Abstract
CD4+ T cells play a vital role in the immune response, and their function requires T cell receptor (TCR) recognition of peptide epitopes presented in complex with MHC class II (MHCII) molecules. Consequently, rapidly identifying peptides that bind MHCII is critical to understanding and treating infectious disease, cancer, autoimmunity, allergy, and transplant rejection. Computational methods provide a fast, ultrahigh-throughput approach to predict MHCII-binding peptides but lack the accuracy of experimental methods. In contrast, experimental methods offer accurate, quantitative results at the expense of speed. To address the gap between these two approaches, we developed a high-throughput, semiquantitative experimental screening strategy termed microsphere-assisted peptide screening (MAPS). Here, we use the Zika virus envelope protein as an example to demonstrate the rapid identification of MHCII-binding peptides from a single pathogenic protein using MAPS. This process involves several key steps including peptide library design, peptide exchange into MHCII, peptide-MHCII loading onto microspheres, flow cytometry screening, and data analysis to identify peptides that bind to one or more MHCII alleles.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chng MHY, Lim MQ, Rouers A et al (2019) Large-scale HLA tetramer tracking of T cells during dengue infection reveals broad acute activation and differentiation into two memory cell fates. Immunity 51:1119–1135
Newell EW, Sigal N, Nair N et al (2013) Combinatorial tetramer staining and mass cytometry analysis facilitate T-cell epitope mapping and characterization. Nat Biotechnol 31:623–629
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
Sillito F, Holler A, Stauss HJ (2020) Engineering CD4+ T cells to enhance cancer immunity. Cell 9:234–236
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
Sturniolo T, Bono E, Ding J et al (1999) Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat Biotechnol 17:555–561
Wen F, Rubin-pitel SB, Zhao H (2009) Engineering of therapeutic proteins. In: Park SJ, Cochran JR (eds) Protein Eng. Des. CRC Press, Taylor & Francis Group, Boca Raton, FL, pp 153–177
Yee CM, Zak AJ, Hill BD et al (2018) The coming age of insect cells for manufacturing and development of protein therapeutics. Ind Eng Chem Res 57:10061–10070
Ma XY, Hill BD, Hoang T et al (2021) Virus-inspired strategies for cancer therapy. Semin Cancer Biol. https://doi.org/10.1016/j.semcancer.2021.06.021
Tarke A, Sidney J, Kidd CK et al (2021) Comprehensive analysis of T cell immunodominance and immunoprevalence of SARS-CoV-2 epitopes in COVID-19 cases. Cell Reports Med 2:100204
Sohail MS, Ahmed SF, Quadeer AA et al (2021) In silico T cell epitope identification for SARS-CoV-2: Progress and perspectives. Adv Drug Deliv Rev 171:29–47
Weiskopf D, Schmitz KS, Raadsen MP et al (2020) Phenotype and kinetics of SARS-CoV-2-specific T cells in COVID-19 patients with acute respiratory distress syndrome. Sci Immunol 5:1–15
Dan JM, Mateus J, Kato Y et al (2021) Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection. Science 371:eabf4063
Unanue ER, Turk V, Neefjes J (2016) Variations in MHC class II antigen processing and presentation in health and disease. Annu Rev Immunol 34:265–297
Robinson J, Barker DJ, Georgiou X et al (2020) IPD-IMGT/HLA Database. Nucleic Acids Res 48:D948–D955
Peters B, Nielsen M, Sette A (2020) T cell epitope predictions. Annu Rev Immunol 38:123–145
Sidney J, Peters B, Sette A (2020) Epitope prediction and identification- adaptive T cell responses in humans. Semin Immunol 50:101418
Lim HX, Lim J, Poh CL (2021) Identification and selection of immunodominant B and T cell epitopes for dengue multi-epitope-based vaccine. Med Microbiol Immunol 210:1–11
Wen F, Zhao H (2013) Construction and screening of an antigen-derived peptide library displayed on yeast cell surface for CD4+ T cell epitope identification. Methods Mol Biol 1061:245–264
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
Mösch A, Raffegerst S, Weis M et al (2019) Machine learning for cancer immunotherapies based on epitope recognition by T cell receptors. Front Genet 10:1–17
Sidney J, Southwood S, Moore C et al (2013) Measurement of MHC/peptide interactions by gel filtration or monoclonal antibody capture. Curr Protoc Immunol 100:18.3.1–18.3.36
Sidney J, Southwood S, Oseroff C et al (1999) Measurement of MHC/peptide interactions by gel filtration. Curr Protoc Immunol 31:18.3.1–18.319
Zarutskie JA, Busch R, Zavala-Ruiz Z et al (2001) The kinetic basis of peptide exchange catalysis by HLA-DM. Proc Natl Acad Sci U S A 98:12450–12455
Joshi RV, Zarutskie JA, Stern LJ (2000) A three-step kinetic mechanism for peptide binding to MHC class II proteins. Biochemistry 39:3751–3762
Yin L, Stern LJ (2014) Measurement of peptide binding to MHC class II molecules by fluorescence polarization. Curr Protoc Immunol 106:5.10.1–5.1012
Justesen S, Harndahl M, Lamberth K et al (2009) Functional recombinant MHC class II molecules and high-throughput peptide-binding assays. Immunome Res 5:2
Jensen PE, Moore JC, Lukacher AE (1998) A europium fluoroimmunoassay for measuring peptide binding to MHC class I molecules. J Immunol Methods 215:71–80
Jiang W, Boder ET (2010) High-throughput engineering and analysis of peptide binding to class II MHC. Proc Natl Acad Sci U S A 107:13258–13263
Narayan K, Su KW, Chou C-L et al (2009) HLA-DM mediates peptide exchange by interacting transiently and repeatedly with HLA-DR1. Mol Immunol 46:3157–3162
Liu G, Carter B, Bricken T et al (2020) Computationally optimized SARS-CoV-2 MHC class I and II vaccine formulations predicted to target human haplotype distributions. Cell Syst 11:131–144.e6
Dar H, Zaheer T, Rehman MT et al (2016) Prediction of promiscuous T-cell epitopes in the Zika virus polyprotein: an in silico approach. Asian Pac J Trop Med 9:844–850
Liao WWP, Arthur JW (2011) Predicting peptide binding to major histocompatibility complex molecules. Autoimmun Rev 10:469–473
Nielsen M, Lund O, Buus S et al (2010) MHC class II epitope predictive algorithms. Immunology 130:319–328
Luo H, Ye H, Ng HW et al (2015) Machine learning methods for predicting HLA-peptide binding activity. Bioinform Biol Insights 9:21–29
Vita R, Mahajan S, Overton JA et al (2019) The immune epitope database (IEDB): 2018 update. Nucleic Acids Res 47:D339–D343
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
Lin HH, Zhang GL, Tongchusak S et al (2008) Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research. BMC Bioinformatics 9:S22
Smith MR, Bugada LF, Wen F (2020) Rapid microsphere-assisted peptide screening (MAPS) of promiscuous MHCII-binding peptides in Zika virus envelope protein. AICHE J 66:e16697
Reynolds C, Goudet A, Jenjaroen K et al (2015) T cell immunity to the alkyl Hydroperoxide reductase of Burkholderia pseudomallei : a correlate of disease outcome in acute Melioidosis. J Immunol 194:4814–4824
Maiers M, Gragert L, Klitz W (2007) High-resolution HLA alleles and haplotypes in the United States population. Hum Immunol 68:779–788
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Bugada, L.F., Smith, M.R., Wen, F. (2022). Rapid Identification of MHCII-Binding Peptides Through Microsphere-Assisted Peptide Screening (MAPS). In: Huang, H., Davis, M.M. (eds) T-Cell Repertoire Characterization. Methods in Molecular Biology, vol 2574. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2712-9_11
Download citation
DOI: https://doi.org/10.1007/978-1-0716-2712-9_11
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-2711-2
Online ISBN: 978-1-0716-2712-9
eBook Packages: Springer Protocols