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

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 r

SUBMITTER: Cao Y 

PROVIDER: S-EPMC7374013 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature