Elvira Bombino, Hesam Arabnejad, Baoyan Liu, Peter A. Jekel, Xiang Sheng, Dick B. Janssen, Fahmi Himo, Hein J. Wijma
{"title":"Unraveling a Case Where the NAC Approach Fails─MD and QM Modeling of Regioselective cis-Stilbene Oxide Hydrolysis by Engineered Epoxide Hydrolases","authors":"Elvira Bombino, Hesam Arabnejad, Baoyan Liu, Peter A. Jekel, Xiang Sheng, Dick B. Janssen, Fahmi Himo, Hein J. Wijma","doi":"10.1021/acs.jcim.6c02029","DOIUrl":null,"url":null,"abstract":"<p>Molecular dynamics\r\n(MD)-based methods to predict the enantioselectivity\r\nor product specificity of enzymes typically use a near attack conformation\r\n(NAC) approach. The computational efficiency of this ground-state\r\napproach, taking minutes to hours, allows to in silico screen thousands\r\nof enzyme variants. During the computational design and screening\r\nof new variants of limonene epoxide hydrolase for improved stereoselectivity,\r\nit was noticed that, while this approach gave satisfactory prediction\r\naccuracy for most substrates, the enantioselectivity predictions were\r\nfar less accurate for variants designed for <em>cis</em>-stilbene\r\noxide. Here, to investigate whether inaccurate predictions with the\r\nNAC-based approach were typical for this substrate, first, a larger\r\ndata set was generated by experimentally testing and computationally\r\npredicting the enantioselectivity of an additional 18 enzyme variants\r\nfor <em>cis</em>-stilbene oxide. Subsequent MD simulations\r\nconfirmed that a NAC analysis resulted in inaccurate enantioselectivity\r\npredictions throughout the data set. Density functional theory (DFT)\r\ncalculations using the B3LYP-D3(BJ) functional, with an active site\r\ncluster model of >300 atoms, provided a rationale for why the NAC\r\napproach gave poor predictions with <em>cis</em>-stilbene\r\noxide. The aromatic rings of the substrate influence the nucleophilic\r\nattack on the epoxide group, by providing a blend of stereoelectronic\r\nactivation and steric hindrance to the transition state, which is\r\nnot considered in the NAC approach. With this substrate, the DFT accurately\r\npredicted the enantioselectivity. The results offer valuable insights\r\ninto the applicability of the NAC approach for predicting enzyme selectivity,\r\nsuggesting that more expensive methodologies, which do explicitly\r\nconsider the transition state structure, can also become required\r\nwhen bulky or stereoelectronically activating groups are bound to\r\nthe reacting atoms.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 19","pages":"12877–12890"},"PeriodicalIF":6.4000,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemical Information and Modeling ","FirstCategoryId":"92","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acs.jcim.6c02029","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/9/22 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
引用次数: 0
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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