Calculating Glycoprotein Similarities From Mass Spectrometric Data

Abstract

Complex protein glycosylation occurs through biosynthetic steps in the secretory pathway that create macro- and microheterogeneity of structure and function. Required for all life forms, glycosylation diversifies and adapts protein interactions with binding partners that underpin interactions at cell surfaces and pericellular and extracellular environments. Because these biological effects arise from heterogeneity of structure and function, it is necessary to measure their changes as part of the quest to understand nature. Quite often, however, the assumption behind proteomics that post-translational modifications are discrete additions that can be modeled using the genome as a template does not apply to protein glycosylation. Rather, it is necessary to quantify the glycosylation distribution at each glycosite and to aggregate this information into a population of mature glycoproteins that exist in a given biological system. To date, mass spectrometric methods for assigning singly glycosylated peptides are well-established. But it is necessary to quantify glycosylation heterogeneity accurately in order to gauge the alterations that occur during biological processes. The task is to quantify the glycosylated peptide forms as accurately as possible and then apply appropriate bioinformatics algorithms to the calculation of micro- and macro-similarities. In this review, we summarize current approaches for protein quantification as they apply to this glycoprotein similarity problem.

Mass Spectrometry-based Proteomics and Glycoproteomics in COVID-19 Biomarkers Identification: A Mini-review

“…Mucin-type O -glycosylation usually has high peptide-level valence and micro-heterogeneity, which may facilitate the formation of multiple O -glycosylations in the neighborhood, but the glycans at each site may be different [ 129 , 130 ]. Although MS-based glycoproteomics is capable of heterogeneity analysis of all classes of glycosylation, the complexity of glycoforms and variability in the chemical properties of glycoproteins relative to non-modified proteins make enrichment a necessary step before MS analysis [ 131 – 134 ]. Enrichment of glycopeptides isolates the glycans and glycoconjugates from the non-glycosylated background, thus greatly improving the sensitivity of MS analysis.…”

Methods to improve quantitative glycoprotein coverage from bottom‐up LC‐MS data

“…The drawback with targeted acquisition is the limited number of precursor ions selected in a time window, even with today's fast analyzer speeds (Hackett & Zaia, 2021). N ‐Linked glycopeptides typically display heterogeneous glycoforms that result in overlapping reversed‐phase chromatographic elution profiles, often with dozens of glycoforms in the same narrow retention time window (Hong et al, 2013).…”

Multiattribute Glycan Identification and FDR Control for Glycoproteomics

“…Furthermore, N-glycans are comprised of a common core and various extensions, often containing repeating carbohydrate units. There are some residue combinations that are isomeric (e.g., N-glycolyl neuraminic acid (NeuGc) plus fucose has the same atomic composition and exact mass as N-acetyl neuraminic acid (NeuAc) plus hexose), and several more that are very similar in mass to other combinations or to peptide modifications (28)(29)(30). Errors in assigning the monoisotopic mass of the precursor, also called "off-by-X" or peakpicking errors, result in a glycan mass that is off by 1 (or several) Da, and there are several additional combinations of common carbohydrate residues or peptide modifications that are very similar in mass to another combination plus such an isotope error (20).…”