Evaluating Mechanical-Embedding ML/MM for Predicting Mutation Effects in Chorismate Mutase Catalysis

Abstract

Predicting how mutations alter enzyme catalysis remains a central challenge in enzymology and enzyme engineering. Although QM/MM simulations can in principle compute the activation free energy associated with enzymatic reactions, their high computational cost limits systematic studies across many variants. Here, we benchmark a mechanical-embedding machine learning potential/molecular mechanics (ML/MM) protocol for predicting mutation effects on chorismate mutase catalysis, a model system extensively studied both experimentally and computationally. In this framework, the QM-region potential energy surface is represented by an actively learned machine-learning potential, while QM/MM electrostatic interactions are treated classically using partial charges predicted from instantaneous geometries for the QM region. Combined with umbrella sampling, the ML/MM approach enables efficient estimation of activation free energies and direct comparison with experimental kinetics. The method shows reasonable correlations with experiment across both nonpolar and polar active-site mutations and is quantitatively accurate for nonpolar mutations despite their narrow energetic range (< 1 kcal mol−1). However, it substantially underestimates the activation free energy for polar mutations. The results highlight both the promise and limitations of mechanical-embedding ML/MM approaches for predicting mutation effects on enzyme catalysis.