MassIVE MSV000093110

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Can we boost N-glycopeptide identification confidence? Smart collision energy choice taking into account structure and search engine

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Description

We investigated how the structural features of N-glycopeptides and the choice of the search engine influence the optimal collision energy, delivering highest identification confidence. We carried out LC-MS/MS measurements using a series of collision energies on a large set of N-glycopeptides with both the glycan and peptide part varied, and studied the behavior of Byonic, pGlyco, and GlycoQuest scores. [doi:10.25345/C5HM52W2Q] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: tandem mass spectrometry ; bottom-up proteomics ; N-glycosylation ; glyan structure ; identification score ; search engine ; collision energy optimization ; general linear model ; lasso regression

Contact

Principal Investigators:
(in alphabetical order) 		Agnes Revesz, HUN-REN Research Centre for Natural Sciences, Hungary
Submitting User: reveszagnes

Publications

Hevér H, Xue A, Nagy K, Komka K, Vékey K, Drahos L, Révész Á.
Can We Boost N-Glycopeptide Identification Confidence? Smart Collision Energy Choice Taking into Account Structure and Search Engine.
J Am Soc Mass Spectrom. 2024 Feb 7;35(2):333-343. Epub 2024 Jan 29.

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