ArticleGenome medicine2023
A robust deep learning workflow to predict CD8 + T-cell epitopes.
Article in Genome medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Transfer learning for T-cell response prediction.BMC bioinformatics · 2026Article
- Computational identification of B- and T-cell epitopes: a unified task taxonomy and review of databases, datasets, predictive pipelines, and gaps.Frontiers in immunology · 2026Review
- ImmUQBench: a benchmark on uncertainty quantification of protein immunogenicity prediction.Oxford open immunology · 2026Article
- Computational neoantigen prediction for cancer immunotherapy.Genes and immunity · 2025Review
- PredIG: an interpretable predictor of T-cell epitope immunogenicity.Genome medicine · 2025Article
- Computational Methods in Immunoinformatics: Epitope Discovery and Diagnostic Applications.ACS omega · 2025Review
- Mapping T cell infiltration patterns in glioma tumor tissue.medRxiv : the preprint server for health sciences · 2025Article
- Similarity to Self-Antigens Shapes Epitope Recognition from Viruses Under Autoimmune and Infectious Disease.International journal of molecular sciences · 2025Article
- Large-scale transcript variants dictate neoepitopes for cancer immunotherapy.Science advances · 2025Article
- Feature Selection Enhances Peptide Binding Predictions for TCR-Specific Interactions.bioRxiv : the preprint server for biology · 2024Article
- A journey to your self: The vague definition of immune self and its practical implications.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Review
- Feature selection enhances peptide binding predictions for TCR-specific interactions.Frontiers in immunology · 2024Article
- A robust deep learning workflow to predict CD8 + T-cell epitopes.Genome medicine · 2023Article
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9 authors.
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Abstract
backgroundT-cells play a crucial role in the adaptive immune system by triggering responses against cancer cells and pathogens, while maintaining tolerance against self-antigens, which has sparked interest in the development of various T-cell-focused immunotherapies. However, the identification of antigens recognised by T-cells is low-throughput and laborious. To overcome some of these limitations, computational methods for predicting CD8 + T-cell epitopes have emerged. Despite recent developments, most immunogenicity algorithms struggle to learn features of peptide immunogenicity from small datasets, suffer from HLA bias and are unable to reliably predict pathology-specific CD8 + T-cell epitopes.
methodsWe developed TRAP (T-cell recognition potential of HLA-I presented peptides), a robust deep learning workflow for predicting CD8 + T-cell epitopes from MHC-I presented pathogenic and self-peptides. TRAP uses transfer learning, deep learning architecture and MHC binding information to make context-specific predictions of CD8 + T-cell epitopes. TRAP also detects low-confidence predictions for peptides that differ significantly from those in the training datasets to abstain from making incorrect predictions. To estimate the immunogenicity of pathogenic peptides with low-confidence predictions, we further developed a novel metric, RSAT (relative similarity to autoantigens and tumour-associated antigens), as a complementary to 'dissimilarity to self' from cancer studies.
resultsTRAP was used to identify epitopes from glioblastoma patients as well as SARS-CoV-2 peptides, and it outperformed other algorithms in both cancer and pathogenic settings. TRAP was especially effective at extracting immunogenicity-associated properties from restricted data of emerging pathogens and translating them onto related species, as well as minimising the loss of likely epitopes in imbalanced datasets. We also demonstrated that the novel metric termed RSAT was able to estimate immunogenic of pathogenic peptides of various lengths and species. TRAP implementation is available at: https://github.com/ChloeHJ/TRAP .
conclusionsThis study presents a novel computational workflow for accurately predicting CD8 + T-cell epitopes to foster a better understanding of antigen-specific T-cell response and the development of effective clinical therapeutics.
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