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POODLE: Tools Predicting Intrinsically Disordered Regions of Amino Acid Sequence

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Protein Structure Prediction

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1137))

Abstract

Protein intrinsic disorder, a widespread phenomenon characterized by a lack of stable three-dimensional structure, is thought to play an important role in protein function. In the last decade, dozens of computational methods for predicting intrinsic disorder from amino acid sequences have been developed. They are widely used by structural biologists not only for analyzing the biological function of intrinsic disorder but also for finding flexible regions that possibly hinder successful crystallization of the full-length protein. In this chapter, I introduce Prediction Of Order and Disorder by machine LEarning (POODLE), which is a series of programs accurately predicting intrinsic disorder. After giving the theoretical background for predicting intrinsic disorder, I give a detailed guide to using POODLE. I then also briefly introduce a case study where using POODLE for functional analyses of protein disorder led to a novel biological findings.

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Acknowledgments

I thank the co-developers of the POODLE series: Dr. Shuichi Hirose, Dr. Satoru Kanai, Dr. Yoichi Muraoka, and Dr. Tamotsu Noguchi. I also thank Dr. Kentaro Tomii, and Dr. Hiroyuki Toh for fruitful discussions.

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Authors and Affiliations

  1. Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan

    Kana Shimizu

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  1. Kana Shimizu
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Editors and Affiliations

  1. Purdue University Dept. Biological Science, West Lafayette, Indiana, USA

    Daisuke Kihara

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Shimizu, K. (2014). POODLE: Tools Predicting Intrinsically Disordered Regions of Amino Acid Sequence. In: Kihara, D. (eds) Protein Structure Prediction. Methods in Molecular Biology, vol 1137. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-0366-5_10

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  • DOI: https://doi.org/10.1007/978-1-4939-0366-5_10

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-0365-8

  • Online ISBN: 978-1-4939-0366-5

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