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Front. Mol. Biosci. | doi: 10.3389/fmolb.2021.647915

Protein Docking Model Evaluation by Graph Neural Networks Provisionally accepted The final, formatted version of the article will be published soon. Notify me

  • 1Department of Computer Science, Purdue University, United States
  • 2Purdue University, United States
  • 3Department of Biological Sciences, College of Science, Purdue University, United States

Physical interactions of proteins play key functional roles in many important cellular processes. To understand molecular mechanisms of such functions it is crucial to determine the structure of protein complexes. To complement experimental approaches, which usually take a considerable amount of time and resources, various computational methods have been developed for predicting the structures of protein complexes. In computational modeling, one of the challenges is to identify near-native structures from a large pool of generated models. Here, we developed a deep learning-based approach named Graph Neural Network-based DOcking decoy eValuation scorE (GNN-DOVE). To evaluate a protein docking model, GNN-DOVE extracts the interface area and represents it as a graph. The chemical properties of atoms and the inter-atom distances are used as features of nodes and edges in the graph. GNN-DOVE was trained and tested on docking models in the Dockground database and further tested on a combined dataset of Dockground and ZDOCK benchmark as well as a CAPRI scoring dataset. GNN-DOVE performed better than existing methods including DOVE, which is our previous development that uses a convolutional neural network on voxelized structure models.

Keywords: protein docking, docking model evaluation, Graph neural networks, deep learning, protein structure prediction

Received: 30 Dec 2020; Accepted: 26 Apr 2021.

Copyright: © 2021 Wang, Flannery and Kihara. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Daisuke Kihara, Purdue University, West Lafayette, United States,