Abstract
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.
Original language | English |
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Pages (from-to) | 3259-3269 |
Number of pages | 11 |
Journal | JACS Au |
Volume | 3 |
Issue number | 12 |
DOIs | |
State | Published - 25 Dec 2023 |
Bibliographical note
Publisher Copyright:© 2023 The Authors. Published by American Chemical Society.
Keywords
- Catalysis
- De Novo Enzyme Design
- Designed-Local Electric Fields
- Enzyme Engineering
- Machine Learning