Startseite Minimum energy path methods and reactivity for enzyme reaction mechanisms: a perspective
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Minimum energy path methods and reactivity for enzyme reaction mechanisms: a perspective

  • Neil R. McFarlane ORCID logo und Jeremy N. Harvey ORCID logo EMAIL logo
Veröffentlicht/Copyright: 21. Juli 2025
Pure and Applied Chemistry
Aus der Zeitschrift Pure and Applied Chemistry

Abstract

In this perspective article, we discuss the link between minimum energy paths and activation parameters for reactions on complex potential energy surfaces involving many possible local minima, as are typically found for enzyme-substrate complexes. Such systems are frequently tackled with hybrid QM/MM methods in order to characterize reactivity. The link between local energy barriers along a minimum energy path, the so-called exponential average of such local energy barriers, and multiconformational transition state theory is discussed. Also, it is shown that in case of positive skewness of the distribution of barrier heights across sets of minimum energy paths, exponential averaging converges relatively quickly with the number of paths used.


Corresponding author: Jeremy N. Harvey, Department of Chemistry and Division of Quantum Chemistry and Physical Chemistry, KU Leuven, Celestijnenlaan 200F, B-3001, Leuven, Belgium, e-mail:
Article note: A collection of invited papers to celebrate the UN’s proclamation of 2025 as the International Year of Quantum Science and Technology.

Funding source: KU Leuven

Award Identifier / Grant number: C14/22/087

Acknowledgments

The authors thank Profs. Julianna Olah and Ulf Ryde for helpful discussions. They also acknowledge funding from KU Leuven research through grant C14/22/087.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. The text was written by both authors. The Monte Carlo analysis was performed by N.M.

  4. Use of Large Language Models, AI and Machine Learning Tools: Non declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: Yes, from KULeuven research funding, grant ref. C14/22/087.

  7. Data availability: Not applicable.

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Received: 2025-04-16
Accepted: 2025-06-24
Published Online: 2025-07-21

© 2025 IUPAC & De Gruyter

Heruntergeladen am 8.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/pac-2025-0484/pdf
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