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Simulating enzyme catalysis with electrostatically embedded machine learning potentials
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DOI：10.1039/D6SC01156J.png)
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Abstract
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To simulate enzyme reactions; multiscale quantum mechanics/molecular mechanics (QM/MM) approaches are well established and popular. However; accurately and efficiently estimating enzyme activity is a challenge; because in general; precise methods are too computationally expensive. Here; we demonstrate that enzyme catalysis can be captured by coupling efficient; reactive machine-learned potentials (MLPs) trained on gas phase data to the wider enzyme environment using electrostatic machine learning embedding (EMLE). The EMLE scheme is first applied to the natural Diels–Alderase AbyU; showing that it correctly differentiates the catalytic action on different enzyme–substrate conformations. Then; we show that training a reaction-specific EMLE model allows us to accurately capture the enzyme catalytic effects of the conversion of chorismate to prephenate; a reaction with a highly polarizable and charged transition state. In both cases; in contrast to mechanical embedding approaches; the EMLE scheme allows accurate and efficient predictions of enzyme catalysis; agreeing with high-level QM/MM reference calculations. This approach facilitates the use of gas phase-trained MLPs in MLP/molecular mechanics (ML/MM) simulations and should thus be highly beneficial for computational activity screening of enzyme biocatalysts.
Keywords:
enzyme catalysis
machine-learned potentials
QM/MM
electrostatic embedding
enzyme simulation
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