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

Shiwei Liu · Tian Zhu · Milong Ren · Chungong Yu · Dongbo Bu · Haicang Zhang

Great Hall & Hall B1+B2 (level 1) #110
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Tue 12 Dec 8:45 a.m. PST — 10:45 a.m. PST


Many crucial biological processes rely on networks of protein-protein interactions. Predicting the effect of amino acid mutations on protein-protein binding is important in protein engineering, including 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 novel 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 the generation of protein backbone structures.

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