title: Improving the prediction accuracy of protein structural class: approached with alternating word frequency and normalized Lempel-Ziv complexity.
abstract: 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.
authors.affiliation: School of Mathematics and Statistics, Xidian University, Xi'an 710071, PR China. Electronic address: shengli0201@163.com.
authors.forename: Shengli
authors.initials: S
authors.lastname: Zhang
authors.affiliation: School of Mathematics and Statistics, Xidian University, Xi'an 710071, PR China.
authors.forename: Yunyun
authors.initials: Y
authors.lastname: Liang
authors.affiliation: School of Computer Science and Technology, Xidian University, Xi'an 710071, PR China.
authors.forename: Xiguo
authors.initials: X
authors.lastname: Yuan
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
chemical_list: D011506:Proteins
keywords: Feature extraction; Low-similarity; Protein structure prediction; Support vector machine