, Volume 65, Issue 10, pp 711–724 | Cite as

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

  • Edita KarosieneEmail author
  • Michael Rasmussen
  • Thomas Blicher
  • Ole Lund
  • Søren Buus
  • Morten Nielsen
Original Paper


Major histocompatibility complex class II (MHCII) molecules play an important role in cell-mediated immunity. They present specific peptides derived from endosomal proteins for recognition by T helper cells. The identification of peptides that bind to MHCII molecules is therefore of great importance for understanding the nature of immune responses and identifying T cell epitopes for the design of new vaccines and immunotherapies. Given the large number of MHC variants, and the costly experimental procedures needed to evaluate individual peptide–MHC interactions, computational predictions have become particularly attractive as first-line methods in epitope discovery. However, only a few so-called pan-specific prediction methods capable of predicting binding to any MHC molecule with known protein sequence are currently available, and all of them are limited to HLA-DR. Here, we present the first pan-specific method capable of predicting peptide binding to any HLA class II molecule with a defined protein sequence. The method employs a strategy common for HLA-DR, HLA-DP and HLA-DQ molecules to define the peptide-binding MHC environment in terms of a pseudo sequence. This strategy allows the inclusion of new molecules even from other species. The method was evaluated in several benchmarks and demonstrates a significant improvement over molecule-specific methods as well as the ability to predict peptide binding of previously uncharacterised MHCII molecules. To the best of our knowledge, the NetMHCIIpan-3.0 method is the first pan-specific predictor covering all HLA class II molecules with known sequences including HLA-DR, HLA-DP, and HLA-DQ. The NetMHCpan-3.0 method is available at


MHC class II T cell epitope MHC binding specificity Peptide–MHC binding Human leukocyte antigens Artificial neural networks 



MN is a researcher at the Argentinean national research council (CONICET). This project has been funded in whole or in part with federal funds from the National Institutes of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under contract nos. HHSN272201200010C and HHSN272200900045C.

Supplementary material

251_2013_720_MOESM1_ESM.doc (203 kb)
ESM 1 (DOC 203 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Edita Karosiene
    • 1
    Email author
  • Michael Rasmussen
    • 2
  • Thomas Blicher
    • 3
  • Ole Lund
    • 1
  • Søren Buus
    • 2
  • Morten Nielsen
    • 1
    • 4
  1. 1.Center for Biological Sequence Analysis, Department of Systems BiologyTechnical University of DenmarkLyngbyDenmark
  2. 2.Laboratory of Experimental Immunology, Faculty of Health SciencesUniversity of CopenhagenCopenhagenDenmark
  3. 3.The Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical SciencesUniversity of CopenhagenCopenhagenDenmark
  4. 4.Instituto de Investigaciones BiotecnológicasUniversidad Nacional de San MartínSan MartínArgentina

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