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

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Prediction of Protein Secondary Structure

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1484))

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).

Author information

Authors and Affiliations

  1. Institute for Glycomics and School of Information and Communication Technology, Griffith University, Gold Coast Campus, Science 1 (G24) 2.10, Parklands Drive, Southport, QLD, 4222, Australia

    Yuedong Yang & Yaoqi Zhou

  2. Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, QLD, Australia

    Rhys Heffernan, Kuldip Paliwal & James Lyons

  3. Department of Psychiatry, Medical Research Center, University of Iowa, Iowa City, IA, USA

    Abdollah Dehzangi

  4. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Alok Sharma & Abdul Sattar

  5. School of Engineering and Physics, University of the South Pacific, Private Mail Bag, Laucala Campus, Suva, Fiji

    Alok Sharma

  6. Shandong Provincial Key Laboratory of Functional Macromolecular Biophysics, Dezhou University, Dezhou, Shandong, China

    Jihua Wang

  7. National ICT Australia (NICTA), Brisbane, QLD, Australia

    Abdul Sattar

Authors
  1. Yuedong Yang
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  2. Rhys Heffernan
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  3. Kuldip Paliwal
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  4. James Lyons
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  5. Abdollah Dehzangi
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  6. Alok Sharma
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  7. Jihua Wang
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  8. Abdul Sattar
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  9. Yaoqi Zhou
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Corresponding author

Correspondence to Yaoqi Zhou .

Editor information

Editors and Affiliations

  1. Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, Queensland, Australia

    Yaoqi Zhou

  2. Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, Ohio, USA

    Andrzej Kloczkowski

  3. Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA

    Eshel Faraggi

  4. Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, Queensland, Australia

    Yuedong Yang

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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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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6404-8

  • Online ISBN: 978-1-4939-6406-2

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