RT Journal Article
SR Electronic
T1 SugarPy facilitates the universal, discovery-driven analysis of intact glycopeptides
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.10.21.349399
DO 10.1101/2020.10.21.349399
A1 Stefan Schulze
A1 Anne Oltmanns
A1 Christian Fufezan
A1 Julia Krägenbring
A1 Michael Mormann
A1 Mechthild Pohlschröder
A1 Michael Hippler
YR 2020
UL http://biorxiv.org/content/early/2020/10/22/2020.10.21.349399.abstract
AB 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 The source code is freely available on GitHub (https://github.com/SugarPy/SugarPy), and its implementation in Python ensures support for all operating systems.Contact mhippler{at}uni-muenster.de and pohlschr{at}uni-muenster.deSupplementary information Supplementary data are available online.Competing Interest StatementThe authors have declared no competing interest.