TCRMatch: Predicting T-Cell Receptor Specificity Based on Sequence Similarity to Previously Characterized Receptors

Abstract

The adaptive immune system in vertebrates has evolved to recognize non-self antigens, such as proteins expressed by infectious agents and mutated cancer cells. T cells play an important role in antigen recognition by expressing a diverse repertoire of antigen-specific receptors, which bind epitopes to mount targeted immune responses. Recent advances in high-throughput sequencing have enabled the routine generation of T-cell receptor (TCR) repertoire data. Identifying the specific epitopes targeted by different TCRs in these data would be valuable. To accomplish that, we took advantage of the ever-increasing number of TCRs with known epitope specificity curated in the Immune Epitope Database (IEDB) since 2004. We compared seven metrics of sequence similarity to determine their power to predict if two TCRs have the same epitope specificity. We found that a comprehensive k -mer matching approach produced the best results, which we have implemented into TCRMatch, an openly accessible tool ( http://tools.iedb.org/tcrmatch/ ) that takes TCR β-chain CDR3 sequences as an input, identifies TCRs with a match in the IEDB, and reports the specificity of each match. We anticipate that this tool will provide new insights into T cell responses captured in receptor repertoire and single cell sequencing experiments and will facilitate the development of new strategies for monitoring and treatment of infectious, allergic, and autoimmune diseases, as well as cancer.

References 29

1. 1.

   Buchholz VR Schumacher TNM Busch DH. T cell fate at the single-cell level. Annu. Rev. Immunol. (2016 ) 34:65–92.

   Not in our library10.1146/annurev-immunol-032414-112014

2. 2.

   Bradley P Thomas PG. Using T cell receptor repertoires to understand the principles of adaptive immune recognition. Annu. Rev. Immunol. (2019 ) 37:547–70.

   Not in our library10.1146/annurev-immunol-042718-041757

3. 3.

   Rudolph MG Stanfield RL Wilson IA. HOW TCRS bind MHCS peptides, and coreceptors. Annu Rev Immunol. (2006 ) 24:419–66.

   Not in our library10.1146/annurev.immunol.23.021704.115658

4. 4.

   Rossjohn J Gras S Miles JJ Turner SJ Godfrey DI McCluskey J. T cell antigen receptor recognition of antigen-presenting molecules. Annu Rev Immunol. (2015 ) 33:169–200.

   Not in our library10.1146/annurev-immunol-032414-112334

5. 5.

   Calis JJA Rosenberg BR. Characterizing immune repertoires by high throughput sequencing: strategies and applications. Trends Immunol. (2014 ) 35:581–90.

   Not in our library10.1016/j.it.2014.09.004

6. 6.
   Massively parallel digital transcriptional profiling of single cells · 2017

   No notice
7. 7.

   Glanville J Huang H Nau A Hatton O Wagar LE Rubelt F. Identifying specificity groups in the T cell receptor repertoire. Nature. (2017 ) 547:94–98.

   Not in our library10.1038/nature22976

8. 8.

   Huang H Wang C Rubelt F Scriba TJ Davis MM. Analyzing the Mycobacterium tuberculosis immune response by T-cell receptor clustering with GLIPH2 and genome-wide antigen screening. Nat Biotechnol. (2020 ) 4:1–9.

   Not in our library10.1038/s41587-020-0505-4

9. 9.
   Detection of Enriched T Cell Epitope Specificity in Full T Cell Receptor Sequence Repertoires · 2019

   No notice
10. 10.

    Fischer DS Wu Y Schubert B Theis FJ. Predicting antigen specificity of single T cells based on TCR CDR3 regions. Mol Syst Biol. (2020 ) 16:e9416.

    Not in our library10.15252/msb.20199416

11. 11.

    Dash P Fiore-Gartland AJ Hertz T Wang GC Sharma S Souquette A. Quantifiable predictive features define epitope-specific T cell receptor repertoires. Nature. (2017 ) 547:89–93.

    Not in our library10.1038/nature22383

12. 12.
    A Framework for Annotation of Antigen Specificities in High-Throughput T-Cell Repertoire Sequencing Studies · 2019

    Not checked
13. 13.

    Tickotsky N Sagiv T Prilusky J Shifrut E Friedman N. McPAS-TCR: a manually curated catalogue of pathology-associated T cell receptor sequences. Bioinformatics. (2017 ) 33:2924–929.

    Not in our library10.1093/bioinformatics/btx286

14. 14.
    VDJdb in 2019: database extension, new analysis infrastructure and a T-cell receptor motif compendium · 2019

    No notice
15. 15.

    Zhang W Wang L Liu K Wei X Yang K Du W. PIRD: pan immune repertoire database. Bioinformatics. (2020 ) 36:897–903.

    Not in our library10.1093/bioinformatics/btz614

16. 16.
    The Immune Epitope Database (IEDB): 2018 update · 2018

    No notice
17. 17.
    Epitope Specific Antibodies and T Cell Receptors in the Immune Epitope Database · 2018

    Not checked
18. 18.

    Shen W.-J. Wong H.-S. Xiao Q.-W.. Towards a mathematical foundation of immunology and amino acid chains. arXiv [Preprint] arXiv:1205.6031 (2012 ).

    Not in our library

19. 19.
    IMGT®, the international ImMunoGeneTics information system® 25 years on · 2014

    No notice
20. 20.

    A New Way of Exploring Immunity—Linking Highly Multiplexed Antigen Recognition to Immune Repertoire and Phenotype. (2020 ). 10x Genomics Available online at: https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/

    Not in our library

21. 21.
    Parasail: SIMD C library for global, semi-global, and local pairwise sequence alignments · 2016

    No notice
22. 22.

    Meysman P De Neuter N Gielis S Bui Thi D Ogunjimi B Laukens K. On the viability of unsupervised T-cell receptor sequence clustering for epitope preference. Bioinformatics. (2019 ) 35:1461–8.

    Not in our library10.1093/bioinformatics/bty821

23. 23.

    Friedensohn S Khan TA Reddy ST. Advanced methodologies in high-throughput sequencing of immune repertoires. Trends Biotechnol. (2017 ) 35:203–14.

    Not in our library10.1016/j.tibtech.2016.09.010

24. 24.

    Wong EB Gold MC Meermeier EW Xulu BZ Khuzwayo S Sullivan ZA. TRAV1-2+ CD8+ T-cells including oligoconal expansions of MAIT cells are enriched in the airways in human tuberculosis. Commun Biol. (2019 ) 2:203.

    Not in our library10.1038/s42003-019-0442-2

25. 25.

    Christley S Scarborough W Salinas E Rounds WH Toby IT Fonner JM. VDJServer: a cloud-based analysis portal and data commons for immune repertoire sequences and rearrangements. Front Immunol. (2018 ) 9:976.

    Not in our library10.3389/fimmu.2018.00976

26. 26.

    Peters B Nielsen M Sette A. T cell epitope predictions. Annu Rev Immunol. (2020 ) 38:123–45.

    Not in our library10.1146/annurev-immunol-082119-124838

27. 27.

    Dhanda SK Mahajan S Paul S Yan Z Kim H Jespersen MC. IEDB-AR: immune epitope database—analysis resource in 2019. Nucleic Acids Res. (2019 ) 47:W502–W6.

    Not in our library10.1093/nar/gkz452

28. 28.

    Jokinen E Huuhtanen J Mustjoki S Heinonen M Lähdesmäki H. Determining epitope specificity of T cell receptors with TCRG. bioRxiv P. 542332. (2019 ).

    Not in our library10.1101/542332

29. 29.

    Jurtz VI Jessen LE Bentzen AK Jespersen MC Mahajan S Vita R. NetTCR: sequence-based prediction of TCR binding to peptide-MHC complexes using convolutional neural networks. bioRxiv 433706. (2018 ).

    Not in our library10.1101/433706

Cited by papers we hold 3

• Therapeutic cancer vaccines: advancements, challenges and prospects · 2023
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• TITAN: T-cell receptor specificity prediction with bimodal attention networks · 2021
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• NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data · 2021
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