Protein Secondary Structure Prediction Based on Wavelet Feature Extraction and Support Vector Machine

Abstract

The structure of proteins is very important for understanding the biological function of proteins. The prediction of protein structure can predict and understand the function of biological functions of unknown proteins; however, the prediction of protein secondary structure plays a decisive role in the prediction of protein structure. In the study of protein secondary structure prediction, a single residue of a protein is encoded by position-specific-score-matrix (PSSM). After a data window is taken, a protein residue can be represented as a 2-dimensional pseudo-image plane, thus could further use the wavelet method to extract multi-resolution based features both on high frequency and low frequency from original pseudo-image, these extracted wavelet-based features with the PSSM matrix together can be taken as sample information carried by a protein residue, and the training model used is support vector machine.