EGRET: edge aggregated graph attention networks and transfer learning improve protein–protein interaction site prediction
Sazan Mahbub, Md. Shamsuzzoha Bayzid
Abstract
MOTIVATION: Protein-protein interactions (PPIs) are central to most biological processes. However, reliable identification of PPI sites using conventional experimental methods is slow and expensive. Therefore, great efforts are being put into computational methods to identify PPI sites. RESULTS: We present Edge Aggregated GRaph Attention NETwork (EGRET), a highly accurate deep learning-based method for PPI site prediction, where we have used an edge aggregated graph attention network to effectively leverage the structural information. We, for the first time, have used transfer learning in PPI site prediction. Our proposed edge aggregated network, together with transfer learning, has achieved notable improvement over the best alternate methods. Furthermore, we systematically investigated EGRET's network behavior to provide insights about the causes of its decisions. AVAILABILITY: EGRET is freely available as an open source project at https://github.com/Sazan-Mahbub/EGRET. CONTACT: [email protected].