SweetNET: A Bioinformatics Workflow for Glycopeptide MS/MS Spectral Analysis

Area of Science:

• Glycoproteomics
• Bioinformatics
• Analytical Chemistry

Background:

• Glycoproteomics is crucial for understanding protein glycosylation's biological roles.
• Current glycopeptide analysis is labor-intensive and requires significant expertise.
• High-throughput glycopeptide analysis is hindered by data interpretation challenges.

Purpose of the Study:

• To develop an efficient bioinformatics workflow for analyzing large glycopeptide MS/MS datasets.
• To create a tool that facilitates high-throughput glycopeptide characterization.
• To address the need for advanced glyco-related bioinformatics tools.

Main Methods:

• Developed SweetNET, a data-oriented bioinformatics workflow.
• Utilized molecular networking to organize glycopeptide MS/MS data by spectral similarity.
• Employed spectral clustering, oxonium ion profiles, and precursor ion m/z shifts for glycopeptide classification.

Main Results:

• Successfully analyzed hundreds of thousands of glycopeptide MS/MS spectra.
• Organized and classified N-, O-, and chondroitin sulfate (CS)-glycopeptides and their glycoforms.
• Identified novel deoxyhexose modifications in chondroitin sulfate proteoglycans.

Conclusions:

• SweetNET enables efficient and high-throughput analysis of glycopeptide MS/MS data.
• The workflow aids in classifying glycopeptide classes and glycoforms.
• This tool advances the field of glycoproteomics by facilitating complex data interpretation.