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A Quantitative Glycomics and Proteomics Combined Purification Strategy
Published on: March 8, 2016
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Library-based virtual match-between-runs quantification in GlyPep-Quant improves site-specific glycan identification.
He Zhu1, Zheng Fang1, Lei Liu1,2
1State Key Laboratory of Medical Proteomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China.
Nature Communications
|July 14, 2025
Summary
GlyPep-Quant enhances site-specific glycan analysis in proteomics, identifying more glycans and enabling the discovery of gastric cancer biomarkers. This tool improves glycopeptide spectra interpretation for disease research.
Area of Science:
- Proteomics
- Glycobiology
- Computational Biology
Background:
- Glycosylation alterations are linked to diseases like cancer.
- Quantitative analysis of site-specific glycans is challenging due to low glycopeptide spectra interpretation.
Purpose of the Study:
- To present GlyPep-Quant, a tool for sensitive quantification and identification of site-specific glycans.
- To develop a glycoproteomic biomarker discovery method for gastric cancer detection.
Main Methods:
- Utilized a machine learning model for glycan quantification.
- Implemented an MS1 feature library-based "virtual match-between-runs" quantification scheme.
- Developed a method calculating site-specific glycan abundance ratios for biomarker discovery.
Main Results:
- GlyPep-Quant quantified 25.1%-178.9% more site-specific glycans than existing tools.
- The virtual match-between-runs scheme enabled over eightfold more identifications/quantifications.
- Two site-specific glycan ratios identified as high-performance biomarkers for gastric cancer (AUC > 0.95).
- Biomarkers achieved up to 85% diagnostic accuracy in an independent cohort.
Conclusions:
- GlyPep-Quant significantly improves site-specific glycan quantification and identification.
- The developed glycoproteomic biomarker discovery method shows promise for gastric cancer diagnostics.
- GlyPep-Quant is suitable for broad glycoproteomic applications.
