4.8 Article

In-Depth Structural Characterization of N-Linked Glycopeptides Using Complete Derivatization for Carboxyl Groups Followed by Positive- and Negative-Ion Tandem Mass Spectrometry

Journal

ANALYTICAL CHEMISTRY
Volume 86, Issue 11, Pages 5360-5369

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/ac500340t

Keywords

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Funding

  1. Japan Society for the Promotion of Science (JSPS)

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Tandem mass spectrometry (MS/MS or MSn) is a powerful tool for characterizing N-linked glycopeptide structures. However, it is still difficult to obtain detailed structural information on the glycan moiety directly from glycopeptide ions. Here, we propose a new method for in-depth analysis of the glycopeptide structure using MS/MS. This method involves complete derivatization of carboxyl groups in glycopeptides. Methylamidation using PyAOP as a condensing reagent has been optimized for derivatizing all carboxyl groups in glycopeptides. By derivatizing carboxyl groups on the peptide moiety (i.e., Asp, Glu, and C-terminus), the glycopeptides efficiently produce informative glycan fragment ions, including the nonreducing end of the glycan moiety under negative-ion collision-induced dissociation (CID) conditions. These glycan fragment ions can define detailed structural features on the glycan moiety (e.g., the specific composition of the two antennae, the location of fucose residues, and the presence/absence of bisecting GlcNAc residues). For sialylated glycopeptides, carboxyl groups on sialic acid residues are simultaneously derivatized using methylamidation, suppressing preferential loss of residues during MS analysis. As a result, both sialylated and nonsialylated glycopeptides can be analyzed in the same manner. Positive-ion CID of methylamine-derivatized glycopeptides mainly provides information on peptide sequence and glycan composition, whereas negative-ion CUD provides in-depth structural information on the glycan moiety. The derivatization step can be readily incorporated into conventional pretreatment for glycopeptide MS analysis without loss of sensitivity, making derivatization suitable for practical use.

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