Optimized local protein structure with support vector machine to predict protein secondary structure

Abstract

Protein includes many substances, such as enzymes, hormones and antibodies that are necessary for the organisms. Living cells are controlled by proteins and genes that interact through complex molecular pathways to achieve a specific function. These proteins have different shapes and structures which distinct them from each other. By having unique structures, only proteins able to carried out their function efficiently. Therefore, determination of protein structure is fundamental for the understanding of the cell's functions. The function of a protein is also largely determined by its structure. The importance of understanding protein structure has fueled the development of protein structure databases and prediction tools. Computational methods which were able to predict protein structure for the determination of protein function efficiently and accurately are in high demand. In this study, local protein structure with Support Vector Machine is proposed to predict protein secondary structure.