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 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.
Hoof
Ilka
I
Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Building 208, 2800, Lyngby, Denmark. ilka@cbs.dtu.dk
Peters
Bjoern
B
Sidney
John
J
Pedersen
Lasse Eggers
LE
Sette
Alessandro
A
Lund
Ole
O
Buus
Søren
S
Nielsen
Morten
M
D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D003198:Computer Simulation; D018984:Epitopes, T-Lymphocyte / Q000737:chemistry / Q000276:immunology / Q000378:metabolism*; D005805:Genes, MHC Class I; D006071:Gorilla gorilla / Q000235:genetics / Q000276:immunology*; D006680:HLA Antigens / Q000235:genetics / Q000276:immunology / Q000378:metabolism; D015395:Histocompatibility Antigens Class I / Q000276:immunology / Q000378:metabolism*; D006801:Humans; D007111:Immunity, Cellular; D008253:Macaca mulatta / Q000235:genetics / Q000276:immunology*; D051379:Mice; D008954:Models, Biological; D016571:Neural Networks, Computer; D009842:Oligopeptides / Q000276:immunology / Q000378:metabolism*; D002679:Pan troglodytes / Q000235:genetics / Q000276:immunology*; D011485:Protein Binding; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping; D013045:Species Specificity; D034421:Sus scrofa / Q000235:genetics / Q000276:immunology*
D018984:Epitopes, T-Lymphocyte; D006680:HLA Antigens; D015395:Histocompatibility Antigens Class I; D009842:Oligopeptides