Binding affinity prediction of S. cerevisiae 14-3-3 and GYF peptide-recognition domains using support vector regression

Volkan Uslan, Huseyin Seker

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Citations (Scopus)

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.
Original languageEnglish
Title of host publication2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Place of PublicationPiscataway
PublisherIEEE
Pages3445-3448
ISBN (Print)978-1-4577-0219-8
DOIs
Publication statusPublished - 18 Oct 2016

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