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. 2021:20:100060.
doi: 10.1074/mcp.R120.002090. Epub 2021 Feb 6.

Recent Advances in Software Tools for More Generic and Precise Intact Glycopeptide Analysis

Affiliations

Recent Advances in Software Tools for More Generic and Precise Intact Glycopeptide Analysis

Weiqian Cao et al. Mol Cell Proteomics. 2021.

Abstract

Intact glycopeptide identification has long been known as a key and challenging barrier to the comprehensive and accurate understanding the role of glycosylation in an organism. Intact glycopeptide analysis is a blossoming field that has received increasing attention in recent years. MS-based strategies and relative software tools are major drivers that have greatly facilitated the analysis of intact glycopeptides, particularly intact N-glycopeptides. This article provides a systematic review of the intact glycopeptide-identification process using MS data generated in shotgun proteomic experiments, which typically focus on N-glycopeptide analysis. Particular attention is paid to the software tools that have been recently developed in the last decade for the interpretation and quality control of glycopeptide spectra acquired using different MS strategies. The review also provides information about the characteristics and applications of these software tools, discusses their advantages and disadvantages, and concludes with a discussion of outstanding tools.

Keywords: bottom-up experimental strategies; intact glycopeptide analysis; mass spectrometry; quality control; software applications; software tools.

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Conflict of interest statement

Conflict of interest The authors declare no competing interests.

Figures

None
Graphical abstract
Fig. 1
Fig. 1
General approaches for matching of the glycan moiety and carrier peptide.
Fig. 2
Fig. 2
A dedicated software pGlyco 2.0 for site-specific glycan interpretation.A, the flowchart of pGlyco 2.0. B, the FDR validation workflow. Reprinted with permission from Ref (26), under Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). FDR, false discovery rate.
Fig. 3
Fig. 3
Glycopeptide spectrum annotation and result display.A, automated annotation of glycopeptide spectra by pGlyco 2.0. B, glycoprotein–glycan network maps. C, a glycan co-occurrence heat map represents the number of times glycan pairs appeared together at the same glycosite. Reprinted with permission from Ref (26) and Ref (35), under Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

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