Generative Artificial Intelligence for Function-Driven De Novo Enzyme Design

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Generative Artificial Intelligence for Function-Driven De Novo Enzyme Design

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1
Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
2
National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
3
School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
*
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Received: 28 August 2025 Accepted: 26 September 2025 Published: 29 September 2025

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© 2025 The authors. This is an open access article under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

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Synth. Biol. Eng. 2025, 3(3), 10015; DOI: 10.70322/sbe.2025.10015
ABSTRACT: The de novo design of artificial enzymes with customized catalytic functions represents a long-standing challenge in synthetic biology. Recent breakthroughs in deep learning, particularly the rise of Generative Artificial Intelligence (GAI), have transformed enzyme design from structure-centric strategies toward function-oriented paradigms. This review outlines the emerging computational frameworks that now span the entire design pipeline, including active site design, backbone generation, inverse folding, and virtual screening. Detailed description of active site, called a theozyme, is designed to stabilize transition states and can be guided by density functional theory (DFT) calculations that define the geometry of key catalytic components. Guided by the theozyme, GAI approaches such as diffusion and flow-matching models enable the generation of protein backbones pre-configured for catalysis. Inverse folding methods, exemplified by ProteinMPNN and LigandMPNN, further incorporate atomic-level constraints to optimize sequence–function compatibility. To assess and optimize catalytic performance, virtual screening platforms such as PLACER allow evaluation of protein–ligand conformational dynamics under catalytically relevant conditions. Through representative case studies, we illustrate how GAI-driven frameworks facilitate the rational creation of artificial enzymes with architectures distinct from natural homologs, thereby enabling catalytic activities not observed in nature. With the rapid progress and widespread adoption of GAI, we anticipate that de novo enzyme design with customized catalytic functions will soon evolve into a mature and broadly applicable methodology.
Keywords: De novo enzyme design; Generative artificial intelligence; Backbone design; Inverse folding

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