A Graph Convolutional Neural Network to predict protein binding site.
- Clone this repo
- Create a conda environment or a Docker container with provided files. Dockerfile and YAML files are provided in the
./venvdirectory - Install with
pip install .
- Install pytest
pip install pytest - Run
pytest -v
./scripts/download_pdbbind.sh
scripts/create_graphs.py
usage: create_graphs.py [-h] [-config CONFIG]
options:
-h, --help show this help message and exit
-config CONFIG Path to config file, default = config.iniscripts/trainer.py
usage: trainer.py [-h] [-nb_epochs NB_EPOCHS] [-config CONFIG] -batch_size BATCH_SIZE [-learning_rate LEARNING_RATE] [-dropout DROPOUT] [-non_linearity NON_LINEARITY]
[-dbscan_eps DBSCAN_EPS] [-threshold THRESHOLD] [-hidden_channels HIDDEN_CHANNELS] [-mlp_channels MLP_CHANNELS]
options:
-h, --help show this help message and exit
-nb_epochs NB_EPOCHS, -ep NB_EPOCHS
The maximum number of epochs (defaults to 100)
-config CONFIG Path to config file, default = config.ini
-batch_size BATCH_SIZE, -bs BATCH_SIZE
Batch size
-learning_rate LEARNING_RATE, -lr LEARNING_RATE
Learning Rate
-dropout DROPOUT, -dp DROPOUT
Dropout
-non_linearity NON_LINEARITY, -nl NON_LINEARITY
non linearity
-dbscan_eps DBSCAN_EPS, -eps DBSCAN_EPS
Min distance between to AA to be considered in the same pocket
-threshold THRESHOLD, -th THRESHOLD
Threshold to classify each node
-hidden_channels HIDDEN_CHANNELS
-mlp_channels MLP_CHANNELSModels are saved in experiments/SGPocket_exp/version_X.
scripts/predict.py
usage: predict.py [-h] [-config CONFIG] -model MODEL -pdb PDB -threshold THRESHOLD -dbscan_eps DBSCAN_EPS
options:
-h, --help show this help message and exit
-config CONFIG Path to config file, default = config.ini
-model MODEL, -m MODEL
Path to the model
-pdb PDB Path to the protein PDB
-threshold THRESHOLD, -th THRESHOLD
Threshold used to class each node
-dbscan_eps DBSCAN_EPS, -eps DBSCAN_EPS
The DBScan EPSExtracted pockets are saved in the protein direcory in a subdirectory named SGPocket_out.
scripts/predict_dataset.py
Create a csv files with a protein path per line.
usage: predict_dataset.py [-h] [-config CONFIG] -model MODEL -dataset DATASET -threshold THRESHOLD -dbscan_eps DBSCAN_EPS
options:
-h, --help show this help message and exit
-config CONFIG Path to config file, default = config.ini
-model MODEL, -m MODEL
Path to the model
-dataset DATASET, -d DATASET
Path to the CSV dataset
-threshold THRESHOLD, -th THRESHOLD
Threshold used to class each node
-dbscan_eps DBSCAN_EPS, -eps DBSCAN_EPS
The DBScan EPSExtracted pockets are saved in each protein direcory in a subdirectory named SGPocket_out.
We use the bin/voronata-linux and bin/sh-featurizer-linux binary files provided by https://gitlab.inria.fr/GruLab/s-gcn.
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