ArticleBMC bioinformatics2019
Balancing sensitivity and specificity in distinguishing TCR groups by CDR sequence similarity.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 26 citations in OpenAlex.
- Unsupervised identification of low-frequency antigen-specific TCRs using distance-based anomaly scoring.Briefings in bioinformatics · 2026Article
- TCR representation learning with protein language models: a comprehensive review.International immunology · 2026Review
- DiffPaSS-high-performance differentiable pairing of protein sequences using soft scores.Bioinformatics (Oxford, England) · 2024Article
- A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity.eLife · 2023Article
- The screening, identification, design and clinical application of tumor-specific neoantigens for TCR-T cells.Molecular cancer · 2023Review
- TCR-engineered T cell therapy in solid tumors: State of the art and perspectives.Science advances · 2023Review
- Antigen-specificity measurements are the key to understanding T cell responses.Frontiers in immunology · 2023Review
- Progress in kidney transplantation: The role for systems immunology.Frontiers in medicine · 2022Review
- Predicting recognition between T cell receptors and epitopes with TCRGP.PLoS computational biology · 2021Article
- Machine Learning Detects Anti-DENV Signatures in Antibody Repertoire Sequences.Frontiers in artificial intelligence · 2021Article
- Antigen-Specific Treg Therapy in Type 1 Diabetes - Challenges and Opportunities.Frontiers in immunology · 2021Review
- Prospective Artificial Intelligence to Dissect the Dengue Immune Response and Discover Therapeutics.Frontiers in immunology · 2021Review
- Persistent T Cell Repertoire Perturbation and T Cell Activation in HIV After Long Term Treatment.Frontiers in immunology · 2021Article
- T cell receptor sequence clustering and antigen specificity.Computational and structural biotechnology journal · 2020Review
- Predicting Cross-Reactivity and Antigen Specificity of T Cell Receptors.Frontiers in immunology · 2020Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
backgroundRepertoire sequencing is enabling deep explorations into the cellular immune response, including the characterization of commonalities and differences among T cell receptor (TCR) repertoires from different individuals, pathologies, and antigen specificities. In seeking to understand the generality of patterns observed in different groups of TCRs, it is necessary to balance how well each pattern represents the diversity among TCRs from one group (sensitivity) vs. how many TCRs from other groups it also represents (specificity). The variable complementarity determining regions (CDRs), particularly the third CDRs (CDR3s) interact with major histocompatibility complex (MHC)-presented epitopes from putative antigens, and thus encode the determinants of recognition.
resultsWe here systematically characterize the predictive power that can be obtained from CDR3 sequences, using representative, readily interpretable methods for evaluating CDR sequence similarity and then clustering and classifying sequences based on similarity. An initial analysis of CDR3s of known structure, clustered by structural similarity, helps calibrate the limits of sequence diversity among CDRs that might have a common mode of interaction with presented epitopes. Subsequent analyses demonstrate that this same range of sequence similarity strikes a favorable specificity/sensitivity balance in distinguishing twins from non-twins based on overall CDR3 repertoires, classifying CDR3 repertoires by antigen specificity, and distinguishing general pathologies.
conclusionWe conclude that within a fairly broad range of sequence similarity, matching CDR3 sequences are likely to share specificities.
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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.