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Intrinsic Disorder and Semi-disorder Prediction by SPINE-D

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Prediction of Protein Secondary Structure

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

Abstract

Over the past decade, it has become evident that a large proportion of proteins contain intrinsically disordered regions, which play important roles in pivotal cellular functions. Many computational tools have been developed with the aim of identifying the level and location of disorder within a protein. In this chapter, we describe a neural network based technique called SPINE-D that employs a unique three-state design and can accurately capture disordered residues in both short and long disordered regions. SPINE-D was trained on a large database of 4229 non-redundant proteins, and yielded an AUC of 0.86 on a cross-validation test and 0.89 on an independent test. SPINE-D can also detect a semi-disordered state that is associated with induced folders and aggregation-prone regions in disordered proteins and weakly stable or locally unfolded regions in structured proteins. We implement an online web service and an offline stand-alone program for SPINE-D, they are freely available at http://sparks-lab.org/SPINE-D/. We then walk you through how to use the online and offline SPINE-D in making disorder predictions, and examine the disorder and semi-disorder prediction in a case study on the p53 protein.

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Acknowledgements

This study was financially supported by National Health and Medical Research Council (1059775 and 1083450) of Australia and Australian Research Council’s Linkage Infrastructure, Equipment and Facilities funding scheme (project number LE150100161) to Y.Z. 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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Authors and Affiliations

  1. Department of Microbiology and Immunology, Weill Cornell Medical College, New York, NY, 10065, USA

    Tuo Zhang

  2. Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, 46032, USA

    Eshel Faraggi

  3. Translational Genomics Group, Institute of Health and Biomedical Innovation, Queensland University of Technology at Translational Research Institute, 37 Kent Street, Woolloongabba, QLD, 4102, Australia

    Zhixiu Li

  4. 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

    Yaoqi Zhou

  5. Research and Information Systems, LLC, Indianapolis, IN, USA

    Eshel Faraggi

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  1. Tuo Zhang
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  2. Eshel Faraggi
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  3. Zhixiu Li
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  4. Yaoqi Zhou
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Corresponding author

Correspondence to Yaoqi Zhou .

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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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Zhang, T., Faraggi, E., Li, Z., Zhou, Y. (2017). Intrinsic Disorder and Semi-disorder Prediction by SPINE-D. 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_12

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  • DOI: https://doi.org/10.1007/978-1-4939-6406-2_12

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