Literature DB >> 28589442

Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions.

Fanchi Meng1, Vladimir N Uversky2,3, Lukasz Kurgan4.   

Abstract

Computational prediction of intrinsic disorder in protein sequences dates back to late 1970 and has flourished in the last two decades. We provide a brief historical overview, and we review over 30 recent predictors of disorder. We are the first to also cover predictors of molecular functions of disorder, including 13 methods that focus on disordered linkers and disordered protein-protein, protein-RNA, and protein-DNA binding regions. We overview their predictive models, usability, and predictive performance. We highlight newest methods and predictors that offer strong predictive performance measured based on recent comparative assessments. We conclude that the modern predictors are relatively accurate, enjoy widespread use, and many of them are fast. Their predictions are conveniently accessible to the end users, via web servers and databases that store pre-computed predictions for millions of proteins. However, research into methods that predict many not yet addressed functions of intrinsic disorder remains an outstanding challenge.

Entities:  

Keywords:  Function of disordered proteins; Intrinsic disorder; MoRF; Prediction; Protein–DNA interactions; Protein–RNA interactions; Protein–protein interactions; SLiM

Mesh:

Substances:

Year:  2017        PMID: 28589442     DOI: 10.1007/s00018-017-2555-4

Source DB:  PubMed          Journal:  Cell Mol Life Sci        ISSN: 1420-682X            Impact factor:   9.261


  134 in total

1.  Orderly order in protein intrinsic disorder distribution: disorder in 3500 proteomes from viruses and the three domains of life.

Authors:  Bin Xue; A Keith Dunker; Vladimir N Uversky
Journal:  J Biomol Struct Dyn       Date:  2012

2.  Computational identification of MoRFs in protein sequences.

Authors:  Nawar Malhis; Jörg Gsponer
Journal:  Bioinformatics       Date:  2015-01-30       Impact factor: 6.937

3.  Intrinsic disorder in transcription factors.

Authors:  Jiangang Liu; Narayanan B Perumal; Christopher J Oldfield; Eric W Su; Vladimir N Uversky; A Keith Dunker
Journal:  Biochemistry       Date:  2006-06-06       Impact factor: 3.162

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

Review 5.  Unfoldomics of human genetic diseases: illustrative examples of ordered and intrinsically disordered members of the human diseasome.

Authors:  Uros Midic; Christopher J Oldfield; A Keith Dunker; Zoran Obradovic; Vladimir N Uversky
Journal:  Protein Pept Lett       Date:  2009       Impact factor: 1.890

Review 6.  Intrinsically disordered proteins in human diseases: introducing the D2 concept.

Authors:  Vladimir N Uversky; Christopher J Oldfield; A Keith Dunker
Journal:  Annu Rev Biophys       Date:  2008       Impact factor: 12.981

7.  Protein disorder prediction: implications for structural proteomics.

Authors:  Rune Linding; Lars Juhl Jensen; Francesca Diella; Peer Bork; Toby J Gibson; Robert B Russell
Journal:  Structure       Date:  2003-11       Impact factor: 5.006

8.  IDEAL: Intrinsically Disordered proteins with Extensive Annotations and Literature.

Authors:  Satoshi Fukuchi; Shigetaka Sakamoto; Yukiko Nobe; Seiko D Murakami; Takayuki Amemiya; Kazuo Hosoda; Ryotaro Koike; Hidekazu Hiroaki; Motonori Ota
Journal:  Nucleic Acids Res       Date:  2011-11-08       Impact factor: 16.971

9.  Development of an accurate classification system of proteins into structured and unstructured regions that uncovers novel structural domains: its application to human transcription factors.

Authors:  Satoshi Fukuchi; Keiichi Homma; Yoshiaki Minezaki; Takashi Gojobori; Ken Nishikawa
Journal:  BMC Struct Biol       Date:  2009-04-30

10.  D²P²: database of disordered protein predictions.

Authors:  Matt E Oates; Pedro Romero; Takashi Ishida; Mohamed Ghalwash; Marcin J Mizianty; Bin Xue; Zsuzsanna Dosztányi; Vladimir N Uversky; Zoran Obradovic; Lukasz Kurgan; A Keith Dunker; Julian Gough
Journal:  Nucleic Acids Res       Date:  2012-11-29       Impact factor: 16.971

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  52 in total

1.  Codon selection reduces GC content bias in nucleic acids encoding for intrinsically disordered proteins.

Authors:  Christopher J Oldfield; Zhenling Peng; Vladimir N Uversky; Lukasz Kurgan
Journal:  Cell Mol Life Sci       Date:  2019-06-07       Impact factor: 9.261

2.  PRISMOID: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact.

Authors:  Fuyi Li; Cunshuo Fan; Tatiana T Marquez-Lago; André Leier; Jerico Revote; Cangzhi Jia; Yan Zhu; A Ian Smith; Geoffrey I Webb; Quanzhong Liu; Leyi Wei; Jian Li; Jiangning Song
Journal:  Brief Bioinform       Date:  2020-05-21       Impact factor: 11.622

Review 3.  Features of molecular recognition of intrinsically disordered proteins via coupled folding and binding.

Authors:  Jing Yang; Meng Gao; Junwen Xiong; Zhengding Su; Yongqi Huang
Journal:  Protein Sci       Date:  2019-09-04       Impact factor: 6.725

4.  Sequence Reversal Prevents Chain Collapse and Yields Heat-Sensitive Intrinsic Disorder.

Authors:  Lance R English; Alexander Tischer; Aysha K Demeler; Borries Demeler; Steven T Whitten
Journal:  Biophys J       Date:  2018-07-17       Impact factor: 4.033

5.  IDPology of the living cell: intrinsic disorder in the subcellular compartments of the human cell.

Authors:  Bi Zhao; Akila Katuwawala; Vladimir N Uversky; Lukasz Kurgan
Journal:  Cell Mol Life Sci       Date:  2020-09-30       Impact factor: 9.261

6.  Accurately Predicting Disordered Regions of Proteins Using Rosetta ResidueDisorder Application.

Authors:  Stephanie S Kim; Justin T Seffernick; Steffen Lindert
Journal:  J Phys Chem B       Date:  2018-03-29       Impact factor: 2.991

7.  Structural Description of the Nipah Virus Phosphoprotein and Its Interaction with STAT1.

Authors:  Malene Ringkjøbing Jensen; Filip Yabukarski; Guillaume Communie; Eric Condamine; Caroline Mas; Valentina Volchkova; Nicolas Tarbouriech; Jean-Marie Bourhis; Viktor Volchkov; Martin Blackledge; Marc Jamin
Journal:  Biophys J       Date:  2020-04-18       Impact factor: 4.033

8.  DISOselect: Disorder predictor selection at the protein level.

Authors:  Akila Katuwawala; Christopher J Oldfield; Lukasz Kurgan
Journal:  Protein Sci       Date:  2019-11-07       Impact factor: 6.725

9.  Granulins modulate liquid-liquid phase separation and aggregation of the prion-like C-terminal domain of the neurodegeneration-associated protein TDP-43.

Authors:  Anukool A Bhopatkar; Vladimir N Uversky; Vijayaraghavan Rangachari
Journal:  J Biol Chem       Date:  2020-01-06       Impact factor: 5.157

10.  Protein kinases phosphorylate long disordered regions in intrinsically disordered proteins.

Authors:  Ryotaro Koike; Mutsuki Amano; Kozo Kaibuchi; Motonori Ota
Journal:  Protein Sci       Date:  2019-11-28       Impact factor: 6.725

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