期刊
ANALYTICAL CHEMISTRY
卷 90, 期 24, 页码 14412-14422出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.analchem.8b03967
关键词
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资金
- 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, 61272318, 31270909, 31600650, 31671369, 31770775]
- National Key Research and Development Program of China [2018YFC0910405]
- March of Dimes research centre grant
- Francis Crick Institute
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.
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