Hybrid based SVM model for prediction of CDKs and cyclins

Abstract

The cyclin-dependent kinases (Cdks) are a family of serine/threonine protein kinases whose members are small proteins (∼34–40 kDa) composed of little more than the catalytic core shared by all protein kinases. All Cdks share the feature that their enzymatic activation requires the binding of a regulatory cyclin subunit. Various combination of both cyclin dependent kinases (CDKs) and cyclin proteins are responsible for progression of cell cycle through various phases like G1, S, G2 and M.. CDKs are also essential for proliferation of specialized cells. Realizing the importance of both these proteins in various aspects of life a new efficient computational model has been developed using parameters like hybrid composition for prediction of these proteins. In order to improve the prediction accuracy, we have developed a hybrid module using all features of a protein, which consisted of amino acid composition, dipeptide and pseudo amino acid composition and resulted in an input vector of 450 dimensions (400 dipeptide compositions, 30 pseudo, 20 amino acid compositions of the protein ). The overall prediction accuracy of SVM modules based on dipeptide composition, amino acid and pseudo composition hybrid approach was 99.7914% respectively. The accuracy of all the modules was evaluated using a 10-fold cross-validation technique.