Accurate prediction of protein structural classes by incorporating PSSS and PSSM into Chou's general PseAAC

Abstract

Determination of protein structural class using a fast and suitable computational method has become a hot issue in protein science. Prediction of protein structural class for low-similarity sequences remains a challenge problem. In this study, a 111-dimensional feature vector is constructed to predict protein structural classes. Among the 111 features, 100 features based on pseudo-position specific scoring matrix (PsePSSM) are selected to reflect the evolutionary information and the sequence-order information, and the other 11 rational features based on predicted protein secondary structure sequences (PSSS) are designed in the previous works. To evaluate the performance of the proposed method (named by PSSS–PsePSSM), jackknife cross-validation tests are performed on three widely used benchmark datasets: 1189, 25PDB and 640. Our method achieves competitive performance on prediction accuracies, especially for the overall prediction accuracies for datasets 1189, 25PDB and 640, which reach 86.6%, 89.5% and 81.0%, respectively. The PSSS–PsePSSM algorithm also outperforms other existing methods, indicating that our proposed method is a cost-effective computational tool for protein structural class prediction.