Datasets:
Cysteine Structure Database
The [Cysteine Structure Database] is a dataset compiled of strucutral data for 6515 cysteine sites in hundreds of proteins. This dataset was published in Cell and is also available at the official DrugMap Github repo.
For each cysteine site, this database includes numerical values for Solvent Accessible Surface Area (SASA), Cysteine Depth, etc. Additionally, each cysteine site has a probe engagement score derived from isotopic tandem orthogonal proteolysis-activity-based protein profiling (isoTOP-ABPP) that is represented as True or False in this dataset for three probes: KB02, KB03, KB05.
Probes
KB02
SMILES: COC1=CC=C2C(CCCN2C(CCl)=O)=C1 Depiction:
KB03
SMILES: FC(F)(F)C1=CC(C(F)(F)F)=CC(NC(CCl)=O)=C1 Depiction:
KB05
SMILES: O=C(C=C)N(C1=CC=C(Br)C=C1)C2=CC=CC=C2 Depiction:
Quickstart Usage
Load a Dataset in Python
Each subset can be loaded into python using the Huggingface datasets library.
Install the datasets
library
$ pip install datasets
then, in Python, load the datasets
library
>>> import datasets
and load one of the Cysteine Structure Database
datasets, e.g.,
>>> KB03_data = datasets.load_dataset('ymanasa2000/DrugMap_Ligandability', name='KB03')
Downloadingβreadme: 100%|ββββββββββββββββββββββββ| 4.04k/4.04kβ[00:00<00:00,β281kB/s]
Downloadingβdata:β100%|ββββββββββββββββββββββββ| 30.5k/30.5kβ[00:00<00:00,β470kB/s]
Downloadingβdata:β100%|ββββββββββββββββββββββββ| 218k/218kβ[00:00<00:00,β2.55MB/s]
Downloadingβdata: 100%|ββββββββββββββββββββββββ| 54.8k/54.8kβ[00:00<00:00,β915kB/s]
Generatingβtestβsplit: 100%|ββββββββββββββββββββββββ| 143/0β[00:00<00:00,β4939.35βexamples/s]
Generatingβtrainβsplit: 100%|ββββββββββββββββββββββββ| 1029/0β[00:00<00:00,β40692.99βexamples/s]
Generatingβvalidationβsplit:β100%|ββββββββββββββββββββββββ| 258/0β[00:00<00:00,β22803.78βexamples/s]
Then, inspect the loaded dataset
>>> KB03_data
DatasetDict({
test: Dataset({
features: ['Unnamed: 0', 'site', 'depth', 'absolute_sasa', 'hse_up', 'hse_down', 'coord_number', 'rsa', 'h_nho1', 'h_ohn1', 'h_nho2', 'h_ohn2', 'tco', 'kappa', 'alpha', 'phi', 'psi', 'pocket', 'interface', 'basic', 'acidic', 'polar', 'cysteine', 'structural', 'aliphatic', 'aromatic', 'KB03', 'struct_motif_B', 'struct_motif_E', 'struct_motif_G', 'struct_motif_H', 'struct_motif_I', 'struct_motif_P', 'struct_motif_S', 'struct_motif_T'],
num_rows: 143
})
train: Dataset({
features: ['Unnamed: 0', 'site', 'depth', 'absolute_sasa', 'hse_up', 'hse_down', 'coord_number', 'rsa', 'h_nho1', 'h_ohn1', 'h_nho2', 'h_ohn2', 'tco', 'kappa', 'alpha', 'phi', 'psi', 'pocket', 'interface', 'basic', 'acidic', 'polar', 'cysteine', 'structural', 'aliphatic', 'aromatic', 'KB03', 'struct_motif_B', 'struct_motif_E', 'struct_motif_G', 'struct_motif_H', 'struct_motif_I', 'struct_motif_P', 'struct_motif_S', 'struct_motif_T'],
num_rows: 1029
})
validation: Dataset({
features: ['Unnamed: 0', 'site', 'depth', 'absolute_sasa', 'hse_up', 'hse_down', 'coord_number', 'rsa', 'h_nho1', 'h_ohn1', 'h_nho2', 'h_ohn2', 'tco', 'kappa', 'alpha', 'phi', 'psi', 'pocket', 'interface', 'basic', 'acidic', 'polar', 'cysteine', 'structural', 'aliphatic', 'aromatic', 'KB03', 'struct_motif_B', 'struct_motif_E', 'struct_motif_G', 'struct_motif_H', 'struct_motif_I', 'struct_motif_P', 'struct_motif_S', 'struct_motif_T'],
num_rows: 258
})
})
Use a Dataset to Train a Model
One way to use the dataset is by training a Baseline Random Forest Classifier to predict intereaction of a cysteine with one of the three probes (KB02, KB03, KB05). In this example, we will train and test on KB03 data.
First, install scikit-learn
>>> pip install scikit-learn
then load, split, featurize, fit and evaluate the Random Forest model
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
KB03_data = datasets.load_dataset('ymanasa2000/DrugMap_Ligandability', name='KB03')
# split into train and test
KB03_train = KB03_data['train']
KB03_test = KB03_data['test']
train_set = pd.DataFrame(KB03_train)
test_set = pd.DataFrame(KB03_test)
# featurize
X_train = train_set.drop(columns=['site', 'KB03'])
y_train = train_set['KB03']
X_test = test_set.drop(columns=['site', 'KB03'])
y_test = test_set['KB03']
# fit
model_1 = RandomForestClassifier()
model_1.fit(X_train, y_train)
# evaluate
print(model_1.score(X_test, y_test)) # output: 0.5944
About the Cysteine Structure Database
Features of the DB
This DB features a csv with structural data for ~6,500 bindable cysteines in hundreds of protein active sites. Each cysteine has structural data such as,
numerical values for Solvent Accessible Surface Area (SASA), Cysteine Depth, etc.
Additionally, this DB contains probe read-outs from an experiment described in Takahashi_et_al_2024. They integrated the isotopic tandem orthogonal
proteolysis-activity-based protein profiling (isoTOP-ABPP) platform with tandem mass tag (TMT)-based mass spectrometry quantification (iso-TMT)
to measure cysteine reactivity. In this approach, cell lysates are first treated with cysteine-reactive βscoutβ compounds or vehicle control,
allowing reactive cysteines a chance to form covalent adducts, and then this is followed by a chase with a pan-cysteine-reactive probe
(iodoacetamide-desthiobiotin DBIA
), which reacts with all remaining free cysteine thiolate groups.
Crucially, cysteines that reacted with the scout compound will escape being tagged by DBIA.
Ligandable cysteines are defined as those that are engaged (Ξ΅-value) >60% by cysteine-reactive compounds.
Data splits
The authors of this dataset suggested using a Stratified Split
via the train_test_split() method which was used to produce the datasets in this Hugging Face DB.
Citation
Please use the following citation in any publication using our Cysteine Structure Dataset:
@article{
Takahashi_et_al_2024,
author={Takahashi, Chong, Harrison, Bar-Peled, et al},
doi={10.1016/j.cell.2024.03.027},
journal={Cell},
number={10},
month={May}
title={DrugMap: A quantitative pan-cancer analysis of Cysteine ligandability},
volume={187},
year={2024}
url = {https://www.biorxiv.org/content/10.1101/2023.10.20.563287v1}
}
- Downloads last month
- 342