Literature DB >> 22430291

POODLE-I: disordered region prediction by integrating POODLE series and structural information predictors based on a workflow approach.

Shuichi Hirose1, Kana Shimizu, Tamotsu Noguchi.   

Abstract

Under physiological conditions, many proteins that include a region lacking well-defined three-dimensional structures have been identified, especially in eukaryotes. These regions often play an important biological cellular role, although they cannot form a stable structure. Therefore, they are biologically remarkable phenomena. From an industrial perspective, they can provide useful information for determining three-dimensional structures or designing drugs. For these reasons, disordered regions have attracted a great deal of attention in recent years. Their accurate prediction is therefore anticipated to provide annotations that are useful for wide range of applications. POODLE-I (where "I" stands for integration) is a web-based disordered region prediction system. POODLE-I integrates prediction results obtained from three kinds of disordered region predictors (POODLEs) developed from the viewpoint that the characteristics of disordered regions change according to their length. Furthermore, POODLE-I combines that information with predicted structural information by application of a workflow approach. When compared with server teams that showed best performance in CASP8, POODLE-I ranked among the top and exhibited the highest performance in predicting unfolded proteins. POODLE-I is an efficient tool for detecting disordered regions in proteins solely from the amino acid sequence. The application is freely available at http://mbs.cbrc.jp/poodle/poodle-i.html.

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Year:  2010        PMID: 22430291     DOI: 10.3233/ISB-2010-0426

Source DB:  PubMed          Journal:  In Silico Biol        ISSN: 1386-6338


  10 in total

1.  How disordered is my protein and what is its disorder for? A guide through the "dark side" of the protein universe.

Authors:  Philippe Lieutaud; François Ferron; Alexey V Uversky; Lukasz Kurgan; Vladimir N Uversky; Sonia Longhi
Journal:  Intrinsically Disord Proteins       Date:  2016-12-21

2.  An optimized Npro-based method for the expression and purification of intrinsically disordered proteins for an NMR study.

Authors:  Natsuko Goda; Naoki Matsuo; Takeshi Tenno; Sonoko Ishino; Yoshizumi Ishino; Satoshi Fukuchi; Motonori Ota; Hidekazu Hiroaki
Journal:  Intrinsically Disord Proteins       Date:  2015-02-23

3.  Evaluation of disorder predictions in CASP9.

Authors:  Bohdan Monastyrskyy; Krzysztof Fidelis; John Moult; Anna Tramontano; Andriy Kryshtafovych
Journal:  Proteins       Date:  2011-09-16

4.  Unsupervised Integration of Multiple Protein Disorder Predictors: The Method and Evaluation on CASP7, CASP8 and CASP9 Data.

Authors:  Ping Zhang; Zoran Obradovic
Journal:  Proteome Sci       Date:  2011-10-14       Impact factor: 2.480

Review 5.  An Overview of Predictors for Intrinsically Disordered Proteins over 2010-2014.

Authors:  Jianzong Li; Yu Feng; Xiaoyun Wang; Jing Li; Wen Liu; Li Rong; Jinku Bao
Journal:  Int J Mol Sci       Date:  2015-09-29       Impact factor: 5.923

6.  The role of balanced training and testing data sets for binary classifiers in bioinformatics.

Authors:  Qiong Wei; Roland L Dunbrack
Journal:  PLoS One       Date:  2013-07-09       Impact factor: 3.240

7.  Intrinsically disordered regions of nucleophosmin/B23 regulate its RNA binding activity through their inter- and intra-molecular association.

Authors:  Miharu Hisaoka; Kyosuke Nagata; Mitsuru Okuwaki
Journal:  Nucleic Acids Res       Date:  2013-10-07       Impact factor: 16.971

8.  DNA binding properties of human Cdc45 suggest a function as molecular wedge for DNA unwinding.

Authors:  Anna Szambowska; Ingrid Tessmer; Petri Kursula; Christian Usskilat; Piotr Prus; Helmut Pospiech; Frank Grosse
Journal:  Nucleic Acids Res       Date:  2013-11-28       Impact factor: 16.971

9.  Discovery of Cryoprotective Activity in Human Genome-Derived Intrinsically Disordered Proteins.

Authors:  Naoki Matsuo; Natsuko Goda; Kana Shimizu; Satoshi Fukuchi; Motonori Ota; Hidekazu Hiroaki
Journal:  Int J Mol Sci       Date:  2018-01-30       Impact factor: 5.923

10.  Decision-Tree Based Meta-Strategy Improved Accuracy of Disorder Prediction and Identified Novel Disordered Residues Inside Binding Motifs.

Authors:  Bi Zhao; Bin Xue
Journal:  Int J Mol Sci       Date:  2018-10-07       Impact factor: 5.923

  10 in total

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