Skip to main content

Yeast Display for the Identification of Peptide-MHC Ligands of Immune Receptors

  • Protocol
  • First Online:
Yeast Surface Display

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2491))

Abstract

T cells detect peptide antigens presented by major histocompatibility complex (MHC) proteins via their T cell receptor (TCR). The sequence diversity of possible antigens, with trillions of potential peptide-MHC targets, makes it challenging to study, characterize, and manipulate the peptide repertoire of a given TCR. Yeast display has been utilized to study the interactions between peptide-MHCs and T cell receptors to facilitate high-throughput screening of peptide-MHC libraries. Here we present insights on designing and validating a peptide-MHC yeast display construct, designing and constructing peptide libraries, conducting selections, and preparing, processing, and analyzing peptide library sequencing data. Applications for this approach are broad, including characterizing peptide-MHC recognition profiles for a TCR, screening for high-affinity mimotopes of known TCR-binding peptides, and identifying natural ligands of TCRs from expanded T cells.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Matsui K, Boniface J, Reay P et al (1991) Low affinity interaction of peptide-MHC complexes with T cell receptors. Science 254:1788–1791

    Article  CAS  Google Scholar 

  2. Birnbaum ME, Mendoza JL, Sethi DK et al (2014) Deconstructing the peptide-MHC specificity of T cell recognition. Cell 157:1073–1087

    Article  CAS  Google Scholar 

  3. Wooldridge L, Ekeruche-Makinde J, van den Berg HA et al (2012) A single autoimmune T cell receptor recognizes more than a million different peptides*. J Biol Chem 287:1168–1177

    Article  CAS  Google Scholar 

  4. Gee MH, Han A, Lofgren SM et al (2018) Antigen identification for orphan T cell receptors expressed on tumor-infiltrating lymphocytes. Cell 172:549–563.e16

    Article  CAS  Google Scholar 

  5. Adams JJ, Narayanan S, Liu B et al (2011) T cell receptor signaling is limited by docking geometry to peptide-major histocompatibility complex. Immunity 35:681–693

    Article  CAS  Google Scholar 

  6. Chao G, Lau WL, Hackel BJ et al (2006) Isolating and engineering human antibodies using yeast surface display. Nat Protoc 1:755–768

    Article  CAS  Google Scholar 

  7. Ramachandiran V, Grigoriev V, Lan L et al (2007) A robust method for production of MHC tetramers with small molecule fluorophores. J Immunol Methods 319:13–20

    Article  CAS  Google Scholar 

  8. Boder ET, Dane Wittrup K (1997) Yeast surface display for screening combinatorial polypeptide libraries. Nat Biotechnol 15:553–557

    Article  CAS  Google Scholar 

  9. Hansen T, Lawrence Yu YY, Fremont DH (2009) Preparation of stable single-chain trimers engineered with peptide, β2 microglobulin, and MHC heavy chain. Curr Protoc Immunol 87

    Google Scholar 

  10. Lybarger L, Lawrence Yu YY, Miley MJ et al (2003) Enhanced immune presentation of a single-chain major histocompatibility complex class I molecule engineered to optimize linkage of a C-terminally extended peptide. J Biol Chem 278:27105–27111

    Article  CAS  Google Scholar 

  11. Pedersen LØ, Stryhn A, Holtet TL et al (1995) The interaction of beta 2-microglobulin (β2m) with mouse class I major histocompatibility antigens and its ability to support peptide binding. A comparison of human and mouse β2m. Eur J Immunol 25:1609–1616

    Article  CAS  Google Scholar 

  12. Fernandes RA, Li C, Wang G et al (2020) Discovery of surrogate agonists for visceral fat Treg cells that modulate metabolic indices in vivo. Elife 9:e58463

    Article  Google Scholar 

  13. Rappazzo CG, Huisman BD, Birnbaum ME (2020) Repertoire-scale determination of class II MHC peptide binding via yeast display improves antigen prediction. Nat Commun 11:4414

    Article  CAS  Google Scholar 

  14. Almagro Armenteros JJ, Tsirigos KD, Sønderby CK et al (2019) SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol 37:420–423

    Article  CAS  Google Scholar 

  15. Kall L, Krogh A, Sonnhammer ELL (2007) Advantages of combined transmembrane topology and signal peptide prediction—the Phobius web server. Nucleic Acids Res 35:W429–W432

    Article  Google Scholar 

  16. von Heijne G (1986) A new method for predicting signal sequence cleavage sites. Nucleic Acids Res 14:4683–4690

    Article  Google Scholar 

  17. Sussman JL, Lin D, Jiang J et al (1998) Protein data Bank (PDB): database of three-dimensional structural information of biological macromolecules. Acta Crystallogr D Biol Crystallogr 54:1078–1084

    Article  CAS  Google Scholar 

  18. Kaas Q (2004) IMGT/3Dstructure-DB and IMGT/StructuralQuery, a database and a tool for immunoglobulin, T cell receptor and MHC structural data. Nucleic Acids Res 32:208D–210D

    Article  Google Scholar 

  19. O’Brien C, Flower DR, Feighery C (2008) Peptide length significantly influences in vitro affinity for MHC class II molecules. Immunome Res 4:6

    Article  Google Scholar 

  20. Zavala-Ruiz Z, Strug I, Anderson MW et al (2004) A polymorphic pocket at the P10 position contributes to peptide binding specificity in class II MHC proteins. Chem Biol 11:1395–1402

    Article  CAS  Google Scholar 

  21. Lovitch SB, Pu Z, Unanue ER (2006) Amino-terminal flanking residues determine the conformation of a peptide-class II MHC complex. J Immunol 176:2958–2968

    Article  CAS  Google Scholar 

  22. Rossjohn J, Gras S, Miles JJ et al (2015) T cell antigen receptor recognition of antigen-presenting molecules. Annu Rev Immunol 33:169–200

    Article  CAS  Google Scholar 

  23. Wieczorek M, Abualrous ET, Sticht J et al (2017) Major histocompatibility complex (MHC) class I and MHC class II proteins: conformational plasticity in antigen presentation. Front Immunol 8:292

    Article  Google Scholar 

  24. Andreatta M, Nielsen M (2016) Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics 32:511–517

    Article  CAS  Google Scholar 

  25. Nielsen M, Lundegaard C, Worning P et al (2003) Reliable prediction of T cell epitopes using neural networks with novel sequence representations. Protein Sci 12:1007–1017

    Article  CAS  Google Scholar 

  26. Vita R, Mahajan S, Overton JA et al (2019) The immune epitope database (IEDB): 2018 update. Nucleic Acids Res 47:D339–D343

    Article  CAS  Google Scholar 

  27. Rammensee H, Bachmann J, Emmerich NP et al (1999) SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50:213–219

    Article  CAS  Google Scholar 

  28. Falk K, Rötzschke O, Stevanović S et al (1991) Allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules. Nature 351:290–296

    Article  CAS  Google Scholar 

  29. Fairhead M, Howarth M (2015) Site-specific biotinylation of purified proteins using BirA. Methods Mol Biol 1266:171–184

    Article  CAS  Google Scholar 

  30. Mitra A, Skrzypczak M, Ginalski K, Rowicka M (2015) Strategies for achieving high sequencing accuracy for low diversity samples and avoiding sample bleeding using illumina platform. PLoS One 10:e0120520

    Article  Google Scholar 

  31. Magoč T, Salzberg SL (2011) FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27:2957–2963

    Article  Google Scholar 

  32. Masella AP, Bartram AK, Truszkowski JM et al (2012) PANDAseq: paired-end assembler for illumina sequences. BMC Bioinform 13:31

    Article  CAS  Google Scholar 

  33. Sibener LV, Fernandes RA, Kolawole EM et al (2018) Isolation of a structural mechanism for uncoupling T cell receptor signaling from peptide-MHC binding. Cell 174:672–687.e27

    Article  CAS  Google Scholar 

  34. Dai Z, Huisman BD, Zeng H et al (2021) Machine learning optimization of peptides for presentation by class II MHCs. Bioinformatics 37(19):3160–3167. https://doi.org/10.1093/bioinformatics/btab131

    Article  CAS  PubMed Central  Google Scholar 

Download references

Acknowledgments

We would like to thank K. Christopher Garcia (Stanford University) for generous sharing of reagents and Christine Devlin for aiding in the creation of our yeast display protocols. This work was supported by National Science Foundation Graduate Research Fellowships to B.D.H, B.E.G., and P.V.H., and a Melanoma Research Alliance grant, the AACR-TESARO Career Development Award for Immuno-oncology Research (17-20-47-BIRN), Schmidt Futures, and the National Institutes of Health (P30CA14051 and 5U19AI110495) to M.E.B.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michael E. Birnbaum .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Huisman, B.D., Grace, B.E., Holec, P.V., Birnbaum, M.E. (2022). Yeast Display for the Identification of Peptide-MHC Ligands of Immune Receptors. In: Traxlmayr, M.W. (eds) Yeast Surface Display. Methods in Molecular Biology, vol 2491. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2285-8_15

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-2285-8_15

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2284-1

  • Online ISBN: 978-1-0716-2285-8

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics