ER stress and cause CDGs . Ito et al. introduced methotrexate (MTX) at the reducing end of high-mannose N-glycans and prepared a complex with dihydrofolate reductase (DHFR), which recognizes MTX. Such glycoprotein mimics were used to analyze the interaction with UGGT. They also investigated various aglycone structures as substrates of UGGT. In addition, chemically synthesized glycoproteins were used for the analysis of substrate recognition by UGGT. UGGT showed higher enzymatic activity against high-mannose N-glycans on misfolded interleukin-8 (IL-8) than against those on the folded one. Furthermore, they synthesized several glycoproteins and isotope-labeled glycopeptides and revealed that UGGT recognizes hydrophobic patches on misfolded proteins. As shown above, they elucidated the molecular basis of the quality-control mechanism based on high-mannose N-glycans using glycoprotein mimics and chemically synthesized glycoproteins. Maintaining the appropriate folding is also critical for in the degradation process. Mutations in human N-glycanase 1 (NGLY1) cause the congenital disorder of deglycosylation (CDDG). Suzuki revealed that N-GlcNAc proteins are accumulated by the action of Endo-β-N-acetylglucosaminidase (ENGase) in Ngly1-defective cells . During ER-associated degradation, N-GlcNAc proteins form aggregates that seem to be toxic. Suzuki also revealed that lethality of Ngly1-KO mice is partially rescued by the additional deletion of the Engase gene, suggesting that ENGase inhibitors are targets for CDDG. In recent years, the influence of N-glycan modifications on the bioactivity of proteins has been gradually elucidated using synthetic glycoproteins. Hematopoietic hormone erythropoietin (EPO), which is used to treat renal anemia, has three N-glycan-modification sites. EPO with various glycoforms is used as a drug. Several groups have reported the synthesis of EPO with homogeneous glycoforms, and the effect of N-glycans on their biological activities has been investigated. In addition, various neoglycoprotein analogues of EPO have been reported. Kajihara et al. synthesized five types of EPO, which is introduced sialic acid containing N-glycans into three N-glycosylation sites with different patterns, and showed the relationship between glycosylation sites and hematopoietic activities. Increasing the number of sialic acids containing N-glycans on EPO improved the stability in blood, leading to an improvement in hematopoietic activity. Moreover, the metabolic stability of EPO was highly correlated with hydrophobicity, suggesting that glycan modifications enhance the in vivo stability by covering hydrophobic sites on the protein surface. Kajihara et al. also synthesized two types of interferon-β (IFN-β) with sialic acid-containing and noncontaining (asialo) N-glycans, and their activities were evaluated. IFN-β modified with sialic acid-containing N-glycans exhibited higher activity than that modified with asialo N-glycans, suggesting that sialic acid extended the in vivo half-life of IFN-β. Thus, N-glycans are closely related to the stability of glycoproteins in vivo. Indeed, Tanaka et al. demonstrated the effect of N-glycans on protein metabolic stability by positron emission tomography (PET) imaging using glycodendrimers as pseudoglycoproteins. On the other hand, N-glycosylation can also affect binding affinity to a receptor. Okamoto et al. synthesized two types of chemokine CCL1 with and without N-glycan, in which N-glycosylation reduced the activity of CCL1, suggesting that CCL1 biological activity can be regulated by N-glycan modification. Thus, it should be noted that the role of N-glycan modifications can be different between proteins. We reported that dectin-1 specifically recognized core fucosylated IgG and did not interact with other core fucosylated proteins, suggesting that core fucose on IgG has specific physiological functions. The role of N-glycans on distinct proteins is an important topic for future work. Use of N-glycans for Drug Development The increased supply of N-glycans has led to an increase in the use of N-glycans for drug development. Because N-glycans are endogenous molecules, they are unlikely to be toxic or immunogenic and, thus, are expected to have high safety profiles. Next-Generation Protein/Peptide Drugs Modified with Homogeneous N-Glycans Controlling the glycan structure is an important issue in the preparation of glycoprotein and glycopeptide drugs. Biopharmaceuticals, including antibodies, are common pharmaceuticals. Although many proteins utilized in biopharmaceuticals are glycoproteins, their actual glycan structures are often neglected or ignored. However, the significance of the role of glycans on the function of glycoproteins has recently been illuminated, and the importance of the glycan structure has been highlighted. The preparation of glycoproteins with homogeneous glycans is also important from the viewpoint of quality control. IgG antibodies have N-glycans at Asn297 in the Fc region of the heavy chain, and their structures affect activity, dynamics, and safety (Figure \(8\)). The importance of core fucose on these N-glycans is well known. The removal of the core fucose from IgG antibodies dramatically enhances antibody-dependent cellular cytotoxicity (ADCC) activity . Mogamulizumab, the antibody without core fucose, is actually in current use. Bisecting glucosamine and terminal galactose have been reported to affect ADCC and complement-dependent cytotoxicity (CDC) activities. Therefore, modifications of N-glycans on IgG antibodies have been extensively investigated. ENGase provides a powerful tool. N-Glycans on antibodies can be trimmed, and other N-glycans can be introduced by ENGase. Antibody–drug conjugates (ADCs) have also been prepared using this method in which N-glycans were changed into a structure with a tag for subsequent reactions, and small molecular drugs were introduced via bio-orthogonal reactions. This approach allows for the introduction of drugs into the Fc region without affecting antigen recognition. Furthermore, the N-glycan structure can be made homogeneous. Wong et al. introduced N-glycans with 3-position fluorinated sialic acids into antibodies. Because this fluorinated N-glycan was not degraded by sialidase and modification with sialic acid containing N-glycan can enhance the metabolic stability of proteins, this antibody is expected to show a significant improvement in pharmacokinetics. N-Glycans play important roles not only in antibodies, but also in many other glycoproteins. As described above, the structure–activity relationship study of N-glycans on EPO demonstrates the importance of the N-glycan structure on the bioavailability and bioactivity of proteins. Hossain and Wade et al. reported that the physical properties of insulin can be improved by adding N-glycan to insulin, which originally has no glycans. Introduction of sialic acid containing N-glycans to insulin successfully inhibited problematic fibril formation. In addition, N-glycan-modified insulin bound to its receptor with almost the same affinity as the natural form, and further improvements in its metabolic stability were observed. Currently, PEGylation has been generally used to enhance the bioavailability of proteins; however, PEG is not without adverse effects. Considering that N-glycans are endogenous glycans and are expected to be extremely safe, “N-glycan modification” has the potential to become a common strategy for improving the protein/peptide bioactivity. The structure of N-glycans is also important for vaccine development. Viruses use host biosynthetic systems to synthesize proteins. Consequently, viral proteins are subjected to glycan modification. Therefore, glycoproteins and glycopeptides are candidate antigens for vaccine development, and their glycan structures influence their functions. HIV vaccine candidates containing N-glycans have been designed and synthesized . Wang et al. reported that the glycan structure on the antigen was critical for the neutralization activity of antibodies, clearly demonstrating the importance of the glycan structure in vaccine design. In addition, Wang showed the importance of glycan structures in the development of influenza HA-based vaccines. For the development of vaccines against COVID-19, the spike protein is a promising antigen candidate. This protein is heavily glycosylated, but N-glycan modifications of spike proteins have been reported to reduce their antigenicity. However, N-glycan-modified antigens may induce antibodies against endogenous N-glycans, which should be carefully examined. Overall, glycans are likely to be important for developing highly efficient and safe vaccines. Drug Delivery Systems (DDSs) Using N-Glycans N-Glycans interact with various biomolecules, including many lectins, and thus show distinct dynamics in vivo. Therefore, DDSs using N-glycans have been investigated. Because glycan–lectin interactions are weak, multivalent materials, including polymers, dendrimers, and liposomes, are usually utilized to enhance their interactions. We synthesized dendrimers of sialic acid containing N-glycans and evaluated their dynamics in vivo using PET imaging. We revealed that the structure of N-glycans affected the uptake of dendrimers into specific organs. In addition, Tanaka et al. developed an N-glycan-based DDS using albumin as a multivalent scaffold (Figure \(9\)). The albumins modified with N-glycans were used as carriers of metal catalysts to realize chemical reactions at the desired organ in vivo. It should be noted that they achieved metal-catalyzed reactions in vivo by utilizing the hydrophobic pocket of albumin. Siglecs, which recognize sialic acid, are expressed on immune cells and are involved in immune regulation. Immune cells can be targeted by utilizing sialyl glycan–Siglec interactions. Paulson et al. developed high-affinity Siglec ligands by the derivatization of sialic acid. They synthesized N-glycans containing these artificial structures, which exhibited a high affinity for Siglec-2. They achieved B cell targeting using liposomes displaying this N-glycan. Utilization of different sialyl glycans enables the targeting of various immune cells. In addition to Siglecs, DDSs targeting galectins, which recognize galactose, have also been investigated. Future Perspectives Glycans exist as polysaccharides in nature and are involved in multivalent interactions for pattern recognition. Conformational control via the formation of polysaccharides also plays an important role in glycan functions. In addition, many glycans function only when they are linked to proteins or lipids. Such emergent glycan functions can only be revealed by analysis using the whole glycan structure or glycoconjugates. As described herein, the increased availability of various N-glycans has led to the elucidation of the significance of complex N-glycan structures. The influence of N-glycan modification on some protein functions was also discussed. On the other hand, the molecular basis of glycan functions on membrane proteins remains to be elucidated, although glycans are attached to almost all membrane proteins and have diverse functions. Recent advances in the engineering of cell-surface glycans are expected to provide a powerful approach to tackle this challenging issue. Bertozzi et al. developed metabolic labeling of cell surface glycan by incorporating unnatural sugar analogs using "click chemistry" having the reaction tag followed by the bioorthogonal reactions. We'll discuss these techniques further below. In addition to glycan function analysis, the therapeutic application of metabolic glycan labeling is being vigorously investigated . Glycan engineering by chemical and chemoenzymatic methods has also been investigated. In addition, de novo glycans on cell surfaces have also been reported, such as the direct introduction of defined glycan structures into plasma membranes by lipid insertion, liposomal fusion, and tag technology. Such glycan editing technique enables glycan functions to be explored on membrane proteins on living cell surfaces. A major feature of glycans is their heterogeneity. Glycans attached to the same site on the same protein can have diverse structures. In addition, many proteins have multiple glycosylation sites to which various glycans can be added. Although studies using pure N-glycans have revealed the functions of individual N-glycans, little is known about their function in combination with each other. Kurbangalieva and Tanaka et al. prepared albumins labeled with several N-glycans and observed their dynamics in vivo. Interestingly, their dynamics were altered depending on the N-glycosylation pattern. These results suggest that the simultaneous interaction of multiple N-glycans may result in the expression of functions different from those of individual N-glycans. Little is known about whether the interactions of glycans with multiple lectins work collaboratively or competitively. A bottom-up approach to the construction of controlled glycoforms is expected to be a powerful strategy to address this difficult issue. Glycans are considered to be the third most important life chain and have attracted increasing attention in recent years. However, unlike nucleic acids and proteins, their functional analysis and regulation have been delayed due to the lack of simple preparation methods. Recent advances in the preparation of N-glycans are expected to accelerate functional studies. Click Chemistry and Bioorthogonal Reactions Click chemistry is a powerful way to covalently connect two molecules - hence the name click chemistry. Sharpless, Meldal and Bertozzi were awarded the Nobel Prize in Chemistry in 2022 for its development and application. It has been use to synthesize active site inhibitors for enzymes as well as to label glycan and other biomolecules in vitro and in vivo. In click chemistry, two molecules are "stitched" together to form a new molecule, much like two activated amino acids condense to form a dipeptide. Click chemistry was developed to emulate the simple solutions found in nature to produce polymers. Click chemistry reactions should not be sensitive to water and oxygen, easy to purify products, and proceed with a favorable ΔG (< - 20 kcal/mol, -84 kJ/mol). Azides and acetylenes were the first used but the addition of Cu1+ as a catalyst made the reaction very fast. The reaction can take place easily in blood and even urine. Drugs that inhibit enzymes typically bind to the active site of the enzyme where catalysis occurs. Binding of an inhibitor precludes binding of the normal reactants (substrates) for the enzyme, inhibiting its activity. Using click chemistry, two small reactive molecules selected to bind independently in the active site can covalently react with each other to form a new drug with very high specificity and very high binding affinity (low KD). This has been used to synthesize noncovalent inhibitors of the enzyme acetylcholinesterase (Barry Sharpless Lab, Scripps Lab). The reactive groups chosen in the example below are azide and acetylene derivatives, which when held in close approximation in the binding site of the enzyme undergo a cycloaddition reaction to form a triazole. Figure \(10\) shows the reaction of an azide and acetylene in solution (without a "binding template"), which leads to equal amounts of the syn and anti products. Figure \(10\): Click chemistry reaction between azide and acetylide without a directing template The actual mechanism (not the simplified version shown) requires catalysis by copper ions (Cu1+), which forms a complex with the acetylide (deprotonated acetylene). This decreases the pKa of the acetylene functional group, making it a better nucleophile. A dicopper intermediate is suggested in which the azide interacts with the second copper. Subsequent rearrangement lead to the triazole products. The reaction of the an azide and acetylide in an extended active site binding site leads to the production of only the syn product as shown for a click inhibitor of the enzyme acetylcholinesterase in Figure \(11\). The product would be a potent inhibitor of the enzyme. Figure \(11\): Click chemistry reaction between azide and acetylide with a directing template This target-guided synthesis creates a bivalent inhibitor (one that binds at two different regions of an extended binding site). It would have a lower KD than either of the separate inhibitors. The enzyme has a catalytic site at the end of very deep ( 20 Å) and narrow pocket. It also has a peripheral site near the surface of the extended binding pocket. Hence it makes a great potential target for click-chemistry inhibitors. Figure \(12\) shows interactive iCn3D models of human acetylcholinesterase in complex with peripheral site inhibitor dihydrotanshinone I (4M0E) and the protein in complex with peripheral and active site-spanning inhibitor territrem B (4M0F) to illustrate the structural features of the enzyme that make it ideal for click chemistry inhibitors. Human acetylcholinesterase in complex with the peripheral site inhibitor dihydrotanshinone I (4M0E) Human acetylcholinesterase in complex with the peripheral and active site-spanning inhibitor territrem B (4M0F) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...y8btPa8WMYNj4A (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...veBZM1DLSxzKX6 Click chemistry can be used in vivo in bioorthogonal reactions. These are specific reactions that take place in potentially reactive biological environments but without interfering with normal biological functions and activities. The reaction is different than when it occurs in a test tube in that a "bioorthogonal reporter such as a fluorphore" (that can be used for instance to track a target molecule in vivo when linked to it) covalently links to a target biomolecule (such as a surface glycan) without influencing its activity. Instead of using Cu ions as catalyst as in in vitro click chemistry, normal catalytic mechanisms of the cell are used. Some more classical condensation reaction can be considered early examples of bioorthogonal reactions. Azides are reactive and are rare biologically, but biomolecules (lipids, proteins, nucleic acids, etc) can be easily labeled with azides or alkynes, facilitating their reaction with click chemistry. Copper ions however can generate ROS, which limits its potential for in vivo click reactions. Here are some classical and more modern bioorthogonal condensing reactions. • carbonyls with hydrazines and alkoxylamines to form oximes and hydrazones. • carbonyls and amines to form Schiff bases • triarylphosphines and organic azides (Staudinger ligation) • copper(I)-catalyzed azide–alkyne cycloaddition (CuAAC) described above • strained cyclooctynes and azides in strain-promoted azide–alkyne cycloadditions (SPAAC) which occur fast enough and don't require a copper ion catalyst • 1,2,4,5-tetrazine and an alkene or alkyne dienophile ([4 + 2]/retro [4 + 2]) cycloaddition to form a dihydropyridazine or pyridazine conjugate in a inverse electron-demand Diels–Alder reaction (IEDDA) Figure \(13\) illustrates newer generation click reactions for connecting two molecules Figure \(13\): Newer generation click/bioorthogonal reactions Idiago-L'opez et al., Nanoscale Adv., 2021, 3, 1261. DOI: 10.1039/d0na00873g. Creative Commons Attribution-NonCommercial 3.0 Unported Panels (a–c) show the main click chemistry reaction use in biochemical labeling reactions. Panel (d) shows a comparison of reaction kinetics of CuAAC, SPAAC and IEDDA.Figure Figure \(14\) shows some applications of bioorthogonal reactions Figure \(14\): Applications for labeling different molecule types in cells. Scinto et al. Nat Rev Methods Primers. 2021 ; 1: . doi:10.1038/s43586-021-00028-z. Creative Commons (https://creativecommons.org/licenses/by/4.0/). Panel a shows a model for metabolic engineering for cell labeling and imaging. Panel b shows fluorescence microscopy of CHO cells incubated in the presence (left) or absence (right) of peracetylated N-azidoacetylmannosamine (Ac4ManNAz) and labeled with a fluorophore by the Staudinger ligation. Panel c shows a model for genetic code expansion as a strategy for cell labeling and imaging. Panel d shows fluorescence and direct stochastic optical reconstruction microscopy (dSTORM) super-resolution images of COS-7 cells where microtubule- microtubule-associated protein was encoded with an unnatural trans-cyclooctene (TCO) amino acid and tetrazine ligation was used to attach a microscopy dye. Panel e shows structured illumination microscopy (SIM) images of Escherichia coli, where N-azidoacetyl-muramic acid (NAM) was metabolically incorporated into the bacterial peptidoglycan and fluorophore-labeled by copper(I)-catalyzed azide–alkyne cycloaddition (CuAAC). Panel f shows electron microscopy images of HeLa cells, where azido-choline was metabolically incorporated, and cyclooctyne/azide click chemistry was used to conjugate electron microscopy imaging agents. The arrows indicate sites of endoplasmic reticulum–mitochondria contacts. aaRS, aminoacyl- aminoacyl-tRNA synthetase. Figure \(15\) shows the application of click chemistry to label surface glycoproteins called integrins, which we will explore in greater detail in Chapter 12.10. Figure \(15\): Bioorthogonal labeling of integrin a5 membrane proteins using azide-modified antibodies and alkyne-HPGFNDs: (a) flow cytometry analysis of fluorescence signals from HFW cells preincubated with Azido-5aAb (red) and control cells (blue). (b) HFW cells labeled with Alexa Fluor 488-conjugated wheat germ agglutinin (i–iii) and 100 nm alkyne-HPGFNDs (iv–vi). White arrows indicate the cell migration route and blue arrows show the migration of integrin a5 on cells filopodia. Scale bars: 20 mm. Idiago-L'opez et al., ibid 7.06: Chapter 7 Problems - Answer Key Search Fundamentals of Biochemistry Chapter 7: Carbohydrates and Glycobiology - Answer Key for Problems Note: Tables, graphs, diagrams, and figures can be copied from the research papers, the papers must have Creative Commons licenses. Science Advances, Nature Communications, JBC and PLOS journals usually have to right kind of CC licenses. 1. jdkfjdkf 2. djkfjdkjf 3. kjfkdjfkjdk 7.07: Chapter 7 Problems Search Fundamentals of Biochemistry Chapter 7: Carbohydrates and Glycobiology - Problems Note: Tables, graphs, diagrams, and figures can be copied from the research papers, the papers must have Creative Commons licenses. Science Advances, Nature Communications, JBC and PLOS journals usually have to right kind of CC licenses. 1. jdkfjdkf 2. djkfjdkjf 3. kjfkdjfkjdk

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/07%3A_Carbohydrates_and_Glycobiology/7.05%3A_Working_with_Carbohydrates.txt

• 8.1: Nucleic Acids - Structure and Function Alongside proteins, lipids and complex carbohydrates (polysaccharides), nucleic acids are one of the four major types of macromolecules that are essential for all known forms of life. The nucleic acids consists of two major macromolecules, Deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) that carry the genetic instructions for the development, functioning, growth and reproduction of all known organisms and viruses. • 8.2: Nucleic Acids - RNA Structure and Function Ribonucleic acids are very similar in chemical structure to DNA except they contain ribose instead of deoxyribose. They also have the pyrimidine base uracil instead of thymine. These two small changes (but mostly the first) confer on it a very different set of biological functions than DNA. This should not surprise us and the basis of all chemistry and biochemistry is that chemical structure determines chemical and biochemical functions and activities. • 8.3: Nucleic Acids - Comparison of DNA and RNA Now that we have an understanding of the structures of DNA and the structures and various functions of RNA, we can now more fully explore how their chemical similarities and difference contribute to different functions. • 8.4: Chromosomes and Chromatin • 8.5: References • 8.6: Enzymes for Genetic modifications 08: Nucleotides and Nucleic Acids Search Fundamentals of Biochemistry Introduction to Nucleic Acids Alongside proteins, lipids, and complex carbohydrates (polysaccharides), nucleic acids are one of the four major types of macromolecules that are essential for all known forms of life. The nucleic acids consist of two major macromolecules, Deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) that carry the genetic instructions for the development, functioning, growth, and reproduction of all known organisms and viruses. Both consist of polymers of a sugar-phosphate-sugar backbone with organic heterocyclic bases attached to the sugars. The sugar in DNA is deoxyribose while in RNA it is ribose. DNA contains four bases, cytosine and thymine (pyrimidine bases) and guanine and adenine (purine bases). DNA in vivo consists of two antiparallel strands intertwined to form the iconic DNA double-stranded helix. RNA is single-stranded but may adopt many secondary and tertiary conformations not unlike that of a protein. Figure \(1\) shows a low-resolution comparison of the structure of DNA and RNA. The biological function of DNA is quite simple, to carry and protect the genetic code. Its structure serves that purpose well. In the next section, we will study the functions of RNA, which are much more numerous and complicated. The structure of RNA has evolved to serve those added functions. The core structure of a nucleic acid monomer is the nucleoside, which consists of a sugar residue + a nitrogenous base that is attached to the sugar residue at the 1′ position as shown in Figure \(2\). The sugar utilized for RNA monomers is ribose, whereas DNA monomers utilize deoxyribose that has lost the hydroxyl functional group at the 2′ position of ribose. For the DNA molecule, four nitrogenous bases are incorporated into the standard DNA structure. These include the Purines: Adenine (A) and Guanine (G), and the Pyrimidines: Cytosine (C) and Thymine (T). RNA uses the same nitrogenous bases as DNA, except for Thymine. Thymine is replaced with Uracil (U) in the RNA structure. When one or more phosphate groups are attached to a nucleoside at the 5′ position of the sugar residue, it is called a nucleotide. Nucleotides come in three flavors depending on how many phosphates are included: the incorporation of one phosphate forms a nucleoside monophosphate, the incorporation of two phosphates forms a nucleoside diphosphate, and the incorporation of three phosphates forms a nucleoside triphosphate as shown in Figure \(2\). DNA and RNA Hydrogen-bonded structures Figure \(3\) below shows a "flattened" structure of double-stranded B-DNA that best shows the backbone and hydrogen-bonded base pairs between two antiparallel strands of the DNA. Unlike the protein α-helix, where the R-groups of the amino acids are positioned to the outside of the helix, in the DNA double-stranded helix, the nitrogenous bases are positioned inward and face each other. The backbone of the DNA is made up of repeating sugar-phosphate-sugar-phosphate residues. Bases fit in the double-helical model if pyrimidine on one strand is always paired with purine on the other. From Chargaff’s rules, the two strands will pair A with T and G with C. This pairs a keto base with an amino base, and a purine with a pyrimidine. Two H‑bonds can form between A and T, and three can form between G and C. This third H-bond in the G:C base pair is between the additional exocyclic amino group on G and the C2 keto group on C. The pyrimidine C2 keto group is not involved in hydrogen bonding in the A:T base pair. Furthermore, the orientation of the sugar molecule within the strand determines the directionality of the strands. The phosphate group that makes up part of the nucleotide monomer is always attached to the 5′ position of the deoxyribose sugar residue. The free end that can accept a new incoming nucleotide is the 3′ hydroxyl position of the deoxyribose sugar. Thus, DNA is directional and is always synthesized in the 5′ to 3′ direction. Interestingly, the two strands of the DNA double helix lie in opposite directions or have a head-to-tail orientation. By analogy to proteins, DNA and RNA can be loosely thought to have primary and secondary structures. For a single strand, the primary sequence is just the base sequence read from the 5' to 3' end of the strand, with the bases thought of as "side chains" as illustrated in Figure \(4\) for an RNA strand which contains U instead of T. Since it is found partnered with another molecule (strand) of DNA, the double-stranded DNA, which consists of two molecules held together by hydrogen bonds, might be considered to have secondary structure (analogous to alpha and beta structure in proteins). Of course, the hydrogen bonds are not between backbone atoms but between side chain bases in double-stranded DNA. Figure \(5\) shows an interactive iCn3D model of the iconic structure of a short oligomer of double-stranded DNA (1BNA). The backbones of the antiparallel strands are magenta (chain A) and cyan (chain B). The 5' sugar-phosphate end of each chain is shown in spacefill and colored magenta (chain A) and cyan (chain B). The hydrogen-bonded interstrand base pairs are shown alternatively in spacefill and sticks to illustrate how the bases stack on top of each other. Figure \(6\) shows types of "secondary (flat representations) and their 3D or tertiary representations found in nucleic acids. Figure \(7\) shows an interactive iCn3D model of the tertiary structure of the T4 hairpin loop on a Z-DNA stem (1D16). The hairpin shown is from a synthetic DNA oligomer C-G-C-G-C-G-T-T-T-T-C-G-C-G-C-G which adopts an alternative Z-DNA conformation (which we will explore below) with a loop at one end. The thymine bases 7, 8, and 9 are generally perpendicular to one another and stack together, along with the ribose of T7. Figure \(8\) shows an interactive iCn3D model of pseudoknot in RNA (437D). The pseudoknot has two stems that form a "helix" and two loops. The knot consists of a hairpin in the nucleic acid structure with the loop between the helices paired to another part of the nucleic acid. Pseudoknots can be found in mRNA and ribosomal RNA and affect the translation of the RNA (decoding to instruct the synthesis of a protein sequence). RNA viruses have pseudoknots which likewise affect protein synthesis as well as RNA replication. Pseudoknots also occur in DNA. Synthesis and structure of DNA The nucleotide that is required as the monomer for the synthesis of both DNA and RNA is nucleoside triphosphate. During the incorporation of the nucleotide into the polymeric structure, two phosphate groups, (Pi-Pi , called pyrophosphate) from each triphosphate are cleaved from the incoming nucleotide and further hydrolyzed during the reaction, leaving a nucleoside monophosphate that is incorporated into the growing RNA or DNA chain as shown in Figure \(9\) below. Incorporation of the incoming nucleoside triphosphate is mediated by the nucleophilic attack of the 3′-OH of the growing DNA polymer. Thus, DNA synthesis is directional, only occurring at the 3′-end of the molecule. The further hydrolysis of the pyrophosphate (Pi-Pi) releases a large amount of energy ensuring that the overall reaction has a negative ΔG. Hydrolysis of Pi-Pi ↔ 2Pi has a ΔG = -7 kcal/mol (-29 kJ/mol) and is essential to provide the overall negative ΔG (-6.5 kcal/mol, -27 kJ/mol) of the DNA synthesis reaction. Hydrolysis of the pyrophosphate also ensures that the reverse reaction, pyrophosphorolysis, will not take place removing the newly incorporated nucleotide from the growing DNA chain. This reaction is mediated in DNA by a family of enzymes known as DNA polymerases. Similarly, RNA polymerases are required for RNA synthesis. A more detailed description of polymerase reaction mechanisms will be covered in Chapters X and Y, covering DNA Replication and Repair, and DNA Transcription. DNA was first isolated by Friedrich Miescher in 1869. The double-helix model of DNA structure was first published in the journal Nature by James Watson and Francis Crick in 1953 based upon the crucial X-ray diffraction image of DNA from Rosalind Franklin in 1952, followed by her more clarified DNA image with Raymond Gosling, Maurice Wilkins, Alexander Stokes, and Herbert Wilson, and base-pairing chemical and biochemical information by Erwin Chargaff. The prior model was triple-stranded DNA. The realization that the structure of DNA is that of a double-helix elucidated the mechanism of base pairing by which genetic information is stored and copied in living organisms and is widely considered one of the most important scientific discoveries of the 20th century. Crick, Wilkins, and Watson each received one-third of the 1962 Nobel Prize in Physiology or Medicine for their contributions to the discovery. (Franklin, whose breakthrough X-ray diffraction data was used to formulate the DNA structure, died in 1958, and thus was ineligible to be nominated for a Nobel Prize.) Watson and Crick proposed two strands of DNA – each in a right-hand helix – wound around the same axis. The two strands are held together by H-bonding between the complementary base pairs (A pairs with T and G pairs with C) as shown in Figure \(10\) below. Note that when looking from the top view, down on a DNA base pair, that the position where the base pairs attach to the DNA backbone is not equidistant, but that attachment favors one side over the other. This creates unequal gaps or spaces in the DNA known as the major groove for the larger gap, and the minor groove for the smaller gap (Figure 4.5). Based on the DNA sequence within the region, the hydrogen-bond potential created by the nitrogen and oxygen atoms present in the nitrogenous base pairs causes unique recognition features within the major and minor grooves, allowing for specific protein recognition sites to be created. Figure \(1\) shows a schematic representation of available hydrogen bond donors and acceptors in the major and minor grove for TA and CG base pairs. Figure \(12\) shows an interactive iCn3D model of DNA showing the major and minor grooves. The two sugar-phosphate backbones are shown in green and yellow. Some of the red (oxygen) and blue (nitrogen) atoms in the major grove (and to a much less extent in the minor groove) are not involved in inter-strand G-C and A-T base pairing and so would be available to hydrogen bond donors with specific binding proteins that would display complementary shape and hydrogen bonds acceptors and donors. Figure \(13\) shows an interactive iCn3D model of the N-terminal fragment of the yeast transcriptional activator GAL4 bound to DNA (1D66). The N-terminal fragment binds to conserved CCG triplets found at both ends of the DNA in the major grove. The protein shown is a dimer held together by a short coiled-coil interaction domain so the site has 2-fold symmetry. A small Zn2+-containing secondary structure motif in each member of the dimer interacts with the major grove. An extended chain connects the DNA binding and interaction domains of each protein. In addition to the major and minor grooves providing variation within the double helix structure, the axis alignment of the helix along with other influencing factors such as the degree of solvation can give rise to three forms of the double helix, the A-form (A-DNA), the B-form (B-DNA), and the Z-form (Z-DNA) as shown in Figure \(14\). Both the A- and B-forms of the double helix are right-handed spirals, with the B-form being the predominant form found in vivo. The A-form helix arises when conditions of dehydration below 75% of normal occur and have mainly been observed in vitro during X-ray crystallography experiments when the DNA helix has become desiccated. However, the A-form of the double helix can occur in vivo when RNA adopts a double-stranded conformation, or when RNA-DNA complexes form. The 2′-OH group of the ribose sugar backbone in the RNA molecule prevents the RNA-DNA hybrid from adopting the B-conformation due to steric hindrance. The third type of double helix formed is a left-handed helical structure known as the Z-form or Z-DNA. Within this structural motif, the phosphates within the backbone appear to zigzag, providing the name Z-DNA. In vitro, the Z-form of DNA is adopted in short sequences that alternate pyrimidine and purines when high salinity is present. However, the Z-form has been identified in vivo, within short regions of the DNA, showing that DNA is quite flexible and can adopt a variety of conformations. A comparison of features between A-, B-, and Z-form DNA is shown in Table 4.1. Table 4.1 Comparisons of B-DNA, A-DNAand Z‑DNA B-DNA A-DNA Z-DNA helix sense Right Handed Right Handed Left Handed base pairs per turn 10 11 12 vertical rise per bp 3.4 Å 2.56 Å 19 Å rotation per bp +36° +33° -30° helical diameter 19 Å 19 Å 19 Å The double-stranded helix of DNA is not always stable. This is because the stair step links between the strands are noncovalent, reversible interactions. Depending on the DNA sequence, denaturation (melting) can be local or widespread and enables various crucial cellular processes to take place, including DNA replication, transcription, and repair. Both sequence specificity and interaction (whether covalent or not) with a small compound or a protein can induce tilt, roll, and twist effects that rotate the base pairs in the x, y, or z axis, respectively as seen in Figure \(15\), and can therefore change the helix’s overall organization. Furthermore, slide or flip effects can also modify the geometrical orientation of the helix. Hence the flip effects, and (to a lesser extent) the other above-defined movements modulate the double-strand stability within the helix or at its ends. Indeed, under physiological conditions, local DNA ‘breathing’ has been evidenced at both ends of the DNA helix and B- to Z-DNA structural transitions have been observed in internal DNA regions. These types of locally open DNA structures are good substrates for specific proteins which can also induce the opening of a ‘closed’ helix. The processes of DNA replication and repair will be discussed in more detail in Chapter 28. Figure \(16\) shows interactive iCn3D models of A-DNA (top), B-DNA (center), and Z-DNA (bottom). (Copyright; author via source). Click the image for a popup or use the external links in column 1. A-DNA (440D) B-DNA (1BNA) Z-DNA (4OCB) Figure \(16\): A, B and Z-DNA. Click the image for a popup or use the links in column 1 We studied the structure of proteins in-depth, discussing resonance in the peptide backbone, allowed backbone angles φ, ψ, and ω, side chain rotamers, Ramachandran plots, and different structural motifs. We also explored them dynamically using molecular dynamic simulations. We also discussed the thermodynamics of protein stability, and how stability could be altered by changing environmental factors such as solution composition and temperature. In contrast, our understanding of the structural parameters and the dynamics of nucleic acids is less advanced. This may seem paradoxical, especially given the apparent simplicity of the iconic structure of DNA presented in textbooks. Yet look at the types of secondary structures of nucleic acid presented and then the complicated tertiary and quaternary structures of RNA. The backbone of nucleic acid has a 5-membered sugar ring, which adds rigidity to the backbone, linked to another sugar ring by CH2O(PO3)O- connectors, which add some additional conformational freedom. We'll explore the effects of the pentose ring geometry in RNA and DNA in chapter section 8.3. To illustrate a yet unexplored complexity of nucleic acid structure, consider just the orientation of rings in double-stranded DNA and in regions of RNA where double-stranded structures form. The variants in the orientation of the hydrogen-bonded base pairs and the corresponding parameters that define them are shown in Figure \(17\). Figure \(17\): Base pair orientation and corresponding parameters in nucleic acids. http://x3dna.org/highlights/schemati...air-parameters (with permission). 2008 3DNA Nature Protocols paper (NP08), the initial 3DNA Nucleic Acids Research paper . Consider just two of these, the propellor and twist angles. If you examine the iCn3D models of nucleic acids presented above, you will see that the base pairs are not perfectly flat but are twisted. Larger propeller angles are associated with increased rigidity. The propellor angles for A, B, and Z DNA are +18o, + 16 +/-7 o, and about 0o, respectively. The twist angles A, B, and Z DNA are +33o, +36 o, and -30o, respectively. The lower the twist angle, the higher the number of base pairs per turn. This of course affects the pitch of the helix (the length of one complete turn). All of these terms should be minimized to computationally determine the lowest energy state for a given double-stranded nucleic acid. Alternative Base Pairing in DNA and RNA A first glance at a DNA or RNA structure reveals a myriad of possible hydrogen bond donors and acceptors in the bases of the nucleic acid. Hence it should come as no surprise that a variety of alternative or noncanonical (not in the canon or dogma) intermolecular hydrogen bonds can form between and among bases, leading to alternatives to the classical Watson-Crick base pairing. There are 28 possible base pairs with two hydrogen bonds between them. As structure determines function and activity, these alternative structures also influence DNA/RNA function. We will consider four different types of noncanonical base pairing: reverse Watson Crick, wobble, Hoogsteen, and reverse Hoogsteen base pairs. In DNA, these types of noncanonical base pairs can occur when bases become mismatched in double-stranded regions. In RNA, which we will explore more fully in Chapter 8.2, double-stranded molecules form by separate RNA molecules aren't common. Instead, the molecule folds on itself in 3D space to form a complex tertiary structure containing regions of helical secondary structure. RNAs also form quaternary structures when bound to other nucleic acids and proteins. Larger RNAs have loops with complex secondary and tertiary structures which often require noncanonical base pairing, stabilizesbilize the alternative structures. The noncanonical structures are also important for RNA-protein interactions in the RNA region which binds proteins. If one considers RNA and protein binding as a coupled equilibrium, it should be clear that protein binding to RNA might also induce conformation changes, specifically noncanonical base pairs, in the RNA. For example, the HIV Rev peptide binds to a target site in the envelop gene of HIV (which has an RNA genome) and leads to the formation of an RNA loop with hydrogen bonding between two purines. Figure \(18\) shows an interactive iCn3D model of the REV Response element RNA complexed with REV peptide (1ETF). The peptide is shown in cyan and its arginine side chains are shown as cyan lines. There are an extraordinary number of arginines that form ion-ion interactions with the negatively charged phosphates in the major grove of this double-stranded A-RNA. The noncanonical base pairs are shown in CPK-colored sticks. A wobble base, U43-G77, see below, is shown as well as three homopurine base pairs, G47-A73, G55-A58, and G48-G71. The solitary A68 base is shown projecting away from the RNA. Figure \(19\) shows the Watson Crick and the first set of alternative non-canonical base pairs. Figure \(19\): Some noncanonical base nucleic acid base pairs Let's look at them in more detail. Reverse Watson Crick: The reverse Watson-Crick AT (AU) and GC pairs can sometimes be found at the end of DNA strands and also in RNA. In forming the reverse base pairs, the pyrimidine can rotate 180o along the axis shown and then rotate in the plane to align the hydrogen bond donors and acceptors as shown in the top part of the figure. The glycosidic bond between the N in the base and the sugar (the circled R group) is now in an "antiparallel" arrangement in the reverse base pair. Wobble Base Pairs The bases in nucleic acids can undergo tautomerization to produce forms that can base pair noncanonically. They are termed wobble base pairs and include G-T(U) base pairs from keto–enol tautomerism and A-C base pairs from amino–imino tautomerism, as illustrated in Figure 18 above. Figure \(20\) shows an interactive iCn3D model of the GT Wobble Base-Pairing in Z-DNA form of d(CGCGTG) (1VTT). Two such GT pairs are found in the structure. The water around the wobble base pairs can form hydrogen bonds and stabilize the pair if a hydrogen bond is missing. Figure \(21\) shows an interactive iCn3D model of dsRNA with G-U wobble base pairs (6L0Y). The structure contains many GU wobble base pairs as well as two CU base pairs between two pyrimidine bases. Inosine, a variant of the base adenine, can be found in RNA. It is formed by the deamination of adenosine by the enzyme adenosine deaminase. A nucleotide having inosine is named hypoxanthine. Hypoxanthine can form the wobble base pairs I-U, I-A, and I-C when incorporated into RNA, as illustrated in Figure \(22\). Figure \(22\): Wobble bases pairs using hypoxanthine with the base inosine Wobble base pair interactions are especially important in translation when a protein sequence is made from a messenger RNA template (which will be discussed in Unit III). For that decoding process to occur, two RNA molecules, messenger RNA (mRNA) and a transfer RNA (t-RNA) covalently attached to a specific amino acid like glutamic acid, must bind to each other through a 3 base pair interaction. The 3 bases on the mRNA are called the codon, and the 3 complementary bases on the tRNA are called the anticodon. The triplet base pair are antiparallel to each other. The interaction between mRNA and tRNA are illustrated in Figure \(23\). Figure \(23\): The wobble uridine (U34) of tRNA molecules that recognize both AAand AG-ending codons for Lys, Gln, and Glu, is modified by the addition of both a thiol (s2) and a methoxy-carbonyl-methyl (mcm5). This double modification enhances the translational efficiency of AA-ending codons. Goffena, J et al. Nat Commun 9, 889 (2018). https://doi.org/10.1038/s41467-018-03221-z. Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. The third 3' base on the mRNA is less restricted and can form noncanonical, specifically, wobble base pairs, with the 5' base in the anti-codon triplet of tRNA. The term wobble arises from the subtle conformational changes used to optimize the pairing of the triplets. Wobble bases occur much more in tRNA than other nucleic acids. Hoogsteen base pairing Flexibility in DNA allows rotation around the C1'-N glycosidic bond connecting the deoxyribose and base in DNA, allowing different orientations of AT and GC base pairs with each other. The normal "anti" orientation allows "Watson-Crick" (WC) base pairing between AT and GC base pairs while the altered rotation allows "Hoogsteen" base pairs. The different orientations for an AT base pair are shown in Figure \(24\). Figure \(24\): Xu, Y., McSally, J., Andricioaei, I. et al. Modulation of Figure \(\PageIndex{xx}\)Hoogsteen dynamics on DNA recognition. Nat Commun 9, 1473 (2018). https://doi.org/10.1038/s41467-018-03516-1Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/. Hoogsteen base pairing is usually seen when DNA is distorted through interactions with bound proteins and drugs that intercalate between base pairs. Figure \(25\) shows an interactive iCn3D model of a Hoogsteen base pair embedded in undistorted B-DNA - MATAlpha2 homeodomain bound to DNA (1K61). The same DNA without bound protein has no Hoogsteen base pairs. To form Hoogsteen base pairs, a rotation around the glycosidic-base bond must occur. Hoogsteen base pairs between G and C can also occur on rotation but in addition, the N3 of cytosine is protonated, as shown in Figure 14 above. Evidence suggests that Hoogsteen base pairing may be important in DNA replication, binding, damage, or repair. They can induce kinking of the DNA near the major grove. There are also examples of reverse Hoogsteen base pairing, as shown in Figure \(26\). Figure \(26\): The reverse Hoogsteen AT base pair Additional Alternative Structures: Quadruplexes,  Triple Helices, and 4-Way Junctions Quadruplexes These can be formed in DNA and RNA from G-rich sequences involving tetrads of guanine bases that are hydrogen bonded. They are a bit hard to describe in words so let's first examine one particular structure. In human cells, telomeres (the ends of chromosomes) contain 300-8000 repeats of a simple TTAGGG sequence. The repetitive TTAGGG sequences in telomeric DNA can form quadruplexes. Figure \(27\) shows an interactive iCn3D model of parallel quadruplexes from human telomeric DNA (1KF1). The structure contains a single DNA strand (5'-AGGGTTAGGGTTAGGGTTAGGG-3') which contains four TTAGGG repeats. Figure \(27\): parallel quadruplexes from human telomeric DNA (1KF1). (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...y5joFHDgWJQsQ6 Rotate the model to see 3 parallel layers of quadruplexes. In each layer, 4 noncontiguous guanine bases interact with a K+ ion. Hover over the guanine bases in one layer and you will find that one layer consists of guanines 4, 10, 16, and 22, which derive from the last G in each of the repeats in the sequence of the oligomer used (5'-AGGGTTAGGGTTAGGGTTAGGG-3'). These quadruplexes certainly serve in recognition and as binding site for telomerase proteins. The guanine-rich telomere sequences which can form quadruplex may also function to stabilize chromosome ends A Quadruplex can be formed in 1 strand of nucleic acid (as in the above model) or from 2 or 4 separate strands. They also must have at least 2 stacked triads. As in the example above, single-stranded sections can form intramolecular G-quadruplex from a GmXnGmXoGmXpGm sequence, where m is the number of Gs in each short segment (3 in the structure above). If a segment is longer than others, a G might be in a loop. Triple Helices These structures can occur in DNA (and also RNA) that contain homopurine and homopyrimidine sequences that have a mirror repeat symmetry. Hence they can occur naturally. A mirror repeat contains a center of symmetry on a single strand. Here is an example: 5'-GCATGGTACG-3'. They can also occur when a third single-strand DNA (called a triplex-forming oligonucleotide or TFO) binds to a double-stranded DNA. The TFOs bind through Hoogsteen base pairing in the major grove of the ds-DNA. They can bind tightly and specifically in a parallel or antiparallel fashion. Specific and locally higher concentrations of divalent cations or positively charged polyamines like spermine act to stabilize the extra negative charge density from the binding of a third polyanionic DNA strand. An example of a triple helix system that has been studied in vitro is shown in Figure \(28\). Figure \(28\): Intermolecular triplex formation and their oligonucleotide sequences (where “•” and “-” indicates Hoogsteen and Watson–Crick base pairings, respectively). Inset: chemical structure of a parallel T•AT triplet. Guerrini, L. and Alvarez-Puebla, R.A. Nanomaterials 2021, 11, 326. https://doi.org/10.3390/nano11020326. Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/) The double-stranded canonical helix (D1D2) consists of 31 base pairs in which strand D1 is pyrimidine-rich and D2 is purine-rich strand (D2). A 22-nucleotide Triple helix forming oligonucleotide (TFO) that is rich in pyrimidines binds the 19 AT and 2 C-GC base triplets. The TFO binds along the major grove of the D2 strand which is purine rich. If the binding of the third strand in the major groove occurs at the site where RNA polymerase binds to a gene, then the third strand can inhibit gene transcription. Binding can also lead to a mutation or recombination at the site. Figure \(29\) shows the base pairing of purine and pyrimidines of the third strand to the canonical AT dn GC base pairs of the original double-stranded DNA. Figure \(29\): Base pairing in triple helix motifs. (after Jain et al. Biochimie. 2008. doi: 10.1016/j.biochi.2008.02.011 Figure \(30\) shows an interactive iCn3D model of a solution conformation of a parallel DNA triple helix (1BWG). Triple helix formation can also occur within a single strand of DNA. The resulting structure is called H-DNA. An example is shown below. Note that the central blue, black, and red sequences are all mirror image repeats (around a central nucleotide). During processes that unravel DNA (replication, transcription, repair), the self-association of individual mirror repeats can form a locally stable triple helix, as shown in Figure \(31\). Figure \(31\): Schematic illustrations of (A) the H-DNA or intramolecular triplex structure used in this study; del Mundo et al. (2019) Nucleic acids research. 47. e73. 10.1093/nar/gkz237. Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) The * between in the G*G and A*A denote Hoogsteen hydrogen bonding (purine motifs) in this intramolecular triple helix. Reverse Hoogsteen hydrogen bonds can also occur. Triple helices can form when single-stranded DNA formed during replication, transcription or DNA repair with half of the required mirror symmetry folds back into the adjacent major grove and base pairs using Hoogsteen/reverse Hoogsteen bonding, which can be stabilized by Mg2+. Recent Updates: Four-Way Junctions Nonhelical sections of DNA can, as we will see in the next section on RNA, can bind small target molecules through noncovalent interactions.  (RNA examples that we will see in the next chapter section include aptamers, ribozymes and riboswitches.)  One example is "lettuce" single-stranded DNA that can bind small fluorophores modeled after the intrinsic fluorophore of the green fluorescent protein.  When bound to the lettuce DNA, the fluorescence of the fluorophore is dramatically enhanced.  Figure \(32\) below shows the structure of extrinsic DNA fluorophores based on GFP that bind to the single-stranded "lettuce" DNA. Figure \(32\): Structure of extrinsic DNA fluorophores based on GFP that bind to DNA.  The font color of the names indicates the color of the emitted fluorescence. Figure \(33\) shows an interactive iCn3D model of a solution conformation of a ssDNA:DFAME fluorophore complex (8FI0). The blue dotted lines shown π-π stacking interactions and the green dotted line a hydrogen bond. The DFAME ligand as shown in sticks. Figure \(34\) shows a closeup of DFAME (colored spacefill) bound to the lettuce DNA. Figure \(34\): Pi stacking interactions (blue dotted lines) between the extrinsic fluorophore DFAME (spacefill) and ssDNA. The DNA fold is characterized as a four-way junction (also seen in RNA but they are more L or H-shaped).  On either end are B-DNA duplexes and the ssDNA between them forms stem-loops with odd base pairing in the stems.  The overall structure is like a cloverleaf.  Two coaxial stacks of nucleotides form what is called a G-quadruplex where the fluorophore binds.  Pi base stacking between diagonally packed bases, along with the binding of Mg2+ and K+, stabilize the structure. Stability of nucleic acids After looking at the myriad of structures showing the nearly parallel hydrogen-bonded base pairs, and from ideas from most textbooks and classes you have taken, you probably think that double-stranded DNA is held together and stabilized by hydrogen bonds between the bases. It is well known that the greater the percentage of GC compared to AT, the greater the stability of the dsDNA, which translates into a higher "melting temperature (TM)", the temperature at which the dsDNA is converted to ssDNA. There is a linear relationship between GC content and TM. The figures above show that GC base pairs have 3 inter-base hydrogen bonds compared to 2 in AT base pairs. These observations support the simple notion that inter-base hydrogen bonds are the source of dsDNA stability. You would be in general correct in this belief, but you'd be missing the more important contributor to ds-DNA stability, base (π) stacking, and the noncovalent interactions associated with the stacking. The main contributors to stability are hydrophobic interactions in the anhydrous hydrogen-bonded base pairs in the helix. Given that the hydrogen bond donors and acceptors that contribute to base pairing exist in the absence of competing water, the donors and acceptors are free to fully engage in bonding. The hydrogen bond interaction energy is hence more favorable in the stack. The stacking energy is similar for an AT-AT stack and a GC-GC stack (about -9.8 kcal/mol, 41 kJ/mol). Hence AT and GC base pairs contribute equally to stability. The excess stability of dsDNA enriched in GC base pairs can still be explained by the extra stabilization for an additional hydrogen bond per GC base pair Proteins are stabilized by a myriad of interactions, but the folded state is marginally more stable than the ensemble of the unfolded state. Marginal stability is important as protein conformation often must be perturbed on binding and ensuing function. The same must be true of double-stranded DNA, which must "unfold' or separate on replication, transcription, and repair. It is well known that dsDNA structure is sensitive to hydration (see the section on A, B, and Z DNA). Small molecules like urea, as we saw with proteins, can also denature DNA into single strands. DNA must be stable enough to be the carrier of genetic information but dynamic enough to allow events that required partial unfolding. Other water-soluble molecules like ethylene glycol ethers (polyethylene glycol-400) and diglyme (dimethyl ether of diethylene glycol), which are more hydrophobic than water, appear to reduce base stacking interactions while maintaining them, and at the same time allow a longitudinal extension or breathing of the helix. This dynamic extension may be required for transitions of B-DNA to Z-DNA, for example. The extensions also allow transient "hole" to appear between base pairs which might assist in the binding of intercalating agents like some transition metal complexes. The extension caused by these ethers and natural extensions would decrease base stacking but appear at the same time to strengthen the hydrogen bonding between bases. Longitudinal helical extensions might be important when homologous gene recombine. In that process the homologous DNA strand but exchange with a paired homolog. This processing is associated with strand extension and disruption of base pair at every third base. Recombination also must allow chain extension as it maintains base-pairing fidelity. DNA structures get obviously more complicated as it packs into the nucleus of a cell and form chromosomes, as shown in Figure \(35\). We will study the packing of DNA in other sections.

textbooks/bio/Biochemistry/Fundamentals_of_Biochemistry_(Jakubowski_and_Flatt)/01%3A_Unit_I-_Structure_and_Catalysis/08%3A_Nucleotides_and_Nucleic_Acids/8.01%3A_Nucleic_Acids_-_Structure_and_Function.txt