Classification of Proteins Using Naïve Bayes Classifier and Surface-Invariant Coordinates

Abstract

Protein classification is one of the challenging problems in computational biology and bioinformatics. Our aim here is to classify proteins into different families using the surface roughness similarity of proteins as a criterion. Because Protein Data Bank (PDB) (http://www.rcsb.org/pdb/ [1]) coordinates give no indication of the orientation of the protein, we designed an invariant coordinate system (ICS) in which we took as the origin the protein’s center of gravity (CG). From PDB we found the surface residue coordinates. We then divided those coordinates into eight octants based on the sign of x, y and z coordinates. For the residues in each octant, we found the standard deviation of the coordinates and created a parameter called the surface-invariant coordinate (SIC). Thus, for every protein we obtained eight SIC values. We also made use of the Structural Classification of Proteins (SCOP) (http://scop.mrc-lmb.cam.ac.uk/scop/ [2]) database. SCOP classifies proteins on the basis of the surface structure of the protein. As it is a classification problem, we used the naïve Bayes classifier algorithm for the classification to achieve better results.