美国华盛顿大学David Baker研究小组的最新研究提出了丝氨酸水解酶的计算设计。这一研究成果于2025年2月13日发表在国际顶尖学术期刊《科学》上。

以丝氨酸水解酶为模型系统，该研究组将RFdiffthemion的生成能力与用于评估活性位点预组织的集成生成方法结合起来，从最小活性位点描述开始设计酶。实验表征表明，催化效率（k_cat/K_m）可达2.2x10⁵ M^-1 s^-1和晶体结构与设计模型(Cα RMSDs <1 Å)。通过选择反应坐标上的结构相容性，可以在低通量筛选中鉴定出具有与天然丝氨酸水解酶不同的五种不同褶皱的新催化剂。他们的新方法提供了对催化的几何基础的洞察，并为设计催化多步骤转化的酶提供了路线图。

据悉，设计具有复杂活性位点介导多步反应的酶仍然是一个突出的挑战。

附：英文原文

Title: Computational design of serine hydrolases

Author: Anna Lauko, Samuel J. Pellock, Kiera H. Sumida, Ivan Anishchenko, David Juergens, Woody Ahern, Jihun Jeung, Alex Shida, Andrew Hunt, Indrek Kalvet, Christoffer Norn, Ian R. Humphreys, Cooper Jamieson, Rohith Krishna, Yakov Kipnis, Alex Kang, Evans Brackenbrough, Asim K. Bera, Banumathi Sankaran, K. N. Houk, David Baker

Issue&Volume: 2025-02-13

Abstract: The design of enzymes with complex active sites that mediate multistep reactions remains an outstanding challenge. With serine hydrolases as a model system, we combined the generative capabilities of RFdiffusion with an ensemble generation method for assessing active site preorganization to design enzymes starting from minimal active site descriptions. Experimental characterization revealed catalytic efficiencies (k_cat/K_m) up to 2.2x10⁵ M^-1 s^-1 and crystal structures that closely match the design models (Cα RMSDs < 1 Å). Selection for structural compatibility across the reaction coordinate enabled identification of new catalysts in low-throughput screens with five different folds distinct from those of natural serine hydrolases. Our de novo approach provides insight into the geometric basis of catalysis and a roadmap for designing enzymes that catalyze multistep transformations.

DOI: adu2454

Source: https://www.science.org/doi/10.1126/science.adu2454