HLA-II immunopeptidome profiling and deep learning reveal features of antigenicity to inform antigen discovery.
Immunity
; 56(7): 1681-1698.e13, 2023 Jul 11.
Artículo
en Inglés
| MEDLINE | ID: covidwho-20243335
ABSTRACT
CD4+ T cell responses are exquisitely antigen specific and directed toward peptide epitopes displayed by human leukocyte antigen class II (HLA-II) on antigen-presenting cells. Underrepresentation of diverse alleles in ligand databases and an incomplete understanding of factors affecting antigen presentation in vivo have limited progress in defining principles of peptide immunogenicity. Here, we employed monoallelic immunopeptidomics to identify 358,024 HLA-II binders, with a particular focus on HLA-DQ and HLA-DP. We uncovered peptide-binding patterns across a spectrum of binding affinities and enrichment of structural antigen features. These aspects underpinned the development of context-aware predictor of T cell antigens (CAPTAn), a deep learning model that predicts peptide antigens based on their affinity to HLA-II and full sequence of their source proteins. CAPTAn was instrumental in discovering prevalent T cell epitopes from bacteria in the human microbiome and a pan-variant epitope from SARS-CoV-2. Together CAPTAn and associated datasets present a resource for antigen discovery and the unraveling genetic associations of HLA alleles with immunopathologies.
Palabras clave
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Aprendizaje Profundo
/
COVID-19
Tipo de estudio:
Estudio pronóstico
Tópicos:
Variantes
Límite:
Humanos
Idioma:
Inglés
Revista:
Immunity
Asunto de la revista:
Alergia e Inmunología
Año:
2023
Tipo del documento:
Artículo
País de afiliación:
J.immuni.2023.05.009
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