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


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

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

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 199

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3


  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 59:16–24

  2. Anderson MW, Gorski J (2003) Cutting edge: TCR contacts as anchors: effects on affinity and HLA-DM stability. J Immunol 171:5683–5687

  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

  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

  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 169:739–749

  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 164:5788–5796

  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

  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

  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

  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

  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

  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

  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 7:387–399

  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 54:159–169

  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 178:27–47

  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 19:2263–2270

  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

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

  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 166:6720–6727

  20. Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Trans Pattern Anal Mach Intell 12:993–1001. doi:10.1109/34.58871

  21. Henikoff S, Henikoff JG (1992) Amino acid substitution matrices from protein blocks. Proc Natl Acad Sci U S A 89:10915–10919

  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

  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

  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

  25. Nielsen M, Lund O (2009) NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC Bioinf 10:296

  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

  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

  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

  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

  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

  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

  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

  33. Sette A, Adorini L, Colon SM et al (1989) Capacity of intact proteins to bind to MHC class II molecules. J Immunol 143:1265–1267

  34. Sewell AK (2012) Why must T cells be cross-reactive? Nat Rev Immunol 12:669–677. doi:10.1038/nri3279

  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

  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

  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

  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

  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

  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

Download references


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).

Author information

Correspondence to Morten Nielsen.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation


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