ArticleFrontiers in immunology2020
Prediction of Specific TCR-Peptide Binding From Large Dictionaries of TCR-Peptide Pairs.
Article in Frontiers in immunology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 122 papers.
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Who cites it
122 citing papers in PubMed.
- Phase II study of ceralasertib (AZD6738) in combination with durvalumab in patients with advanced gastric cancer.Journal for immunotherapy of cancer · 2022Trial
- Randomized trial of neoadjuvant vaccination with tumor-cell lysate induces T cell response in low-grade gliomas.The Journal of clinical investigation · 2022Trial
- Toward next-generation therapeutic HPV vaccines: A review on expanding the antigen repertoire and an integrated bioinformatics selection framework.Human vaccines & immunotherapeutics · 2026Review
- Structural T-Cell Receptor Analysis in the Age of Machine Learning.Immunological reviews · 2026Review
- Mitigating Goodhart's law in epitope-conditioned TCR generation using plug-and-play reward designs.Bioinformatics (Oxford, England) · 2026Article
- Technical review of artificial intelligence in TCR-T therapy.Journal of the National Cancer Center · 2026Review
- TcrDesign: de novo design of epitope-specific full-length T cell receptors.Science China. Life sciences · 2026Article
- Machine Learning of Personal Repertoires From Public T Cell Receptors.Immunological reviews · 2026Review
- Ensembles ofbioRxiv : the preprint server for biology · 2026Article
- Computational identification of antigen-specific T cell groups through generative epitope modeling.iScience · 2026Article
- T-cell receptor alpha to beta chains binding prediction.Briefings in bioinformatics · 2026Article
- 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
- General prediction of T cell receptor antigen specificity from sequence using AlphaFold 3.bioRxiv : the preprint server for biology · 2026Article
- A lightweight TcrLM model predicts T cell receptor and epitope binding specificity.Cell reports methods · 2026Article
- Deep peptide recognition profiling decodes TCR specificity and enables disease-associated antigen discovery.Nature biotechnology · 2026Article
- Supervised fine-tuning enhances unsupervised learning from 45 million amino acids in TCR and peptide sequences.Bioinformatics (Oxford, England) · 2026Article
- AI-Driven BCR Modeling for Precision Immunology.International journal of molecular sciences · 2026Review
- TCR sequencing in cancer immunology and immunotherapy: what, when, where, why, and how.Journal for immunotherapy of cancer · 2026Review
- 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
62 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current sequencing methods allow for detailed samples of T cell receptors (TCR) repertoires. To determine from a repertoire whether its host had been exposed to a target, computational tools that predict TCR-epitope binding are required. Currents tools are based on conserved motifs and are applied to peptides with many known binding TCRs. We employ new Natural Language Processing (NLP) based methods to predict whether any TCR and peptide bind. We combined large-scale TCR-peptide dictionaries with deep learning methods to produce ERGO (pEptide tcR matchinG predictiOn), a highly specific and generic TCR-peptide binding predictor. A set of standard tests are defined for the performance of peptide-TCR binding, including the detection of TCRs binding to a given peptide/antigen, choosing among a set of candidate peptides for a given TCR and determining whether any pair of TCR-peptide bind. ERGO reaches similar results to state of the art methods in these tests even when not trained specifically for each test. The software implementation and data sets are available at https://github.com/louzounlab/ERGO. ERGO is also available through a webserver at: http://tcr.cs.biu.ac.il/.
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