A Machine Learning-Driven, Probability-Based Approach to Enzyme Catalysis

01 April 2025, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

The workings of enzymes depend crucially on transition state structures, which encode critical chemical information necessary to control their efficiency and selectivity. However, capturing these configurations and describing them on a statistical basis remains a significant challenge due to their transient nature. Here, we leverage a novel enhanced sampling scheme based on a machine learned committor function to provide a probabilistic characterization of transition states in enzymatic reactions. Applied to the glycolysis reaction of maltopentaose catalyzed by human pancreatic α-amylase, this approach successfully reveals the critical role of water molecules in shaping the catalytic landscape, dictating whether the reaction follows a water-assisted or water-mediated mechanism, and providing atomistic insight on how specific hydrogen bonding interactions within the catalytic pocket can influence the stability of transition states. Our findings highlight the potential of this machine-learning-based enhanced sampling scheme to study rare events in complex biochemical systems, offering a powerful tool for unveiling mechanistic details that are often elusive with traditional simulation approaches, and paving the way for accelerating the rational design of novel enzymes through more accurate structure-activity correlations targeting the transition state ensemble.

Keywords

molecular dynamics
machine learning
enzyme catalysis
reaction mechanisms

Supplementary materials

Title
Description
Actions
Title
Supplementary Information
Description
Supplementary Information
Actions

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.