HLA-II immunopeptidome profiling and deep learning reveal features of antigenicity to inform antigen discovery.

Journal: Immunity
Published Date: Jul 11, 2023

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.

Authors

  • Martin Stražar
    Faculty of Computer and Information Science, University of Ljubljana, 1000, Ljubljana, Slovenia.
  • Jihye Park
    Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, 11794, USA.
  • Jennifer G Abelin
    Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Hannah B Taylor
    Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Thomas K Pedersen
    Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Technical University of Denmark, Kongens Lyngby, Denmark.
  • Damian R Plichta
    Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Eric M Brown
    Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
  • Basak Eraslan
    Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Yuan-Mao Hung
    Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
  • Kayla Ortiz
    Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • Karl R Clauser
    Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Steven A Carr
    Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Ramnik J Xavier
    Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Daniel B Graham
    Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Electronic address: dgraham@broadinstitute.org.

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