Dehzangi, A. and Paliwal, K.K. and Lyons, J. and Sharma, Alokanand and Sattar, A. (2014) A segmentation - based method to extract structural and evolutionary features for protein fold recognition. IEEE-ACM Transactions on Computational Biology and Bioinformatics, PP (99). pp. 1-11. ISSN 1557-9964
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Abstract
Protein fold recognition (PFR) is considered as an important step towards the protein structure prediction problem. Despite all the efforts that have been made so far, finding an accurate and fast computational approach to solve the PFR still remains a challenging problem for bioinformatics and computational biology. In this study, we propose the concept of segmented-based feature extraction technique to provide local evolutionary information embedded in Position Specific Scoring Matrix (PSSM) and structural information embedded in the predicted secondary structure of proteins using SPINE-X. We also employ the concept of occurrence feature to extract global discriminatory information from PSSM and SPINE-X. By applying a Support Vector Machine (SVM) to our extracted features, we enhance the protein fold prediction accuracy for 7.4% over the best results reported in the literature. We also report 73.8% prediction accuracy for a data set consisting of proteins with less than 25% sequence similarity rates and 80.7% prediction accuracy for a data set with proteins belonging to 110 folds with less than 40% sequence similarity rates.We also investigate the relation
between the number of folds and the number of features being used and show that the number of features should be increased to get better protein fold prediction results when the number of folds is relatively large.
| Item Type: | Journal Article |
|---|---|
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics |
| Depositing User: | Alokanand Sharma |
| Date Deposited: | 30 Mar 2014 22:53 |
| Last Modified: | 12 Sep 2016 01:05 |
| URI: | https://repository.usp.ac.fj/id/eprint/7335 |
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