Automatic Chemical Structure Annotation of an LC-MSn Based Metabolic Profile from Green Tea
Automatic Chemical Structure Annotation of an LC-MSn Based Metabolic Profile from Green Tea
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DOI：
10.1021/ac400861a
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发表时间：
2013-06-18
影响因子：
7.4
通讯作者：
Vervoort, Jacques
中科院分区：
文献类型：
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作者：
Ridder, Lars;van der Hooft, Justin J. J.;Vervoort, Jacques
液相色谱-多级精确质谱联用技术（LC-MSn）可以获得粗提物中代谢物的全面光谱信息。为了支持这种复杂样品中存在的许多代谢物的结构表征，我们提出了一种新的方法（http：//www.emetabolomics.org/magma），以基于来自化学数据库（例如PubChem或人类代谢物数据库）的候选分子来自动处理和注释LC-MSn数据集。多级MSn光谱数据自动注释与层次树的计算机生成的子结构的候选分子，以解释所观察到的碎片离子和替代的候选人排名的基础上计算的匹配得分。我们在非靶向LC-MSn（n）上测试了该方法。
Liquid chromatography coupled with multistage accurate mass spectrometry (LC-MSn) can generate comprehensive spectral information of metabolites in crude extracts. To support structural characterization of the many metabolites present in such complex samples, we present a novel method (http://www.emetabolomics.org/magma) to automatically process and annotate the LC-MSn data sets on the basis of candidate molecules from chemical databases, such as PubChem or the Human Metabolite Database. Multistage MSn spectral data is automatically annotated with hierarchical trees of in silico generated substructures of candidate molecules to explain the observed fragment ions and alternative candidates are ranked on the basis of the calculated matching score. We tested this method on an untargeted LC-MSn (n