GNN_pocket is a tool to predict the pocket regions in a protein structure, where the residues that are at the edges of a pocket get tagged as active.
Copyright (C) 2022 Yuanyuan Zhang, Xiao Wang, Charles Christoffer, & Daisuke Kihara, and Purdue University.
License: GPL v3 for academic use. (For commercial use, please contact us for different licensing.)
Contact: Daisuke Kihara ([email protected])
1. Install git
git clone https://github.com/kiharalab/GNN_pocket
cd GNN_pocket
python3 main.py -h:
--mode Running Mode
--gpu specify the gpu to use
--test_dir specify the directory of dataset
--test_odir specify the directory that you want to save the processed data
python main.py --mode=1 --gpu=1 --test_dir="../dataset/test" --test_odir="../dataset/test_processed"
id_structure.pqr: the prediction is at column 9, which 1.000 means pocket 1, 0.000 means not a pocket atom.
Two examples about pocktes, the first one is example 23 from test set, the second one is example 103 from validation set.
