Predict protein structural class by incorporating two different modes of evolutionary information into Chou's general pseudo amino acid composition.

Structural class is an important attribute of protein, which plays an important role in both the experiment and theoretical research of protein science. Prediction of protein structural classes has been a challenging task, specifically for low-similarity datasets. In this paper, we develop a feature extraction method PSSS-SOMA-PSSM by incorporating two different modes of evolutionary information into Chou's general pseudo amino acid composition (PseAAC) based on predicted secondary structure sequence (PSSS) and position-specific scoring matrix (PSSM). We construct a 170-dimensional (170D) feature vector for each protein sequence sample, which contains 10D PSSS features that reflect content, alternating word frequency and novel position information, and contains 160D PSSM features that are calculated by second-order moving average (SOMA) algorithm. The SVM classifier with RBF kernel function and the jackknife test are used to predict and evaluate on 1189, 25PDB and 640 datasets with sequence similarity lower than 40%, 25%, and 25%, respectively. Comparison of our results with other methods shows that the proposed method provides the state-of-the-art performance and a cost-effective alternative to structural classes prediction for low-similarity datasets.