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Updated: Nov 25, 2025

The Application of Open Searching-based Approaches for the Identification of Acinetobacter baumannii O-linked Glycopeptides
Published on: November 2, 2021
SugarPy facilitates the universal, discovery-driven analysis of intact glycopeptides
Stefan Schulze1,2, Anne Oltmanns2, Christian Fufezan2,3
1Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Motivation:
Protein glycosylation is a complex post-translational modification with crucial cellular functions in all domains of life. Currently, large-scale glycoproteomics approaches rely on glycan database dependent algorithms and are thus unsuitable for discovery-driven analyses of glycoproteomes.
Results:
Therefore, we devised SugarPy, a glycan database independent Python module, and validated it on the glycoproteome of human breast milk. We further demonstrated its applicability by analyzing glycoproteomes with uncommon glycans stemming from the green alga Chlamydomonas reinhardtii and the archaeon Haloferax volcanii. SugarPy also facilitated the novel characterization of glycoproteins from the red alga Cyanidioschyzon merolae.
Availability And Implementation:
The source code is freely available on GitHub (https://github.com/SugarPy/SugarPy), and its implementation in Python ensures support for all operating systems.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
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