3D Protein Classification using Topological, Geometrical and Biological Information

Computational approaches for protein classification have been proposed over the last years in order to speed up the analysis of the biological mechanics in living organisms. Most of the approaches tend to focus in geometrical comparison of the 3D molecules to reach their goals. In this paper a method suitable for partial (sub)graph matching of 3D proteinic models, in order to achieve fast and accurate classification, is proposed. The 3D objects are firstly segmented to their molecular structure. Then, descriptors are extracted for each segment using spherical harmonics algorithms, and graphs are constructed for the molecules. Next, a sub-graph matching procedure is utilized and the results are refined using biochemical properties to get biological meaningful classification. The experimental results proved that the proposed method achieves accurate classification of the proteinic data.