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

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State: Public
Version: Final published version
License: CC BY-NC 4.0
Serval ID
serval:BIB_9BA5A00EAA19
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes.
Journal
Immunity
Author(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
Publication state
Published
Issued date
13/06/2023
Peer-reviewed
Oui
Volume
56
Number
6
Pages
1359-1375.e13
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
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.
Keywords
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
Create date
11/04/2023 16:13
Last modification date
24/07/2024 5:58
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