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J Biomol Struct Dyn. 2012;29(6):634-42. doi: 10.1080/07391102.2011.672627.

Using principal component analysis and support vector machine to predict protein structural class for low-similarity sequences via PSSM.

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Department of Mathematics, Xidian University, Xi'an, P.R. China.


The accurate identification of protein structure class solely using extracted information from protein sequence is a complicated task in the current computational biology. 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 fusing the sequence information and evolution information to represent a protein sample. To evaluate the performance of the proposed method, jackknife cross-validation tests are performed on two widely used benchmark data-sets, 1189 and 25PDB with sequence similarity lower than 40 and 25%, respectively. Comparison of our results with other methods shows that the proposed method by us is very promising and may provide a cost-effective alternative to predict protein structural class in particular for low-similarity data-sets.

[Indexed for MEDLINE]

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