Prediction of protein structural class using a combined representation of protein-sequence information and support vector machine

Knowledge of structural classes is useful in understanding of folding patterns in proteins. Although numerous methods were proposed and achieved promising results in structural class prediction, some problems in using protein-sequence information have impeded the development. In this paper, a combined representation of protein-sequence information is proposed for prediction of protein structural class, which combines word frequencies, word position information and physicochemical properties of amino acids. Then the support vector machine classifier is adopted to classify attributes of protein. To check the validity, we use three benchmark datasets and jackknife cross-validation to evaluate the proposed method. Results show that the proposed combined representation of protein-sequence information is more efficient, which indicates that the necessity for protein structural class prediction method to extract more information as possible.