Literature DB >> 32696351

Computational Prediction of Intrinsic Disorder in Protein Sequences with the disCoP Meta-predictor.

Christopher J Oldfield1, Xiao Fan2, Chen Wang3, A Keith Dunker4, Lukasz Kurgan5.   

Abstract

Intrinsically disordered proteins are either entirely disordered or contain disordered regions in their native state. These proteins and regions function without the prerequisite of a stable structure and were found to be abundant across all kingdoms of life. Experimental annotation of disorder lags behind the rapidly growing number of sequenced proteins, motivating the development of computational methods that predict disorder in protein sequences. DisCoP is a user-friendly webserver that provides accurate sequence-based prediction of protein disorder. It relies on meta-architecture in which the outputs generated by multiple disorder predictors are combined together to improve predictive performance. The architecture of disCoP is presented, and its accuracy relative to several other disorder predictors is briefly discussed. We describe usage of the web interface and explain how to access and read results generated by this computational tool. We also provide an example of prediction results and interpretation. The disCoP's webserver is publicly available at http://biomine.cs.vcu.edu/servers/disCoP/ .

Entities:  

Keywords:  Bioinformatics; IDP; Intrinsically disordered proteins; Meta-architecture; Webserver

Mesh:

Substances:

Year:  2020        PMID: 32696351     DOI: 10.1007/978-1-0716-0524-0_2

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  77 in total

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Journal:  Proteins       Date:  2000-11-15

2.  Genome-scale prediction of proteins with long intrinsically disordered regions.

Authors:  Zhenling Peng; Marcin J Mizianty; Lukasz Kurgan
Journal:  Proteins       Date:  2013-09-17

3.  The intrinsic disorder status of the human hepatitis C virus proteome.

Authors:  Xiao Fan; Bin Xue; Patrick T Dolan; Douglas J LaCount; Lukasz Kurgan; Vladimir N Uversky
Journal:  Mol Biosyst       Date:  2014-04-22

Review 4.  Introducing protein intrinsic disorder.

Authors:  Johnny Habchi; Peter Tompa; Sonia Longhi; Vladimir N Uversky
Journal:  Chem Rev       Date:  2014-04-17       Impact factor: 60.622

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6.  Protein intrinsic disorder within the Potyvirus genus: from proteome-wide analysis to functional annotation.

Authors:  Justine Charon; Sébastien Theil; Valérie Nicaise; Thierry Michon
Journal:  Mol Biosyst       Date:  2016-02

7.  Unstructural biology of the Dengue virus proteins.

Authors:  Fanchi Meng; Reaid A Badierah; Hussein A Almehdar; Elrashdy M Redwan; Lukasz Kurgan; Vladimir N Uversky
Journal:  FEBS J       Date:  2015-07-15       Impact factor: 5.542

8.  Prediction and functional analysis of native disorder in proteins from the three kingdoms of life.

Authors:  J J Ward; J S Sodhi; L J McGuffin; B F Buxton; D T Jones
Journal:  J Mol Biol       Date:  2004-03-26       Impact factor: 5.469

9.  RAPID: fast and accurate sequence-based prediction of intrinsic disorder content on proteomic scale.

Authors:  Jing Yan; Marcin J Mizianty; Paul L Filipow; Vladimir N Uversky; Lukasz Kurgan
Journal:  Biochim Biophys Acta       Date:  2013-06-01

Review 10.  What's in a name? Why these proteins are intrinsically disordered: Why these proteins are intrinsically disordered.

Authors:  A Keith Dunker; M Madan Babu; Elisar Barbar; Martin Blackledge; Sarah E Bondos; Zsuzsanna Dosztányi; H Jane Dyson; Julie Forman-Kay; Monika Fuxreiter; Jörg Gsponer; Kyou-Hoon Han; David T Jones; Sonia Longhi; Steven J Metallo; Ken Nishikawa; Ruth Nussinov; Zoran Obradovic; Rohit V Pappu; Burkhard Rost; Philipp Selenko; Vinod Subramaniam; Joel L Sussman; Peter Tompa; Vladimir N Uversky
Journal:  Intrinsically Disord Proteins       Date:  2013-04-01
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  2 in total

1.  Predicting Protein Conformational Disorder and Disordered Binding Sites.

Authors:  Ketty C Tamburrini; Giulia Pesce; Juliet Nilsson; Frank Gondelaud; Andrey V Kajava; Jean-Guy Berrin; Sonia Longhi
Journal:  Methods Mol Biol       Date:  2022

Review 2.  Deep learning in prediction of intrinsic disorder in proteins.

Authors:  Bi Zhao; Lukasz Kurgan
Journal:  Comput Struct Biotechnol J       Date:  2022-03-08       Impact factor: 7.271

  2 in total

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