ArticleFrontiers in immunology2025
NetMHCpan-4.2: improved prediction of CD8+ epitopes by use of transfer learning and structural features.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed.
- Predicting Neoantigen Immunogenicity frombioRxiv : the preprint server for biology · 2026Article
- Comparative Genomics-Guided Epitope Prioritization and in Silico Design of a Multi-Epitope DNA Vaccine Candidate Against Megalocytivirus pagrus 1.Marine biotechnology (New York, N.Y.) · 2026Article
- The challenge and promise of studying human antigen-specific T cells.Nature reviews. Immunology · 2026Review
- A population-based retrospective machine learning study of COVID-19 severity using integrated clinical and viral genomic data in Jiangsu Province, China.BMC microbiology · 2026Article
- Understanding the structure of swine leukocyte antigen class I molecules.Cellular & molecular biology letters · 2026Review
- Advances in cancer immunotherapy: adoptive cell therapy and immune cell engagers in solid tumours.British journal of cancer · 2026Review
- Recombinant Production and Immunological Evaluation of Tobamovirus fructirugosum (ToBRFV) Capsid and Movement Proteins.Molecular biotechnology · 2026Article
- Coxsackievirus B escapes antiviral CD8Science advances · 2026Article
- Human iPSC-derived neurons stimulated with IFNγ present HLA class I-restricted antigens to cytotoxic CD8Journal of neuroinflammation · 2026Article
- MHC Attention: Identifying HLA-E presented cancer antigens through deep learning and high-throughput screening.bioRxiv : the preprint server for biology · 2026Article
- PMGen: from peptide-MHC structure prediction to peptide generation.Bioinformatics (Oxford, England) · 2026Article
- Human neurons stimulated with IFNγ present HLA class I-restricted autoantigens to cytotoxic CD8bioRxiv : the preprint server for biology · 2026Article
- A generalizable system for antigenic peptide targeting across HLA-I allotypes.bioRxiv : the preprint server for biology · 2026Article
- The digital keystone: how artificial intelligence is reshaping HLA research and clinical practice.Immunogenetics · 2026Review
- Humanized extracellular vesicles for efficient RNA delivery.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A Framework for Comparing Mouse Neoantigen Immunogenicity.bioRxiv : the preprint server for biology · 2026Article
- Beyond counting: how single-cell long-read sequencing turns transcriptome complexity into precision targets.Frontiers in oncology · 2026Review
- Antimicrobial Peptides Against Antimicrobial-Resistant Bacteria: Focus on Machine Learning.Infection and drug resistance · 2026Review
- HLA class I-naturally presented synovial tissue peptides are recognized by CD8+ T lymphocytes from rheumatoid arthritis patients.Frontiers in immunology · 2026Article
- TCRLens: structure-aware equivariant graph learning for TCR-pMHC-I recognition and immunogenic epitope discovery.Bioinformatics advances · 2026Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Introduction: Identification of CD8+ T cell epitopes is crucial for advancing vaccine development and immunotherapy strategies. Traditional methods for predicting T cell epitopes primarily focus on MHC presentation, leveraging immunopeptidome data. Recent advancements however suggest significant performance improvements through transfer learning and refinement using epitope data. Methods: To further investigate this, we here develop an enhanced MHC class I (MHC-I) antigen presentation predictor by integrating newly curated binding affinity and eluted ligand datasets, expanding MHC allele coverage, and incorporating novel input features related to the structural constraints of the MHC-I peptide-binding cleft. We next apply transfer learning using experimentally validated pathogen- and cancer-derived epitopes from public databases to refine our prediction method, ensuring comprehensive data partitioning to prevent performance overestimation. Results: Integration of structural features results in improved predictive power and enhanced identification of peptide residues likely to interact with the MHC. However, our findings indicate that fine-tuning on epitope data only yields a minor accuracy boost. Moreover, the transferability between cancer and pathogen-derived epitopes is limited, suggesting distinct properties between these data types. Discussion: In conclusion, while transfer learning can enhance T cell epitope prediction, the performance gains are modest and data type specific. Our final NetMHCpan-4.2 model is publicly accessible at https://services.healthtech.dtu.dk/services/NetMHCpan-4.2, providing a valuable resource for immunological research and therapeutic development.
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