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An automated framework for NMR chemical shift calculations of small organic molecules.
Journal of Cheminformatics ( IF 8.6 ) Pub Date : 2018-10-26 , DOI: 10.1186/s13321-018-0305-8
Yasemin Yesiltepe 1, 2 , Jamie R Nuñez 2 , Sean M Colby 2 , Dennis G Thomas 2 , Mark I Borkum 2 , Patrick N Reardon 3 , Nancy M Washton 2 , Thomas O Metz 2 , Justin G Teeguarden 2 , Niranjan Govind 2 , Ryan S Renslow 1, 2
Affiliation  

When using nuclear magnetic resonance (NMR) to assist in chemical identification in complex samples, researchers commonly rely on databases for chemical shift spectra. However, authentic standards are typically depended upon to build libraries experimentally. Considering complex biological samples, such as blood and soil, the entirety of NMR spectra required for all possible compounds would be infeasible to ascertain due to limitations of available standards and experimental processing time. As an alternative, we introduce the in silico Chemical Library Engine (ISiCLE) NMR chemical shift module to accurately and automatically calculate NMR chemical shifts of small organic molecules through use of quantum chemical calculations. ISiCLE performs density functional theory (DFT)-based calculations for predicting chemical properties—specifically NMR chemical shifts in this manuscript—via the open source, high-performance computational chemistry software, NWChem. ISiCLE calculates the NMR chemical shifts of sets of molecules using any available combination of DFT method, solvent, and NMR-active nuclei, using both user-selected reference compounds and/or linear regression methods. Calculated NMR chemical shifts are provided to the user for each molecule, along with comparisons with respect to a number of metrics commonly used in the literature. Here, we demonstrate ISiCLE using a set of 312 molecules, ranging in size up to 90 carbon atoms. For each, calculation of NMR chemical shifts have been performed with 8 different levels of DFT theory, and with solvation effects using the implicit solvent Conductor-like Screening Model. The DFT method dependence of the calculated chemical shifts have been systematically investigated through benchmarking and subsequently compared to experimental data available in the literature. Furthermore, ISiCLE has been applied to a set of 80 methylcyclohexane conformers, combined via Boltzmann weighting and compared to experimental values. We demonstrate that our protocol shows promise in the automation of chemical shift calculations and, ultimately, the expansion of chemical shift libraries.

中文翻译:

小型有机分子NMR化学位移计算的自动化框架。

当使用核磁共振(NMR)协助复杂样品中的化学鉴定时,研究人员通常依赖于数据库进行化学位移光谱分析。但是,通常会根据真实标准来建立实验库。考虑到复杂的生物样品,例如血液和土壤,由于可用标准品和实验处理时间的限制,无法确定所有可能的化合物所需的NMR光谱的整体。作为替代方案,我们引入了计算机化学图书馆引擎(ISiCLE)NMR化学位移模块,以通过使用量子化学计算来准确自动计算小有机分子的NMR化学位移。ISiCLE通过开源的高性能计算化学软件NWChem,执行基于密度泛函理论(DFT)的计算,以预测化学性质,特别是本手稿中的NMR化学位移。ISiCLE使用用户选择的参考化合物和/或线性回归方法,使用DFT方法,溶剂和NMR活性核的任何可用组合来计算分子组的NMR化学位移。将计算出的NMR化学位移提供给用户每个分子,以及与文献中常用的许多指标进行比较。在这里,我们演示了使用一组312个分子的ISiCLE,分子大小不超过90个碳原子。对于每种化合物,已经使用8种不同水平的DFT理论进行了NMR化学位移的计算,并使用隐含溶剂类导体筛选模型具有溶剂化作用。DFT方法对计算出的化学位移的依赖性已经通过基准测试进行了系统地研究,随后与文献中提供的实验数据进行了比较。此外,将ISiCLE应用于一组80个甲基环己烷构象异构体,通过Boltzmann加权进行组合,并与实验值进行比较。我们证明了我们的协议在化学位移计算的自动化以及最终化学位移库的扩展方面显示出了希望。DFT方法对计算出的化学位移的依赖性已经通过基准测试进行了系统地研究,随后与文献中提供的实验数据进行了比较。此外,将ISiCLE应用于一组80个甲基环己烷构象异构体,通过Boltzmann加权进行组合,并与实验值进行比较。我们证明了我们的协议在化学位移计算的自动化以及最终化学位移库的扩展方面显示出了希望。DFT方法对计算出的化学位移的依赖性已经通过基准测试进行了系统地研究,随后与文献中提供的实验数据进行了比较。此外,将ISiCLE应用于一组80个甲基环己烷构象异构体,通过Boltzmann加权进行组合,并与实验值进行比较。我们证明了我们的协议在化学位移计算的自动化以及最终化学位移库的扩展方面显示出了希望。
更新日期:2018-10-26
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