title: Binding affinity prediction of S. cerevisiae 14-3-3 and GYF peptide-recognition domains using support vector regression.
abstract: Proteins interact with other proteins and bio-molecules to carry out biological processes in a cell. Computational models help understanding complex biochemical processes that happens throughout the life of a cell. Domain-mediated protein interaction to peptides one such complex problem in bioinformatics that requires computational predictive models to identify meaningful bindings. In this study, domain-peptide binding affinity prediction models are proposed based on support vector regression. Proposed models are applied to yeast bmh 14-3-3 and syh GYF peptide-recognition domains. The cross validated results of the domain-peptide binding affinity data sets show that predictive performance of the support vector based models are efficient.
authors.forename: Volkan
authors.initials: V
authors.lastname: Uslan
authors.forename: Huseyin
authors.initials: H
authors.lastname: Seker
mesh_terms: D048948:14-3-3 Proteins / Q000737:chemistry / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D010455:Peptides / Q000737:chemistry / Q000378:metabolism; D011485:Protein Binding; D000072417:Protein Domains; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry* / Q000378:metabolism*; D060388:Support Vector Machine
chemical_list: D048948:14-3-3 Proteins; D010455:Peptides; D029701:Saccharomyces cerevisiae Proteins