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<p>Enzymes catalyze complex chemical transformations with remarkable efficiency and selectivity, yet their atomistic mechanisms remain challenging to capture because conventional simulations trade accuracy for efficiency. Here we introduce a reactive machine learning/molecular mechanics (ML/MM) framework that bridges quantum chemistry with long-timescale sampling, enabling direct exploration of enzymatic transition states and free-energy landscapes. Coupled with metadynamics, this approach achieves nanosecond sampling of bond-forming reactions and quantitatively predicts activation barriers, mutational effects, and stereoselectivity. Applied to Diels-Alderases, the framework not only reproduces experimental activity and <i>endo</i>/<i>exo</i> preferences with sub-kcal mol<sup>−1</sup> accuracy but also uncovers how pathway dynamics and local electrostatics preorganize substrates for selective outcomes. By uniting reactivity, conformational dynamics, and predictive power, this work establishes reactive ML/MM as a broadly applicable strategy for mechanistic enzymology and a foundation for the rational design of new biocatalysts.</p>

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Multiscale machine learning molecular mechanics for mechanism and stereoselectivity of Diels-Alderase catalysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月13日

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  • Xujian Wang,
  • Haocheng Tang,
  • Xiongwu Wu,
  • Bernard R. Brooks,
  • Junmei Wang,
  • Wan-Lu Li

摘要

Enzymes catalyze complex chemical transformations with remarkable efficiency and selectivity, yet their atomistic mechanisms remain challenging to capture because conventional simulations trade accuracy for efficiency. Here we introduce a reactive machine learning/molecular mechanics (ML/MM) framework that bridges quantum chemistry with long-timescale sampling, enabling direct exploration of enzymatic transition states and free-energy landscapes. Coupled with metadynamics, this approach achieves nanosecond sampling of bond-forming reactions and quantitatively predicts activation barriers, mutational effects, and stereoselectivity. Applied to Diels-Alderases, the framework not only reproduces experimental activity and endo/exo preferences with sub-kcal mol−1 accuracy but also uncovers how pathway dynamics and local electrostatics preorganize substrates for selective outcomes. By uniting reactivity, conformational dynamics, and predictive power, this work establishes reactive ML/MM as a broadly applicable strategy for mechanistic enzymology and a foundation for the rational design of new biocatalysts.