Advancing the large-scale CCS database for metabolomics and lipidomics at the machine-learning era. (February 2018)

• Record Type: Journal Article
• Authors: Zhou, Zhiwei; Tu, Jia; Zhu, Zheng-Jiang
• Is Part Of: Current opinion in chemical biology. Volume 42(2018)
• Page Start: 34
• Page End: 41
• Publication Date: 2018-02
• Subjects: Bioorganic chemistry -- Periodicals; Biology -- Periodicals; Biochemistry -- Periodicals; Clinical biochemistry -- Periodicals; Biochemistry -- Periodicals; Chimie bio-organique -- Périodiques; Biologie -- Périodiques; 572.05
• DOI: 10.1016/j.cbpa.2017.10.033
• Languages: English
• ISSNs: 1367-5931

Abstract

Highlights: Ion mobility–mass spectrometry (IM–MS) supports the metabolomics and lipidomics applications. Collision cross-section (CCS) value is a valuable physiochemical property for metabolite/lipid identification. Machine-learning based prediction generates the CCS values in a large scale to facilitate IM–MS based metabolomics and lipidomics. Abstract : Metabolomics and lipidomics aim to comprehensively measure the dynamic changes of all metabolites and lipids that are present in biological systems. The use of ion mobility–mass spectrometry (IM–MS) for metabolomics and lipidomics has facilitated the separation and the identification of metabolites and lipids in complex biological samples. The collision cross-section (CCS) value derived from IM–MS is a valuable physiochemical property for the unambiguous identification of metabolites and lipids. However, CCS values obtained from experimental measurement and computational modeling are limited available, which significantly restricts the application of IM–MS. In this review, we will discuss the recently developed machine-learning based prediction approach, which could efficiently generate precise CCS databases in a large scale. We will also highlight the applications of CCS databases to support metabolomics and lipidomics.