GraphPPIS

GraphPPIS is a novel framework for structure-based protein-protein interaction site prediction using deep graph convolutional network via initial residual and identity mapping, which is able to capture information from high-order spatially neighboring amino acids.

System requirement

GraphPPIS is developed under Linux environment with:
python 3.7.7
numpy 1.19.1
pandas 1.1.0
torch 1.6.0
scikit-learn 0.23.2

Dataset and Feature

The datasets used in this study (Train_335, Test_60, Test_315 and UBtest_31) are stored in ./Dataset in python dictionary format:

Dataset[ID] = [seq, label]

The distance maps(L * L) and normalized feature matrixes PSSM(L * 20), HMM(L * 20) and DSSP(L * 14) are stored in ./Feature in numpy format.

Running GraphPPIS

Train the model with default parameters:

python train.py

Test the model you just trained on the three test sets:

python test.py

You can adjust the parameters via GraphPPIS_model.py
The pre-trained GraphPPIS model and the simplified version using BLOSUM62 can be found under ./Model

Web server and contact

The GraphPPIS web server is freely available in here.

Feel free to contact us:
Qianmu Yuan ([email protected])
Yuedong Yang ([email protected])