Artificial Intelligence-Driven Enzyme Engineering from Structural Prediction to De Novo Design

作者：Hongling Shi, Xueyang Bai, Fangyuan Tian, Yangwan Li, Dandan Li, Lunguang Yao, Chuang Xue, Cunduo Tang · 发表于：Journal of Agricultural and Food Chemistry · 年份：2026 · DOI：10.1021/acs.jafc.6c01781 · 被引用次数：20 · 研究领域：Microbial Metabolic Engineering and Bioproduction、Machine Learning in Materials Science、RNA and protein synthesis mechanisms

The rapid maturation of artificial intelligence (AI) has catalyzed a fundamental transition in biocatalysis, moving from structural analysis toward the prescriptive design of bespoke enzymes. This review synthesizes this AI-driven revolution, evaluating breakthroughs like AlphaFold2 and CLEAN that now bridge sequences with catalytic properties, including kinetic parameters and substrate specificity. We critically compare rational design strategies, contrasting evolutionary-guided redesign with the emerging generative de novo paradigm, where diffusion models and protein language models (PLMs) explore uncharacterized sequence space. By dissecting algorithms such as Graph Neural Networks and Transformers, we illustrate their role in deciphering protein chemistry's linguistic "grammar". Grounded in industrial cases, we demonstrate how AI overcomes bottlenecks like the stability-activity trade-off. Finally, we delineate the trajectory toward autonomous biofoundries and virtual cell modeling, envisioning engineered biocatalysts systematically integrated into complex metabolic networks─providing a roadmap for next-generation computational enzymology.