Generative Artificial Intelligence-Empowered Virtual Evolution of Enzyme with the VERnet Model

Large language models (LLMs) have demonstrated their limitations in addressing the design of active proteins that rely on intricate intramolecular interactions, particularly in the engineering of biocatalysts. Conducting real-world studies from targeted laboratory assays has become the de facto standard for artificial intelligence (AI) research in complex biological tasks. In this study, we present a standardized strategy using function-targeted models to decode the subtle effect of sequence variations on the function. Unlike affinity-oriented protein–protein interaction studies using LLMs, our model targets the specific functional interpretation, thereby guiding enzyme evolution. We established the VERnet model using deep mutation scanning data that underwent self-distillation, achieving an optimal accuracy of 93.5% for interpreting CYP2C9 variants. Through directed evolution at conserved positions enhanced by generative AI, we identified multiple CYP2C9 variants exhibiting a broad range of functional alterations. Additionally, a fine-tuned model optimized by AlphaFold3 significantly improved the prediction of variants involving the substitution of two amino acids. Molecular dynamics simulations revealed the structural and dynamic features of the catalytic alterations in evolved variants. The in vitro validation of metabolic activity strongly corroborated the in silico predictions, highlighting the substantial potential of AI models in predicting functional evolution.大语言模型（Large Language Models，LLMs）在设计依赖复杂分子内相互作用的活性蛋白，尤其是生物催化剂工程领域时，已展现出其局限性。针对特定实验室实验开展真实世界研究，已成为复杂生物任务中人工智能（AI）研究的事实标准。本研究提出了一种基于功能靶向模型的标准化策略，用于解析序列变异对蛋白质功能的细微影响。与使用大语言模型的亲和力导向蛋白质-蛋白质相互作用研究不同，本模型聚焦于特定的功能解读，从而指导酶的进化改造。本研究利用经过自蒸馏处理的深度突变扫描数据构建了VERnet模型，在解读CYP2C9变异体时实现了93.5%的最优准确率。通过生成式 AI (Generative AI)辅助的保守位点定向进化实验，本研究鉴定出了多种表现出广泛功能改变的CYP2C9变异体。此外，经AlphaFold3优化的微调模型，显著提升了涉及双氨基酸替换的变异体的预测性能。分子动力学模拟揭示了进化后变异体催化特性改变的结构与动态特征。代谢活性的体外实验验证有力佐证了计算机模拟预测结果，凸显了人工智能模型在预测功能进化方面的巨大潜力。