Electronic rearrangements associated with bond forming/breaking in catalytic enzymes require quantum mechanical (QM) treatment beyond classical molecular mechanics (MM). Hybrid QM/MM methods enable tractable simulations but require system-specific setup and are sensitive to the QM region choice and treatment of the QM/MM interface. We demonstrate quantum-accurate treatment of all-atom, complete enzymes in explicit solvent comprising up to 54k atoms and 1 microsecond of total simulation time using the machine-learned interatomic potential (MLIP) eSEN-omol. We reproduce experimental barrier trends for Claisen rearrangement in chorismate mutase, resolve critical intermediate states in PETase catalyzed polymer depolymerization, and distinguish mechanistic alternatives for metal-activated phosphoryl transfer in nucleoside diphosphate kinase. We realize 1000x speedups relative to typical QM/MM calculations without system-specific tuning. These results establish MLIPs as a practical route to QM-accurate simulations of enzyme catalysis.
QuantaMind is presented, a machine learning force field framework that attains density functional theory–level accuracy, enabling fully reactive simulations of complex molecular systems, and establishes a paradigm for long-timescale, fully reactive molecular simulation.
Song Xia, De-Qiang Zhang, Xue-Dong Shang et al.· Science Advances· 0 citations
This work presents a robust and fully periodic QM/MM interface between the open-source molecular dynamics engine GROMACS and the electronic structure theory code CP2K that enables efficient and reproducible QM/MM molecular dynamics and enhanced sampling simulations with a consistent treatment of long-range electrostati...
D. Morozov, C. Blau, Ole Schütt et al.· Journal of Chemical Informat...· 0 citations
Enzymes can be computationally designed for an increasing range of reactions, yet the catalytic efficiencies achieved are typically well below those of natural biocatalysts. Directed evolution can narrow this gap, often improving activity through mutations with non-obvious effects on catalysis. Here, we dissect how dir...
Abbie Lear, H. Bunzel, Adrian J. Mulholland· bioRxiv· 0 citations
We propose a framework for incorporating quantum-computing-based electronic-structure calculations into universal machine-learning interatomic potentials (uMLIPs). Rather than constructing an interatomic potential from scratch, we refine a pretrained DFT-based uMLIP using a small set of accurate reference energies obta...
Classical force fields (FFs) remain the workhorse for large-scale simulations even as machine-learned interatomic potentials (MLIPs) approach ab initio accuracy. They decompose total configuration energies into simple effective interactions whose parameters are traditionally assigned based on atom or bond types, enabli...
Berkay Gunes, Leif Seute, Jigyasa Nigam et al.· 0 citations
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