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

详细信息    查看全文

• 作者：Shengli Zhang ; Yunyun Liang ; Xiguo Yuan
• 关键词：Protein structure prediction ; Low-similarity ; Feature extraction ; Support vector machine
• 刊名：Journal of Theoretical Biology
• 出版年：21 January, 2014
• 年：2014
• 卷：341
• 期：Complete
• 页码：71-77
• 全文大小：392 K

文摘

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.