Introducing novel physicochemical based features to enhance protein fold prediction accuracy

One of the most important goals of bioinformatics is the ability to predict tertiary structure of a protein from its amino acids sequence. In pattern recognition terms, protein fold prediction problem can be mapped to a multi class classification problem. Feature analysis and pattern classification techniques have been applied on proteins´ sequence to reveal underlying tertiary structures. In this paper, new features are proposed based on the physicochemical properties of the amino acids. Proposed features are extracted by using a modified feature extraction method adopted by Dubchak et al. AdaBoost.M1 and Multi Layer Perceptron attained good result constantly with existing features. Therefore, we assess the prediction accuracy of our new features and modified feature extraction with these methods. Our experimental results showed that the new features and modified feature extraction method outperformed the prediction accuracy that was reported in previous works.