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  3. Simulating Enzyme Catalysis With Electrostatically Embedded Machine Learning Potentials.
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  3. Simulating Enzyme Catalysis With Electrostatically Embedded Machine Learning Potentials.

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Simulating enzyme catalysis with electrostatically embedded machine learning potentials.

Valentin Gradisteanu1, Elliot W Chan2, Lester Hedges2,3

  • 1Departamento de Química Física, Universidad de Valencia 46100 Burjassot Spain kirill.zinovjev@uv.es.

Chemical Science
|March 12, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

We developed a new computational method using machine-learned potentials and electrostatic embedding to efficiently and accurately simulate enzyme reactions. This approach enables precise enzyme activity screening, overcoming the limitations of expensive traditional methods.

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Area of Science:

  • Computational Chemistry
  • Biochemistry
  • Enzyme Catalysis

Background:

  • Multiscale quantum mechanics/molecular mechanics (QM/MM) methods are standard for simulating enzyme reactions.
  • Accurate and efficient estimation of enzyme activity remains a challenge due to the high computational cost of precise methods.

Purpose of the Study:

  • To develop and validate a novel computational approach for accurately and efficiently simulating enzyme catalysis.
  • To demonstrate the effectiveness of coupling machine-learned potentials (MLPs) with electrostatic machine learning embedding (EMLE) for enzyme simulations.

Main Methods:

  • Coupling efficient, reactive MLPs trained on gas-phase data with the enzyme environment using EMLE.
  • Applying the EMLE scheme to the Diels-Alderase AbyU and the chorismate to prephenate conversion.
  • Comparing EMLE predictions with high-level QM/MM reference calculations.

    • Main Results:

    • The EMLE scheme accurately differentiates catalytic actions on various enzyme-substrate conformations for Diels-Alderase AbyU.
    • A reaction-specific EMLE model accurately captures enzyme catalytic effects for chorismate to prephenate conversion, including a polarizable transition state.
    • EMLE provides accurate and efficient enzyme catalysis predictions, consistent with QM/MM calculations, outperforming mechanical embedding approaches.

    Conclusions:

    • The EMLE scheme offers an accurate and efficient method for simulating enzyme catalysis by integrating gas-phase trained MLPs into ML/MM simulations.
    • This approach significantly enhances the feasibility of computational activity screening for enzyme biocatalysts.
    • EMLE represents a beneficial advancement for computational enzymology and biocatalyst design.