ArticleBioinformatics (Oxford, England)2023
epiTCR: a highly sensitive predictor for TCR-peptide binding.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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42 citing papers in PubMed, 56 citations in OpenAlex.
- When multimodal fusion fails: contrastive alignment as a necessary stabilizer for TCR-peptide binding prediction.Bioinformatics (Oxford, England) · 2026Article
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- DTCR: generating realistic, diverse, and epitope-specific T cell receptor sequences via a discrete diffusion model.Briefings in bioinformatics · 2026Article
- Modeling TCR-Epitope Recognition Specificity: What We Should Learn to Succeed.Immunological reviews · 2026Review
- Mapping the TCR landscape: computational tools empowering translational immunology and therapy design.Journal for immunotherapy of cancer · 2026Review
- Revised Adaptive Immune Receptor Data in the Immune Epitope Database.bioRxiv : the preprint server for biology · 2026Article
- Supervised fine-tuning enhances unsupervised learning from 45 million amino acids in TCR and peptide sequences.Bioinformatics (Oxford, England) · 2026Article
- Advances in predicting T cell epitope recognition for cancer immunotherapy.Nature cancer · 2026Review
- Single-cell profiling unveils nephritis-related circulating immunological signatures in systemic lupus erythematosus patients.Communications biology · 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
- 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
- Disrupting explicit encoding paradigms: property-interactive transformers decode T-cell receptor specificity beyond dataset biases.Briefings in bioinformatics · 2025Article
- Article
- T cell receptor cross-reactivity prediction improved by a comprehensive mutational scan database.Cell systems · 2025Article
- Benchmarking of T cell receptor-epitope predictors with ePytope-TCR.Cell genomics · 2025Article
- Computational methods and data resources for predicting tumor neoantigens.Briefings in bioinformatics · 2025Review
- nuTCRacker: Predicting the Recognition of HLA-I-Peptide Complexes by αβTCRs for Unseen Peptides.European journal of immunology · 2025Article
- TCR-epiDiff: solving dual challenges of TCR generation and binding prediction.Bioinformatics (Oxford, England) · 2025Article
- Phage display enables machine learning discovery of cancer antigen-specific TCRs.Science advances · 2025Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationPredicting the binding between T-cell receptor (TCR) and peptide presented by human leucocyte antigen molecule is a highly challenging task and a key bottleneck in the development of immunotherapy. Existing prediction tools, despite exhibiting good performance on the datasets they were built with, suffer from low true positive rates when used to predict epitopes capable of eliciting T-cell responses in patients. Therefore, an improved tool for TCR-peptide prediction built upon a large dataset combining existing publicly available data is still needed.
resultsWe collected data from five public databases (IEDB, TBAdb, VDJdb, McPAS-TCR, and 10X) to form a dataset of >3 million TCR-peptide pairs, 3.27% of which were binding interactions. We proposed epiTCR, a Random Forest-based method dedicated to predicting the TCR-peptide interactions. epiTCR used simple input of TCR CDR3β sequences and antigen sequences, which are encoded by flattened BLOSUM62. epiTCR performed with area under the curve (0.98) and higher sensitivity (0.94) than other existing tools (NetTCR, Imrex, ATM-TCR, and pMTnet), while maintaining comparable prediction specificity (0.9). We identified seven epitopes that contributed to 98.67% of false positives predicted by epiTCR and exerted similar effects on other tools. We also demonstrated a considerable influence of peptide sequences on prediction, highlighting the need for more diverse peptides in a more balanced dataset. In conclusion, epiTCR is among the most well-performing tools, thanks to the use of combined data from public sources and its use will contribute to the quest in identifying neoantigens for precision cancer immunotherapy. AVAILABILITY AND IMPLEMENTATION: epiTCR is available on GitHub (https://github.com/ddiem-ri-4D/epiTCR).
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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.