ac702081a_si_001.pdf (686.59 kB)
Maximizing Coverage of Glycosylation Heterogeneity in MALDI-MS Analysis of Glycoproteins with Up to 27 Glycosylation Sites
journal contribution
posted on 2008-05-01, 00:00 authored by Ying Zhang, Eden P. Go, Heather DesaireGlycosylation affects various biological functions of proteins (e.g., protein binding, inter- or intracell signaling,
etc.), and it can serve as an indicator of disease. Therefore, characterization of the glycosylation in proteins is
one important step in developing a comprehensive understanding of the biological significance of glycosylation
and in facilitating disease diagnosis. Glycopeptide-based
MS analysis has proven to be a viable tool for glycopeptide
analysis. However, when glycopeptides coexist with peptides, glycopeptide signals are usually suppressed by the
strongly ionizing peptides. Toward this end, it would be
desirable to seek methods to improve glycopeptide detection. Herein, we performed an in-depth study of maximizing glycosylation coverage on model glycoproteins by
optimizing all the aspects of glycopeptide-based analysis,
including sample preparation methods, mass spectral
techniques, and data analysis strategies. For sample
preparation, several approaches, including reversed-phase high-performance liquid chromatography, lectin-based affinity, and hydrophilic affinity using a carbohydrate-based resin, were compared and tested individually as
well as in parallel. For mass spectral techniques, profiling
glycopeptides in both positive and negative ion mode is
essential to obtain complete glycan profiles. For data
analysis, incorporating variable modifications in the database search of GlycoPep DB enhances glycopeptide
coverage. In addition, the use of PNGase F helps to
confirm the presence of weakly ionized glycopeptides
when they coelute with strongly ionizing species. In doing
so, we created a work flow that is designed specifically to
optimize the coverage of glycosylation heterogeneity in
terms of the number of glycosylation sites detected and
their corresponding glycan profiles. To test the effectiveness of this approach, a glycoprotein with 27 potential
glycosylation sites, and an unknown glycosylation profile,
was analyzed; on this protein, more than 300 glycoforms
from 23 detected glycosylation sites were identified. This
work demonstrates that these strategies significantly
improve the glycopeptide detection, thereby, facilitating
understanding the functional properties of glycans on the
glycoproteins.