GearBind

For the latest version of GearBind code and the link to the datasets, please refer to https://github.com/DeepGraphLearning/GearBind.

Overview

GearBind is a pretrainable geometric graph neural network for protein-protein binding affinity change (ddG_bind) prediction. It is pretrained on CATH using contrastive learning and fine-tuned on SKEMPI with a regression loss. Here we provide the inference code of GearBind.

This codebase is based on PyTorch and TorchDrug. It supports training and inference with multiple GPUs or multiple machines.

Installation

You may install the dependencies via either conda or pip. Generally, GearBind works with Python 3.8/3.9 and PyTorch version >= 1.8.0.

Windows, Mac OS X and Linux should all be supported.

From Conda

If internet connection is smooth, the installation should be completed within 15 minutes. Using mamba as the conda solver can potentially speed up the installation process.

conda install pyg pytorch=1.8.0 cudatoolkit=11.1 torchdrug -c pyg -c pytorch -c conda-forge
conda install rdkit easydict pyyaml biopython gdown -c conda-forge

Inference on HER2 and CR3022

Now we show how to use our (pre-)trained models for inference on new wild-type proteins. Here we take the HER2 and CR3022 proteins used in the paper as examples. First, you need to download the checkpoints to the ./checkpoints directory. Note that we can not provide FoldX-generated HER2 and CR3022 mutant structures due to license restrictions. Please prepare the wild-type and mutant structures yourself. The prepared dataset should have the following file structure:

• data.csv: a csv file with columns "pdb_id", "mutation", "chain_a", "chain_b", "wt_protein", "mt_protein", where
  • "pdb_id" is the stem of the protein complex structure file name
  • "mutation" is the comma-separated mutation list
  • "chain_a" and "chain_b" are interacting chains in the complex (e.g., HL and C),
  • "wt_protein" and "mt_protein" are the file names of the wild-type and mutant structures, respectively.
• data: folder storing the wild-type and mutant structures.

The PDB structures used to prepare the HER2 (1n8z.pdb) and CR3022 dataset (6xc3_wt.pdb) are provided in the data directory. 6xc3_ba4.pdb and 6xc3_ba11.pdb are the PDB structures of CR3022 against the RBD of BA.4 and BA.1.1 strains of SARS-CoV-2, respectively, modelled by SWISS-MODEL.

# Downloading model checkpoints
cd checkpoints
gdown 1nFEjbjdlRWFwYz7LUNv_D6oLnEsZ5beJ
unzip new-gearbind-model-weights.zip
mv new-gearbind-model-weights/*.pth ./
rm -rf new-gearbind-model-weights
cd ..

We have prepared the config file in the ./config/predict directory. To get the prediction results of the pre-trained models on different variants, you can run the following commands.

# Run GearBind-P models on CR3022 datasets
python script/predict.py -c config/predict/CR3022_GearBindP.yaml

# Run GearBind models on HER2 datasets
python script/predict.py -c config/predict/HER2_GearBind.yaml

The inference should take about 2 minutes on a single A100 GPU. The expected output for HER2 binders are stored in results/GearBind_HER2_1n8z_renum.pdb_HL_C.csv. After finishing the prediction, you are expected to get an output file called <model_class>_<dataset_class>_<test_split>.csv. For the second case, the name of the output file is GearBind_HER2_1n8z_renum.pdb_HL_C.csv. You can compare this output with the results we provide in ./results.

To run the model on your own protein complexes, you need to

1. prepare the dataset with FoldX
2. write a customized dataset class following dataset.HER2 and dataset.CR3022
3. add a .yaml file by modifying the configuration of the dataset class

SKEMPI preprocesssing

The following commands process SKEMPI from raw data, including downloading the raw data, processing the data so that it is ready for FoldX mutagenesis.

python script/process_skempi.py --csv-path $SKEMPI_CSV_PATH --pdb-dir $SKEMPI_PDB_DIR --output-csv-path $PROCESSED_SKEMPI_CSV_PATH --output-pdb-dir $PROCESSED_SKEMPI_PDB_DIR --no-repair

where

• SKEMPI_CSV_PATH: the path to the raw SKEMPI csv file.
• SKEMPI_PDB_DIR: the directory containing the raw SKEMPI pdb files.
• PROCESSED_SKEMPI_CSV_PATH: the path to the processed SKEMPI csv file.
• PROCESSED_SKEMPI_PDB_DIR: the directory to store the processed SKEMPI pdb files.

The processed SKEMPI dataset and all model predictions can be found in data/skempi_v2_with_all_results_0415.csv.