moPPIt: De Novo Generation of Motif-Specific Peptide Binders with Protein Language Models

Motif-specific targeting of protein-protein interactions (PPIs) is crucial for developing highly selective therapeutics, yet remains a significant challenge in drug discovery. The ability to precisely target specific motifs or epitopes within these proteins is essential for modulating their function while minimizing off-target effects, but current methods struggle to achieve this specificity without structural information. In this work, we introduce a motif-specific PPI targeting algorithm, moPPIt, for de novo generation of motif-specific peptide binders using only protein sequence information. At the core of moPPIt is BindEvaluator, a transformer-based model that interpolates protein language model embeddings via a series of multi-headed self-attention blocks, with a key focus on local interaction changes. Trained on over 510,000 PPI-hotspot triplets from the PPIRef dataset, BindEvaluator accurately predicts binding hotspots between two proteins with a test AUC > 0.94, improving to AUC > 0.96 when fine-tuned on peptide-protein pairs. By combining BindEvaluator with our PepMLM peptide generator and genetic algorithm-based optimization, moPPIt generates peptides that bind specifically to user-defined motifs on target proteins.

Colab Notebook for Binding Site Prediction and Motif-Specific Binder Generation: Link

Colab Notebook for PeptiDerive: Link

0. Conda Environment Preparation

conda env create -f environment.yml

conda activate moppit

1. Dataset Preparation

Pre-training dataset: dataset/pretrain_dataset.csv

Fine-tuning dataset: dataset/finetune_dataset.csv

To accelerate training and fine-tuning, datasets need to be processed into HuggingFace Dataset in advance.

Before pre-training, run:

python dataset/pretrain_preprocessing.py -dataset_pth dataset/pretrain_dataset.csv -output_dir dataset

Before fine-tuning, run:

python dataset/pretrain_preprocessing.py -dataset_pth dataset/finetune_dataset.csv -output_dir dataset

The processed datasets will be saved in output_dir

2. Model Training and Fine-tuning

To train BindEvaluator with dilated CNN modules, run scripts/train.sh

To fine-tune the pre-trained BindEvaluator, run scripts/finetune.sh

To test the performance of BindEvaluator, run scripts/test.sh

Ensure you adjust the hyper-parameters according to your specific requirements.

3. Binding site prediction

Protein-protein interaction binding sites can be predicted using the pre-trained BindEvaluator (model_path/pretrained_BindEvaluator.ckpt)

Peptide-protein interaction binding sites can be predicted using the fine-tuned BindEvaluator (model_path/finetuned_BindEvaluator.ckpt)

We provide an example script to use BindEvaluator to predict binding sites (scripts/predict.sh)

NOTE: amino acid indices start from 0 on a protein sequence

usage: python predict_motifs.py -sm MODEL_PATH -target Target -binder Binder
                        [-gt] [-n_layers] [-d_model] [-d_hidden] [-n_head] [-d_inner]

arguments:
  -sm         The path to the BindEvaluator model weights
  -target     Target protein sequence
  -binder     Binder sequence
  -gt         Ground Truth binding motifs if known. If specified, the prediction accuracy, F1 score, and MCC score will be calculated.
  -n_layers, -d_model, -d_hidden, -n_head, -d_inner   Model parameters for BindEvaluator, which should be the same as the model specified in -sm used

4. Motif-Specific Binder Generation

We provide an example script to use moPPIt for generating motif-specific binders based on a target sequence (scripts/generation.sh)

usage: python moppit.py -sm MODEL_PATH --protein_seq PROTEIN --peptide_length LENGTH --motif MOTIF
                        [--top_k] [--num_binders] [--num_display] [-max_iterations] [-n_layers] [-d_model] [-d_hidden] [-n_head] [-d_inner]

arguments:
  -sm               The path to the BindEvaluator model weights
  --protein_seq     Target protein sequence
  --peptide_length  The length for the generated binders
  --motif           The binding motifs (NOTE: amino acid indices start from 0 on a protein sequence)
  --top_k           Sampling argument for each position used in PepMLM
  --num_binders     The size of the pool of candidates in the genetic algorithm
  --num_display     The number of top binders to display after each generation
  -max_iterations   Maximum no improvement iterations
  -n_layers, -d_model, -d_hidden, -n_head, -d_inner   Model parameters for BindEvaluator, which should be the same as the model specified in -sm used

5. PeptiDerive

We provide the Python script to run PeptiDerive locally.

pyrosetta needs to be installed in the conda environment before running this script. (Installation Guideline)

NOTE: In PeptiDerive results, amino acid indices start from 1 on protein sequences.

usage: python peptiderive.py --pdb PDB_PATH [--binder_chain]

arguments:
  --pdb             The path to the binder-target protein complex structure
  --binder_chain    Whether the binder is chain A or chain B in the protein complex structure

Please sign the academic-only, non-commercial license to access moPPIt.

Repository Authors

Tong Chen, Visiting Student at Duke University
Pranam Chatterjee, Assistant Professor at Duke University

Reach out to us with any questions!

ChatterjeeLab
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moPPIt