Prediction of protein structural class using tri-gram probabilities of position-specific scoring matrix and recursive feature elimination

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• 作者：Peiying Tao ; Taigang Liu ; Xiaowei Li ; Lanming Chen
• 关键词：Tri ; gram ; Position ; specific scoring matrix ; Protein structural class ; Support vector machine ; Feature selection
• 刊名：Amino Acids
• 出版年：2015
• 出版时间：March 2015
• 年：2015
• 卷：47
• 期：3
• 页码：461-468
• 全文大小：310 KB
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• 作者单位：Peiying Tao (1)
  Taigang Liu (2)
  Xiaowei Li (1)
  Lanming Chen (1)
  
  1. College of Food Science and Technology, Shanghai Ocean University, Shanghai, 201306, China
  2. College of Information Technology, Shanghai Ocean University, Shanghai, 201306, China
  
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Life Sciences
  Biochemistry
  Analytical Chemistry
  Biochemical Engineering
  Life Sciences
  Proteomics
  Neurobiology
  
• 出版者：Springer Wien
• ISSN：1438-2199

文摘

Knowledge of structural class plays an important role in understanding protein folding patterns. As a transitional stage in recognition of three-dimensional structure of a protein, protein structural class prediction is considered to be an important and challenging task. In this study, we firstly introduce a feature extraction technique which is based on tri-grams computed directly from position-specific scoring matrix (PSSM). A total of 8,000 features are extracted to represent a protein. Then, support vector machine-recursive feature elimination (SVM-RFE) is applied for feature selection and reduced features are input to a support vector machine (SVM) classifier to predict structural class of a given protein. To examine the effectiveness of our method, jackknife tests are performed on six widely used benchmark datasets, i.e., Z277, Z498, 1189, 25PDB, D640, and D1185. The overall accuracies of 97.1, 98.6, 92.5, 93.5, 94.2, and 95.9?% are achieved on these datasets, respectively. Comparison of the proposed method with other prediction methods shows that our method is very promising to perform the prediction of protein structural class.