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

Détails

ID Serval
serval:BIB_7C66F6F02EE5
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Unsupervised HLA Peptidome Deconvolution Improves Ligand Prediction Accuracy and Predicts Cooperative Effects in Peptide-HLA Interactions.
Périodique
Journal of Immunology (baltimore, Md. : 1950)
Auteur(s)
Bassani-Sternberg M., Gfeller D.
ISSN
1550-6606 (Electronic)
ISSN-L
0022-1767
Statut éditorial
Publié
Date de publication
2016
Peer-reviewed
Oui
Volume
197
Numéro
6
Pages
2492-2499
Langue
anglais
Résumé
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
Oui
Création de la notice
15/09/2016 21:38
Dernière modification de la notice
08/05/2019 20:53
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