Siddiqui Shakir Ali, Stuyver Thijs, Shaik Sason, Dubey Kshatresh Dutta
Molecular Simulation Lab, Department of Chemistry, School of Natural Sciences, Shiv Nadar Institution of Eminence, Delhi NCR, India 201314.
Ecole Nationale Supérieure de Chimie de Paris, Université PSL, CNRS, Institute of Chemistry for Life and Health Sciences, 75 005 Paris, France.
JACS Au. 2023 Dec 1;3(12):3259-3269. doi: 10.1021/jacsau.3c00536. eCollection 2023 Dec 25.
Designing efficient catalysts is one of the ultimate goals of chemists. In this Perspective, we discuss how local electric fields (LEFs) can be exploited to improve the catalytic performance of supramolecular catalysts, such as enzymes. More specifically, this Perspective starts by laying out the fundamentals of how local electric fields affect chemical reactivity and review the computational tools available to study electric fields in various settings. Subsequently, the advances made so far in optimizing enzymatic electric fields through targeted mutations are discussed critically and concisely. The Perspective ends with an outlook on some anticipated evolutions of the field in the near future. Among others, we offer some pointers on how the recent data science/machine learning revolution, engulfing all science disciplines, could potentially provide robust and principled tools to facilitate rapid inference of electric field effects, as well as the translation between optimal electrostatic environments and corresponding chemical modifications.
设计高效催化剂是化学家的终极目标之一。在这篇综述文章中，我们讨论了如何利用局域电场（LEF）来提高超分子催化剂（如酶）的催化性能。更具体地说，本文开篇阐述了局域电场影响化学反应活性的基本原理，并回顾了用于研究不同环境中电场的计算工具。随后，对通过靶向突变优化酶电场方面迄今取得的进展进行了批判性和简洁的讨论。本文最后展望了该领域在不久的将来的一些预期发展。其中，我们给出了一些指导意见，说明席卷所有科学学科的近期数据科学/机器学习革命如何有可能提供强大且有原则的工具，以促进电场效应的快速推断，以及最佳静电环境与相应化学修饰之间的转化。
