AI-driven de novo enzyme design: Strategies, applications, and future prospects.

Zhiguang Yuchi, Ying-Jin Yuan, Yan Zheng, Ye Liu, Xi-Chen Cui

Journal: Biotechnology advances 2025;82():108603

PMID: 40368118

Full Text on Elsevier Science View on PubMed

Abstract

Enzymes are indispensable for biological processes and diverse applications across industries. While top-down modification strategies, such as directed evolution, have achieved remarkable success in optimizing existing enzymes, bottom-up de novo enzyme design has emerged as a transformative approach for engineering novel enzymes with customized catalytic functions, independent of natural templates. Recent advancements in artificial intelligence (AI) and computational power have significantly accelerated this field, enabling breakthroughs in enzyme engineering. These technologies facilitate the rapid generation of enzyme structures and amino acid sequences optimized for specific functions, thereby enhancing design efficiency. They also support functional validation and activity optimization, improving the catalytic performance, stability, and robustness of de novo designed enzymes. This review highlights recent advancements in AI-driven de novo enzyme design, discusses strategies for validation and optimization, and examines the challenges and future prospects of integrating these technologies into enzyme development.

Address: State Key Laboratory of Synthetic Biology, Tianjin University, Tianjin 30072, PR China; Frontiers Science Center for Synthetic Biology(Ministry of Education), School of Synthetic Biology and Biomanufacturing, Tianjin University, Tianjin 300072, PR China.; State Key Laboratory of Synthetic Biology, Tianjin University, Tianjin 30072, PR China; Frontiers Science Center for Synthetic Biology(Ministry of Education), School of Synthetic Biology and Biomanufacturing, Tianjin University, Tianjin 300072, PR China; School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, PR China.; State Key Laboratory of Synthetic Biology, Tianjin University, Tianjin 30072, PR China; Frontiers Science Center for Synthetic Biology(Ministry of Education), School of Synthetic Biology and Biomanufacturing, Tianjin University, Tianjin 300072, PR China; School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, PR China. Electronic address: [email protected].; State Key Laboratory of Synthetic Biology, Tianjin University, Tianjin 30072, PR China; Frontiers Science Center for Synthetic Biology(Ministry of Education), School of Synthetic Biology and Biomanufacturing, Tianjin University, Tianjin 300072, PR China. Electronic address: [email protected].

DOI: 10.1016/j.biotechadv.2025.108603

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MeSH Terms: Protein Engineering, Artificial Intelligence, Enzymes