G-RANK: an equivariant graph neural network for the scoring of protein–protein docking models

• Published in: Bioinformatics advances Vol. 3; no. 1; p. vbad011
• Main Authors: Kim, Ha Young, Kim, Sungsik, Park, Woong-Yang, Kim, Dongsup
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 2023
• Subjects: Original Paper
• ISSN: 2635-0041; 2635-0041
• DOI: 10.1093/bioadv/vbad011

Abstract Motivation Protein complex structure prediction is important for many applications in bioengineering. A widely used method for predicting the structure of protein complexes is computational docking. Although many tools for scoring protein–protein docking models have been developed, it is still a challenge to accurately identify near-native models for unknown protein complexes. A recently proposed model called the geometric vector perceptron–graph neural network (GVP-GNN), a subtype of equivariant graph neural networks, has demonstrated success in various 3D molecular structure modeling tasks. Results Herein, we present G-RANK, a GVP-GNN-based method for the scoring of protein-protein docking models. When evaluated on two different test datasets, G-RANK achieved a performance competitive with or better than the state-of-the-art scoring functions. We expect G-RANK to be a useful tool for various applications in biological engineering. Availability and implementation Source code is available at https://github.com/ha01994/grank. Contact kds@kaist.ac.kr Supplementary information Supplementary data are available at Bioinformatics Advances online.