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

Shen, Long-Chen, Zhang, Yumeng, Wang, Zhikang, Littler, Dene R., Liu, Yan, Tang, Jinhui, Rossjohn, Jamie ORCID: https://orcid.org/0000-0002-2020-7522, Yu, Dong-Jun and Song, Jiangning 2025. Self-iterative multiple-instance learning enables the prediction of CD4+ T cell immunogenic epitopes. Nature Machine Intelligence 10.1038/s42256-025-01073-z

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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.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Schools > Medicine
Publisher:	Nature Research
ISSN:	2522-5839
Date of First Compliant Deposit:	15 July 2025
Date of Acceptance:	6 June 2025
Last Modified:	15 Jul 2025 14:15
URI:	https://orca.cardiff.ac.uk/id/eprint/179797

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