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Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning

Heffernan, R. and Paliwal, K.K. and Lyons, J. and Dehzangi, A. and Sharma, Alokanand and Wang, J. and Sattar, A. and Yang, Y. and Zhou, Y. (2015) Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning. Scientific Reports, 5 (11476). pp. 1-11. ISSN NA

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    Abstract

    Direct prediction of protein structure from sequence is a challenging problem. An effective approach is to break it up into independent sub-problems. These sub-problems such as prediction of protein secondary structure can then be solved independently. In a previous study, we found that an iterative use of predicted secondary structure and backbone torsion angles can further improve secondary structure and torsion angle prediction. In this study, we expand the iterative features to include solvent accessible surface area and backbone angles and dihedrals based on Cα atoms. By using a deep learning neural network in three iterations, we achieved 82% accuracy for secondary structure prediction, 0.76 for the correlation coefficient between predicted and actual solvent accessible surface area, 19° and 30° for mean absolute errors of backbone φ and ψ angles, respectively, and 8° and 32° for mean absolute errors of Cα-based θ and τ angles, respectively, for an independent test dataset of 1199 proteins. The accuracy of the method is slightly lower for 72 CASP 11 targets but much higher than those of model structures from current state-of-the-art techniques. This suggests the potentially beneficial use of these predicted properties for model assessment and ranking.

    Item Type: Journal Article
    Additional Information: Online Publication
    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: 13 Jan 2016 15:35
    Last Modified: 27 Apr 2016 15:13
    URI: http://repository.usp.ac.fj/id/eprint/8641
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