Prediction of secondary Structure of Proteins using Signal Processing Methods

Article history: Received 26 Sept. 2012 Accepted 30 Sept. 2012 Available online 01 October 2012 The prediction of protein folding is important because the structure of a protein is related to its function. The study of protein structure therefore produces valuable practical benefits for medicine, agriculture and industry. The understanding of enzyme function allows the design of drugs which inhibit specific enzyme targets for therapeutic purposes. Structural information can provide insight into protein function, and therefore, highaccuracy prediction 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. Present methods of prediction based on the statistical methods and machine learning methods typically have 76% approximate level of accuracy on an average. Thus, there is a considerable room for improvement. Digital Signal Processing (DSP) is an Engineering discipline concerning the creation, manipulation and analysis of digital signals. New approach for the secondary structure prediction based on the DSP techniques can take major role for fast and accurate result. Unknown secondary structure of a targetprotein can be predicted by using the appropriate digital signal processing tools for a baseprotein of a significant amino acid sequence-similarity and whose secondary structure is known. In this study we present an extensive review of existing methods of secondary structure prediction of proteins. © 2012 International Journal of Advanced Research in Science and Technology (IJARST). All rights reserved.