Cell cycle kinases predicted from conserved biophysical properties.

ABSTRACT: Machine-learning techniques can classify functionally related proteins where homology-transfer as well as sequence and structure motifs fail. Here, we present a method that aimed at complementing homology-transfer in the identification of cell cycle control kinases from sequence alone. First, we identified functionally significant residues in cell cycle proteins through their high sequence conservation and biophysical properties. We then incorporated these residues and their features into support vector machines (SVM) to identify new kinases and more specifically to differentiate cell cycle kinases from other kinases and other proteins. As expected, the most informative residues tend to be highly conserved and tend to localize in the ATP binding regions of the kinases. Another observation

SUBMITTER: Wrzeszczynski KO 

PROVIDER: S-EPMC2629806 | biostudies-literature | 2009 Feb

REPOSITORIES: biostudies-literature