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Identification of MHC Ligands and Establishing MHC Class I Peptide Motifs

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1988))

Abstract

MHC class I peptide motifs are used on a regular basis to identify and predict MHC class I ligands and CD8+ T cell epitopes. This approach is above all an invaluable tool for the identification of disease-associated epitopes ranging from pathogen associated epitopes, tumor associated natural and neoepitopes to autoimmune disease associated epitopes. As a matter of fact, the vast majority of T cell epitopes discovered during the past two decades was identified by means of epitope prediction and MHC ligand identification. Here we describe the steps which are necessary to identify MHC epitopes from monoallelic and multiallelic cells and establish MHC class I peptide motifs to compose a reliable scoring matrix for epitope prediction. As an example, the ligands of monoallelic C1R cells and multiallelic peripheral blood mononuclear cell tissue will be identified and a scoring matrix for the prediction of HLA-C*01:02-presented T cell epitopes will be developed.

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References

  1. Rammensee HG, Weinschenk T, Gouttefangeas C, Stevanović S (2002) Towards patient-specific tumor antigen selection for vaccination. Immunol Rev 188:164–176

    Article  CAS  Google Scholar 

  2. Singh-Jasuja H, Emmerich NP, Rammensee HG (2004) The Tübingen approach: identification, selection, and validation of tumor-associated HLA peptides for cancer therapy. Cancer Immunol Immunother 53(3):187–195

    Article  CAS  Google Scholar 

  3. Freudenmann LK, Marcu A, Stevanović S (2018) Mapping the tumour human leukocyte antigen (HLA) ligandome by mass spectrometry. Immunology. https://doi.org/10.1111/imm.12936

    Article  CAS  Google Scholar 

  4. Germain RN, Margulies DH (1993) The biochemistry and cell biology of antigen processing and presentation. Annu Rev Immunol 11:403–450

    Article  CAS  Google Scholar 

  5. Serwold T, Gonzalez F, Kim J, Jacob R, Shastri N (2002) ERAAP customizes peptides for MHC class I molecules in the endoplasmic reticulum. Nature 419(6906):480–483

    Article  CAS  Google Scholar 

  6. Germain RN (1995) The biochemistry and cell biology of antigen presentation by MHC class I and class II molecules. Implications for development of combination vaccines. Ann N Y Acad Sci 754:114–125

    Article  CAS  Google Scholar 

  7. Falk K, Rötzschke O, Stevanović S, Jung G, Rammensee HG (1991) Allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules. Nature 351(6324):290–296

    Article  CAS  Google Scholar 

  8. Rammensee HG, Falk K, Rötzschke O (1993) Peptides naturally presented by MHC class I molecules. Annu Rev Immunol 11:213–244

    Article  CAS  Google Scholar 

  9. Thomsen MCF, Nielsen M (2012) Seq2Logo: a method for construction and visualization of amino acid binding motifs and sequence profiles including sequence weighting, pseudo counts and two-sided representation of amino acid enrichment and depletion. Nucleic Acids Res 40:281–287

    Article  Google Scholar 

  10. Bouvier M, Wiley DC (1994) Importance of peptide amino and carboxyl termini to the stability of MHC class I molecules. Science 265(5170):398–402

    Article  CAS  Google Scholar 

  11. Andreatta M, Alvarez B, Nielsen M (2017) GibbsCluster: unsupervised clustering and alignment of peptide sequences. Nucleic Acids Res 45:458–463

    Article  Google Scholar 

  12. Di Marco M, Schuster H, Backert L, Ghosh M, Rammensee HG, Stevanović S (2017) Unveiling the peptide motifs of HLA-C and HLA-G from naturally presented peptides and generation of binding prediction matrices. J Immunol 199:2639–2651

    Article  Google Scholar 

  13. Dönnes P, Elofsson A (2002) Prediction of MHC class I binding peptides, using SVMHC. BMC Bioinformatics 3:25

    Article  Google Scholar 

  14. Soam SS, Bhasker B, Mishra BN (2011) Improved prediction of MHC class I binders/non-binders peptides through artificial neural network using variable learning rate: SARS corona virus, a case study. Adv Exp Med Biol 696:223–229

    Article  CAS  Google Scholar 

  15. Rammensee HG, Bachmann J, Emmerich NPN, Bachor OA, Stevanović S (1999) SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50(3–4):213–219

    Article  CAS  Google Scholar 

  16. Bassani-Sternberg M, Chong C, Guillaume P, Solleder M, Pak H, Gannon PO, Kandalaft LE, Coukos G, Gfeller D (2017) Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity. PLoS Comput Biol 13:e1005725

    Article  Google Scholar 

  17. Trautwein N, Stevanović S (2013) Establishing MHC class I peptide motifs. Methods Mol Biol 960:159–168

    Article  CAS  Google Scholar 

  18. Keller BO, Sui J, Young AB, Whittal RM (2008) Interferences and contaminants encountered in modern mass spectrometry. Anal Chim Acta 627(1):71–81

    Article  CAS  Google Scholar 

  19. Schubert B, Walzer M, Brachvogel HP, Szolek A, Mohr C, Kohlbacher O (2016) FRED 2: an immunoinformatics framework for Python. Bioinformatics 32:2044–2046

    Article  CAS  Google Scholar 

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Correspondence to Stefan Stevanović .

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Ghosh, M., Di Marco, M., Stevanović, S. (2019). Identification of MHC Ligands and Establishing MHC Class I Peptide Motifs. In: van Endert, P. (eds) Antigen Processing. Methods in Molecular Biology, vol 1988. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9450-2_11

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  • DOI: https://doi.org/10.1007/978-1-4939-9450-2_11

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9449-6

  • Online ISBN: 978-1-4939-9450-2

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