Abstract
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.
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Acknowledgments
This work was supported by the Innovation Program of Shanghai Municipal Education Commission (No. 13YZ098), the Foundation for University Youth Teachers of Shanghai (No. ZZhy12028), the National Natural Science Foundation of China (Nos. 31271830, 41376135), and the Doctoral Fund of Shanghai Ocean University.
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Tao, P., Liu, T., Li, X. et al. Prediction of protein structural class using tri-gram probabilities of position-specific scoring matrix and recursive feature elimination. Amino Acids 47, 461–468 (2015). https://doi.org/10.1007/s00726-014-1878-9
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DOI: https://doi.org/10.1007/s00726-014-1878-9