ML/MM toolkit – Towards Accelerated Mechanistic Investigation of Enzymatic Reactions

Quantum mechanics/molecular mechanics (QM/MM) methods have long been used to analyze enzymatic reaction mechanisms in silico. While treating the active site with QM and the remainder with MM reduces the computational cost, the inherently high computational cost of the QM calculation is still a major limitation for their application to a wide variety of enzymes. Replacing QM with machine-learning (ML) interatomic potentials yields ML/MM approaches that can further reduce computational cost while retaining accuracy. Here, we developed ML/MM toolkit, an open-source collection of a core implementation of the ML/MM methods, ML/MM calculator, and associated command-line tools, employing Meta’s Universal Model for Atoms (UMA) as the ML interatomic potentials. This toolkit streamlines the workflow necessary for the enzymatic reaction mechanism analyses such as energy minimization, transition-state (TS) search, and vibrational analysis to calculate the reaction free energy (∆G) and activation free energy (∆G‡) for a given enzyme–substrate complex structure. A link atom boundary treatment is implemented to include amino-acid residues in the ML region and full-system Hessians are available for TS searches and vibrational analyses. To accelerate TS searches in systems comprising ≈ 10,000 atoms, we developed a Partial Hessian Guided Dimer (PHG-Dimer) method that uses the active-site Hessian to determine initial dimer orientation. We also integrated a mass-scaled flattening loop to suppress spurious imaginary modes. A test calculation on an enzymatic Claisen rearrangement reproduced its experimental and high-level QM/MM activation barriers to within a few kcal mol−1, while a full reaction-path workflow completed in under two hours on a single consumer GPU. Collectively, the ML/MM toolkit makes enzymatic mechanistic investigations more accessible.