ImmunoFoundation: A Multimodal Foundation Model for Immunogenicity Prediction and Peptide Optimization

João Felipe Rocha, Hiren Madhu, Jenny Yongjia Liu, Apurva Mishra, Chen Liu, Rishabh Anand, Rex Ying, Smita Krishnaswamy

Keywords: foundation models, immunogenicity, AI4Science, AI4Cancer, computational biology

TL;DR: We introduce ImmunoFoundation a multimodal foundation model for immunogenicity prediction that pretrains on folded protein complexes.

Abstract: Peptide immunogenicity, whether a peptide presented by an MHC molecule elicits a T-cell response, is central to designing vaccines, cancer immunotherapy, and therapeutic proteins. Existing tools rely on a single modality, such as peptide sequences or peptide-MHC interactions, and often ignore the T-cell response that depends on the TCR-peptide-MHC complex (TCR-pMHC) and its three-dimensional structure. The scarcity of labeled TCR-pMHC data with known structures makes it difficult to build a model that captures how all components of the TCR-pMHC contribute to immunogenicity. However, a foundation model of TCR-pMHCs can learn transferable representations across components, which can be adapted to immunogenicity, binding, and TCR specificity tasks, even with limited labeled data. We introduce **ImmunoFoundation**, a self-supervised multimodal backbone for protein-complex representation, fine-tuned for peptide--MHC immunogenicity. The model couples an ESM-2 sequence encoder with a graph transformer over structure, fused via cross-modal attention. Pretraining follows a curriculum that progressively introduces structural inductive bias. **ImmunoFoundation** ourperforms prior multimodal class-I predictors on cancer neoepitope and infectious-disease tasks.

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Submission Number: 64