Understanding the Enzyme (S)-Norcoclaurine Synthase Promiscuity to Aldehydes and Ketones

IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL Journal of Chemical Information and Modeling Pub Date : 2024-05-22 DOI:10.1021/acs.jcim.3c01773
Brunno A. Salvatti, Marcelo A. Chagas, Phillipe O. Fernandes, Yan F. X. Ladeira, Aline S. Bozzi, Veronica S. Valadares, Ana Paula Valente, Amanda S. de Miranda, Willian R. Rocha, Vinicius G. Maltarollo and Adolfo H. Moraes*, 
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Abstract

The (S)-norcoclaurine synthase from Thalictrum flavum (TfNCS) stereoselectively catalyzes the Pictet–Spengler reaction between dopamine and 4-hydroxyphenylacetaldehyde to give (S)-norcoclaurine. TfNCS can catalyze the Pictet–Spengler reaction with various aldehydes and ketones, leading to diverse tetrahydroisoquinolines. This substrate promiscuity positions TfNCS as a highly promising enzyme for synthesizing fine chemicals. Understanding carbonyl-containing substrates’ structural and electronic signatures that influence TfNCS activity can help expand its applications in the synthesis of different compounds and aid in protein optimization strategies. In this study, we investigated the influence of the molecular properties of aldehydes and ketones on their reactivity in the TfNCS-catalyzed Pictet–Spengler reaction. Initially, we compiled a library of reactive and unreactive compounds from previous publications. We also performed enzymatic assays using nuclear magnetic resonance to identify some reactive and unreactive carbonyl compounds, which were then included in the library. Subsequently, we employed QSAR and DFT calculations to establish correlations between substrate-candidate structures and reactivity. Our findings highlight correlations of structural and stereoelectronic features, including the electrophilicity of the carbonyl group, to the reactivity of aldehydes and ketones toward the TfNCS-catalyzed Pictet–Spengler reaction. Interestingly, experimental data of seven compounds out of fifty-three did not correlate with the electrophilicity of the carbonyl group. For these seven compounds, we identified unfavorable interactions between them and the TfNCS. Our results demonstrate the applications of in silico techniques in understanding enzyme promiscuity and specificity, with a particular emphasis on machine learning methodologies, DFT electronic structure calculations, and molecular dynamic (MD) simulations.

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了解 (S)-Norcoclaurine Synthase 酶对醛和酮的亲和性。
来自 Thalictrum flavum(TfNCS)的 (S)-norcoclaurine 合酶立体选择性地催化多巴胺与 4-hydroxyphenylacetaldehyde 之间的 Pictet-Spengler 反应,生成 (S)-norcoclaurine。TfNCS 可催化与各种醛和酮的 Pictet-Spengler 反应,生成多种四氢异喹啉。这种底物杂合性使 TfNCS 成为一种极有潜力合成精细化学品的酶。了解影响 TfNCS 活性的含羰基底物的结构和电子特征有助于扩大其在不同化合物合成中的应用,并有助于蛋白质优化策略。在本研究中,我们研究了醛和酮的分子特性对其在 TfNCS 催化的 Pictet-Spengler 反应中的反应性的影响。首先,我们从以前发表的文章中整理出一个反应性和非反应性化合物库。我们还利用核磁共振进行了酶测定,以确定一些有反应和无反应的羰基化合物,并将其纳入化合物库。随后,我们利用 QSAR 和 DFT 计算建立了底物候选结构与反应性之间的相关性。我们的研究结果强调了结构和立体电子学特征(包括羰基的亲电性)与醛和酮在 TfNCS 催化的 Pictet-Spengler 反应中的反应性之间的相关性。有趣的是,在 53 种化合物中,有 7 种化合物的实验数据与羰基的亲电性无关。对于这七种化合物,我们发现它们与 TfNCS 之间存在不利的相互作用。我们的研究结果证明了硅学技术在理解酶的杂合性和特异性方面的应用,其中特别强调了机器学习方法、DFT 电子结构计算和分子动力学 (MD) 模拟。
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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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