ArticleProceedings of the National Academy of Sciences of the United States of America2024
Limits on inferring T cell specificity from partial information.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Fundamental limits incorporating logical reasoning into Shannon's information theory.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Structural T-Cell Receptor Analysis in the Age of Machine Learning.Immunological reviews · 2026Review
- Systematic analysis of CDR contacts and pairing constraints between T cell receptor αβ chains.Bioinformatics (Oxford, England) · 2026Article
- Modeling TCR-Epitope Recognition Specificity: What We Should Learn to Succeed.Immunological reviews · 2026Review
- TCRBinder: Unified pre-trained language model with paired-chain synergy for predicting T-cell receptor binding specificity.PLoS computational biology · 2026Article
- Pre-transplant TCR Network Topology Predicts Kidney Allograft Rejection Independent of HLA Mismatch.bioRxiv : the preprint server for biology · 2026Article
- Evaluating the utility of amino acid similarity-aware kmers to represent TCR repertoires for classification.PLoS computational biology · 2026Article
- Article
- Evolution of the tuberculin skin test reveals generalisable Mtb-reactive T cell metaclones.Nature communications · 2026Article
- HELP-TCR: Harmonized Explainable Language Processing Toolkit for T Cell Antigen Receptor Repertoires.Computational and structural biotechnology journal · 2026Article
- Article
- Conditional generation of real antigen-specific T cell receptor sequences.Nature machine intelligence · 2025Article
- Quantifying conformational changes in the TCR:pMHC-I binding interface.Frontiers in immunology · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A key challenge in molecular biology is to decipher the mapping of protein sequence to function. To perform this mapping requires the identification of sequence features most informative about function. Here, we quantify the amount of information (in bits) that T cell receptor (TCR) sequence features provide about antigen specificity. We identify informative features by their degree of conservation among antigen-specific receptors relative to null expectations. We find that TCR specificity synergistically depends on the hypervariable regions of both receptor chains, with a degree of synergy that strongly depends on the ligand. Using a coincidence-based approach to measuring information enables us to directly bound the accuracy with which TCR specificity can be predicted from partial matches to reference sequences. We anticipate that our statistical framework will be of use for developing machine learning models for TCR specificity prediction and for optimizing TCRs for cell therapies. The proposed coincidence-based information measures might find further applications in bounding the performance of pairwise classifiers in other fields.
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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.