TY - JOUR

T1 - NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence

AU - Nielsen, Morten

AU - Lundegaard, Claus

AU - Blicher, Thomas

AU - Lamberth, Kasper

AU - Harndahl, Mikkel

AU - Justesen, Sune

AU - Røder, Gustav

AU - Peters, Bjoern

AU - Sette, Alessandro

AU - Lund, Ole

AU - Buus, Søren

PY - 2007

Y1 - 2007

N2 - BACKGROUND: 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 class I system (HLA-I) is extremely polymorphic. The number of registered HLA-I molecules has now surpassed 1500. Characterizing the specificity of each separately would be a major undertaking. PRINCIPAL FINDINGS: Here, we have drawn on a large database of known peptide-HLA-I interactions to develop a bioinformatics method, which takes both peptide and HLA sequence information into account, and generates quantitative predictions of the affinity of any peptide-HLA-I interaction. Prospective experimental validation of peptides predicted to bind to previously untested HLA-I molecules, cross-validation, and retrospective prediction of known HIV immune epitopes and endogenous presented peptides, all successfully validate this method. We further demonstrate that the method can be applied to perform a clustering analysis of MHC specificities and suggest using this clustering to select particularly informative novel MHC molecules for future biochemical and functional analysis. CONCLUSIONS: Encompassing all HLA molecules, this high-throughput computational method lends itself to epitope searches that are not only genome- and pathogen-wide, but also HLA-wide. Thus, it offers a truly global analysis of immune responses supporting rational development of vaccines and immunotherapy. It also promises to provide new basic insights into HLA structure-function relationships. The method is available at https://www.cbs.dtu.dk/services/NetMHCpan.

AB - BACKGROUND: 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 class I system (HLA-I) is extremely polymorphic. The number of registered HLA-I molecules has now surpassed 1500. Characterizing the specificity of each separately would be a major undertaking. PRINCIPAL FINDINGS: Here, we have drawn on a large database of known peptide-HLA-I interactions to develop a bioinformatics method, which takes both peptide and HLA sequence information into account, and generates quantitative predictions of the affinity of any peptide-HLA-I interaction. Prospective experimental validation of peptides predicted to bind to previously untested HLA-I molecules, cross-validation, and retrospective prediction of known HIV immune epitopes and endogenous presented peptides, all successfully validate this method. We further demonstrate that the method can be applied to perform a clustering analysis of MHC specificities and suggest using this clustering to select particularly informative novel MHC molecules for future biochemical and functional analysis. CONCLUSIONS: Encompassing all HLA molecules, this high-throughput computational method lends itself to epitope searches that are not only genome- and pathogen-wide, but also HLA-wide. Thus, it offers a truly global analysis of immune responses supporting rational development of vaccines and immunotherapy. It also promises to provide new basic insights into HLA structure-function relationships. The method is available at https://www.cbs.dtu.dk/services/NetMHCpan.

U2 - 10.1371/journal.pone.0000796

DO - 10.1371/journal.pone.0000796

M3 - Journal article

C2 - 17726526

VL - 2

SP - e796

JO - PLoS ONE

JF - PLoS ONE

SN - 1932-6203

IS - 8

ER -