Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes.

Détails

ID Serval
serval:BIB_D9A8D08A5BC6
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes.
Périodique
Nature biotechnology
Auteur⸱e⸱s
Racle J., Michaux J., Rockinger G.A., Arnaud M., Bobisse S., Chong C., Guillaume P., Coukos G., Harari A., Jandus C., Bassani-Sternberg M. (co-dernier), Gfeller D.
ISSN
1546-1696 (Electronic)
ISSN-L
1087-0156
Statut éditorial
Publié
Date de publication
11/2019
Peer-reviewed
Oui
Volume
37
Numéro
11
Pages
1283-1286
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Résumé
Predictions of epitopes presented by class II human leukocyte antigen molecules (HLA-II) have limited accuracy, restricting vaccine and therapy design. Here we combined unbiased mass spectrometry with a motif deconvolution algorithm to profile and analyze a total of 99,265 unique peptides eluted from HLA-II molecules. We then trained an epitope prediction algorithm with these data and improved prediction of pathogen and tumor-associated class II neoepitopes.
Mots-clé
Algorithms, Cell Line, Computational Biology/methods, Epitopes/metabolism, Histocompatibility Antigens Class II/chemistry, Histocompatibility Antigens Class II/metabolism, Humans, Mass Spectrometry, Peptides/analysis, Peptides/immunology
Pubmed
Web of science
Création de la notice
16/10/2019 19:11
Dernière modification de la notice
11/12/2020 6:26
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