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

• URL: http://arxiv.org/abs/2310.19849v1
• Date: Mon, 30 Oct 2023 15:23:42 GMT
• Title: Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model
• Authors: Shiwei Liu, Tian Zhu, Milong Ren, Chungong Yu, Dongbo Bu, Haicang Zhang
• Abstract summary: We propose SidechainDiff, a representation learning-based approach that leverages unlabelled experimental protein structures. 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.
• Score: 14.949807579474781
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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