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Molecular dynamics (MD)-based methods to predict the enantioselectivity or product specificity of enzymes typically use a near attack conformation (NAC) approach. The computational efficiency of this ground-state approach, taking minutes to hours, allows to in silico screen thousands of enzyme variants. During the computational design and screening of new variants of limonene epoxide hydrolase for improved stereoselectivity, it was noticed that, while this approach gave satisfactory prediction accuracy for most substrates, the enantioselectivity predictions were far less accurate for variants designed for cis-stilbene oxide. Here, to investigate whether inaccurate predictions with the NAC-based approach were typical for this substrate, first, a larger data set was generated by experimentally testing and computationally predicting the enantioselectivity of an additional 18 enzyme variants for cis-stilbene oxide. Subsequent MD simulations confirmed that a NAC analysis resulted in inaccurate enantioselectivity predictions throughout the data set. Density functional theory (DFT) calculations using the B3LYP-D3(BJ) functional, with an active site cluster model of >300 atoms, provided a rationale for why the NAC approach gave poor predictions with cis-stilbene oxide. The aromatic rings of the substrate influence the nucleophilic attack on the epoxide group, by providing a blend of stereoelectronic activation and steric hindrance to the transition state, which is not considered in the NAC approach. With this substrate, the DFT accurately predicted the enantioselectivity. The results offer valuable insights into the applicability of the NAC approach for predicting enzyme selectivity, suggesting that more expensive methodologies, which do explicitly consider the transition state structure, can also become required when bulky or stereoelectronically activating groups are bound to the reacting atoms.
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Bombino et al. (2026) studied this question.
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