title: The quantitative prediction of HLA-B*2705 peptide binding affinities using Support Vector Regression to gain insights into its role for the Spondyloarthropathies.
abstract: Computational methods are increasingly utilised in many immunoinformatics problems such as the prediction of binding affinity of peptides. The peptides could provide valuable insight into the drug design and development such as vaccines. Moreover, they can be used to diagnose diseases. The presence of human class I MHC allele HLA-B*2705 is one of the strong hypothesis that would lead spondyloarthropathies. In this paper, Support Vector Regression is used in order to predict binding affinity of peptides with the aid of experimentally determined peptide-MHC binding affinities of 222 peptides to HLA-B*2705 to get more insight into this problematic disease. The results yield a high correlation coefficient as much as 0.65 and the SVR-based predictive models can be considered as a useful tool in order to predict the binding affinities for newly discovered peptides.
authors.forename: Volkan
authors.initials: V
authors.lastname: Uslan
authors.forename: Huseyin
authors.initials: H
authors.lastname: Seker
mesh_terms: D000483:Alleles; D019295:Computational Biology / Q000379:methods*; D015796:HLA-B27 Antigen / Q000378:metabolism*; D006801:Humans; D010455:Peptides / Q000378:metabolism; D011485:Protein Binding; D025242:Spondylarthropathies / Q000276:immunology*; D060388:Support Vector Machine
chemical_list: C464936:HLA-B*27:05 antigen; D015796:HLA-B27 Antigen; D010455:Peptides