Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes.
Détails
Télécharger: 1-s2.0-S1074761323001292-main.pdf (6858.59 [Ko])
Etat: Public
Version: Final published version
Licence: CC BY-NC 4.0
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
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
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