Prediction Secondary Protein Structure from Images of Amino Acid by Using Harr - like Features in Support Vector Machine

Abstract

In this study Haar features are extracted from images of sub-sequences of amino acid and classified by Support Vector Machine (SVM). We apply a novel approach of integration Haar-like features extraction from primary protein structure to predication three states of secondary protein structure. The sequences of primary protein are divided into different slides windows for representation images and then Haar-like feature have been extracted from these images to classify three-category of secondary protein structure helix (H), strand (E) and coil (C). The final prediction results were generated from SVM overall per residue accuracies are: - accuracy of helix(H) reached 83.93%, accuracy of Sheet(E) is 85.15% and accuracy of the Coil (C) is 81.0126 %. Images are scanned from amino acid sequences are specified by the selection window sizes, when the size of window is small the important information of predicting secondary structure relay outside the window. It has taken only local sequence information. When the size of window has been increased the performance has been deteriorated. Haar-like gives a perfect input data of SVM with a huge amount of data, also for improvement of support vector machine are used varying cost parameters.