SugarPy facilitates the universal, discovery-driven analysis of intact glycopeptides

Stefan Schulze; Anne Oltmanns; Christian Fufezan; Julia Krägenbring; Michael Mormann; Mechthild Pohlschröder; Michael Hippler

doi:10.1093/bioinformatics/btaa1042

SugarPy facilitates the universal, discovery-driven analysis of intact glycopeptides

Bioinformatics. 2021 Apr 1;36(22-23):5330-5336. doi: 10.1093/bioinformatics/btaa1042.

Authors

Stefan Schulze^{1

2}, Anne Oltmanns², Christian Fufezan^{2

3}, Julia Krägenbring^{2

4}, Michael Mormann⁴, Mechthild Pohlschröder¹, Michael Hippler^{2

5}

Affiliations

¹ Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA.
² Faculty of Biology, Institute of Plant Biology and Biotechnology, University of Münster, Münster, Germany.
³ Faculty of Biology, Institute of Pharmacy and Molecular Biotechnology, Heidelberg University, Heidelberg, Germany.
⁴ Faculty of Medicine, Institute for Hygiene, University of Münster, Münster, Germany.
⁵ Faculty of Sciences, Institute of Plant Science and Resources, Okayama University, Kurashiki, Japan.

PMID: 33325487
DOI: 10.1093/bioinformatics/btaa1042

Abstract

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.

Abstract

Grants and funding