Automatic Chemical Structure Annotation of an LC鈥揗Sn Based Metabolic Profile from Green Tea

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• 作者：Lars Ridder ; Justin J. J. van der Hooft ; Stefan Verhoeven ; Ric C. H. de Vos ; Raoul J. Bino ; Jacques Vervoort
• 刊名：Analytical Chemistry
• 出版年：2013
• 出版时间：June 18, 2013
• 年：2013
• 卷：85
• 期：12
• 页码：6033-6040
• 全文大小：577K
• 年卷期：v.85,no.12(June 18, 2013)
• ISSN：1520-6882

文摘

Liquid chromatography coupled with multistage accurate mass spectrometry (LC鈥揗Sⁿ) 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鈥揗Sⁿ 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鈥揗Sⁿ (n 鈮?3) data set of a green tea extract, generated on an LC-LTQ/Orbitrap hybrid MS system. For the 623 spectral trees obtained in a single LC鈥揗Sⁿ run, a total of 116鈥?40 candidate molecules with monoisotopic masses matching within 5 ppm mass accuracy were retrieved from the PubChem database, ranging from 4 to 1327 candidates per molecular ion. The matching scores were used to rank the candidate molecules for each LC鈥揗Sⁿ component. The median and third quartile fractional ranks for 85 previously identified tea compounds were 3.5 and 7.5, respectively. The substructure annotations and rankings provided detailed structural information of the detected components, beyond annotation with elemental formula only. Twenty-four additional components were putatively identified by expert interpretation of the automatically annotated data set, illustrating the potential to support systematic and untargeted metabolite identification.