Prediction of protein structure classes

Abstract

Prediction of protein structural are crucial in Bioinformatics. More and more evidences demonstrate that an great number of prediction methods has been employed to predict these structures based on the sequences of protein and biostatistics. The accuracy of such methods, nevertheless, is strongly affected by the efficiency and the robustness of classification model and other several factors. In our present research, the features based on the correlation coefficient of dipeptide or polypeptide were put forward. For one thing, flexible neutral tree (FNT), a novel classification model which is a variable structure neural network, is employed as the base classifiers. For another, the alterable tree structure based on FNT, such model may take advantage of the selection of available information, which aimed at the improvement of efficiency. It is important to find out the tree structural of protein structure classification model. To examine the performance of such method, ASTRAL, 1189 and 640 are selected as benchmark datasets of protein tertiary structure. Fortunately, the results show that a higher prediction accuracy compared with other methods. With the selected features running in the flexible neutral tree, several redundant information of features may be cut off and the accuracy of such model may be improved in some degree and the time of running such model could be hold down.