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

Details

Serval ID
serval:BIB_D9A8D08A5BC6
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes.
Journal
Nature biotechnology
Author(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., Gfeller D.
ISSN
1546-1696 (Electronic)
ISSN-L
1087-0156
Publication state
Published
Issued date
11/2019
Peer-reviewed
Oui
Volume
37
Number
11
Pages
1283-1286
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
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.
Keywords
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
Create date
16/10/2019 19:11
Last modification date
30/01/2020 6:20
Usage data