Application of spectral library prediction for parallel reaction monitoring of viral peptides.
PRM
SARS-CoV-2
parallel reaction monitoring
tandem mass spectra prediction
virus proteomics
Journal
Proteomics
ISSN: 1615-9861
Titre abrégé: Proteomics
Pays: Germany
ID NLM: 101092707
Informations de publication
Date de publication:
04 2021
04 2021
Historique:
revised:
08
02
2021
received:
22
08
2020
accepted:
17
02
2021
pubmed:
23
2
2021
medline:
27
4
2021
entrez:
22
2
2021
Statut:
ppublish
Résumé
A major part of the analysis of parallel reaction monitoring (PRM) data is the comparison of observed fragment ion intensities to a library spectrum. Classically, these libraries are generated by data-dependent acquisition (DDA). Here, we test Prosit, a published deep neural network algorithm, for its applicability in predicting spectral libraries for PRM. For this purpose, we targeted 1529 precursors derived from synthetic viral peptides and analyzed the data with Prosit and DDA-derived libraries. Viral peptides were chosen as an example, because virology is an area where in silico library generation could significantly improve PRM assay design. With both libraries a total of 1174 precursors were identified. Notably, compared to the DDA-derived library, we could identify 101 more precursors by using the Prosit-derived library. Additionally, we show that Prosit can be applied to predict tandem mass spectra of synthetic viral peptides with different collision energies. Finally, we used a spectral library predicted by Prosit and a DDA library to identify SARS-CoV-2 peptides from a simulated oropharyngeal swab demonstrating that both libraries are suited for peptide identification by PRM. Summarized, Prosit-derived viral spectral libraries predicted in silico can be used for PRM data analysis, making DDA analysis for library generation partially redundant in the future.
Identifiants
pubmed: 33615696
doi: 10.1002/pmic.202000226
pmc: PMC7995018
doi:
Substances chimiques
Peptide Library
0
Peptides
0
Viral Proteins
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2000226Informations de copyright
© 2021 The Authors. Proteomics published by Wiley-VCH GmbH.
Références
J Proteome Res. 2020 Nov 6;19(11):4380-4388
pubmed: 33090795
Nucleic Acids Res. 2015 Jul 1;43(W1):W326-30
pubmed: 25990723
J Proteome Res. 2020 Nov 6;19(11):4393-4397
pubmed: 32786682
Anal Chem. 2017 Dec 5;89(23):12690-12697
pubmed: 29125736
Anal Chem. 2019 Aug 6;91(15):9724-9731
pubmed: 31283184
Nat Biotechnol. 2008 Dec;26(12):1367-72
pubmed: 19029910
Proteomics. 2021 Jan;21(1):e2000198
pubmed: 33236484
Nat Methods. 2019 Jun;16(6):519-525
pubmed: 31133761
Bioinformatics. 2010 Apr 1;26(7):966-8
pubmed: 20147306
Nat Methods. 2019 Jun;16(6):509-518
pubmed: 31133760
Nat Commun. 2020 Dec 3;11(1):6201
pubmed: 33273458
J Proteome Res. 2020 Nov 6;19(11):4389-4392
pubmed: 32568543
Proteomics. 2020 Nov;20(21-22):e1900345
pubmed: 32574431
Mol Cell Proteomics. 2019 Oct;18(10):2099-2107
pubmed: 31249099
Proteomics. 2021 Apr;21(7-8):e2000226
pubmed: 33615696
Nat Methods. 2017 Mar;14(3):259-262
pubmed: 28135259
Mol Cell Proteomics. 2020 Sep;19(9):1503-1522
pubmed: 32591346
Proteomics. 2020 Jul;20(14):e2000107
pubmed: 32462744