NetMHCpan,a method for MHC class I binding prediction beyond humans |
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Authors: | Ilka Hoof Bjoern Peters John Sidney Lasse Eggers Pedersen Alessandro Sette Ole Lund Søren Buus Morten Nielsen |
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Institution: | (1) Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Building 208, 2800 Lyngby, Denmark;(2) La Jolla Institute for Allergy and Immunology, San Diego, CA, USA;(3) Laboratory of Experimental Immunology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark |
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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 .
Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users. |
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Keywords: | MHC class I Binding specificity Non-human primates Artificial neural networks CTL epitopes |
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