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.
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Acknowledgements
This work was supported in part by National Health and Medical Research Council (1059775) of Australia and Australian Research Council’s Linkage Infrastructure, Equipment and Facilities funding scheme (project number LE150100161), the Taishan Scholars Program of Shandong province of China, National Natural Science Foundation of China (61540025) to Y.Z. and National Natural Science Foundation of China (61271378) to Y.Y. and J.W. We also gratefully acknowledge the support of the Griffith University eResearch Services Team and the use of the High Performance Computing Cluster “Gowonda” to complete this research. This research/project has also been undertaken with the aid of the research cloud resources provided by the Queensland Cyber Infrastructure Foundation (QCIF).
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Yang, Y. et al. (2017). SPIDER2: A Package to Predict Secondary Structure, Accessible Surface Area, and Main-Chain Torsional Angles by Deep Neural Networks. In: Zhou, Y., Kloczkowski, A., Faraggi, E., Yang, Y. (eds) Prediction of Protein Secondary Structure. Methods in Molecular Biology, vol 1484. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6406-2_6
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DOI: https://doi.org/10.1007/978-1-4939-6406-2_6
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