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

Shen, L.-C.; Zhang, Y.; Wang, Z.; Littler, D. R.; Yan, L.; Tang, J.; Rossjohn, J.; Yu, D.-J.; Song, J.

doi:10.1101/2025.02.02.636141

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bioRxiv · 10.1101/2025.02.02.636141

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

Shen, L.-C.·Zhang, Y.·Wang, Z.·Littler, D. R.·Yan, L.·Tang, J.·Rossjohn, J.·Yu, D.-J.·Song, J.

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Abstract

Accurately predicting the antigen presentation to CD4+ T cells and subsequent induction of immune response is fundamentally important for vaccine development, autoimmune disease treatments, and cancer neoepitope identification. In immunopeptidomics, single-allelic data are highly specific but limited in allele scope, while multi-allelic data contain broader coverage at the cost of weakly labeling. Existing computational approaches either overlook the massive multi-allelic data or introduce label ambiguity due to inadequate modeling strategies. Here, we introduce ImmuScope, a weakly supervised deep-learning framework integrating precise MHC-II antigen presentation, CD4+ T cell epitopes, and immunogenicity predictions. ImmuScope leverages self-iterative multiple-instance learning with positive-anchor triplet loss to explore peptide-MHC-II (pMHC-II) binding from weakly labeled multi-allelic data and single-allelic data, comprising over 600,000 ligands across 142 alleles. Moreover, ImmuScope can also interpret the MHC-II binding specificity and motif deconvolution of immunopeptidomics data. We successfully applied ImmuScope to discover melanoma neoantigens, revealing variations in pMHC-II binding and immunogenicity upon epitope mutations. We further employed ImmuScope to assess the effects of SARS-CoV-2 epitope mutations on immune escape, with its predictions aligned well with experimentally determined immune escape dynamics. Overall, ImmuScope provides a comprehensive solution for CD4+ T cell antigen recognition and immunogenicity assessment, with broad potential for advancing vaccine design and personalized immunotherapy.

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Shen, L.-C., Zhang, Y., Wang, Z., Littler, D. R., Yan, L., Tang, J., Rossjohn, J., Yu, D.-J., Song, J.. 2025-02-04. Self-iterative multiple instance learning enables the prediction of CD4+ T cell immunogenic epitopes. https://doi.org/10.1101/2025.02.02.636141

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