Protein-protein bindings play a key role in a variety of fundamental
biological processes, and thus predicting the effects of amino acid mutations
on protein-protein binding is crucial. To tackle the scarcity of annotated
mutation data, pre-training with massive unlabeled data has emerged as a
promising solution. However, this process faces a series of challenges: (1)
complex higher-order dependencies among multiple (more than paired) structural
scales have not yet been fully captured; (2) it is rarely explored how
mutations alter the local conformation of the surrounding microenvironment; (3)
pre-training is costly, both in data size and computational burden. In this
paper, we first construct a hierarchical prompt codebook to record common
microenvironmental patterns at different structural scales independently. Then,
we develop a novel codebook pre-training task, namely masked microenvironment
modeling, to model the joint distribution of each mutation with their residue
types, angular statistics, and local conformational changes in the
microenvironment. With the constructed prompt codebook, we encode the
microenvironment around each mutation into multiple hierarchical prompts and
combine them to flexibly provide information to wild-type and mutated protein
complexes about their microenvironmental differences. Such a hierarchical
prompt learning framework has demonstrated superior performance and training
efficiency over state-of-the-art pre-training-based methods in mutation effect
prediction and a case study of optimizing human antibodies against SARS-CoV-2.

本研究使用预训练模型来预测蛋白质结合中的氨基酸突变效应，通过构建分层提示编码表和建立掩码微环境模型，成功捕捉到多个结构尺度上的高阶依赖关系以及突变对周围微环境的局部构象变化，该分层提示学习框架在突变效应预测和优化人类抗体对抗 SARS-CoV-2 中表现出卓越的性能和训练效率。

通过微环境感知的分层提示学习，预测蛋白质相互作用的突变效应

Learning to Predict Mutation Effects of Protein-Protein Interactions by  Microenvironment-aware Hierarchical Prompt Learning

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.

利用未标记的实验蛋白质结构，通过基于表示学习的方法，我们提出了 SidechainDiff，它利用了 Riemannian 扩散模型来学习侧链构象的生成过程，并且可以给出蛋白质结合界面上突变的结构背景表示。通过利用这些学习到的表示，我们在预测蛋白质结合突变效果方面取得了最新的研究成果。另外，SidechainDiff 是第一个基于扩散的侧链生成模型，与以往主要集中在生成蛋白质骨架结构的努力有所区别。