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Construction and Screening of an Antigen-Derived Peptide Library Displayed on Yeast Cell Surface for CD4+ T Cell Epitope Identification

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Immunoproteomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2024))

Abstract

Antigenic peptides (termed T cell epitopes) are assembled with major histocompatibility complex (MHC) molecules and presented on the surface of antigen-presenting cells (APCs) for T cell recognition. T cells engage these peptide-MHCs using T cell receptors (TCRs). Because T cell epitopes determine the specificity of a T cell immune response, their prediction and identification are important steps in developing peptide-based vaccines and immunotherapies. In recent years, a number of computational methods have been developed to predict T cell epitopes by evaluating peptide-MHC binding; however, the success of these methods has been limited for MHC class II (MHCII) due to the structural complexity of MHCII antigen presentation. Moreover, while peptide-MHC binding is a prerequisite for a T cell epitope, it alone is not sufficient. Therefore, T cell epitope identification requires further functional verification of the MHC-binding peptide using professional APCs, which are difficult to isolate, expand, and maintain. To address these issues, we have developed a facile, accurate, and high-throughput method for T cell epitope mapping by screening antigen-derived peptide libraries in complex with MHC protein displayed on yeast cell surface. Here, we use hemagglutinin and influenza A virus X31/A/Aichi/68 as examples to describe the key steps in identification of CD4+ T cell epitopes from a single antigenic protein and the entire genome of a pathogen, respectively. Methods for single-chain peptide MHC vector design, yeast surface display, peptide library generation in Escherichia coli, and functional screening in Saccharomyces cerevisiae are discussed.

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References

  1. Germain RN (1994) MHC-dependent antigen processing and peptide presentation: providing ligands for T lymphocyte activation. Cell 76:287–299

    Article  CAS  Google Scholar 

  2. Wen F, Rubin-Pitel SB, Zhao H (2009) Engineering of therapeutic proteins. In: Park SJ, Cochran JR (eds) Protein engineering and design. Tayler & Francis Group, Boca Raton, FL

    Google Scholar 

  3. Park H, Li Z, Yang XO et al (2005) A distinct lineage of CD4 T cells regulates tissue inflammation by producing interleukin 17. Nat Immunol 6:1133–1141

    Article  CAS  Google Scholar 

  4. Altman JD, Moss PA, Goulder PJ et al (1996) Phenotypic analysis of antigen-specific T lymphocytes. Science 274:94–96

    Article  CAS  Google Scholar 

  5. Klein L, Kyewski B (2000) “Promiscuous” expression of tissue antigens in the thymus: a key to T-cell tolerance and autoimmunity? J Mol Med 78:483–494

    Article  CAS  Google Scholar 

  6. Huang CH, Peng S, He L et al (2005) Cancer immunotherapy using a DNA vaccine encoding a single-chain trimer of MHC class I linked to an HPV-16 E6 immunodominant CTL epitope. Gene Ther 12:1180–1186

    Article  CAS  Google Scholar 

  7. Coulie PG, Karanikas V, Lurquin C et al (2002) Cytolytic T-cell responses of cancer patients vaccinated with a MAGE antigen. Immunol Rev 188:33–42

    Article  CAS  Google Scholar 

  8. Hill BD, Zak A, Khera E et al (2018) Engineering virus-like particles for antigen and drug delivery. Curr Protein Pept Sci 19:112–127

    CAS  PubMed  Google Scholar 

  9. Jenkins MK, Khoruts A, Ingulli E et al (2001) In vivo activation of antigen-specific CD4 T cells. Annu Rev Immunol 19:23–45

    Article  CAS  Google Scholar 

  10. Robbins PF, Morgan RA, Feldman SA et al (2011) Tumor regression in patients with metastatic synovial cell sarcoma and melanoma using genetically engineered lymphocytes reactive with NY-ESO-1. J Clin Oncol 29:917–924

    Article  Google Scholar 

  11. Morgan RA, Dudley ME, Wunderlich JR et al (2006) Cancer regression in patients after transfer of genetically engineered lymphocytes. Science 314:126–129

    Article  CAS  Google Scholar 

  12. Phan GQ, Yang JC, Sherry RM et al (2003) Cancer regression and autoimmunity induced by cytotoxic T lymphocyte-associated antigen 4 blockade in patients with metastatic melanoma. Proc Natl Acad Sci U S A 100:8372–8377

    Article  CAS  Google Scholar 

  13. Matsushita H, Vesely MD, Koboldt DC et al (2012) Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature 482:400–404

    Article  CAS  Google Scholar 

  14. (2017) The problem with neoantigen prediction. Nat Biotechnol 35:97

    Google Scholar 

  15. Schumacher TN, Schreiber RD (2015) Neoantigens in cancer immunotherapy. Science 348:69–74

    Article  CAS  Google Scholar 

  16. Reche PA, Glutting JP, Reinherz EL (2002) Prediction of MHC class I binding peptides using profile motifs. Hum Immunol 63:701–709

    Article  CAS  Google Scholar 

  17. Nielsen M, Lundegaard C, Lund O (2007) Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinformatics 8:238

    Article  Google Scholar 

  18. Hattotuwagama CK, Doytchinova IA, Flower DR (2007) Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity: in silico bioinformatic step-by-step guide using quantitative structure-activity relationships. Methods Mol Biol 409:227–245

    Article  CAS  Google Scholar 

  19. Huang M, Huang W, Wen F et al (2017) Efficient estimation of binding free energies between peptides and an MHC class II molecule using coarse-grained molecular dynamics simulations with a weighted histogram analysis method. J Comput Chem 38:2007–2019

    Article  CAS  Google Scholar 

  20. 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 

  21. Andreatta M, Karosiene E, Rasmussen M et al (2015) Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification. Immunogenetics 67:641–650

    Article  CAS  Google Scholar 

  22. Luo H, Ye H, Ng HW et al (2015) Machine learning methods for predicting HLA-peptide binding activity. Bioinform Biol Insights 9:21–29

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Nielsen M, Lund O, Buus S et al (2010) MHC class II epitope predictive algorithms. Immunology 130:319–328

    Article  CAS  Google Scholar 

  24. Wang P, Sidney J, Dow C et al (2008) A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach. PLoS Comput Biol 4:e1000048

    Article  Google Scholar 

  25. Jensen KK, Andreatta M, Marcatili P et al (2018) Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology 154:394–406

    Article  CAS  Google Scholar 

  26. Vita R, Overton JA, Greenbaum JA et al (2015) The immune epitope database (IEDB) 3.0. Nucleic Acids Res 43:405–412

    Article  Google Scholar 

  27. Lin HH, Zhang GL, Tongchusak S et al (2008) Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research. BMC Bioinformatics 9(Suppl 12):S22

    Article  Google Scholar 

  28. Henderson RA, Cox AL, Sakaguchi K et al (1993) Direct identification of an endogenous peptide recognized by multiple HLA-A2.1-specific cytotoxic T cells. Proc Natl Acad Sci U S A 90:10275–10279

    Article  CAS  Google Scholar 

  29. Richards KA, Chaves FA, Sant AJ (2009) Infection of HLA-DR1 transgenic mice with a human isolate of influenza a virus (H1N1) primes a diverse CD4 T-cell repertoire that includes CD4 T cells with heterosubtypic cross-reactivity to avian (H5N1) influenza virus. J Virol 83:6566–6577

    Article  CAS  Google Scholar 

  30. Babon JA, Cruz J, Orphin L et al (2009) Genome-wide screening of human T-cell epitopes in influenza A virus reveals a broad spectrum of CD4+ T-cell responses to internal proteins, hemagglutinins, and neuraminidases. Hum Immunol 70:711–721

    Article  CAS  Google Scholar 

  31. Novak EJ, Liu AW, Gebe JA et al (2001) Tetramer-guided epitope mapping: rapid identification and characterization of immunodominant CD4+ T cell epitopes from complex antigens. J Immunol 166:6665–6670

    Article  CAS  Google Scholar 

  32. Smith MR, Tolbert SV, Wen F (2018) Protein-scaffold directed nanoscale assembly of T cell ligands: artificial antigen presentation with defined valency, density, and ratio. ACS Synth Biol 7:1629–1639

    Article  CAS  Google Scholar 

  33. Sospedra M, Pinilla C, Martin R (2003) Use of combinatorial peptide libraries for T-cell epitope mapping. Methods 29:236–247

    Article  CAS  Google Scholar 

  34. Hiemstra HS, Duinkerken G, Benckhuijsen WE et al (1997) The identification of CD4+ T cell epitopes with dedicated synthetic peptide libraries. Proc Natl Acad Sci U S A 94:10313–10318

    Article  CAS  Google Scholar 

  35. Wang RF, Wang X, Atwood AC et al (1999) Cloning genes encoding MHC class II-restricted antigens: mutated CDC27 as a tumor antigen. Science 284:1351–1354

    Article  CAS  Google Scholar 

  36. Koelle DM (2003) Expression cloning for the discovery of viral antigens and epitopes recognized by T cells. Methods 29:213–226

    Article  CAS  Google Scholar 

  37. Wen F, Esteban O, Zhao H (2008) Rapid identification of CD4+ T-cell epitopes using yeast displaying pathogen-derived peptide library. J Immunol Methods 336:37–44

    Article  CAS  Google Scholar 

  38. Boen E, Crownover AR, McIlhaney M et al (2000) Identification of T cell ligands in a library of peptides covalently attached to HLA-DR4. J Immunol 165:2040–2047

    Article  CAS  Google Scholar 

  39. Horton RM, Cai ZL, Ho SN et al (1990) Gene splicing by overlap extension: tailor-made genes using the polymerase chain reaction. BioTechniques 8:528–535

    CAS  PubMed  Google Scholar 

  40. Woodyer R, van der Donk WA, Zhao H (2003) Relaxing the nicotinamide cofactor specificity of phosphite dehydrogenase by rational design. Biochemistry 42:11604–11614

    Article  CAS  Google Scholar 

  41. Wen F, Sethi DK, Wucherpfennig KW et al (2011) Cell surface display of functional human MHC class II proteins: yeast display versus insect cell display. Protein Eng Des Sel 24:701–709

    Article  CAS  Google Scholar 

  42. Upcroft P, Healey A (1987) Rapid and efficient method for cloning of blunt-ended DNA fragments. Gene 51:69–75

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Fei Wen & Huimin Zhao

  2. Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA

    Fei Wen & Mason R. Smith

  3. Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Huimin Zhao

  4. Department of Chemistry, Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Huimin Zhao

  5. Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Huimin Zhao

Authors
  1. Fei Wen
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  2. Mason R. Smith
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  3. Huimin Zhao
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Corresponding author

Correspondence to Huimin Zhao .

Editor information

Editors and Affiliations

  1. Human Health Therapeutics Research Centre, National Research Council of Canada, Ottawa, ON, Canada

    Kelly M. Fulton

  2. Human Health Therapeutics Research Centre, National Research Council of Canada, Ottawa, ON, Canada

    Susan M. Twine

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Wen, F., Smith, M.R., Zhao, H. (2019). Construction and Screening of an Antigen-Derived Peptide Library Displayed on Yeast Cell Surface for CD4+ T Cell Epitope Identification. In: Fulton, K., Twine, S. (eds) Immunoproteomics. Methods in Molecular Biology, vol 2024. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9597-4_13

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  • DOI: https://doi.org/10.1007/978-1-4939-9597-4_13

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9596-7

  • Online ISBN: 978-1-4939-9597-4

  • eBook Packages: Springer Protocols

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