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

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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. Contact: kds@kaist.ac.kr © 2023 The Author(s). Published by Oxford University Press.
Publisher
OXFORD UNIV PRESS
Issue Date
2023
Language
English
Article Type
Article
Citation

BIOINFORMATICS ADVANCES, v.3, no.1

ISSN
2635-0041
DOI
10.1093/bioadv/vbad011
URI
http://hdl.handle.net/10203/316123
Appears in Collection
BiS-Journal Papers(저널논문)
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