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Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering

National Science Review 2023年第12期10卷 13-25页

作者： Wen Jun Xie Arieh Warshel Department of Medicinal Chemistry Center for Natural Products Drug Discovery and DevelopmentGenetics InstituteUniversity of Florida Department of Chemistry University of Southern California

Enzymes, as paramount protein catalysts, occupy a central role in fostering remarkable progress across numerous fields. However, the intricacy of sequence-function relationships continues to obscure our grasp of enzyme behaviors and curtails our capabilities in rational enzyme engineering. Generative artificial intelligence(AI), known for its proficiency in handling intricate data distributions, holds the potential to offer novel perspectives in enzyme research. Generative models could discern elusive patterns within the vast sequence space and uncover new functional enzyme sequences. This review highlights the recent advancements in employing generative AI for enzyme sequence analysis. We delve into the impact of generative AI in predicting mutation effects on enzyme fitness, catalytic activity and stability, rationalizing the laboratory evolution of de novo enzymes, and decoding protein sequence semantics and their application in enzyme engineering. Notably, the prediction of catalytic activity and stability of enzymes using natural protein sequences serves as a vital link, indicating how enzyme catalysis shapes enzyme evolution. Overall,we foresee that the integration of generative AI into enzyme studies will remarkably enhance our knowledge of enzymes and expedite the creation of superior biocatalysts.

关键词： generative AI mutation effects evolution–catalysis relationship enzyme engineering enzyme evolution

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