Recent advances in computational algorithms and software for large-scale glycoproteomics

• Published in: Current opinion in chemical biology Vol. 72; p. 102238
• Main Authors: Polasky, Daniel A., Nesvizhskii, Alexey I.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.02.2023
• Subjects: Algorithms; Database Search; False Discovery Rate; Glycopeptide Identification; Glycopeptides; Glycoproteomics; Glycosylation; Polysaccharides - analysis; Software; False Discovery Rate; Glycopeptide Identification; Glycoproteomics; Software; FDR; Database Search
• ISSN: 1367-5931; 1879-0402; 1879-0402
• DOI: 10.1016/j.cbpa.2022.102238

Glycoproteomics, or characterizing glycosylation events at a proteome scale, has seen rapid advances in methods for analyzing glycopeptides by tandem mass spectrometry in recent years. These advances have enabled acquisition of far more comprehensive and large-scale datasets, precipitating an urgent need for improved informatics methods to analyze the resulting data. A new generation of glycoproteomics search methods has recently emerged, using glycan fragmentation to split the identification of a glycopeptide into peptide and glycan components and solve each component separately. In this review, we discuss these new methods and their implications for large-scale glycoproteomics, as well as several outstanding challenges in glycoproteomics data analysis, including validation of glycan assignments and quantitation. Finally, we provide an outlook on the future of glycoproteomics from an informatics perspective, noting the key challenges to achieving widespread and reproducible glycopeptide annotation and quantitation.