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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!