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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/1193650
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- Title
- Toward automated N-glycopeptide identification in glycoproteomics
- Related
- Journal of proteome research, Vol. 15, Issue 10, (2016), p.3904-3915
- Funding Body
- NHMRC
- DOI
- 10.1021/acs.jproteome.6b00438
- Publisher
- American Chemical Society
- Date
- 2016
- Author/Creator
- Lee, Ling Y
- Author/Creator
- Moh, Edward S. X
- Author/Creator
- Parker, Benjamin L
- Author/Creator
- Bern, Marshall
- Author/Creator
- Packer, Nicolle H
- Author/Creator
- Thaysen-Andersen, Morten
- Description
- Advances in software-driven glycopeptide identification have facilitated N-glycoproteomics studies reporting thousands of intact N-glycopeptides, i.e., N-glycan-conjugated peptides, but the automated identification process remains to be scrutinized. Herein, we compare the site-specific glycoprofiling efficiency of the PTM-centric search engine Byonic relative to manual expert annotation utilizing typical glycoproteomics acquisition and data analysis strategies but with a single glycoprotein, the uncharacterized multiple N-glycosylated human basigin. Detailed site-specific reference glycoprofiles of purified basigin were manually established using ion-trap CID–MS/MS and high-resolution Q-Exactive Orbitrap HCD–MS/MS of tryptic N-glycopeptides and released N-glycans. The micro- and macroheterogeneous basigin N-glycosylation was site-specifically glycoprofiled using Byonic with or without a background of complex peptides using Q-Exactive Orbitrap HCD–MS/MS. The automated glycoprofiling efficiencies were assessed against the site-specific reference glycoprofiles and target/decoy proteome databases. Within the limits of this single glycoprotein analysis, the search criteria and confidence thresholds (Byonic scores) recommended by the vendor provided high glycoprofiling accuracy and coverage (both >80%) and low peptide FDRs (<1%). The data complexity, search parameters including search space (proteome/glycome size), mass tolerance and peptide modifications, and confidence thresholds affected the automated glycoprofiling efficiency and analysis time. Correct identification of ambiguous peptide modifications (methionine oxidation/carbamidomethylation) whose mass differences coincide with several monosaccharide mass differences (Fuc/Hex/HexNAc) and of ambiguous isobaric (Hex₁NeuAc₁-R/Fuc₁NeuGc₁-R) or near-isobaric (NeuAc₁-R/Fuc₂-R) monosaccharide subcompositions remains challenging in automated glycoprofiling, arguing particular attention paid to N-glycopeptides displaying such “difficult-to-identify” features. This study provides valuable insights into the automated glycopeptide identification process, stimulating further developments in FDR-based glycoproteomics.
- Description
- 12 page(s)
- Subject Keyword
- N-glycosylation
- Subject Keyword
- LC-MS/MS
- Subject Keyword
- glycopeptide
- Subject Keyword
- glycoproteomics
- Subject Keyword
- basigin
- Subject Keyword
- glycoprofiling
- Subject Keyword
- glycomics
- Subject Keyword
- automated glycopeptide identification
- Subject Keyword
- Byonic
- Resource Type
- journal article
- Organisation
- Macquarie University. Department of Chemistry and Biomolecular Sciences
- Identifier
- http://hdl.handle.net/1959.14/1193650
- Identifier
- mq:61323
- Identifier
- ISSN:1535-3893
- Identifier
- mq-rm-2014006660
- Identifier
- mq_res-se-566912
- Language
- eng
- Reviewed
Macquarie University ResearchOnline
