Protein classification by using four approaches for extraction of the protein ray-based descriptor

Abstract

The knowledge about the protein molecules, and how they influence the processes in the humans is very worth, because it is really needed in order to develop new drugs for diseases. In proteomics, one of the most important tasks is solving the problem of classification of protein molecules. The literature provides plethora of methods that could be used for this task. However, it is still an open issue where still there is a need for fast computational methods that would provide accurate classification of proteins. In this paper, we focus on solving this task. For that purpose, first, we extract feature vectors that hold information about the main features of the proteins. The feature vectors that are used in this study are obtained by following the procedure for extraction of our protein ray-based descriptor that we have introduced in our former studies. For that purpose, the skeleton of the protein is interpolated with predefined number of interpolation points, and then the elements of the feature vector are extracted as Euclidean distances between the interpolation points and center of mass. Besides this approach, in this study we also use three additional approaches for extraction of the feature vectors, where we focus on the change of the Euclidean distance to the center of mass between two consecutive interpolation points. After extracting feature vectors, next we apply several wellknown classification methods in order to generate classification model. We present the results obtained with these four approaches used for extraction of the feature vectors.