Unraveling a Case Where the NAC Approach Fails─MD and QM Modeling of Regioselective cis-Stilbene Oxide Hydrolysis by Engineered Epoxide Hydrolases

IF 6.4 2区 化学 Q1 CHEMISTRY, MEDICINAL
Elvira Bombino, Hesam Arabnejad, Baoyan Liu, Peter A. Jekel, Xiang Sheng, Dick B. Janssen, Fahmi Himo, Hein J. Wijma
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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.

揭示NAC方法失败的一个案例──工程化环氧化物水解酶对区域选择性顺式二苯乙烯氧化物水解的MDand QM模型
基于分子动力学(MD)的预测酶对映体选择性或产物特异性的方法通常使用近攻击构象(NAC)方法。这种基态方法的计算效率,只需几分钟到几小时,就可以在计算机上筛选数千种酶变体。在计算设计和筛选环氧柠檬烯水解酶的新变体以提高立体选择性的过程中,人们注意到,虽然这种方法对大多数底物的预测精度令人满意,但对顺式环氧苯乙烯设计的变体的对映体选择性预测精度要低得多。在这里,为了研究基于nac的方法对这种底物的预测是否不准确,首先,通过实验测试和计算预测另外18种酶变体对顺式二苯乙烯氧化物的对映选择性,生成了一个更大的数据集。随后的MD模拟证实,NAC分析导致整个数据集的对映体选择性预测不准确。密度泛函理论(DFT)计算采用B3LYP-D3(BJ)泛函,活性位点聚类模型为&;gt;300个原子,为NAC方法对顺式二苯乙烯氧化物的预测不准确提供了一个基本原理。底物的芳环通过对过渡态提供立体电子激活和空间位阻的混合作用来影响对环氧化物基团的亲核攻击,这在NAC方法中没有考虑到。利用该底物,DFT准确地预测了对映体选择性。这些结果为NAC方法在预测酶选择性方面的适用性提供了有价值的见解,表明当大体积或立体电子激活基团与反应原子结合时,也可能需要更昂贵的方法,这些方法明确考虑了过渡态结构。
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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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