The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authentic compounds. Using currently available analytical methods, complete chemical characterization of metabolomes is infeasible for both technical and economic reasons. For example, unambiguous identification of metabolites is limited by the availability of authentic chemical standards, which, for the majority of molecules, do not exist. Computationally predicted or calculated data are a viable solution to expand the currently limited metabolite reference libraries, if such methods are shown to be sufficiently accurate. For example, determining nuclear magnetic resonance (NMR) spectroscopy spectra in silico has shown promise in the identification and delineation of metabolite structures. Many researchers have been taking advantage of density functional theory (DFT), a computationally inexpensive yet reputable method for the prediction of carbon and proton NMR spectra of metabolites. However, such methods are expected to have some error in predicted 13C and 1H NMR spectra with respect to experimentally measured values. This leads us to the question–what accuracy is required in predicted 13C and 1H NMR chemical shifts for confident metabolite identification? Using the set of 11,716 small molecules found in the Human Metabolome Database (HMDB), we simulated both experimental and theoretical NMR chemical shift databases. We investigated the level of accuracy required for identification of metabolites in simulated pure and impure samples by matching predicted chemical shifts to experimental data. We found 90% or more of molecules in simulated pure samples can be successfully identified when errors of 1H and 13C chemical shifts in water are below 0.6 and 7.1 ppm, respectively, and below 0.5 and 4.6 ppm in chloroform solvation, respectively. In simulated complex mixtures, as the complexity of the mixture increased, greater accuracy of the calculated chemical shifts was required, as expected. However, if the number of molecules in the mixture is known, e.g., when NMR is combined with MS and sample complexity is low, the likelihood of confident molecular identification increased by 90%.

中文翻译：

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使用量子化学计算的 NMR 化学位移对复杂样品中小分子鉴定所需的准确性进行初步调查

自然界中的大多数初级和次级代谢物尚未确定，这对目前需要来自真实化合物分析的参考库的代谢组学研究来说是一项重大挑战。使用目前可用的分析方法，由于技术和经济原因，代谢组的完整化学表征是不可行的。例如，代谢物的明确鉴定受到可靠化学标准可用性的限制，对于大多数分子而言，这些标准不存在。如果这些方法被证明足够准确，则计算预测或计算的数据是扩展当前有限的代谢物参考库的可行解决方案。例如，用计算机确定核磁共振 (NMR) 光谱在识别和描绘代谢物结构方面显示出前景。许多研究人员一直在利用密度泛函理论 (DFT)，这是一种用于预测代谢物碳和质子 NMR 光谱的计算成本低廉但信​​誉良好的方法。然而，预计此类方法在预测的 13C 和 1H NMR 光谱中相对于实验测量值会有一些误差。这就引出了一个问题——预测的 13C 和 1H NMR 化学位移需要什么准确度才能可靠地鉴定代谢物？使用在人类代谢组数据库 (HMDB) 中发现的 11,716 个小分子集，我们模拟了实验和理论 NMR 化学位移数据库。我们通过将预测的化学位移与实验数据相匹配，研究了鉴定模拟纯和不纯样品中代谢物所需的准确度水平。我们发现，当水中的 1H 和 13C 化学位移误差分别低于 0.6 和 7.1 ppm，以及在氯仿溶剂化中分别低于 0.5 和 4.6 ppm 时，可以成功识别模拟纯样品中 90% 或更多的分子。在模拟的复杂混合物中，随着混合物复杂性的增加，正如预期的那样，需要计算的化学位移的准确性更高。然而，如果混合物中的分子数量已知，例如，当 NMR 与 MS 结合使用且样品复杂性较低时，可靠分子鉴定的可能性增加了 90%。