Self-iterative multiple-instance learning enables the prediction of CD4+ T cell immunogenic epitopes

Abstract Accurate prediction of antigen presentation to CD4 + T cells and subsequent induction of immune response are fundamentally important for vaccine development, autoimmune disease treatment and cancer neoepitope discovery. In immunopeptidomics, single-allelic data offer high specificity but limited allele coverage, whereas multi-allelic data provide broader representation at the expense of weak labelling. Current computational approaches either overlook the abundance of multi-allelic data or suffer from label ambiguity due to inadequate modelling strategies. To address these limitations, we present ImmuScope, a weakly supervised deep learning framework that integrates major histocompatibility complex class II (MHC-II) antigen presentation, CD4 + T cell epitopes and immunogenicity assessment. ImmuScope leverages self-iterative multiple-instance learning with positive-anchor triplet loss to decipher peptide-MHC-II binding from weakly labelled multi-allelic data and high-confidence single-allelic data. The training dataset comprises over 600,000 ligands across 142 alleles. Additionally, ImmuScope enables the interpretation of MHC-II binding specificity and motif deconvolution of immunopeptidomics data. We successfully applied ImmuScope to identify melanoma neoantigens, uncovering mutation-driven variations in peptide-MHC-II binding and immunogenicity. Furthermore, we employed ImmuScope to evaluate the effects of SARS-CoV-2 epitope mutations associated with immune escape, with predictions well aligned with experimentally observed immune escape dynamics. Overall, by offering a unified solution for CD4 + T cell antigen recognition and immunogenicity assessment, ImmuScope holds substantial promise for accelerating vaccine design and advancing personalized immunotherapy.