Semiautomated glycoproteomics data analysis workflow for maximized glycopeptide identification and reliable...

Area of Science:

• Biochemistry
• Bioinformatics
• Analytical Chemistry

Background:

• Glycoproteomic data complexity arises from diverse peptide and glycan structures.
• Advancements in mass spectrometry-driven glycoproteomics necessitate reliable data analysis tools.
• Existing bioinformatic tools require integration for comprehensive glycopeptide analysis.

Purpose of the Study:

• To establish a robust glycopeptide detection and quantification workflow for enriched glycoproteins.
• To evaluate and combine existing bioinformatic tools for improved glycoproteomic data analysis.
• To assess the performance of different quantification software for glycosylation profiling.

Main Methods:

• Analysis of tryptic digests from immunoglobulins G and A using nano-liquid chromatography-tandem mass spectrometry.
• Glycopeptide identification using Byonic (MS/MS) and GlycopeptideGraphMS (MS1-based) software.
• Comparison of LaCyTools, Skyline, and GlycopeptideGraphMS for glycopeptide quantification.

Main Results:

• GlycopeptideGraphMS expanded the set of identified glycopeptides beyond MS/MS-based methods.
• Quantification packages showed comparable glycosylation profiles but differed in robustness and quality control.
• Partial cysteine oxidation was identified as a common modification affecting IgA glycopeptide analysis.

Conclusions:

• A semiautomated workflow combining MS/MS and MS1 identification tools offers reliable glycoproteomic data analysis.
• Integration of analyte quality control and quantification improves the robustness of glycoproteomic studies.
• The developed workflow addresses the need for advanced tools in evolving glycoproteomics research.