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。