Intro

GraphPPIS is a novel framework for structure-based protein-protein interaction site prediction using deep graph convolutional network, which is able to capture information from high-order spatially neighboring amino acids. The GraphPPIS source code is designed for high-throughput predictions, and does not have the limitation of one query protein per run. We recommend you to use the web server (new version) of GraphPPIS if your input is small.

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

Software and database requirement

To run the full & accurate version of GraphPPIS, you need to install the following three software and download the corresponding databases:
BLAST+ and UniRef90
HH-suite and Uniclust30
DSSP
However, if you use the fast version of GraphPPIS, only DSSP is needed.

Build database and set path

1. Use makeblastdb in BLAST+ to build UniRef90 (guide).
2. Build Uniclust30 following this guide.
3. Set path variables UR90, HHDB, PSIBLAST, HHBLITS and DSSP in GraphPPIS_predict.py.

Run GraphPPIS for prediction

For a protein chain in PDB:

python GraphPPIS_predict.py -p PDB_ID -c chain_ID

For a user-custom PDB file:

python GraphPPIS_predict.py -f XXX.pdb -c chain_ID

The program uses the fast model in default. If you want to use the slow & accurate mode, type as follows:

python GraphPPIS_predict.py -p PDB_ID -c chain_ID -m slow

How to reproduce our work

We provide the datasets, pre-computed features, the two pre-trained models, and the training and evaluation codes for those interested in reproducing our paper.
The datasets used in this study (Train_335, Test_60, Test_315 and UBtest_31) are stored in ./Dataset in fasta format.
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.
The pre-trained GraphPPIS full model and the simplified version using BLOSUM62 can be found under ./Model
⭐⭐⭐The training and evaluation codes can be found in here.

Web server, citation and contact

The GraphPPIS web server is freely available in here.

Citation:

@article{10.1093/bioinformatics/btab643,
    author = {Yuan, Qianmu and Chen, Jianwen and Zhao, Huiying and Zhou, Yaoqi and Yang, Yuedong},
    title = "{Structure-aware protein–protein interaction site prediction using deep graph convolutional network}",
    journal = {Bioinformatics},
    volume = {38},
    number = {1},
    pages = {125-132},
    year = {2021},
    month = {09},
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btab643},
    url = {https://doi.org/10.1093/bioinformatics/btab643},
}

Contact:
Qianmu Yuan ([email protected])
Yuedong Yang ([email protected])