Protein-Protein Interaction Sites Prediction Using Graph Convolutional Networks

Protein-protein interactions (PPI) are essential in keeping the cells functioning properly. Identifying PPI binding sites is a fundamental problem in system Biology, and it contributes to a better understanding of lower organisms such as viruses, a multitude-drug design and vaccines. The experimental methods for PPI binding sites identification are slow and expensive. Therefore, great research efforts have been attempted to improve the performance of computational methods. In this paper, we use a deep learning model based on Graph Convolutional Networks (GCN) to predict putative interaction sites on the surface of an isolated protein. We extracted features from both protein sequence and structure to enhance the accuracy of PPI binding sites predictions. Our model achieved higher accuracy compared to other models.