Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction

Yang Yue, Shu Li, Yihua Cheng, Lie Wang, Tingjun Hou, Zexuan Zhu () and Shan He ()
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Yang Yue: Edgbaston
Shu Li: Macao Polytechnic University
Yihua Cheng: Edgbaston
Lie Wang: Institute of Immunology, Zhejiang University School of Medicine
Tingjun Hou: Zhejiang University
Zexuan Zhu: Shenzhen University
Shan He: Edgbaston

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Structure-based machine learning algorithms have been utilized to predict the properties of protein-protein interaction (PPI) complexes, such as binding affinity, which is critical for understanding biological mechanisms and disease treatments. While most existing algorithms represent PPI complex graph structures at the atom-scale or residue-scale, these representations can be computationally expensive or may not sufficiently integrate finer chemical-plausible interaction details for improving predictions. Here, we introduce MCGLPPI, a geometric representation learning framework that combines graph neural networks (GNNs) with MARTINI molecular coarse-grained (CG) models to predict PPI overall properties accurately and efficiently. Extensive experiments on three types of downstream PPI property prediction tasks demonstrate that at the CG-scale, MCGLPPI achieves competitive performance compared with the counterparts at the atom- and residue-scale, but with only a third of computational resource consumption. Furthermore, CG-scale pre-training on protein domain-domain interaction structures enhances its predictive capabilities for PPI tasks. MCGLPPI offers an effective and efficient solution for PPI overall property predictions, serving as a promising tool for the large-scale analysis of biomolecular interactions.

Date: 2024
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DOI: 10.1038/s41467-024-53583-w

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