Multiple Layers of Complexity in O-Glycosylation Illustrated With the Urinary Glycoproteome

While N-glycopeptides are relatively easy to characterize, O-glycosylation analysis is more complex. In this article, we illustrate the multiple layers of O-glycopeptide char-acterization that make this task so challenging. We believe our carefully curated dataset represents perhaps the largest inta...

Teljes leírás

Elmentve itt :
Bibliográfiai részletek
Szerzők: Pap Ádám
Király István Előd
Medzihradszky F. Katalin
Darula Zsuzsanna
Dokumentumtípus: Cikk
Megjelent: 2022
Sorozat:MOLECULAR & CELLULAR PROTEOMICS 21 No. 12
Tárgyszavak:
doi:10.1016/j.mcpro.2022.100439

mtmt:33548024
Online Access:http://publicatio.bibl.u-szeged.hu/34889
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520 3 |a While N-glycopeptides are relatively easy to characterize, O-glycosylation analysis is more complex. In this article, we illustrate the multiple layers of O-glycopeptide char-acterization that make this task so challenging. We believe our carefully curated dataset represents perhaps the largest intact human glycopeptide mixture derived from individuals, not from cell lines. The samples were collected from healthy individuals, patients with superficial or advanced bladder cancer (three of each group), and a single bladder inflammation patient. The data were scru-tinized manually and interpreted using three different search engines: Byonic, Protein Prospector, and O-Pair, and the tool MS-Filter. Despite all the recent advances, reliable automatic O-glycopeptide assignment has not been solved yet. Our data reveal such diversity of site -specific O-glycosylation that has not been presented before. In addition to the potential biological implications, this dataset should be a valuable resource for software developers in the same way as some of our previously released data has been used in the development of O-Pair and O-Glycoproteome Analyzer. Based on the manual evaluation of the performance of the existing tools with our data, we lined up a series of recommendations that if implemented could significantly improve the reliability of glycopeptide assignments. 
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700 0 1 |a Király István Előd  |e aut 
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700 0 2 |a Darula Zsuzsanna  |e aut 
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