魏茨曼科学研究所Sarel J. Fleishman研究团队报道了高效Kemp消除酶的完整计算设计。2025年6月18日，国际知名学术期刊《自然》发表了这一成果。

该研究团队提出了一个完全计算的工作流程，用于在TIM桶折叠中设计高效的酶，从天然蛋白质中提取主干片段，而无需通过突变文库筛选进行优化。三种Kemp消能设计的效率均大于 2,000 M⁻¹ s⁻¹。最有效的方法显示了来自任何天然蛋白质的140多个突变，包括一个新的活性位点。它具有高稳定性（大于85°C）和显著的催化效率（12,700 M⁻¹ s⁻¹）和速率（2.8s^-1），比以前的计算设计高出两个数量级。

此外，设计一种在所有以前的Kemp消除设计中被认为是必不可少的残留物，可将效率提高到10⁵ M⁻¹ s⁻¹，比率提高到30 s⁻¹，实现与天然酶相当的催化参数，挑战基本的生物催化假设。通过克服设计方法上的限制，他们的策略使通过最小的实验努力就能编程出稳定、高效、全新的酶。

据了解，到目前为止，计算设计的酶表现出较低的催化速率，并且需要大量的实验优化才能达到与天然酶相似的活性水平。这些结果暴露了设计方法的局限性，并表明他们对生物催化基础知识的理解存在重大差距。

附：英文原文

Title: Complete computational design of high-efficiency Kemp elimination enzymes

Author: Listov, Dina, Vos, Eva, Hoffka, Gyula, Hoch, Shlomo Yakir, Berg, Andrej, Hamer-Rogotner, Shelly, Dym, Orly, Kamerlin, Shina Caroline Lynn, Fleishman, Sarel J.

Issue&Volume: 2025-06-18

Abstract: 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 2,000M1s1. The most efficient shows more than 140 mutations from any natural protein, including a novel active site. It exhibits high stability (greater than 85°C) and remarkable catalytic efficiency (12,700M1s1) and rate (2.8s1), 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 105M1s1 and rate to 30s1, 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.

DOI: 10.1038/s41586-025-09136-2

Source: https://www.nature.com/articles/s41586-025-09136-2