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Updated: Oct 4, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

EGRET: edge aggregated graph attention networks and transfer learning improve protein-protein interaction site

Sazan Mahbub¹, Md Shamsuzzoha Bayzid²

¹Department of Computer Science University of Maryland, College Park, Maryland 20742, USA.

Briefings in Bioinformatics

|February 2, 2022

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:

shams_bayzid@cse.buet.ac.bd.

Keywords:

deep learning edge aggregation graph neural network protein–protein interaction sites

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