The quantitative prediction of HLA-B*2705 peptide binding affinities using Support Vector Regression to gain insights into its role for the Spondyloarthropathies

Volkan Uslan, Huseyin Seker

Research output: Contribution to conferencePaperpeer-review

3 Citations (Scopus)

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.
Original languageEnglish
DOIs
Publication statusPublished - Aug 2015
Event37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015 ) - Milan
Duration: 1 Aug 2015 → …

Conference

Conference37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015 )
Period1/08/15 → …

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