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
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
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