Unraveling a Case Where the NAC Approach Fails─MD and QM Modeling of Regioselective cis-Stilbene Oxide Hydrolysis by Engineered Epoxide Hydrolases.
- 1Biotransformation and Biocatalysis, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 4, 9747AGGroningen, The Netherlands.
- 2State Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin300308, P. R. China.
- 3Department of Chemistry, Arrhenius Laboratory, Stockholm University, 10691Stockholm, Sweden.
Abstract
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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BibTeX
@article{bombino2026unraveling,
title = {{Unraveling a Case Where the NAC Approach Fails─MD and QM Modeling of Regioselective cis-Stilbene Oxide Hydrolysis by Engineered Epoxide Hydrolases.}},
author = {Elvira Bombino and Hesam Arabnejad and Baoyan Liu and Peter A Jekel and Xiang Sheng and Dick B Janssen and Fahmi Himo and Hein J Wijma},
journal = {Journal of chemical information and modeling},
year = {2026},
month = oct,
doi = {10.1021/acs.jcim.6c02029},
url = {https://europepmc.org/article/MED/42912923},
note = {CC BY},
}
RIS
TY - JOUR
TI - Unraveling a Case Where the NAC Approach Fails─MD and QM Modeling of Regioselective cis-Stilbene Oxide Hydrolysis by Engineered Epoxide Hydrolases.
AU - Elvira Bombino
AU - Hesam Arabnejad
AU - Baoyan Liu
AU - Peter A Jekel
AU - Xiang Sheng
AU - Dick B Janssen
AU - Fahmi Himo
AU - Hein J Wijma
PY - 2026
DA - 2026/10/01
JO - Journal of chemical information and modeling
DO - 10.1021/acs.jcim.6c02029
UR - https://europepmc.org/article/MED/42912923
AB - 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.
SN - 1549-9596
SN - 1549-960X
ER -