Computational Prediction of Intrinsic Disorder in Proteins

Fanchi Meng1, Vladimir Uversky2, Lukasz Kurgan3

1 Department of Electrical and Computer Engineering, University of Alberta, Edmonton, 2 Laboratory of Structural Dynamics, Stability and Folding of Proteins, Institute of Cytology, Russian Academy of Sciences, St. Petersburg, 3 Department of Computer Science, Virginia Commonwealth University, Richmond
Publication Name:  Current Protocols in Protein Science
Unit Number:  Unit 2.16
DOI:  10.1002/cpps.28
Online Posting Date:  April, 2017
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Abstract

Computational prediction of intrinsically disordered proteins (IDPs) is a mature research field. These methods predict disordered residues and regions in an input protein chain. More than 60 predictors of IDPs have been developed. This unit defines computational prediction of intrinsic disorder, summarizes major types of predictors of disorder, and provides details about three accurate and recently released methods. We demonstrate the use of these methods to predict intrinsic disorder for several illustrative proteins, provide insights into how predictions should be interpreted, and quantify and discuss predictive performance. Predictions can be freely and conveniently obtained using webservers. We point to the availability of databases that provide access to annotations of intrinsic disorder determined by structural studies and putative intrinsic disorder pre‐computed by computational methods. Lastly, we also summarize experimental methods that can be used to validate computational predictions. © 2017 by John Wiley & Sons, Inc.

Keywords: intrinsic disorder; intrinsically disordered protein; prediction

     
 
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Table of Contents

  • Introduction
  • Prediction of Intrinsic Disorder from Sequence
  • Selected Computational Predictors of Intrinsic Disorder
  • Assessment of Predictive Performance of Computational Predictors of Intrinsic Disorder
  • Consensus‐Based Predictions
  • Experimental Means for Validation of Predicted Disorder
  • Conclusions
  • Literature Cited
  • Figures
  • Tables
     
 
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Materials

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Literature Cited

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Key References
  Ishida & Kinoshita, 2007. See above.
  Describes PrDOS, one of the most accurate hybrid method for the prediction of intrinsic disorder.
  Jones & Cozzetto, 2015. See above.
  Describes DISOPRED3, one of the most accurate machine learning method for the prediction of intrinsic disorder and disordered protein binding regions.
  Mizianty et al., 2010. See above.
  Describes MFDp, one of the most accurate meta method for the prediction of intrinsic disorder.
  Peng & Kurgan, 2012b. See above.
  Provides comprehensive empirical assessment of predictive performance of modern methods for the prediction of intrinsic disorder.
  Piovesan et al., 2017. See above.
  Introduces and describes the DisProt database of the intrinsically disordered proteins.
  van der Lee et al., 2014. See above.
  Defines intrinsic disorder and discusses the relevant experimental and computational tools.
Internet Resources
  http://bioinf.cs.ucl.ac.uk/psipred/
  DISOPRED3's webserver.
  http://biomine‐ws.ece.ualberta.ca/MFDp
  MFDp's webserver.
  http://d2p2.pro/
  D2P2 database.
  http://mobidb.bio.unipd.it/
  MobiDB database.
  http://prdos.hgc.jp/cgi‐bin/top.cgi
  PrDOS's webserver.
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