Retention time prediction for dereplication of natural products (CxHyOz) in LC-MS metabolite profiling
Retention time prediction for dereplication of natural products (CxHyOz) in LC-MS metabolite profiling
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DOI：
10.1016/j.phytochem.2014.10.005
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发表时间：
2014-12-01
期刊：
影响因子：
3.8
通讯作者：
Carrupt, Pierre-Alain
中科院分区：
文献类型：
--
作者：
Eugster, Philippe J.;Boccard, Julien;Carrupt, Pierre-Alain
检测和早期识别的天然产物（NP）的去复制的目的，需要高效，高分辨率的方法来分析粗天然提取物。这项任务是困难的，因为在这些复杂的生物基质中有大量的NP，并且因为它们具有非常高的化学多样性。使用超高压液相色谱法结合高分辨率质谱法（UHPLC-HR-MS）进行代谢物分析对于复杂混合物的分离非常有效，并作为去复制的第一步提供分子式信息。这种结构信息单独或甚至与化学分类信息相结合，往往是不够的明确的代谢物鉴定。在本研究中，在通用UHPLC-HR-MS分析条件下分析了仅含有C、H和O原子的260个NP的代表性组。基于测定的保留时间和由结构计算的8个简单理化参数，建立了两个易于使用的定量结构保留关系（QSRR）模型。首先，一个原始的方法，使用几个偏最小二乘（PLS）回归，根据植物化学类提供了令人满意的结果与一个简单的计算。其次，一个独特的人工神经网络（ANN）模型提供了类似的结果，对整个NP集，但需要专用的软件。在这项研究中描述的保留预测方法被发现，以提高给定的分析物之间的推定异构体结构的识别的置信水平。它的适用性被验证为模型植物提取物中的NP的去复制。(c)2014爱思唯尔有限公司版权所有。
The detection and early identification of natural products (NPs) for dereplication purposes require efficient, high-resolution methods for the profiling of crude natural extracts. This task is difficult because of the high number of NPs in these complex biological matrices and because of their very high chemical diversity. Metabolite profiling using ultra-high pressure liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HR-MS) is very efficient for the separation of complex mixtures and provides molecular formula information as a first step in dereplication. This structural information alone or even combined with chemotaxonomic information is often not sufficient for unambiguous metabolite identification. In this study, a representative set of 260 NPs containing C, H, and O atoms only was analysed in generic UHPLC-HR-MS profiling conditions. Two easy to use quantitative structure retention relationship (QSRR) models were built based on the measured retention time and on eight simple physicochemical parameters calculated from the structures. First, an original approach using several partial least square (PLS) regressions according to the phytochemical classes provided satisfactory results with an easy calculation. Secondly, a unique artificial neural network (ANN) model provided similar results on the whole set of NPs but required dedicated software. The retention prediction methods described in this study were found to improve the level of confidence of the identification of given analytes among putative isomeric structures. Its applicability was verified for the dereplication of NPs in model plant extracts. (c) 2014 Elsevier Ltd. All rights reserved.