, Volume 64, Issue 3, pp 177–186 | Cite as

NetMHCcons: a consensus method for the major histocompatibility complex class I predictions

  • Edita KarosieneEmail author
  • Claus Lundegaard
  • Ole Lund
  • Morten Nielsen
Original Paper


A key role in cell-mediated immunity is dedicated to the major histocompatibility complex (MHC) molecules that bind peptides for presentation on the cell surface. Several in silico methods capable of predicting peptide binding to MHC class I have been developed. The accuracy of these methods depends on the data available characterizing the binding specificity of the MHC molecules. It has, moreover, been demonstrated that consensus methods defined as combinations of two or more different methods led to improved prediction accuracy. This plethora of methods makes it very difficult for the non-expert user to choose the most suitable method for predicting binding to a given MHC molecule. In this study, we have therefore made an in-depth analysis of combinations of three state-of-the-art MHC–peptide binding prediction methods (NetMHC, NetMHCpan and PickPocket). We demonstrate that a simple combination of NetMHC and NetMHCpan gives the highest performance when the allele in question is included in the training and is characterized by at least 50 data points with at least ten binders. Otherwise, NetMHCpan is the best predictor. When an allele has not been characterized, the performance depends on the distance to the training data. NetMHCpan has the highest performance when close neighbours are present in the training set, while the combination of NetMHCpan and PickPocket outperforms either of the two methods for alleles with more remote neighbours. The final method, NetMHCcons, is publicly available at, and allows the user in an automatic manner to obtain the most accurate predictions for any given MHC molecule.


MHC class I T cell epitope MHC binding specificity Peptide–MHC binding Consensus methods Artificial neural network 



This work was supported by two NIH (National Institute of Health) grants (contract no. HHSN272200900045C, and contract no. HHSNN26600400006C).

Supplementary material

251_2011_579_MOESM1_ESM.doc (706 kb)
ESM 1 (DOC 706 kb)
251_2011_579_Fig2_ESM.tiff (3.2 mb)
High resolution image file (TIFF 3.15 mb)


  1. Bui HH, Sidney J, Peters B, Sathiamurthy M, Sinichi A, Purton KA, Mothe BR, Chisari FV, Watkins DI, Sette A (2005) Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications. Immunogenetics 57(5):304–314. doi: 10.1007/s00251-005-0798-y PubMedCrossRefGoogle Scholar
  2. Erup Larsen M, Kloverpris H, Stryhn A, Koofhethile CK, Sims S, Ndung'u T, Goulder P, Buus S, Nielsen M (2011) HLArestrictor—a tool for patient-specific predictions of HLA restriction elements and optimal epitopes within peptides. Immunogenetics 63(1):43–55. doi: 10.1007/s00251-010-0493-5 PubMedCrossRefGoogle Scholar
  3. Hoof I, Perez CL, Buggert M, Gustafsson RK, Nielsen M, Lund O, Karlsson AC (2010) Interdisciplinary analysis of HIV-specific CD8+ T cell responses against variant epitopes reveals restricted TCR promiscuity. J Immunol 184(9):5383–5391PubMedCrossRefGoogle Scholar
  4. Hoof I, Peters B, Sidney J, Pedersen LE, Sette A, Lund O, Buus S, Nielsen M (2009) NetMHCpan, a method for MHC class I binding prediction beyond humans. Immunogenetics 61(1):1–13. doi: 10.1007/s00251-008-0341-z PubMedCrossRefGoogle Scholar
  5. Jacob L, Vert JP (2008) Efficient peptide-MHC-I binding prediction for alleles with few known binders. Bioinformatics 24(3):358–366PubMedCrossRefGoogle Scholar
  6. Jojic N, Reyes-Gomez M, Heckerman D, Kadie C, Schueler-Furman O (2006) Learning MHC I–peptide binding. Bioinformatics 22(14):e227–e235PubMedCrossRefGoogle Scholar
  7. Lin HH, Ray S, Tongchusak S, Reinherz EL, Brusic V (2008) Evaluation of MHC class I peptide binding prediction servers: applications for vaccine research. BMC Immunol 9:8PubMedCrossRefGoogle Scholar
  8. Lundegaard C, Lamberth K, Harndahl M, Buus S, Lund O, Nielsen M (2008) NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11. Nucleic Acids Res 36 (Web Server issue):W509-512.Google Scholar
  9. Lundegaard C, Lund O, Buus S, Nielsen M (2010) Major histocompatibility complex class I binding predictions as a tool in epitope discovery. Immunology 130(3):309–318PubMedCrossRefGoogle Scholar
  10. Moutaftsi M, Peters B, Pasquetto V, Tscharke DC, Sidney J, Bui HH, Grey H, Sette A (2006) A consensus epitope prediction approach identifies the breadth of murine T(CD8+)-cell responses to vaccinia virus. Nat Biotechnol 24(7):817–819PubMedCrossRefGoogle Scholar
  11. Nielsen M, Lundegaard C, Blicher T, Lamberth K, Harndahl M, Justesen S, Roder G, Peters B, Sette A, Lund O, Buus S (2007) NetMHCpan, a method for quantitative predictions of Peptide binding to any HLA-A and -B locus protein of known sequence. PLoS One 2(8).Google Scholar
  12. Nielsen M, Lundegaard C, Worning P, Lauemoller SL, Lamberth K, Buus S, Brunak S, Lund O (2003) Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci 12(5):1007–1017PubMedCrossRefGoogle Scholar
  13. Peters B, Bui HH, Frankild S, Nielson M, Lundegaard C, Kostem E, Basch D, Lamberth K, Harndahl M, Fleri W, Wilson SS, Sidney J, Lund O, Buus S, Sette A (2006) A community resource benchmarking predictions of peptide binding to MHC-I molecules. PLoS Comput Biol 2(6):e65PubMedCrossRefGoogle Scholar
  14. Peters B, Sette A (2005) Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC Bioinforma 6:132CrossRefGoogle Scholar
  15. Rapin N, Hoof I, Lund O, Nielsen M (2010) The MHC motif viewer: a visualization tool for MHC binding motifs. Curr Protoc Immunol Chapter 18:Unit 18 17. doi: 10.1002/0471142735.im1817s88
  16. Robinson J, Waller MJ, Parham P, Bodmer JG, Marsh SG (2001) IMGT/HLA database—a sequence database for the human major histocompatibility complex. Nucleic Acids Res 29(1):210–213PubMedCrossRefGoogle Scholar
  17. Stranzl T, Larsen MV, Lundegaard C, Nielsen M (2010) NetCTLpan: pan-specific MHC class I pathway epitope predictions. Immunogenetics 62(6):357–368. doi: 10.1007/s00251-010-0441-4 PubMedCrossRefGoogle Scholar
  18. Vita R, Zarebski L, Greenbaum JA, Emami H, Hoof I, Salimi N, Damle R, Sette A, Peters B (2010) The immune epitope database 2.0. Nucleic Acids Res 38(Database issue): D854–D862.Google Scholar
  19. Wang P, Sidney J, Dow C, Mothe B, Sette A, Peters B (2008) A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach. PLoS Comput Biol 4(4):e1000048. doi: 10.1371/journal.pcbi.1000048 PubMedCrossRefGoogle Scholar
  20. Wang P, Sidney J, Kim Y, Sette A, Lund O, Nielsen M, Peters B (2010) Peptide binding predictions for HLA DR, DP and DQ molecules. BMC Bioinforma 11:568CrossRefGoogle Scholar
  21. Yu K, Petrovsky N, Schonbach C, Koh JY, Brusic V (2002) Methods for prediction of peptide binding to MHC molecules: a comparative study. Mol Med 8(3):137–148PubMedGoogle Scholar
  22. Zhang H, Lund O, Nielsen M (2009a) The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC–peptide binding. Bioinformatics 25(10):1293–1299PubMedCrossRefGoogle Scholar
  23. Zhang H, Lundegaard C, Nielsen M (2009b) Pan-specific MHC class I predictors: a benchmark of HLA class I pan-specific prediction methods. Bioinformatics 25(1):83–89PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Edita Karosiene
    • 1
    Email author
  • Claus Lundegaard
    • 1
  • Ole Lund
    • 1
  • Morten Nielsen
    • 1
  1. 1.Center for Biological Sequence Analysis, Department of Systems BiologyTechnical University of DenmarkLyngbyDenmark

Personalised recommendations