Immunogenetics

, 61:1

NetMHCpan, a method for MHC class I binding prediction beyond humans

Authors

    • Center for Biological Sequence Analysis, Department of Systems BiologyTechnical University of Denmark
  • Bjoern Peters
    • La Jolla Institute for Allergy and Immunology
  • John Sidney
    • La Jolla Institute for Allergy and Immunology
  • Lasse Eggers Pedersen
    • Laboratory of Experimental Immunology, Faculty of Health SciencesUniversity of Copenhagen
  • Alessandro Sette
    • La Jolla Institute for Allergy and Immunology
  • Ole Lund
    • Center for Biological Sequence Analysis, Department of Systems BiologyTechnical University of Denmark
  • Søren Buus
    • Laboratory of Experimental Immunology, Faculty of Health SciencesUniversity of Copenhagen
  • Morten Nielsen
    • Center for Biological Sequence Analysis, Department of Systems BiologyTechnical University of Denmark
Original Paper

DOI: 10.1007/s00251-008-0341-z

Cite this article as:
Hoof, I., Peters, B., Sidney, J. et al. Immunogenetics (2009) 61: 1. doi:10.1007/s00251-008-0341-z
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Abstract

Binding of peptides to major histocompatibility complex (MHC) molecules is the single most selective step in the recognition of pathogens by the cellular immune system. The human MHC genomic region (called HLA) is extremely polymorphic comprising several thousand alleles, each encoding a distinct MHC molecule. The potentially unique specificity of the majority of HLA alleles that have been identified to date remains uncharacterized. Likewise, only a limited number of chimpanzee and rhesus macaque MHC class I molecules have been characterized experimentally. Here, we present NetMHCpan-2.0, a method that generates quantitative predictions of the affinity of any peptide–MHC class I interaction. NetMHCpan-2.0 has been trained on the hitherto largest set of quantitative MHC binding data available, covering HLA-A and HLA-B, as well as chimpanzee, rhesus macaque, gorilla, and mouse MHC class I molecules. We show that the NetMHCpan-2.0 method can accurately predict binding to uncharacterized HLA molecules, including HLA-C and HLA-G. Moreover, NetMHCpan-2.0 is demonstrated to accurately predict peptide binding to chimpanzee and macaque MHC class I molecules. The power of NetMHCpan-2.0 to guide immunologists in interpreting cellular immune responses in large out-bred populations is demonstrated. Further, we used NetMHCpan-2.0 to predict potential binding peptides for the pig MHC class I molecule SLA-1*0401. Ninety-three percent of the predicted peptides were demonstrated to bind stronger than 500 nM. The high performance of NetMHCpan-2.0 for non-human primates documents the method’s ability to provide broad allelic coverage also beyond human MHC molecules. The method is available at http://www.cbs.dtu.dk/services/NetMHCpan.

Keywords

MHC class IBinding specificityNon-human primatesArtificial neural networksCTL epitopes

Supplementary material

251_2008_341_MOESM1_ESM.doc (286 kb)
Supplementary Figure S1(DOC 285 KB)
251_2008_341_MOESM2_ESM.doc (223 kb)
Supplementary Figure S2(DOC 223 KB)
251_2008_341_MOESM3_ESM.xls (109 kb)
Supplemental tables(XLS 109 KB)

Copyright information

© Springer-Verlag 2008