1. SugarPy facilitates the universal, discovery-driven analysis of intact glycopeptides
- Author
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Stefan Schulze, Michael Mormann, Christian Fufezan, Michael Hippler, Mechthild Pohlschroder, Anne Oltmanns, and Julia Krägenbring
- Subjects
Protein glycosylation ,Statistics and Probability ,Glycan ,Glycosylation ,Computer science ,Chlamydomonas reinhardtii ,Computational biology ,01 natural sciences ,Biochemistry ,03 medical and health sciences ,chemistry.chemical_compound ,Molecular Biology ,030304 developmental biology ,chemistry.chemical_classification ,0303 health sciences ,biology ,010401 analytical chemistry ,Haloferax volcanii ,biology.organism_classification ,Glycopeptide ,0104 chemical sciences ,Computer Science Applications ,Glycoproteomics ,Computational Mathematics ,Cyanidioschyzon merolae ,Computational Theory and Mathematics ,chemistry ,biology.protein ,Glycoprotein - Abstract
MotivationProtein 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.ResultsTherefore, 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.AvailabilityThe source code is freely available on GitHub (https://github.com/SugarPy/SugarPy), and its implementation in Python ensures support for all operating systems.Contactmhippler@uni-muenster.de and pohlschr@uni-muenster.deSupplementary informationSupplementary data are available online.
- Published
- 2020
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