ArticleNature methods2026
Assessment of computational methods in predicting TCR-epitope binding recognition.
Article in Nature methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Bridging model performance and clinical utility in T cell receptor-epitope binding prediction.Clinical and translational medicine · 2026Article
- Artificial intelligence for translational personalized neoantigen cancer vaccine development.Journal of biomedical science · 2026Review
- Benchmarking AlphaFold and related deep learning approaches for modeling antibody and TCR antigen recognition.bioRxiv : the preprint server for biology · 2026Article
- DTCR: generating realistic, diverse, and epitope-specific T cell receptor sequences via a discrete diffusion model.Briefings in bioinformatics · 2026Article
- Toward mechanistic virtual immune cells.Nature biotechnology · 2026Article
- Machine Learning for TCR Repertoire Epitope Annotation and Pattern Discovery.Immunological reviews · 2026Review
- Modeling TCR-Epitope Recognition Specificity: What We Should Learn to Succeed.Immunological reviews · 2026Review
- Computational prediction of TCR cross-reactivity: principles, challenges and translational opportunities.Journal of translational medicine · 2026Review
- Revised Adaptive Immune Receptor Data in the Immune Epitope Database.bioRxiv : the preprint server for biology · 2026Article
- Turep: Detecting cross-cancer tumor-reactive T cells in single-cell and spatial transcriptomics data.bioRxiv : the preprint server for biology · 2026Article
- A structure-informed deep learning framework for modeling TCR-peptide-HLA interactions.bioRxiv : the preprint server for biology · 2026Article
- AI-driven computational methods and benchmarking for T-cell antigen identification.Briefings in bioinformatics · 2026Review
- Decoding adaptive immunity: advanced strategies in T and B cell repertoire analysis.Journal of translational medicine · 2026Review
- DecoderTCR: Compositional Pretraining and Entropy-Guided Decoding for TCR-pMHC Interactions.bioRxiv : the preprint server for biology · 2026Article
- Structural quality-tier assessment for TCR-pMHC functional enrichment.Frontiers in immunology · 2026Article
- Artificial intelligence in peptide cancer vaccine design: from neoantigen discovery to immunogenicity prediction.Frontiers in genetics · 2026Review
- Assessment of computational methods in predicting TCR-epitope binding recognition.Nature methods · 2026Article
- AI-driven discovery in protein science for immunology and infectious disease research.Frontiers in bioinformatics · 2026Review
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Authors and funding
7 authors.
Funding
Abstract
T cell receptors (TCRs) play a vital role in immune recognition by binding specific epitopes. Accurate prediction of TCR-epitope interactions is fundamental for advancing immunology research. Although numerous computational methods have been developed, a comprehensive evaluation of their performance remains lacking. Here we assessed 50 state-of-the-art TCR-epitope prediction models using 21 datasets covering 762 epitopes and hundreds of thousands binding TCRs. Our analysis revealed that the source of negative TCRs substantially impacts model accuracy, with external negatives potentially introducing uncontrolled confounders. Model performance generally improved with more TCRs per epitope, highlighting the importance of large and diverse datasets. Models incorporating multiple features typically outperformed those using only complementarity-determining region 3β information, yet all struggle to generalize to unseen epitopes. The use of independent test sets proved crucial for unbiased assessment on both seen and unseen epitopes. These insights will guide the development of more accurate and generalizable TCR-epitope prediction models for real-world applications.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.