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

Protein-Protein Interactions (PPIs) are fundamental in various biological
processes and play a key role in life activities. The growing demand and cost
of experimental PPI assays require computational methods for efficient PPI
prediction. While existing methods rely heavily on protein sequence for PPI
prediction, it is the protein structure that is the key to determine the
interactions. To take both protein modalities into account, we define the
microenvironment of an amino acid residue by its sequence and structural
contexts, which describe the surrounding chemical properties and geometric
features. In addition, microenvironments defined in previous work are largely
based on experimentally assayed physicochemical properties, for which the
"vocabulary" is usually extremely small. This makes it difficult to cover the
diversity and complexity of microenvironments. In this paper, we propose
Microenvironment-Aware Protein Embedding for PPI prediction (MPAE-PPI), which
encodes microenvironments into chemically meaningful discrete codes via a
sufficiently large microenvironment "vocabulary" (i.e., codebook). Moreover, we
propose a novel pre-training strategy, namely Masked Codebook Modeling (MCM),
to capture the dependencies between different microenvironments by randomly
masking the codebook and reconstructing the input. With the learned
microenvironment codebook, we can reuse it as an off-the-shelf tool to
efficiently and effectively encode proteins of different sizes and functions
for large-scale PPI prediction. Extensive experiments show that MAPE-PPI can
scale to PPI prediction with millions of PPIs with superior trade-offs between
effectiveness and computational efficiency than the state-of-the-art
competitors.

通过定义氨基酸残基的序列和结构上下文，我们提出了一种微环境感知的蛋白质嵌入（MPAE-PPI）方法，将微环境编码为具有足够大的词汇（例如代码本）的化学有意义的离散代码，用于高效的大规模蛋白质相互作用（PPI）预测。经过广泛实验表明，MPAE-PPI 可以在 PPI 预测中高效而有效地处理数百万个 PPI，具有优异的效果和计算效率。