SugarPy

SugarPy enables discovery-driven, database-independent analysis of intact glycopeptides from in-source collision-induced dissociation mass spectrometry data.

Key Features:

• Glycan Database Independence: Operates without predefined glycan databases, enabling identification of glycosylation patterns absent from existing glycan libraries.
• Intact Glycopeptide Analysis: Analyzes intact glycopeptides derived from in-source collision-induced dissociation (IS-CID) mass spectrometry data to capture combined peptide and glycan information.
• Broad Applicability Across Species: Validated on human breast milk and applied to organisms with uncommon glycans, including Chlamydomonas reinhardtii, Haloferax volcanii, and Cyanidioschyzon merolae.

Scientific Applications:

• Glycoproteomics Research: Enables characterization of protein glycosylation and discovery of novel glycan structures across diverse biological samples.
• Discovery-Driven Studies: Supports exploratory analyses aimed at identifying previously uncharacterized glycopeptides and glycosylation patterns without reliance on prior glycan annotations.

Methodology:

Implemented as a Python module and analyzes intact glycopeptides derived from in-source collision-induced dissociation (IS-CID) mass spectrometry data.