Literature DB >> 16455751

PROFbval: predict flexible and rigid residues in proteins.

Avner Schlessinger1, Guy Yachdav, Burkhard Rost.   

Abstract

UNLABELLED: The mobility of a residue on the protein surface is closely linked to its function. The identification of extremely rigid or flexible surface residues can therefore contribute information crucial for solving the complex problem of identifying functionally important residues in proteins. Mobility is commonly measured by B-value data from high-resolution three-dimensional X-ray structures. Few methods predict B-values from sequence. Here, we present PROFbval, the first web server to predict normalized B-values from amino acid sequence. The server handles amino acid sequences (or alignments) as input and outputs normalized B-value and two-state (flexible/rigid) predictions. The server also assigns a reliability index for each prediction. For example, PROFbval correctly identifies residues in active sites on the surface of enzymes as particularly rigid. AVAILABILITY: http://www.rostlab.org/services/profbval CONTACT: profbval@rostlab.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Year:  2006        PMID: 16455751     DOI: 10.1093/bioinformatics/btl032

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  53 in total

1.  MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins.

Authors:  Fatemeh Miri Disfani; Wei-Lun Hsu; Marcin J Mizianty; Christopher J Oldfield; Bin Xue; A Keith Dunker; Vladimir N Uversky; Lukasz Kurgan
Journal:  Bioinformatics       Date:  2012-06-15       Impact factor: 6.937

2.  Variability of the Cyclin-Dependent Kinase 2 Flexibility Without Significant Change in the Initial Conformation of the Protein or Its Environment; a Computational Study.

Authors:  Mohammad Taghizadeh; Bahram Goliaei; Armin Madadkar-Sobhani
Journal:  Iran J Biotechnol       Date:  2016-06       Impact factor: 1.671

3.  Computational identification of MoRFs in protein sequences.

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

4.  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

Review 5.  Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions.

Authors:  Fanchi Meng; Vladimir N Uversky; Lukasz Kurgan
Journal:  Cell Mol Life Sci       Date:  2017-06-06       Impact factor: 9.261

Review 6.  Multifactorial level of extremostability of proteins: can they be exploited for protein engineering?

Authors:  Debamitra Chakravorty; Mohd Faheem Khan; Sanjukta Patra
Journal:  Extremophiles       Date:  2017-03-10       Impact factor: 2.395

Review 7.  From local structure to a global framework: recognition of protein folds.

Authors:  Agnel Praveen Joseph; Alexandre G de Brevern
Journal:  J R Soc Interface       Date:  2014-04-16       Impact factor: 4.118

8.  FlexPred: a web-server for predicting residue positions involved in conformational switches in proteins.

Authors:  Igor B Kuznetsov; Michael McDuffie
Journal:  Bioinformation       Date:  2008-11-05

9.  Prodepth: predict residue depth by support vector regression approach from protein sequences only.

Authors:  Jiangning Song; Hao Tan; Khalid Mahmood; Ruby H P Law; Ashley M Buckle; Geoffrey I Webb; Tatsuya Akutsu; James C Whisstock
Journal:  PLoS One       Date:  2009-09-17       Impact factor: 3.240

10.  Protein secondary structure appears to be robust under in silico evolution while protein disorder appears not to be.

Authors:  Christian Schaefer; Avner Schlessinger; Burkhard Rost
Journal:  Bioinformatics       Date:  2010-01-16       Impact factor: 6.937

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