ArticleFrontiers in immunology2019
T-Cell Receptor Cognate Target Prediction Based on Paired α and β Chain Sequence and Structural CDR Loop Similarities.
Article in Frontiers in immunology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed, 57 citations in OpenAlex.
- NeoTCRseek: an integrated platform for high-sensitivity identification of neoantigen-specific TCR clonotypes to track clinical T-cell dynamics.Frontiers in immunology · 2026Article
- SageTCR: a structure-based model integrating residue- and atom-level representations for enhanced TCR-pMHC binding prediction.Briefings in bioinformatics · 2025Article
- GRAPE: graph-regularized protein language modeling unlocks TCR-epitope binding specificity.Briefings in bioinformatics · 2025Article
- A roadmap for T cell receptor-peptide-bound major histocompatibility complex binding prediction by machine learning: glimpse and foresight.Briefings in bioinformatics · 2025Review
- TCR-ESM: Employing protein language embeddings to predict TCR-peptide-MHC binding.Computational and structural biotechnology journal · 2024Article
- Predicting Antigen-Specificities of Orphan T Cell Receptors from Cancer Patients with TCRpcDist.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- TULIP: A transformer-based unsupervised language model for interacting peptides and T cell receptors that generalizes to unseen epitopes.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity.Briefings in bioinformatics · 2024Article
- Can AlphaFold's breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity?Nature methods · 2024Review
- Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells.Nature communications · 2024Article
- Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy.Frontiers in immunology · 2024Review
- Editorial: Quantification and prediction of T-cell cross-reactivity through experimental and computational methods.Frontiers in immunology · 2024Article
- Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens.Journal of chemical theory and computation · 2023Review
- Can we predict T cell specificity with digital biology and machine learning?Nature reviews. Immunology · 2023Review
- MIX-TPI: a flexible prediction framework for TCR-pMHC interactions based on multimodal representations.Bioinformatics (Oxford, England) · 2023Article
- Entropic analysis of antigen-specific CDR3 domains identifies essential binding motifs shared by CDR3s with different antigen specificities.Cell systems · 2023Article
- Attentive Variational Information Bottleneck for TCR-peptide interaction prediction.Bioinformatics (Oxford, England) · 2023Article
- Stitchr: stitching coding TCR nucleotide sequences from V/J/CDR3 information.Nucleic acids research · 2022Article
- TCR-L: an analysis tool for evaluating the association between the T-cell receptor repertoire and clinical phenotypes.BMC bioinformatics · 2022Article
- Identification of neoantigens for individualized therapeutic cancer vaccines.Nature reviews. Drug discovery · 2022Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
T-cell receptors (TCR) mediate immune responses recognizing peptides in complex with major histocompatibility complexes (pMHC) displayed on the surface of cells. Resolving the challenge of predicting the cognate pMHC target of a TCR would benefit many applications in the field of immunology, including vaccine design/discovery and the development of immunotherapies. Here, we developed a model for prediction of TCR targets based on similarity to a database of TCRs with known targets. Benchmarking the model on a large set of TCRs with known target, we demonstrated how the predictive performance is increased (i) by focusing on CDRs rather than the full length TCR protein sequences, (ii) by incorporating information from paired α and β chains, and (iii) integrating information for all 6 CDR loops rather than just CDR3. Finally, we show how integration of the structure of CDR loops, as obtained through homology modeling, boosts the predictive power of the model, in particular in situations where no high-similarity TCRs are available for the query. These findings demonstrate that TCRs that bind to the same target also share, to a very high degree, sequence, and structural features. This observation has profound impact for future development of prediction models for TCR-pMHC interactions and for the use of such models for the rational design of T cell based therapies.
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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.