Prediction of protein structure classes with flexible neural tree

Abstract

Prediction of protein structural classes is of great significance to better understand protein folding patterns. An array of methods has been proposed to predict these structures based on sequences. However, the accuracy is strongly affected by the homology of sequences. In the present study, the features based on correlation coefficient of sequence and amino acid composition are extracted. Flexible neutral tree is employed as the classification model. To examine the performance of this method, four benchmark datasets are selected. Altogether, the results show that a higher prediction accuracy of alpha/beta can be achieved by the method compared to others.