Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes.

Détails

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Etat: Public
Version: Final published version
Licence: CC BY-NC 4.0
ID Serval
serval:BIB_9BA5A00EAA19
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes.
Périodique
Immunity
Auteur⸱e⸱s
Racle J., Guillaume P., Schmidt J., Michaux J., Larabi A., Lau K., Perez MAS, Croce G., Genolet R., Coukos G., Zoete V., Pojer F., Bassani-Sternberg M., Harari A., Gfeller D.
ISSN
1097-4180 (Electronic)
ISSN-L
1074-7613
Statut éditorial
Publié
Date de publication
13/06/2023
Peer-reviewed
Oui
Volume
56
Numéro
6
Pages
1359-1375.e13
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Résumé
CD4 <sup>+</sup> T cells orchestrate the adaptive immune response against pathogens and cancer by recognizing epitopes presented on class II major histocompatibility complex (MHC-II) molecules. The high polymorphism of MHC-II genes represents an important hurdle toward accurate prediction and identification of CD4 <sup>+</sup> T cell epitopes. Here we collected and curated a dataset of 627,013 unique MHC-II ligands identified by mass spectrometry. This enabled us to precisely determine the binding motifs of 88 MHC-II alleles across humans, mice, cattle, and chickens. Analysis of these binding specificities combined with X-ray crystallography refined our understanding of the molecular determinants of MHC-II motifs and revealed a widespread reverse-binding mode in HLA-DP ligands. We then developed a machine-learning framework to accurately predict binding specificities and ligands of any MHC-II allele. This tool improves and expands predictions of CD4 <sup>+</sup> T cell epitopes and enables us to discover viral and bacterial epitopes following the aforementioned reverse-binding mode.
Mots-clé
Humans, Animals, Mice, Cattle, Epitopes, T-Lymphocyte, Ligands, Peptides, Protein Binding, Chickens/metabolism, Machine Learning, Histocompatibility Antigens Class II, Alleles, MHC-II binding motifs, MHC-II ligand binding modes, antigen presentation, class II epitope predictions, computational immunology, immunopeptidomics, reverse binding mode
Pubmed
Web of science
Création de la notice
11/04/2023 16:13
Dernière modification de la notice
24/07/2024 5:58
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