Energy-based graph convolutional networks for scoring protein docking models.

Proteins
Yue Cao, Yang Shen

Abstract

Structural information about protein-protein interactions, often missing at the interactome scale, is important for mechanistic understanding of cells and rational discovery of therapeutics. Protein docking provides a computational alternative for such information. However, ranking near-native docked models high among a large number of candidates, often known as the scoring problem, remains a critical challenge. Moreover, estimating model quality, also known as the quality assessment problem, is rarely addressed in protein docking. In this study, the two challenging problems in protein docking are regarded as relative and absolute scoring, respectively, and addressed in one physics-inspired deep learning framework. We represent protein and complex structures as intra- and inter-molecular residue contact graphs with atom-resolution node and edge features. And we propose a novel graph convolutional kernel that aggregates interacting nodes' features through edges so that generalized interaction energies can be learned directly from 3D data. The resulting energy-based graph convolutional networks (EGCN) with multihead attention are trained to predict intra- and inter-molecular energies, binding affinities, and quality measures (int...Continue Reading

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Citations

May 29, 2021·Computational Biology and Chemistry·Sharon Sunny, P B Jayaraj
Aug 17, 2021·Frontiers in Genetics·Xiao-Meng ZhangMing-Jing Tang
Aug 29, 2021·Proteins·Marc F LensinkShoshana J Wodak

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