  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.
