美国华盛顿大学David Baker课题组研究出金属水解酶的计算设计。这一研究成果发表在2025年12月3日出版的国际学术期刊《自然》上。

在这里，课题组人员引入了RFdiffthemion2，它消除了这些要求，并从量子化学衍生的活性位点几何形状开始设计锌金属水解酶。从实验测试的最初96个设计中，最活跃的催化效率（k_cat/K_M）为16000 μ M ^-1/ s^-1。比先前设计的金属水解酶高几个数量级。第二轮96个设计中又产生了3个高活性酶，k_cat/K_M值高达53000 μ M ^-1/ s^-1。催化速率常数（k_cat）可达1.5 s^-1。他们的最活跃设计的设计模型与已知结构不同，彼此之间也不同，最活跃设计的晶体结构与设计模型非常接近，证明了设计方法的准确性。最活跃的酶被PLACER12和Chai-1预测具有预先组织的活性位点，该活性位点有效地定位底物，以便被结合金属激活的水分子进行亲核攻击。直接从计算机生成高活性酶的能力，无需实验优化，将使新一代强大的设计催化剂成为可能。

研究人员表示，De novo酶设计旨在构建含有理想活性位点的蛋白质，其催化残基围绕并稳定目标化学反应的过渡态。生成式人工智能方法RFdiffthemion解决了这个问题，但需要指定每个催化残基的序列位置和主干坐标，限制了采样。

附：英文原文

Title: Computational design of metallohydrolases

Author: Kim, Donghyo, Woodbury, Seth M., Ahern, Woody, Tischer, Doug, Kang, Alex, Joyce, Emily, Bera, Asim K., Hanikel, Nikita, Salike, Saman, Krishna, Rohith, Yim, Jason, Pellock, Samuel J., Lauko, Anna, Kalvet, Indrek, Hilvert, Donald, Baker, David

Issue&Volume: 2025-12-03

Abstract: De novo enzyme design seeks to build proteins containing ideal active sites with catalytic residues surrounding and stabilizing the transition state(s) of the target chemical reaction1,2,3,4,5,6,7. The generative artificial intelligence method RFdiffusion8,9 solves this problem, but requires specifying both the sequence position and backbone coordinates for each catalytic residue, limiting sampling. Here we introduce RFdiffusion2, which eliminates these requirements, and use it to design zinc metallohydrolases starting from quantum chemistry-derived active site geometries. From an initial set of 96 designs tested experimentally, the most active has a catalytic efficiency (kcat/KM) of 16,000M1s1, orders of magnitude higher than previously designed metallohydrolases6,7,10,11. A second round of 96 designs yielded 3 additional highly active enzymes, with kcat/KM values of up to 53,000M1s1 and a catalytic rate constant (kcat) of up to 1.5s1. The design models of the four most active designs differ from known structures and from each other, and the crystal structure of the most active design is very close to the design model, demonstrating the accuracy of the design method. The most active enzymes are predicted by PLACER12 and Chai-1 (ref. 13) to have preorganized active sites that effectively position the substrate for nucleophilic attack by a water molecule activated by the bound metal. The ability to generate highly active enzymes directly from the computer, without experimental optimization, should enable a new generation of potent designer catalysts14,15.

DOI: 10.1038/s41586-025-09746-w

Source: https://www.nature.com/articles/s41586-025-09746-w