SGNG protein classifier by matching 3D structures

Abstract

In this paper, a novel 3D structure-based approach is presented for fast and accurate classification of protein molecules. We have used our voxel and ray based descriptors for feature extraction of protein structures. By using these descriptors, in this paper we propose a novel approach for classifying protein molecules, named Supervised Growing Neural Gas (SGNG). It combines the Growing Neural Gas (GNG) as a hidden layer, and Radial Basis Function (RBF) as an output layer. GNG and its supervised version SGNG have not yet been applied for protein retrieval and classification. Our approach was evaluated according to the SCOP method. The results show that our approach achieves more than 83,5% by using the voxel descriptor and 98,4% classification accuracy by using the ray descriptor, while it is simpler and faster than the SCOP method. We provide some experimental results. © 2009 Springer Berlin Heidelberg.