Toward Automated Identification of Glycan Branching Patterns Using Multistage Mass Spectrometry with Intelligent Precursor Selection

doi:10.1021/acs.analchem.8b03967

	Toward Automated Identification of Glycan Branching Patterns Using Multistage Mass Spectrometry with Intelligent Precursor Selection
	Sun, Shiwei 1,3; Huang, Chuncui 2; Wang, Yaojun 1,3; Liu, Yaming 2,3; Zhang, Jingwei 1,3; Zhou, Jinyu 2,3; Gao, Feng 1,3; Yang, Fei 1,3; Chen, Runsheng 2,3; Mulloy, Barbara 4; Chai, Wengang 4; Li, Yan 2,3; Bu, Dongbo 1,3
	2018-12-18
发表期刊	ANALYTICAL CHEMISTRY
ISSN	0003-2700
卷号	90 期号:24 页码:14412-14422
摘要	Glycans play important roles in a variety of biological processes. Their activities are closely related to the fine details of their structures. Unlike the simple linear chains of proteins, branching is a unique feature of glycan structures, making their identification extremely challenging. Multistage mass spectrometry (MSn) has become the primary method for glycan structural identification. The major difficulty for MSn is the selection of fragment ions as precursors for the next stage of scanning. Widely used strategies are either manual selection by experienced experts, which requires considerable expertise and time, or simply selecting the most intense peaks by which the product-ion spectrum generated may not be structurally informative and therefore fail to make the assignment. We here report a glycan "intelligent precursor selection" strategy (GIPS) to guide MSn experiments. Our approach consists of two key elements, an empirical model to calculate candidate glycan's probability and a statistical model to calculate fragment ion's distinguishing power in order to select the structurally most informative peak as the precursor for next-stage scanning. Using 15 glycan standards, including three pairs with isomeric sequences and eight variously fucosylated oligosaccharides on linear or branched hexasaccharide backbones isolated from a human milk oligosaccharide fraction by HPLC, we demonstrate its successful application to branching pattern analysis with improved efficiency and sensitivity and also the potential for automated operation.
DOI	10.1021/acs.analchem.8b03967
收录类别	SCI
语种	英语
资助项目	National High-Tech Research and Development Project[2014AA021101] ; Scientific Equipment Development Project of Chinese Academy of Sciences[YZ201249] ; National Natural Science Foundation of China[31270834] ; National Natural Science Foundation of China[61272318] ; National Natural Science Foundation of China[31270909] ; National Natural Science Foundation of China[31600650] ; National Natural Science Foundation of China[31671369] ; National Natural Science Foundation of China[31770775] ; National Key Research and Development Program of China[2018YFC0910405] ; March of Dimes research centre grant ; Francis Crick Institute
WOS研究方向	Chemistry
WOS类目	Chemistry, Analytical
WOS记录号	WOS:000454182800044
出版者	AMER CHEMICAL SOC
引用统计	被引频次：25[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/3495
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Chai, Wengang; Li, Yan; Bu, Dongbo
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, 6 Kexueyuan South Rd, Beijing 100080, Peoples R China 2.Chinese Acad Sci, Inst Biophys, 15 Datun Rd, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, 19 Yuquan Rd, Beijing 100049, Peoples R China 4.Imperial Coll London, Glycosci Lab, Fac Med, London W12 0NN, England
推荐引用方式 GB/T 7714	Sun, Shiwei,Huang, Chuncui,Wang, Yaojun,et al. Toward Automated Identification of Glycan Branching Patterns Using Multistage Mass Spectrometry with Intelligent Precursor Selection[J]. ANALYTICAL CHEMISTRY,2018,90(24):14412-14422.
APA	Sun, Shiwei.,Huang, Chuncui.,Wang, Yaojun.,Liu, Yaming.,Zhang, Jingwei.,...&Bu, Dongbo.(2018).Toward Automated Identification of Glycan Branching Patterns Using Multistage Mass Spectrometry with Intelligent Precursor Selection.ANALYTICAL CHEMISTRY,90(24),14412-14422.
MLA	Sun, Shiwei,et al."Toward Automated Identification of Glycan Branching Patterns Using Multistage Mass Spectrometry with Intelligent Precursor Selection".ANALYTICAL CHEMISTRY 90.24(2018):14412-14422.