SPIDER2: A Package to Predict Secondary Structure, Accessible Surface Area, and Main-Chain Torsional Angles by Deep Neural Networks

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Yang, Yuedong
Heffernan, Rhys
Paliwal, Kuldip
Lyons, James
Dehzangi, Abdollah
Sharma, Alok
Wang, Jihua
Sattar, Abdul
Zhou, Yaoqi
Griffith University Author(s)
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Zhou, Y
Kloczkowski, A
Faraggi, E
Yang, Y
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2017
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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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Prediction of Protein Secondary Structure
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LE150100161
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Other chemical sciences
Biochemistry and cell biology
Cognitive neuroscience
Cognition
Medicinal and biomolecular chemistry
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