Predicting protein structural class by incorporating patterns of over-represented k-mers into the general form of Chou's PseAAC.
Computational prediction of protein structural class based on sequence data remains a challenging problem in current protein science. In this paper, a new feature extraction approach based on relative polypeptide composition is introduced. This approach could take into account the background distribution of a given k-mer under a Markov model of order k-2, and avoid the curse of dimensionality with the increase of k by using a T-statistic feature selection strategy. The selected features are then fed to a support vector machine to perform the prediction. To verify the performance of our method, jackknife cross-validation tests are performed on four widely used benchmark datasets. Comparison of our results with existing methods shows that our method provides satisfactory performance for structural class prediction.
Department of Mathematics, Shanghai Normal University, 200234 Shanghai, China.
Yu-Fang
YF
Qin
Chun-Hua
CH
Wang
Xiao-Qing
XQ
Yu
Jie
J
Zhu
Tai-Gang
TG
Liu
Xiao-Qi
XQ
Zheng
D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry*; D019295:Computational Biology; D030562:Databases, Protein; D016571:Neural Networks, Computer; D017510:Protein Folding; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000145:classification; D020539:Sequence Analysis, Protein; D060388:Support Vector Machine
D000596:Amino Acids; D011506:Proteins