Spatom: a graph neural network for structure-based protein-protein interaction site prediction.

Journal: Briefings in bioinformatics
Published Date:

Abstract

Accurate identification of protein-protein interaction (PPI) sites remains a computational challenge. We propose Spatom, a novel framework for PPI site prediction. This framework first defines a weighted digraph for a protein structure to precisely characterize the spatial contacts of residues, then performs a weighted digraph convolution to aggregate both spatial local and global information and finally adds an improved graph attention layer to drive the predicted sites to form more continuous region(s). Spatom was tested on a diverse set of challenging protein-protein complexes and demonstrated the best performance among all the compared methods. Furthermore, when tested on multiple popular proteins in a case study, Spatom clearly identifies the interaction interfaces and captures the majority of hotspots. Spatom is expected to contribute to the understanding of protein interactions and drug designs targeting protein binding.

Authors

  • Haonan Wu
    School of Mathematics and Statistics, Shandong University, Weihai 264209, China.
  • Jiyun Han
    School of Mathematics and Statistics, Shandong University, Weihai 264209, China.
  • Shizhuo Zhang
    School of Mathematics and Statistics, Shandong University, Weihai 264209, China.
  • Gaojia Xin
    School of Mathematics and Statistics, Shandong University, Weihai 264209, China.
  • Chaozhou Mou
    School of Mathematics and Statistics, Shandong University, Weihai 264209, China.
  • Juntao Liu
    State Key Laboratory of Transducer Technology, Aerospace Information Research Institute. Chinese Academy of Sciences, Beijing 100190, China.