Deciphering the Properties and Functions of Glycoproteins Using Quantitative Proteomics

Abstract

Glycosylation is one of the most common and important protein modifications, and it regulates the properties and functions of a wide range of proteins. Aberrant glycosylation is directly related to human diseases. Recently, with the advancement of mass spectrometry (MS) instrumentation and MS-based glycoproteomic methods, global characterization of glycoproteins in complex biological samples has become possible. Using quantitative proteomics, the abundance of glycoproteins in different samples can be quantified, which provides a wealth of information to further our understanding of protein functions, cellular activities, and the molecular mechanisms of diseases. In this review, we discuss quantitative proteomic methods used for comprehensive analysis of protein glycosylation, and cover the applications of quantitative glycoproteomics to unveil the properties and functions of glycoproteins and their association with various diseases. It is expected that quantitative proteomic methods will be extensively applied to explore the role of protein glycosylation in complex biological systems, and to identify glycoproteins as biomarkers for disease detection and as therapeutic targets for disease treatment.

Systematic analysis of the impact of phosphorylation and O-GlcNAcylation on protein subcellular localization

“…This is not surprising because protein O-GlcNAcylation are mainly related to the crosstalk with other modifications such as phosphorylation and the regulation of gene expression while interacting with RNA and DNA. 68 – 70 …”

Integrating the analysis of human biopsies using post‐translational modifications proteomics

“…Glycosylation is prevalent PTM and defined by the addition of a glycan to an amino acid, namely asparagine (N‐linked) or serine and threonine (O‐linked; Cain et al, 2021 ; Girgis et al, 2024 ; Morelle et al, 2006 ). Glycosylation impacts many functions of a protein: its structure, protein–protein interactions and physical dynamics (Xu et al, 2023 ). In complex biological samples, it is challenging to detect and categorize glycosylated residues due to their heterogeneity, stoichiometry constraints, and uncertainty of the specific residue that is glycosylated (Riley et al, 2021 ).…”