An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts.
An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts.
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DOI：
10.1186/s13321-022-00587-7
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发表时间：
2022-09-22
影响因子：
8.6
通讯作者：
中科院分区：
文献类型：
--
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自然界中的大多数初级和次级代谢产物尚未被鉴定，这对代谢组学研究来说是一个重大挑战，目前需要从真实化合物的分析中获得参考库。由于技术和经济原因，使用目前可用的分析方法，对代谢物组进行完整的化学表征是不可行的。例如，代谢物的明确鉴定受到真实化学标准品可用性的限制，对于大多数分子来说，不存在真实化学标准品。计算预测或计算的数据是一个可行的解决方案，以扩大目前有限的代谢物参考库，如果这种方法被证明是足够准确的。例如，在计算机中确定核磁共振（NMR）光谱已经显示出在代谢物结构的鉴定和描绘中的前景。许多研究人员一直在利用密度泛函理论（DFT），一种计算成本低廉但信誉良好的方法来预测代谢物的碳和质子NMR谱。然而，预计这些方法在预测的13 C和1H NMR谱中相对于实验测量值具有一些误差。这就引出了一个问题--在预测的13 C和1H NMR化学位移中需要什么样的准确度才能确定代谢物？使用人类代谢组数据库（HMDB）中的11，716个小分子，我们模拟了实验和理论NMR化学位移数据库。我们研究了在模拟纯和不纯样品中识别代谢物所需的准确度水平，通过将预测的化学位移与实验数据相匹配。当1H和13C化学位移在水中的误差分别小于0.6和7.1ppm，在氯仿溶剂中的误差分别小于0.5和4.6ppm时，可以成功地识别模拟纯样品中90%以上的分子。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，需要更高精度的计算化学位移。然而，如果混合物中的分子数量已知，例如，当NMR与MS相结合且样品复杂性较低时，可靠分子鉴定的可能性增加了90%。在线版本包含补充材料，可通过10.1186/s13321 - 022 - 00587 - 7获得。
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%. The online version contains supplementary material available at 10.1186/s13321-022-00587-7.
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影响因子：
4.4
作者：
BECKE, AD
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影响因子：
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作者：
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