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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

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Published on: August 19, 2025

A Comprehensive, Open-source Platform for Mass Spectrometry-based Glycoproteomics Data Analysis.

Gang Liu¹, Kai Cheng^1,2, Chi Y Lo¹

¹From the ‡Chemical and Biological Engineering.

Molecular & Cellular Proteomics : MCP

|September 10, 2017

Summary

This summary is machine-generated.

Glycoproteomics analysis is challenging due to glycan complexity. GlycoPAT software offers a new computational platform for site-specific glycosylation analysis, improving data interpretation for glycopeptide identification.

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Published on: April 11, 2019

Area of Science:

Proteomics
Glycomics
Biochemistry

Background:

Protein glycosylation is a diverse and abundant post-translational modification.
Analyzing glycosylation is complex due to glycan diversity, branching, and heterogeneity.
Existing computational tools for glycoproteomics data analysis are limited.

Purpose of the Study:

To develop a comprehensive, open-source software, GlycoPAT (GlycoProteomics Analysis Toolbox), for analyzing site-specific glycosylation.
To address the scarcity of robust computational platforms in glycoproteomics.
To facilitate high-throughput analysis of complex glycopeptide data.

Main Methods:

Introduced "SmallGlyPep" for minimal linear glycopeptide representation in MSⁿ analysis.
Developed a novel "Ensemble Score (ES)" for ranking MS/MS spectra of N- and O-linked glycopeptides.
Implemented a unique false discovery rate (FDR) calculation scheme using decoy glycopeptides.

Main Results:

GlycoPAT successfully catalogued site-specific glycosylation in various samples using CID, HCD, and ETD fragmentation modes.
Identified 960 unique glycopeptides in prostate cancer cell lysates.
Demonstrated the necessity of considering both peptide and glycan fragmentation for accurate MSⁿ spectrum annotation.

Conclusions:

GlycoPAT is a suitable tool for analyzing shotgun glycoproteomics data.
The software enhances the accuracy and efficiency of site-specific glycosylation analysis.
The developed methods improve the interpretation of complex glycoproteomics datasets.