Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model

作者：Shiwei Liu, Zhu Tian, Milong Ren, Chungong Yu, Dongbo Bu, Haicang Zhang · 发表于：arXiv (Cornell University) · 年份：2023 · DOI：10.48550/arxiv.2310.19849 · 被引用次数：10 · 研究领域：Protein Structure and Dynamics、Bioinformatics and Genomic Networks、Microbial Metabolic Engineering and Bioproduction

Many crucial biological processes rely on networks of protein-protein interactions. Predicting the effect of amino acid mutations on protein-protein binding is vital in protein engineering and therapeutic discovery. However, the scarcity of annotated experimental data on binding energy poses a significant challenge for developing computational approaches, particularly deep learning-based methods. In this work, we propose SidechainDiff, a representation learning-based approach that leverages unlabelled experimental protein structures. SidechainDiff utilizes a Riemannian diffusion model to learn the generative process of side-chain conformations and can also give the structural context representations of mutations on the protein-protein interface. Leveraging the learned representations, we achieve state-of-the-art performance in predicting the mutational effects on protein-protein binding. Furthermore, SidechainDiff is the first diffusion-based generative model for side-chains, distinguishing it from prior efforts that have predominantly focused on generating protein backbone structures.