Improving the prediction accuracy of protein structural class: Approached with alternating word frequency and normalized Lempel-Ziv complexity

摘要

Prediction of protein structural class for low-similarity sequences remains a challenging problem. In this study, the new computational method has been developed to predict protein structural class by incorporating alternating word frequency and normalized Lempel-Ziv complexity. To evaluate the performance of the proposed method, jackknife cross-validation tests are performed on three widely used benchmark datasets, 25PDB, 1189 and 640, respectively. We report 83.6%, 81.8% and 83.6% prediction accuracies for 25PDB, 1189 and 640 benchmarks, respectively. Comparison of our results with other methods shows that the proposed method is very promising and may provide a cost-effective alternative to predict protein structural class in particular for low-similarity datasets and may at least play an important complementary role to existing methods.