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NetMHCpan - MHC class I binding prediction beyond humans
Hoof I1, Peter B3, Sidney J3, Pedersen LE2
Lund O1, Buus S2, Nielsen M1, Submitted 2008.

1Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Lyngby, Denmark
2Division of Experimental Immunology, Institute of Medical Microbiology and Immunology, University of Copenhagen, Denmark
3La Jolla Institute for Allergy and Immunology, San Diego, California, United States of America

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 different alleles. Each MHC molecule has a potentially unique binding specificity. Even though the quality of binding affinity prediction for the most common HLA-A and HLA-B molecules has increased significantly during the last decade, the specificity of the majority of the more than 1500 HLA alleles that have been identified to date is uncharacterized. In particular, the binding motifs for alleles of the loci HLA-E, HLA-C, and HLA-G remain unsolved due to lack of experimental data. Likewise, the binding motifs for most animal MHCs remain uncharacterized. Here, we present an updated version of NetMHCpan, a method that generates quantitative predictions of the affinity of any peptide-MHC class I interaction. NetMHCpan has been trained on the hitherto largest set of quantitative MHC binding data covering HLA-A, HLA-B, as well as binding data available for chimpanzee, rhesus macaque, gorilla, and mouse MHC class I molecules. We show that the updated NetMHCpan method can accurately predict the binding motif for uncharacterized HLA molecules including HLA-C, HLA-E, and HLA-G. Moreover, NetMHCpan allows prediction of the binding motifs of chimpanzee and macaque MHC class I molecules. We used NetMHCpan to predict potential binding peptides for the pig MHC class I molecule SLA-1*0401. 13 of 14 predicted binders turned out to be binders, five of them being strong binders with an IC50 value of less than 50 nM. This documents the usefulness of NetMHCpan application beyond human MHC molecules. Knowledge of MHC specificities may help understand immune reactions and aid in the design of vaccines and diagnostic tools. The method is available at

PMID: 19002680

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Morten Nielsen,