Yang, Y. and Heffernan, R. and Paliwal, K. and Lyons, J. and Dehzangi, A. and Sharma, Alokanand and Wang, J. and Sattar, A. and Zhou, Y. (2017) SPIDER2: a package to predict secondary structure, accessible surface area, and main - chain torsional angles by deep neural networks. In: Prediction of Protein Secondary Structure. Methods in Molecular Biology. Humana Press, New York, pp. 55-63. ISBN 9781493964048
Full text not available from this repository.Abstract
Predicting one-dimensional structure properties has played an important role to improve prediction of protein three-dimensional structures and functions. The most commonly predicted properties are secondary structure and accessible surface area (ASA) representing local and nonlocal structural characteristics, respectively. Secondary structure prediction is further complemented by prediction of continuous main-chain torsional angles. Here we describe a newly developed method SPIDER2 that utilizes three iterations of deep learning neural networks to improve the prediction accuracy of several structural properties simultaneously. For an independent test set of 1199 proteins SPIDER2 achieves 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. The method provides state-of-the-art, all-in-one accurate prediction of local structure and solvent accessible surface area. The method is implemented, as a webserver along with a standalone package that are available in our website: http://sparks-lab.org.
| Item Type: | Book Chapter |
|---|---|
| Subjects: | Q Science > Q Science (General) |
| Divisions: | Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics |
| Depositing User: | USP RSC Assistant |
| Date Deposited: | 15 Jan 2018 21:43 |
| Last Modified: | 15 Jan 2018 21:43 |
| URI: | http://repository.usp.ac.fj/id/eprint/10518 |
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