Novel O-linked sialoglycan structures in human urinary glycoproteins. (5th February 2020)

• Record Type: Journal Article
• Authors: Pap, Adam; Tasnadi, Ervin; Medzihradszky, Katalin F.; Darula, Zsuzsanna
• Is Part Of: Molecular omics. Volume 16:Number 2(2020)
• Page Start: 156
• Page End: 164
• Publication Date: 2020-02-05
• Subjects: Molecular biology -- Periodicals; Biochemistry -- Periodicals; Biological systems -- Periodicals; Molecular Biology; Computational Biology; Biochemistry; Biological systems; Molecular biology; Periodicals; Electronic journals; Periodicals; Fulltext; Internet Resources; Periodicals
• DOI: 10.1039/c9mo00160c
• Languages: English
• ISSNs: 2515-4184

Abstract

Abstract : Identification of new glycoforms for glycopeptides confidently assigned from primary database searches permitting the most common O -glycans only. Abstract : Glycopeptides represent cross-linked structures between chemically and physically different biomolecules. Mass spectrometric analysis of O -glycopeptides may reveal the identity of the peptide, the composition of the glycan and even the connection between certain sugar units, but usually only the combination of different MS/MS techniques provides sufficient information for reliable assignment. Currently, HCD analysis followed by diagnostic sugar fragment-triggered ETD or EThcD experiments is the most promising data acquisition protocol. However, the information content of the different MS/MS data is handled separately by search engines. We are convinced that these data should be used in concert, as we demonstrate in the present study. First, glycopeptides bearing the most common glycans can be identified from EThcD and/or HCD data. Then, searching for Y0 (the gas-phase deglycosylated peptide) in HCD spectra, the potential glycoforms of these glycopeptides could be lined up. Finally, these spectra and the corresponding EThcD data can be used to verify or discard the tentative assignments and to obtain further structural information about the glycans. We present 18 novel human urinary sialoglycan structures deciphered using this approach. To accomplish this in an automated fashion further software development is necessary.