Abstract：
:Structural class characterizes the overall folding type of a protein or its domain. Many methods have been proposed to improve the prediction accuracy of protein structural class in recent years, but it is still a challenge for the low-similarity sequences. In this study, we introduce a feature extraction technique based on auto cross covariance (ACC) transformation of position-specific score matrix (PSSM) to represent a protein sequence. Then support vector machine-recursive feature elimination (SVM-RFE) is adopted to select top K features according to their importance and these features are input to a support vector machine (SVM) to conduct the prediction. Performance evaluation of the proposed method is performed using the jackknife test on three low-similarity datasets, i.e., D640, 1189 and 25PDB. By means of this method, the overall accuracies of 97.2%, 96.2%, and 93.3% are achieved on these three datasets, which are higher than those of most existing methods. This suggests that the proposed method could serve as a very cost-effective tool for predicting protein structural class especially for low-similarity datasets.
journal_name
Comput Biol Chemjournal_title
Computational biology and chemistryauthors
Li X,Liu T,Tao P,Wang C,Chen Ldoi
10.1016/j.compbiolchem.2015.08.012subject
Has Abstractpub_date
2015-12-01 00:00:00pages
95-100eissn
1476-9271issn
1476-928Xpii
S1476-9271(15)30144-4journal_volume
59 Pt Apub_type
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