A novel protein structure classification model

Protein tertiary structure prediction is an important area of research in bioinformatics. In this paper, we proposed a new method to predict the tertiary structure of the protein, the method by extracting the protein sequence of the amino acid frequencies generalization dipeptide information hydrophobic combination, using neural networks and flexible neural tree classifier for different the integrated structure classification model. To evaluate the efficiency of the proposed method we choose two benchmark protein sequence datasets (640 dataset and 1189 dataset) as the test data set. The final results show that our method is efficient for protein structure prediction.