Advertisement

Immunogenetics

, Volume 67, Issue 11–12, pp 641–650 | Cite as

Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification

  • Massimo Andreatta
  • Edita Karosiene
  • Michael Rasmussen
  • Anette Stryhn
  • Søren Buus
  • Morten Nielsen
Original Paper

Abstract

A key event in the generation of a cellular response against malicious organisms through the endocytic pathway is binding of peptidic antigens by major histocompatibility complex class II (MHC class II) molecules. The bound peptide is then presented on the cell surface where it can be recognized by T helper lymphocytes. NetMHCIIpan is a state-of-the-art method for the quantitative prediction of peptide binding to any human or mouse MHC class II molecule of known sequence. In this paper, we describe an updated version of the method with improved peptide binding register identification. Binding register prediction is concerned with determining the minimal core region of nine residues directly in contact with the MHC binding cleft, a crucial piece of information both for the identification and design of CD4+ T cell antigens. When applied to a set of 51 crystal structures of peptide-MHC complexes with known binding registers, the new method NetMHCIIpan-3.1 significantly outperformed the earlier 3.0 version. We illustrate the impact of accurate binding core identification for the interpretation of T cell cross-reactivity using tetramer double staining with a CMV epitope and its variants mapped to the epitope binding core. NetMHCIIpan is publicly available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.1.

Keywords

MHC class II Peptide binding T cell cross-reactivity Binding core Artificial neural networks Peptide-MHC 

Notes

Funding

This work was supported with Federal funds from the National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under Contract No. HHSN272201200010C, and from the Agencia Nacional de Promoción Científica y Tecnológica, Argentina (PICT-2012-0115). MN is a researcher at the Argentinean national research council (CONICET).

References

  1. Al-Attiyah R, Mustafa AS (2004) Computer-assisted prediction of HLA-DR binding and experimental analysis for human promiscuous Th1-cell peptides in the 24 kDa secreted lipoprotein (LppX) of Mycobacterium tuberculosis. Scand J Immunol 59:16–24CrossRefPubMedGoogle Scholar
  2. Anderson MW, Gorski J (2003) Cutting edge: TCR contacts as anchors: effects on affinity and HLA-DM stability. J Immunol 171:5683–5687CrossRefPubMedGoogle Scholar
  3. Andreatta M, Nielsen M (2012) Characterizing the binding motifs of 11 common human HLA-DP and HLA-DQ molecules using NNAlign. Immunology 136:306–311. doi: 10.1111/j.1365-2567.2012.03579.x PubMedCentralCrossRefPubMedGoogle Scholar
  4. Andreatta M, Schafer-Nielsen C, Lund O et al (2011) NNAlign: a web-based prediction method allowing non-expert end-user discovery of sequence motifs in quantitative peptide data. PLoS One 6:e26781. doi: 10.1371/journal.pone.0026781 PubMedCentralCrossRefPubMedGoogle Scholar
  5. Arnold PY, La Gruta NL, Miller T et al (2002) The majority of immunogenic epitopes generate CD4+ T cells that are dependent on MHC class II-bound peptide-flanking residues. J Immunol 169:739–749CrossRefPubMedGoogle Scholar
  6. Basu D, Horvath S, Matsumoto I et al (2000) Molecular basis for recognition of an arthritic peptide and a foreign epitope on distinct MHC molecules by a single TCR. J Immunol 164:5788–5796CrossRefPubMedGoogle Scholar
  7. Benoist C, Mathis D (2001) Autoimmunity provoked by infection: how good is the case for T cell epitope mimicry? Nat Immunol 2:797–801. doi: 10.1038/ni0901-797 CrossRefPubMedGoogle Scholar
  8. Birnbaum ME, Mendoza JL, Sethi DK et al (2014) Deconstructing the peptide-MHC specificity of T cell recognition. Cell 157:1073–1087. doi: 10.1016/j.cell.2014.03.047 PubMedCentralCrossRefPubMedGoogle Scholar
  9. Braendstrup P, Justesen S, Osterbye T et al (2013) MHC class II tetramers made from isolated recombinant α and β chains refolded with affinity-tagged peptides. PLoS One 8:e73648. doi: 10.1371/journal.pone.0073648 PubMedCentralCrossRefPubMedGoogle Scholar
  10. Braendstrup P, Mortensen BK, Justesen S et al (2014) Identification and HLA-tetramer-validation of human CD4+ and CD8+ T cell responses against HCMV proteins IE1 and IE2. PLoS One 9:e94892. doi: 10.1371/journal.pone.0094892 PubMedCentralCrossRefPubMedGoogle Scholar
  11. Bremel RD, Homan EJ (2014) Frequency patterns of T-cell exposed amino acid motifs in immunoglobulin heavy chain peptides presented by MHCs. Front Immunol 5:541. doi: 10.3389/fimmu.2014.00541 PubMedCentralCrossRefPubMedGoogle Scholar
  12. Bui H-H, Sidney J, Peters B et al (2005) Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications. Immunogenetics 57:304–314. doi: 10.1007/s00251-005-0798-y CrossRefPubMedGoogle Scholar
  13. Carson RT, Vignali KM, Woodland DL, Vignali DA (1997) T cell receptor recognition of MHC class II-bound peptide flanking residues enhances immunogenicity and results in altered TCR V region usage. Immunity 7:387–399CrossRefPubMedGoogle Scholar
  14. Castellino F, Zhong G, Germain RN (1997) Antigen presentation by MHC class II molecules: invariant chain function, protein trafficking, and the molecular basis of diverse determinant capture. Hum Immunol 54:159–169CrossRefPubMedGoogle Scholar
  15. Chicz RM, Urban RG, Gorga JC et al (1993) Specificity and promiscuity among naturally processed peptides bound to HLA-DR alleles. J Exp Med 178:27–47CrossRefPubMedGoogle Scholar
  16. Doytchinova IA, Flower DR (2003) Towards the in silico identification of class II restricted T-cell epitopes: a partial least squares iterative self-consistent algorithm for affinity prediction. Bioinformatics 19:2263–2270CrossRefPubMedGoogle Scholar
  17. Frankild S, de Boer RJ, Lund O et al (2008) Amino acid similarity accounts for T cell cross-reactivity and for “holes” in the T cell repertoire. PLoS One 3:e1831. doi: 10.1371/journal.pone.0001831 PubMedCentralCrossRefPubMedGoogle Scholar
  18. Germain RN (1994) MHC-dependent antigen processing and peptide presentation: providing ligands for T lymphocyte activation. Cell 76:287–299CrossRefPubMedGoogle Scholar
  19. Godkin AJ, Smith KJ, Willis A et al (2001) Naturally processed HLA class II peptides reveal highly conserved immunogenic flanking region sequence preferences that reflect antigen processing rather than peptide-MHC interactions. J Immunol 166:6720–6727CrossRefPubMedGoogle Scholar
  20. Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Trans Pattern Anal Mach Intell 12:993–1001. doi: 10.1109/34.58871 CrossRefGoogle Scholar
  21. Henikoff S, Henikoff JG (1992) Amino acid substitution matrices from protein blocks. Proc Natl Acad Sci U S A 89:10915–10919PubMedCentralCrossRefPubMedGoogle Scholar
  22. 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. doi: 10.1186/1745-7580-5-2 PubMedCentralCrossRefPubMedGoogle Scholar
  23. Karosiene E, Rasmussen M, Blicher T et al (2013) NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ. Immunogenetics 65:711–724. doi: 10.1007/s00251-013-0720-y CrossRefPubMedGoogle Scholar
  24. Lang HLE, Jacobsen H, Ikemizu S et al (2002) A functional and structural basis for TCR cross-reactivity in multiple sclerosis. Nat Immunol 3:940–943. doi: 10.1038/ni835 CrossRefPubMedGoogle Scholar
  25. Nielsen M, Lund O (2009) NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC Bioinf 10:296CrossRefGoogle Scholar
  26. 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. doi: 10.1110/ps.0239403 PubMedCentralCrossRefPubMedGoogle Scholar
  27. Nielsen M, Lundegaard C, Blicher T et al (2007a) NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. PLoS One 2:e796. doi: 10.1371/journal.pone.0000796 PubMedCentralCrossRefPubMedGoogle Scholar
  28. Nielsen M, Lundegaard C, Lund O (2007b) Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinf 8:238. doi: 10.1186/1471-2105-8-238 CrossRefGoogle Scholar
  29. Nielsen M, Lundegaard C, Blicher T et al (2008) Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan. PLoS Comput Biol 4:e1000107. doi: 10.1371/journal.pcbi.1000107 PubMedCentralCrossRefPubMedGoogle Scholar
  30. Nielsen M, Justesen S, Lund O et al (2010) NetMHCIIpan-2.0—improved pan-specific HLA-DR predictions using a novel concurrent alignment and weight optimization training procedure. Immunome Res 6:9. doi: 10.1186/1745-7580-6-9 PubMedCentralCrossRefPubMedGoogle Scholar
  31. Rose PW, Prlić A, Bi C et al (2015) The RCSB Protein Data Bank: views of structural biology for basic and applied research and education. Nucleic Acids Res 43:D345–D356. doi: 10.1093/nar/gku1214 PubMedCentralCrossRefPubMedGoogle Scholar
  32. Rudolph MG, Stanfield RL, Wilson IA (2006) How TCRs bind MHCs, peptides, and coreceptors. Annu Rev Immunol 24:419–466. doi: 10.1146/annurev.immunol.23.021704.115658 CrossRefPubMedGoogle Scholar
  33. Sette A, Adorini L, Colon SM et al (1989) Capacity of intact proteins to bind to MHC class II molecules. J Immunol 143:1265–1267PubMedGoogle Scholar
  34. Sewell AK (2012) Why must T cells be cross-reactive? Nat Rev Immunol 12:669–677. doi: 10.1038/nri3279 CrossRefPubMedGoogle Scholar
  35. 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. doi: 10.1038/9858 CrossRefPubMedGoogle Scholar
  36. Vita R, Overton JA, Greenbaum JA et al (2015) The immune epitope database (IEDB) 3.0. Nucleic Acids Res 43:D405–D412. doi: 10.1093/nar/gku938 PubMedCentralCrossRefPubMedGoogle Scholar
  37. Wan J, Liu W, Xu Q et al (2006) SVRMHC prediction server for MHC-binding peptides. BMC Bioinf 7:463. doi: 10.1186/1471-2105-7-463 CrossRefGoogle Scholar
  38. Welsh RM, Che JW, Brehm MA, Selin LK (2010) Heterologous immunity between viruses. Immunol Rev 235:244–266. doi: 10.1111/j.0105-2896.2010.00897.x PubMedCentralCrossRefPubMedGoogle Scholar
  39. Wilson DB, Wilson DH, Schroder K et al (2004) Specificity and degeneracy of T cells. Mol Immunol 40:1047–1055. doi: 10.1016/j.molimm.2003.11.022 CrossRefPubMedGoogle Scholar
  40. Zhang L, Chen Y, Wong H-S et al (2012) TEPITOPEpan: extending TEPITOPE for peptide binding prediction covering over 700 HLA-DR molecules. PLoS One 7:e30483. doi: 10.1371/journal.pone.0030483 PubMedCentralCrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Massimo Andreatta
    • 1
  • Edita Karosiene
    • 2
  • Michael Rasmussen
    • 3
  • Anette Stryhn
    • 3
  • Søren Buus
    • 3
  • Morten Nielsen
    • 1
    • 4
  1. 1.Instituto de Investigaciones BiotecnológicasUniversidad Nacional de San MartínSan MartínArgentina
  2. 2.Division of Vaccine DiscoveryLa Jolla Institute for Allergy and ImmunologyLa JollaUSA
  3. 3.Laboratory of Experimental Immunology, Faculty of Health SciencesUniversity of CopenhagenCopenhagenDenmark
  4. 4.Center for Biological Sequence Analysis, Department of Systems BiologyTechnical University of DenmarkLyngbyDenmark

Personalised recommendations