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

• Glycoproteomics has rapidly become an independent analytical platform bridging the fields of glycomics and proteomics to address site-specific protein glycosylation and its impact in biology. Current glycopeptide characterization relies on time-consuming manual interpretations and demands high levels of personal expertise. Efficient data interpretation constitutes one of the major challenges to be overcome before true high throughput glycopeptide analysis can be achieved. The development of new glyco-related bioinformatics tools is thus of crucial importance to fulfill this goal. Here we present SweetNET: a data-oriented bioinformatics workflow for efficient analysis of hundreds of thousands of glycopeptide MS/MS-spectra. We have analyzed MS data sets from two separate glycopeptide enrichment protocols targeting sialylated glycopeptides and chondroitin sulfate linkage region glycopeptides, respectively. Molecular networking was performed to organize the glycopeptide MS/MS data based on spectral similarities. The combination of spectral clustering, oxonium ion intensity profiles, and precursor ion m/z shift distributions provided typical signatures for the initial assignment of different N-, O- and CS-glycopeptide classes and their respective glycoforms. These signatures were further used to guide database searches leading to the identification and validation of a large number of glycopeptide variants including novel deoxyhexose (fucose) modifications in the linkage region of chondroitin sulfate proteoglycans.

Subject headings

NATURVETENSKAP  -- Biologi -- Bioinformatik och systembiologi (hsv//swe)

NATURAL SCIENCES  -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)

Keyword

MS/MS molecular networking glycopeptides

oxonium ion

bioinformatics

glycosylation

mass

INTACT N-GLYCOPEPTIDES

TANDEM MASS-SPECTRA

HEPARAN-SULFATE

PROTEOGLYCANS

SPECTROMETRY-BASED PROTEOMICS

O-LINKED GLYCOPEPTIDES

MOLECULAR NETWORKING

GLYCAN STRUCTURE

LC-MS/MS

DE-NOVO

AUTOMATED

IDENTIFICATION

Publication and Content Type

ref (subject category)

art (subject category)