Advertisement

Modeling and Viewing T Cell Receptors Using TCRmodel and TCR3d

  • Ragul Gowthaman
  • Brian G. PierceEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 2120)

Abstract

The past decade has seen a rapid increase in T cell receptor (TCR) sequences from single cell cloning and repertoire-scale high throughput sequencing studies. Many of these TCRs are of interest as potential therapeutics or for their implications in autoimmune disease or effective targeting of pathogens. As it is impractical to characterize the structure or targeting of the vast majority of these TCRs experimentally, advanced computational methods have been developed to predict their 3D structures and gain mechanistic insights into their antigen binding and specificity. Here, we describe the use of a TCR modeling web server, TCRmodel, which generates models of TCRs from sequence, and TCR3d, which is a weekly-updated database of all known TCR structures. Additionally, we describe the use of RosettaTCR, which is a protocol implemented in the Rosetta framework that serves as the command-line backend to TCRmodel. We provide an example where these tools are used to analyze and model a therapeutically relevant TCR based on its amino acid sequence.

Key words

TCR Rosetta MHC Antigen Bioinformatics Immunology 

References

  1. 1.
    Gaud G et al (2018) Regulatory mechanisms in T cell receptor signalling. Nat Rev Immunol 18(8):485–497.  https://doi.org/10.1038/s41577-018-0020-8CrossRefPubMedGoogle Scholar
  2. 2.
    Hinrichs CS, Rosenberg SA (2014) Exploiting the curative potential of adoptive T-cell therapy for cancer. Immunol Rev 257(1):56–71.  https://doi.org/10.1111/imr.12132CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Liddy N et al (2012) Monoclonal TCR-redirected tumor cell killing. Nat Med 18(6):980–987.  https://doi.org/10.1038/nm.2764CrossRefPubMedGoogle Scholar
  4. 4.
    Zhang J, Wang L (2019) The emerging world of TCR-T cell trials against cancer: a systematic review. Technol Cancer Res Treat 18:1533033819831068.  https://doi.org/10.1177/1533033819831068CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Rose PW et al (2011) The RCSB protein data bank: redesigned web site and web services. Nucleic Acids Res 39(Database):D392–D401.  https://doi.org/10.1093/nar/gkq1021CrossRefPubMedGoogle Scholar
  6. 6.
    Glanville J et al (2017) Identifying specificity groups in the T cell receptor repertoire. Nature 547(7661):94–98.  https://doi.org/10.1038/nature22976CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Gowthaman R, Pierce BG (2018) TCRmodel: high resolution modeling of T cell receptors from sequence. Nucleic Acids Res 46(W1):W396–W401.  https://doi.org/10.1093/nar/gky432CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Leaver-Fay A et al (2011) ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol 487:545–574.  https://doi.org/10.1016/B978-0-12-381270-4.00019-6CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Gowthaman R, Pierce BG (2019) TCR3d: The T cell receptor structural repertoire database. Bioinformatics, In PressGoogle Scholar
  10. 10.
    North B et al (2011) A new clustering of antibody CDR loop conformations. J Mol Biol 406(2):228–256.  https://doi.org/10.1016/j.jmb.2010.10.030CrossRefPubMedGoogle Scholar
  11. 11.
    Pierce BG, Weng Z (2013) A flexible docking approach for prediction of T cell receptor-peptide-MHC complexes. Protein Sci 22(1):35–46.  https://doi.org/10.1002/pro.2181CrossRefPubMedGoogle Scholar
  12. 12.
    Shugay M et al (2018) VDJdb: a curated database of T-cell receptor sequences with known antigen specificity. Nucleic Acids Res 46(D1):D419–D427.  https://doi.org/10.1093/nar/gkx760CrossRefPubMedGoogle Scholar
  13. 13.
    Dayhoff M et al (1978) A model of evolutionary change in proteins. In: Atlas of protein sequence and structure, vol 5. National Biomedical Research Foundation Silver Spring, Waltham, MA, pp 345–352Google Scholar
  14. 14.
    Yang X et al (2015) Structural basis for clonal diversity of the public T cell response to a dominant human cytomegalovirus epitope. J Biol Chem 290(48):29106–29119.  https://doi.org/10.1074/jbc.M115.691311CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Stronen E et al (2016) Targeting of cancer neoantigens with donor-derived T cell receptor repertoires. Science 352(6291):1337–1341.  https://doi.org/10.1126/science.aaf2288CrossRefPubMedGoogle Scholar
  16. 16.
    Dunbar J, Deane CM (2016) ANARCI: antigen receptor numbering and receptor classification. Bioinformatics 32(2):298–300.  https://doi.org/10.1093/bioinformatics/btv552CrossRefPubMedGoogle Scholar
  17. 17.
    Alford RF et al (2017) The Rosetta all-atom energy function for macromolecular modeling and design. J Chem Theory Comput 13(6):3031–3048.  https://doi.org/10.1021/acs.jctc.7b00125CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Institute for Bioscience and Biotechnology ResearchUniversity of MarylandRockvilleUSA
  2. 2.Department of Cell Biology and Molecular GeneticsUniversity of MarylandCollege ParkUSA
  3. 3.Marlene and Stewart Greenebaum Comprehensive Cancer CenterUniversity of MarylandBaltimoreUSA

Personalised recommendations