Mechanistic rules for de novo design of enzymes

• 全文链接

酶从头设计的机制规则

关键词：

来源：

Chem Catalysis

全文链接：

//agri.nais.net.cn/topic/downloadFile/9a10114f-fee5-4016-9167-684d0eb8d0f1

来源地址：

https://www.cell.com/chem-catalysis/fulltext/S2667-1093(25)00132-0?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS2667109325001320%3Fshowall%3Dtrue

资源所属：

饲料用酶工程

类型：

学术文献

语种：

英语

原文发布日期：

2025-05-23

摘要：

While the last two decades have witnessed the development of a number of different strategies to build synthetic nanomotors that deliver mechanical work, making systems that possess engineered catalytic functionality has not so far been demonstrated either theoretically or experimentally in the context of (wet) molecular nanotechnology. We describe a fundamentally new paradigm in the bottom-up design of systems that give direction to chemistry, which will enable future technologies to control how catalytic activity is organized. Our work is inspired by the key observation that the non-equilibrium dynamics of an enzyme during catalysis simultaneously involve energy transduction and conformational changes, i.e., displacements. This suggests that mechanical considerations should play a key role in the stochastic dynamics of an enzyme, and consequently, in its optimal design with the aim of achieving the desired catalytic cycle. Our proposed dynamical paradigm, built on appropriate implementation of the relevant physical constraints on the minimal reaction coordinates, allows us to identify the following three golden rules for the optimal function of a fueled enzyme driven by mechanochemical coupling: (1) the enzyme and the molecule should be attached at the smaller end of each (i.e., friction matching); (2) the conformational change of the enzyme must be comparable to or larger than the conformational change required of the molecule; and (3) the conformational change of the enzyme must be fast enough so that the molecule actually stretches, rather than just following the enzyme without stretching. The mechanistic rules can provide useful input to the complementary perspectives of de novo enzyme design based on machine learning, as they can be used for training the algorithm, as well as fine-tuning the force fields and phenomenological parameters in all-atom simulations.