Unsupervised HLA Peptidome Deconvolution Improves Ligand Prediction Accuracy and Predicts Cooperative Effects in Peptide-HLA Interactions.

Details

Serval ID
serval:BIB_7C66F6F02EE5
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Unsupervised HLA Peptidome Deconvolution Improves Ligand Prediction Accuracy and Predicts Cooperative Effects in Peptide-HLA Interactions.
Journal
Journal of Immunology (baltimore, Md. : 1950)
Author(s)
Bassani-Sternberg M., Gfeller D.
ISSN
1550-6606 (Electronic)
ISSN-L
0022-1767
Publication state
Published
Issued date
2016
Peer-reviewed
Oui
Volume
197
Number
6
Pages
2492-2499
Language
english
Abstract
Ag presentation on HLA molecules plays a central role in infectious diseases and tumor immunology. To date, large-scale identification of (neo-)Ags from DNA sequencing data has mainly relied on predictions. In parallel, mass spectrometry analysis of HLA peptidome is increasingly performed to directly detect peptides presented on HLA molecules. In this study, we use a novel unsupervised approach to assign mass spectrometry-based HLA peptidomics data to their cognate HLA molecules. We show that incorporation of deconvoluted HLA peptidomics data in ligand prediction algorithms can improve their accuracy for HLA alleles with few ligands in existing databases. The results of our computational analysis of large datasets of naturally processed HLA peptides, together with experimental validation and protein structure analysis, further reveal how HLA-binding motifs change with peptide length and predict new cooperative effects between distant residues in HLA-B07:02 ligands.
Pubmed
Open Access
Yes
Create date
15/09/2016 20:38
Last modification date
20/08/2019 14:38
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