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

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Date

13 juin 2023

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Périmètre
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Source

Serveur académique Lausannois

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Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.immuni.2023.03.009

Ce document est lié à :
info:eu-repo/semantics/altIdentifier/pmid/37023751

Ce document est lié à :
info:eu-repo/semantics/altIdentifier/eissn/1097-4180

Ce document est lié à :
info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:serval-BIB_9BA5A00EAA197

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Serveur Académique Lausannois

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Université de Lausanne et CHUV

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info:eu-repo/semantics/openAccess , Copying allowed only for non-profit organizations , https://serval.unil.ch/disclaimer

Mots-clés 0

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

Sujets proches En

Antigen determinants Epitopes Antigenic determinant groups Determinants, Antigenic Epitypes

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J. Racle et al., « Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes. », Serveur académique Lausannois, ID : 10.1016/j.immuni.2023.03.009

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CD4 + 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 + 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 + T cell epitopes and enables us to discover viral and bacterial epitopes following the aforementioned reverse-binding mode.

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