The ImmunoFoundation Model (IFM), a multimodal deep learning system that integrates not only peptide sequences, 3D molecular structures, and biochemical properties but also TCR-MHC-peptide to achieve superior immunogenicity prediction and enable peptide optimization for therapeutic applications is developed.
Abstract
Predicting immunogenicity remains a critical challenge in vaccine design, autoimmunity treatment, and pharmaceutical development. Current AI tools rely on limited data inputs, typically only class-I MHC-peptide sequences, missing crucial structural and biochemical information. We developed the ImmunoFoundation Model (IFM), a multimodal deep learning system that integrates not only peptide sequences, 3D molecular structures, and biochemical properties but also TCR-MHC-peptide (class-II and class-I) to achieve superior immunogenicity prediction and enable peptide optimization for therapeutic applications.
IFM comprises three modules: (1) ESM3 transformer for embedding antigen-peptide, MHC, and TCR sequences from vast biomedical data; (2) geometric scattering transformer networks to capture molecular structure from AlphaFold3-predicted peptide-MHC complexes; (3) Autoencoder for biochemical property embedding including surface area and thermal stability. Cross-modal attention layers integrate these representations. Training utilized IEDB, VDJdb, McPAS-TCR, and TCR3d datasets totaling >300,000 samples across MHC class I and II.
The preliminary model achieved state-of-the-art performance on CEDAR cancer neoepitope datasets. Attention mechanism analysis revealed structural motifs influencing immunogenicity, distinguishing between KRAS G12V and G12D mutants. The model successfully predicted vaccine cassette immunogenicity and identified key peptide-MHC interaction sites. Current IFM development shows improved multimodal integration with enhanced predictive accuracy across viral and cancer peptide immunogenicity tasks.
IFM represents a paradigm shift in immunogenicity prediction by comprehensively modeling the complex antigen-MHC-TCR interaction system. Its generative capabilities enable peptide optimization for cancer vaccines and personalized immunotherapy, with potential applications in autoimmunity treatment and biologics development.
Yale Colton Center for Autoimmunity
Computational and Systems Immunology (COMP)
CLDN18.2 is a promising tumor-specific antigen; however, the development of therapeutic antibodies against it is challenged by the need for simultaneous optimization of affinity and developability. To address this, we present cdrGPT, a deep learning framework based on GPT-2 for de novo generation of complementarity-determining region H3 (CDRH3) sequences. Our approach integrates pre-training on the Observed Antibody Space (OAS) database with structural templating derived from the known antibody zolbetuximab. Generated sequences were iteratively refined through rejection sampling and fine-tuned against a multi-parameter objective function encompassing predicted affinity and MHC class II binding risk. From an initial set of 50,000 sequences, this screening pipeline yielded 313 high-confidence candidates. Subsequent analysis using evolutionary scale modeling 2 (ESM2) embeddings, principal component analysis (PCA), and clustering revealed three structurally distinct clusters, with intra-cluster cosine similarities exceeding 0.99. Validation of seven representative sequences from the dominant cluster using AlphaFold3 confirmed high structural fidelity to the zolbetuximab template, demonstrating a root mean square deviation (RMSD) of 1.331 Å for the CDRH3 loop and positional deviations of less than 0.4 Å for key paratope residues. These results indicate that the designed variants preserve the core binding mode of the parent antibody. This study establishes a feasible pipeline for integrating AI-generated CDRH3 loops into functional antibody scaffolds, providing a foundation for the accelerated development of therapeutics targeting CLDN18.2 and other clinically relevant antigens.
Tao Qu, Lingyan Yuan, Weiran Cui et al.· PLoS Computational Biology· 0 citations
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Tomer Cohen, Tanya Hochner, Dina Schneidman-Duhovny· Current Opinion in Structura...· 1 citation
The current role of AI is summarized across the peptide cancer vaccine development pipeline, from neoantigen discovery and epitope prioritization to prediction of peptide–HLA binding, antigen presentation, and T-cell receptor recognition, and the application of modern computational frameworks.
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This work is the first to use a multi-modal foundation model for neoantigen vaccines, overcoming the tumor-type-specific constraints of conventional approaches and facilitating joint modelling of immunogenicity and clinical efficacy, thereby offering an AI decision engine for precision cancer vaccine design that is applicable to all cancer types.
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It is shown that increased sampling and AlphaFold3 generally improve performance relative to default sampling and AlphaFold2, however predictive accuracy and improvement levels varied considerably among interface classes, with antibody-peptide complexes representing a challenge despite their small antigen size.
Rui Yin, S. Saravanakumar, S. Shi et al.· bioRxiv· 1 citation
Understanding peptide-major histocompatibility complex (MHC) class I binding is critical for effective vaccine and immunotherapy design but is a combinatorially complex challenge for which prediction models have become essential. MHC ligands are typically identified at scale via untargeted mass spectrometry (MS), and this has built a strong base for peptide-MHC model training. However, MS incompletely captures the vast peptide-MHC space due to technical, sampling, and biological biases. Although recently developed experimental assays have queried such blind spots yielding complementary information, existing peptide-MHC predictors have not yet incorporated these orthogonal data and are not designed to be updated as new data are generated. Here, we introduce PepCL (Peptide-MHC Continual Learning), a continual learning framework for updating peptide-MHC predictors with new assay data while explicitly preserving prior MS knowledge. To enable PepCL, we also develop MHCPrime, a new state-of-the-art pan-allelic peptide-MHC prediction model, trained on publicly available MS data, that can be effectively updated under our framework. We demonstrate that PepCL allows MHCPrime to learn previously unseen, assay-specific information while preventing catastrophic forgetting that is typically observed with conventional fine-tuning. We evaluate PepCL and MHCPrime in a variety of biological contexts, including infectious disease and cancer, and show improved peptide-MHC prediction that transfers across alleles for broader applicability in clinical settings. Overall, our results establish PepCL as a flexible framework for extending the utility of peptide-MHC models by improving their predictive performance as immunopeptidomics assays continue to evolve and new data become available.
P. Chati, Vishal D. Lashkari, Ankit Salhotra et al.· bioRxiv· 0 citations