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Details

Autor(en) / Beteiligte
Titel
Distinctive and Complementary MS 2 Fragmentation Characteristics for Identification of Sulfated Sialylated N-Glycopeptides by nanoLC-MS/MS Workflow
Ist Teil von
  • Journal of the American Society for Mass Spectrometry, 2018-06, Vol.29 (6), p.1166-1178
Ort / Verlag
United States
Erscheinungsjahr
2018
Link zum Volltext
Quelle
SpringerLink
Beschreibungen/Notizen
  • High sensitivity identification of sulfated glycans carried on specific sites of glycoproteins is an important requisite for investigation of molecular recognition events involved in diverse biological processes. However, aiming for resolving site-specific glycosylation of sulfated glycopeptides by direct LC-MS sequencing is technically most challenging. Other than the usual limiting factors such as lower abundance and ionization efficiency compared to analysis of non-glycosylated peptides, confident identification of sulfated glycopeptides among the more abundant non-sulfated glycopeptides requires additional considerations in the selective enrichment and detection strategies. Metal oxide has been applied to enrich phosphopeptides and sialylated glycopeptides, but its use to capture sulfated glycopeptides has not been investigated. Likewise, various complementary MS fragmentation modes have yet to be tested against sialylated and non-sialylated sulfoglycopeptides due to limited appropriate sample availability. In this study, we have investigated the feasibility of sequencing tryptic sulfated N-glycopeptide and its MS fragmentation characteristics by first optimizing the enrichment methods to allow efficient LC-MS detection and MS analysis by a combination of CID, HCD, ETD, and EThcD on hybrid and tribrid Orbitrap instruments. Characteristic sulfated glyco-oxonium ions and direct loss of sulfite from precursors were detected as evidences of sulfate modification. It is anticipated that the technical advances demonstrated in this study would allow a feasible extension of our sulfoglycomic analysis to sulfoglycoproteomics. Graphical Abstract ᅟ.

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