Prediction of Protein Structural Class Based on Gapped-Dipeptides and a Recursive Feature Selection Approach

International Journal of Molecular Sciences

Taigang Liu¹  Yufang Qin¹  Yongjie Wang²  Chunhua Wang¹ 

[1] College of Information Technology, Shanghai Ocean University, Shanghai 201306, China;;College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China

关键词: feature selection;    gapped-dipeptide;    position-specific score matrix;    protein structural class;    recursive feature elimination;    support vector machine;   

DOI  :  10.3390/ijms17010015

来源: mdpi

【 摘 要 】

The prior knowledge of protein structural class may offer useful clues on understanding its functionality as well as its tertiary structure. Though various significant efforts have been made to find a fast and effective computational approach to address this problem, it is still a challenging topic in the field of bioinformatics. The position-specific score matrix (PSSM) profile has been shown to provide a useful source of information for improving the prediction performance of protein structural class. However, this information has not been adequately explored. To this end, in this study, we present a feature extraction technique which is based on gapped-dipeptides composition computed directly from PSSM. Then, a careful feature selection technique is performed based on support vector machine-recursive feature elimination (SVM-RFE). These optimal features are selected to construct a final predictor. The results of jackknife tests on four working datasets show that our method obtains satisfactory prediction accuracies by extracting features solely based on PSSM and could serve as a very promising tool to predict protein structural class.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.