ArticleNature computational science2021
Rapid Assessment of T-Cell Receptor Specificity of the Immune Repertoire.
Article in Nature computational science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
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Who cites it
34 citing papers in PubMed.
- Technical review of artificial intelligence in TCR-T therapy.Journal of the National Cancer Center · 2026Review
- Biophysical modeling for accurate T cell specificity prediction of viral and tumor antigens.Nature communications · 2026Article
- Computational prediction of TCR cross-reactivity: principles, challenges and translational opportunities.Journal of translational medicine · 2026Review
- A biophysical framework for accurately identifying antigen single-amino acid escape variants and corresponding variant-specific compensatory TCR sequences.bioRxiv : the preprint server for biology · 2026Article
- Structural quality-tier assessment for TCR-pMHC functional enrichment.Frontiers in immunology · 2026Article
- TCRdesign: an antigen-specific generative language model for de novo design of T-cell receptors.Briefings in bioinformatics · 2025Article
- Article
- LightCTL: lightweight contrastive TCR-pMHC specificity learning with context-aware prompt.Briefings in bioinformatics · 2025Article
- A fingerprint approach to pioneer structure-based T cell receptor repertoire analysis and specificity prediction.Frontiers in immunology · 2025Article
- STAG-LLM: Predicting TCR-pHLA binding with protein language models and computationally generated 3D structures.Computational and structural biotechnology journal · 2025Article
- Integration of kinetic data into affinity-based models for improved T cell specificity prediction.Biophysical journal · 2024Article
- Advances of computational methods enhance the development of multi-epitope vaccines.Briefings in bioinformatics · 2024Review
- Limits on inferring T cell specificity from partial information.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Feature Selection Enhances Peptide Binding Predictions for TCR-Specific Interactions.bioRxiv : the preprint server for biology · 2024Article
- Predicting Antigen-Specificities of Orphan T Cell Receptors from Cancer Patients with TCRpcDist.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire.Briefings in bioinformatics · 2024Article
- RACER-m leverages structural features for sparse T cell specificity prediction.Science advances · 2024Article
- Mechanical forces amplify TCR mechanotransduction in T cell activation and function.Applied physics reviews · 2024Review
- Exploring the therapeutic potential of precision T-Cell Receptors (TCRs) in targeting KRAS G12D cancer throughOncology research · 2024Article
- Feature selection enhances peptide binding predictions for TCR-specific interactions.Frontiers in immunology · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Accurate assessment of TCR-antigen specificity at the whole immune repertoire level lies at the heart of improved cancer immunotherapy, but predictive models capable of high-throughput assessment of TCR-peptide pairs are lacking. Recent advances in deep sequencing and crystallography have enriched the data available for studying TCR-p-MHC systems. Here, we introduce a pairwise energy model, RACER, for rapid assessment of TCR-peptide affinity at the immune repertoire level. RACER applies supervised machine learning to efficiently and accurately resolve strong TCR-peptide binding pairs from weak ones. The trained parameters further enable a physical interpretation of interacting patterns encoded in each specific TCR-p-MHC system. When applied to simulate thymic selection of an MHC-restricted T-cell repertoire, RACER accurately estimates recognition rates for tumor-associated neoantigens and foreign peptides, thus demonstrating its utility in helping address the large computational challenge of reliably identifying the properties of tumor antigen-specific T-cells at the level of an individual patient's immune repertoire.
Identifiers
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Registered trials
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.