Effective Prediction of Proteins Secondary Structure using Efficient Integrated Signal Processing and Neural Network Methods Induced by Physico-Chemical Parameters

Abstract

Protein structure analysis and prediction is a core area of research in bioinformatics. Prediction of protein secondary structure from amino acid sequences is one of the most important problems in molecular biology, because the structure of a protein is related to its function. Thus high prediction accuracy of protein structure from its sequence is highly desirable. Considerable research effort has been devoted to predicting the secondary structure of proteins from their amino acid sequences that typically have 76% approximate level of accuracy on an average. Thus, there is a considerable room for improvement. Recently digital signal processing (DSP) tools have been successfully applied in solving problems in the field of bioinformatics. In this paper we have proposed an effective feature extraction method based on discrete wavelet transform (DWT) to detect informative proteins and radial basis function neural network (RBFNN) classifier is used to efficiently predict the sample class which has a low complexity than other classifier in which effective numerical representation based on physico-chemical parameters induces the prediction more accurately. The potential of the proposed approach is evaluated through an exhaustive study by benchmark non-redundant dataset and a prediction accuracy of 93% is achieved.