Prediction of protein structure classes

Prediction of protein special structural plays a significant role to better recognize the protein folding patterns. Multiple prediction methods may be used to predict the structures based on the information of sequences and biostatistics. The accuracy, nevertheless, is strongly affected by the efficiency of classification, the robustness of model and other factors. In our research, flexible neutral tree (FNT), a novel classification model, is employed as the base classifiers. The alterable structural tree take advantage of the selection of available features, aims to improve the efficiency. To examine the performance and efficiency of such algorithm combination, an ASTRAL dataset is selected as the test dataset. Our results show that a higher prediction accuracy could be achieved compared with other methods, the structure of the classification model for prediction of protein structural may make incremental improvements possible.