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
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
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
11/12/2020 6:26