Classification of transporters using efficient radial basis function networks with position‐specific scoring matrices and biochemical properties

Abstract

Transporters are proteins that are involved in the movement of ions or molecules across biological membranes. Transporters are generally classified into channels/pores, electrochemical transporters, and active transporters. Discriminating the specific class of transporters and their subfamilies are essential tasks in computational biology for the advancement of structural and functional genomics. We have systematically analyzed the amino acid composition, residue pair preference and amino acid properties in six different families of transporters. Utilizing the information, we have developed a radial basis function (RBF) network method based on profiles obtained with position specific scoring matrices for discriminating transporters belonging to three different classes and six families. Our method showed a fivefold cross validation accuracy of 76%, 73%, and 69% for discriminating transporters and nontransporters, three different classes and six different families of transporters, respectively. Further, the method was tested with independent datasets, which showed similar level of accuracy. A web server has been developed for discriminating transporters based on three classes and six families, and it is available at http://rbf.bioinfo.tw/ approximately sachen/tcrbf.html. We suggest that our method could be effectively used to identify transporters and discriminating them into different classes and families.