Indexing methods for efficient protein 3D surface search

Abstract

This paper exploits efficient indexing techniques for protein structure search where protein structures are represented as vectors by 3D-Zernike Descriptor (3DZD). 3DZD compactly represents a surface shape of protein tertiary structure as a vector, and the simplified representation accelerates the structural search. However, further speed up is needed to address the scenarios where multiple users access the database simultaneously. We address this need for further speed up in protein structural search by exploiting two indexing techniques, i.e., iDistance and iKernel, on the 3DZDs. The results show that both iDistance and iKernel significantly enhance the searching speed. In addition, we introduce an extended approach for protein structure search based on indexing techniques that use the 3DZD characteristic. In the extended approach, index structure is constructured using only the first few of the numbers in the 3DZDs. To find the top-k similar structures, first top-10 x k similar structures are selected using the reduced index structure, then top-k structures are selected using similarity measure of full 3DZDs of the selected structures. Using the indexing techniques, the searching time reduced 69.6% using iDistance, 77% using iKernel, 77.4% using extended iDistance, and 87.9% using extended iKernel method.