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

Martin Stražar, Jihye Park, Jennifer G. Abelin, Hannah B. Taylor, Thomas K. Pedersen, Damian R. Plichta, Eric M. Brown, Basak Eraslan, Yuan Mao Hung, Kayla Ortiz, Karl R. Clauser, Steven A. Carr, Ramnik J. Xavier, Daniel B. Graham*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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.
Original languageEnglish
JournalImmunity
Volume56
Issue number7
Pages (from-to)1681-1698.e13
Number of pages32
ISSN1074-7613
DOIs
Publication statusPublished - 2023

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