E(3) equivariant graph neural networks for robust and accurate protein–protein interaction site prediction

by Rahmatullah Roche, Bernard Moussad, Md Hossain Shuvo, Debswapna Bhattacharya

published in PLOS Computational Biology

Codebase for our E(3) equivariant graph neural network approach for PPI site prediction, EquiPPIS.

Installation

1.) We recommend conda virtual environment to install dependencies for EquiPPIS. The following command will create a virtual environment named 'EquiPPIS'

conda env create -f EquiPPIS_environment.yml

2.) Then activate the virtual environment

conda activate EquiPPIS

3.) Download the trained model for EquiPPIS here

That's it! EquiPPIS is ready to be used.

Usage

To see usage instructions, run python EquiPPIS.py -h

usage: EquiPPIS.py [-h] [--model MODEL] [--model_state_dict MODEL_STATE_DICT] [--indir INDIR] [--outdir OUTDIR] [--num_workers NUM_WORKERS]

options:
  -h, --help            show this help message and exit
  --model MODEL         String name of model (default 'EGNN')
  --model_state_dict MODEL_STATE_DICT
                        Saved model
  --indir INDIR         Path to input data containing distance maps and input features (default 'Preprocessing/')
  --outdir OUTDIR       Prediction output directory
  --num_workers NUM_WORKERS
                        Number of data loader workers (default=4)

Here is an example of running EquiPPIS:

1.) Input target list and all input files should be inside input preprocessing directory (default Preprocessing/). A detailed preprocessing instructions can be found here

2.) Make an output directory mkdir output

3.) Run python EquiPPIS.py --model_state_dict Trained_model/EquiPPIS_model/E-l10-256.pt --indir Preprocessing/ --outdir output/

4.) The residue-level PPI site predictions are generated at output/.