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

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 MH...

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Bibliographic Details
Published in:	Immunogenetics (New York) 2009-01, Vol.61 (1), p.1-13
Main Authors:	Hoof, Ilka, Peters, Bjoern, Sidney, John, Pedersen, Lasse Eggers, Sette, Alessandro, Lund, Ole, Buus, Søren, Nielsen, Morten
Format:	Article
Language:	eng
Subjects:	Algorithms Allergology Amino Acid Sequence Animals Binding specificity Biomedical and Life Sciences Biomedicine Cell Biology Computer Simulation CTL epitopes Epitopes, T-Lymphocyte - chemistry Epitopes, T-Lymphocyte - immunology Epitopes, T-Lymphocyte - metabolism Gene Function Genes, MHC Class I Gorilla gorilla - genetics Gorilla gorilla - immunology Histocompatibility Antigens Class I - immunology Histocompatibility Antigens Class I - metabolism HLA Antigens - genetics HLA Antigens - immunology HLA Antigens - metabolism Human Genetics Humans Immune system Immunity, Cellular Immunology Macaca mulatta - genetics Macaca mulatta - immunology Medical research MHC class I Mice Models, Biological neural networks Neural Networks, Computer Non-human primates Oligopeptides - immunology Oligopeptides - metabolism Original Paper Pan troglodytes - genetics Pan troglodytes - immunology Peptides Protein Binding Protein Interaction Domains and Motifs Protein Interaction Mapping Species Specificity Sus scrofa - genetics Sus scrofa - immunology
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Summary:	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.
ISSN:	0093-7711 1432-1211