EGRET：边缘聚合图注意力网络和迁移学习提高蛋白质-蛋白质相互作用位点预测。

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

机构信息

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

Department of Computer Science and Engineering Bangladesh University of Engineering and Technology, Dhaka-1205, Bangladesh.

出版信息

Brief Bioinform. 2022 Mar 10;23(2). doi: 10.1093/bib/bbab578.

DOI:10.1093/bib/bbab578

PMID:35106547

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.

摘要

动机

蛋白质-蛋白质相互作用（PPIs）是大多数生物过程的核心。然而，使用传统的实验方法可靠地识别 PPI 位点既缓慢又昂贵。因此，人们正在努力开发计算方法来识别 PPI 位点。

结果

我们提出了 Edge Aggregated GRaph Attention NETwork（EGRET），这是一种基于深度学习的高度准确的 PPI 位点预测方法，我们使用了边缘聚合图注意网络来有效地利用结构信息。我们首次在 PPI 位点预测中使用了迁移学习。我们提出的边缘聚合网络与迁移学习相结合，在性能上优于最佳替代方法。此外，我们系统地研究了 EGRET 的网络行为，以深入了解其决策的原因。