ArticleNature communications2024
Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
What it found
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Who cites it
56 citing papers in PubMed, 75 citations in OpenAlex.
- Calibrating T cell responsiveness through interactions with self.Nature reviews. Immunology · 2026Review
- Structural T-Cell Receptor Analysis in the Age of Machine Learning.Immunological reviews · 2026Review
- Rapid discovery of monoclonal antibodies via high-throughput single BCR affinity sequencing.Nucleic acids research · 2026Article
- Engineered Bacterial Membranes as Next-Generation Platforms for Cancer Immunotherapy.Small science · 2026Review
- TcrDesign: de novo design of epitope-specific full-length T cell receptors.Science China. Life sciences · 2026Article
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- Reliable evaluation and learning in multi-input biological association prediction.Briefings in bioinformatics · 2026Article
- Mitigating negative data bias to enhance TCR-epitope binding and residue interaction prediction.Briefings in bioinformatics · 2026Article
- Ganoderma lucidum spore powder enhances IFN-α-mediated antiviral capacity of COVID-19 vaccine boosters revealed by single-cell multi-omics sequencing.Journal of advanced research · 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
- Revised Adaptive Immune Receptor Data in the Immune Epitope Database.bioRxiv : the preprint server for biology · 2026Article
- TCRBinder: Unified pre-trained language model with paired-chain synergy for predicting T-cell receptor binding specificity.PLoS computational biology · 2026Article
- Deep peptide recognition profiling decodes TCR specificity and enables disease-associated antigen discovery.Nature biotechnology · 2026Article
- A single polypeptide vaccine derived from multiple antigens confers protection against staphylococcus aureus infection in mice.Tropical diseases, travel medicine and vaccines · 2026Article
- Advances in predicting T cell epitope recognition for cancer immunotherapy.Nature cancer · 2026Review
- T-cell repertoire response in individuals with post-acute sequelae of COVID-19.bioRxiv : the preprint server for biology · 2026Article
- The digital keystone: how artificial intelligence is reshaping HLA research and clinical practice.Immunogenetics · 2026Review
- 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
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
T cells have the ability to eliminate infected and cancer cells and play an essential role in cancer immunotherapy. T cell activation is elicited by the binding of the T cell receptor (TCR) to epitopes displayed on MHC molecules, and the TCR specificity is determined by the sequence of its α and β chains. Here, we collect and curate a dataset of 17,715 αβTCRs interacting with dozens of class I and class II epitopes. We use this curated data to develop MixTCRpred, an epitope-specific TCR-epitope interaction predictor. MixTCRpred accurately predicts TCRs recognizing several viral and cancer epitopes. MixTCRpred further provides a useful quality control tool for multiplexed single-cell TCR sequencing assays of epitope-specific T cells and pinpoints a substantial fraction of putative contaminants in public databases. Analysis of epitope-specific dual α T cells demonstrates that MixTCRpred can identify α chains mediating epitope recognition. Applying MixTCRpred to TCR repertoires from COVID-19 patients reveals enrichment of clonotypes predicted to bind an immunodominant SARS-CoV-2 epitope. Overall, MixTCRpred provides a robust tool to predict TCRs interacting with specific epitopes and interpret TCR-sequencing data from both bulk and epitope-specific T cells.
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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.