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

• Published in: Immunogenetics (New York) Vol. 61; no. 1; pp. 1 - 13
• Main Authors: Hoof, Ilka, Peters, Bjoern, Sidney, John, Pedersen, Lasse Eggers, Sette, Alessandro, Lund, Ole, Buus, Søren, Nielsen, Morten
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Berlin/Heidelberg : Springer-Verlag 01.01.2009; Springer-Verlag; Springer Nature B.V
• Subjects: Algorithms; Allergology; Amino Acid Sequence; Animals; Binding specificity; Biomedical and Life Sciences; Biomedicine; Cell Biology; chemistry; Computer Simulation; CTL epitopes; Epitopes, T-Lymphocyte; Epitopes, T-Lymphocyte - chemistry; Epitopes, T-Lymphocyte - immunology; Epitopes, T-Lymphocyte - metabolism; Gene Function; Genes, MHC Class I; genetics; Gorilla gorilla; Gorilla gorilla - genetics; Gorilla gorilla - immunology; Histocompatibility Antigens Class I; Histocompatibility Antigens Class I - immunology; Histocompatibility Antigens Class I - metabolism; HLA Antigens; HLA Antigens - genetics; HLA Antigens - immunology; HLA Antigens - metabolism; Human Genetics; Humans; Immune system; Immunity, Cellular; Immunology; Macaca mulatta; Macaca mulatta - genetics; Macaca mulatta - immunology; Medical research; metabolism; MHC class I; Mice; Models, Biological; neural networks; Neural Networks, Computer; Non-human primates; Oligopeptides; Oligopeptides - immunology; Oligopeptides - metabolism; Original Paper; Pan troglodytes; Pan troglodytes - genetics; Pan troglodytes - immunology; Peptides; Protein Binding; Protein Interaction Domains and Motifs; Protein Interaction Mapping; Species Specificity; Sus scrofa; Sus scrofa - genetics; Sus scrofa - immunology; MHC class I; CTL epitopes; Non-human primates; Binding specificity; Artificial neural networks
• ISSN: 0093-7711; 1432-1211; 1432-1211
• DOI: 10.1007/s00251-008-0341-z

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.