AUTHOR=Han Ye , He Fei , Chen Yongbing , Qin Wenyuan , Yu Helong , Xu Dong TITLE=Quality Assessment of Protein Docking Models Based on Graph Neural Network JOURNAL=Frontiers in Bioinformatics VOLUME=1 YEAR=2021 URL=https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2021.693211 DOI=10.3389/fbinf.2021.693211 ISSN=2673-7647 ABSTRACT=

Protein docking provides a structural basis for the design of drugs and vaccines. Among the processes of protein docking, quality assessment (QA) is utilized to pick near-native models from numerous protein docking candidate conformations, and it directly determines the final docking results. Although extensive efforts have been made to improve QA accuracy, it is still the bottleneck of current protein docking systems. In this paper, we presented a Deep Graph Attention Neural Network (DGANN) to evaluate and rank protein docking candidate models. DGANN learns inter-residue physio-chemical properties and structural fitness across the two protein monomers in a docking model and generates their probabilities of near-native models. On the ZDOCK decoy benchmark, our DGANN outperformed the ranking provided by ZDOCK in terms of ranking good models into the top selections. Furthermore, we conducted comparative experiments on an independent testing dataset, and the results also demonstrated the superiority and generalization of our proposed method.