Complete computational design of high-efficiency Kemp elimination enzymes

• 成果类型: Article
• 署名作者: Listov, Dina; Vos, Eva; Hoffka, Gyula; Hoch, Shlomo Yakir; Berg, Andrej; Hamer-Rogotner, Shelly; Dym, Orly; Kamerlin, Shina Caroline Lynn; Fleishman, Sarel J.
• 署名单位: Weizmann Institute of Science; University System of Georgia; Georgia Institute of Technology; Lund University; University of Debrecen; Uppsala University; Weizmann Institute of Science
• 刊物名称: Nature
• ISSN/ISSBN: 0028-0866
• DOI: 10.1038/s41586-025-09136-2
• 发表日期: 2025-07-31
• 关键词: empirical valence-bond; de-novo design; molecular-dynamics; directed evolution; optimization; software; simulations; STABILITY; SEQUENCES; catalysis

摘要

Until now, computationally designed enzymes exhibited low catalytic rates1, 2, 3, 4-5 and required intensive experimental optimization to reach activity levels observed in comparable natural enzymes5, 6, 7, 8-9. These results exposed limitations in design methodology and suggested critical gaps in our understanding of the fundamentals of biocatalysis10,11. We present a fully computational workflow for designing efficient enzymes in TIM-barrel folds using backbone fragments from natural proteins and without requiring optimization by mutant-library screening. Three Kemp eliminase designs exhibit efficiencies greater than 2,000 M-1 s-1. The most efficient shows more than 140 mutations from any natural protein, including a novel active site. It exhibits high stability (greater than 85 degrees C) and remarkable catalytic efficiency (12,700 M-1 s-1) and rate (2.8 s-1), surpassing previous computational designs by two orders of magnitude1, 2, 3, 4-5. Furthermore, designing a residue considered essential in all previous Kemp eliminase designs increases efficiency to more than 105 M-1 s-1 and rate to 30 s-1, achieving catalytic parameters comparable to natural enzymes and challenging fundamental biocatalytic assumptions. By overcoming limitations in design methodology11, our strategy enables programming stable, high-efficiency, new-to-nature enzymes through a minimal experimental effort.