By Topic

Protein phosphorylation site prediction via feature discovery support vector machine

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
4 Author(s)
Shi, Yi ; Department of Computing Science, University of Alberta, Edmonton T6G 2E8, Canada ; Yuan, Bo ; Lin, Guohui ; Schuurmans, Dale

Protein phosphorylation/dephosphorylation is the central mechanism of post-translational modification which regulates cellular responses and phenotypes. Due to the efficiency and resource constraints of the in vivo methods for identifying phosphorylation sites, there is a strong motivation to computationally predict potential phosphorylation sites. In this work, we propose to use a unique set of features to represent the peptides surrounding the amino acid sites of interest and use feature selection support vector machine to predict whether the serine/threonine sites are potentially phosphorylable, as well as selecting important features that may lead to phosphorylation. Experimental results indicate that the new features and the prediction method can more effectively predict protein phosphorylation sites than the existing state of the art methods. The features selected by our prediction model provide biological insights to the in vivo phosphorylation.

Published in:

Tsinghua Science and Technology  (Volume:17 ,  Issue: 6 )