Literature DB >> 11673241

Prediction of disulfide connectivity in proteins.

P Fariselli1, R Casadio.   

Abstract

MOTIVATION: A major problem in protein structure prediction is the correct location of disulfide bridges in cysteine-rich proteins. In protein-folding prediction, the location of disulfide bridges can strongly reduce the search in the conformational space. Therefore the correct prediction of the disulfide connectivity starting from the protein residue sequence may also help in predicting its 3D structure.
RESULTS: In this paper we equate the problem of predicting the disulfide connectivity in proteins to a problem of finding the graph matching with the maximum weight. The graph vertices are the residues of cysteine-forming disulfide bridges, and the weight edges are contact potentials. In order to solve this problem we develop and test different residue contact potentials. The best performing one, based on the Edmonds-Gabow algorithm and Monte-Carlo simulated annealing reaches an accuracy significantly higher than that obtained with a general mean force contact potential. Significantly, in the case of proteins with four disulfide bonds in the structure, the accuracy is 17 times higher than that of a random predictor. The method presented here can be used to locate putative disulfide bridges in protein-folding. AVAILABILITY: The program is available upon request from the authors. CONTACT: Casadio@alma.unibo.it; Piero@biocomp.unibo.it.

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Year:  2001        PMID: 11673241     DOI: 10.1093/bioinformatics/17.10.957

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


  23 in total

1.  CysView: protein classification based on cysteine pairing patterns.

Authors:  Johann Lenffer; Paulo Lai; Wafaa El Mejaber; Asif M Khan; Judice L Y Koh; Paul T J Tan; Seng H Seah; Vladimir Brusic
Journal:  Nucleic Acids Res       Date:  2004-07-01       Impact factor: 16.971

2.  Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins.

Authors:  Jing Yang; Bao-Ji He; Richard Jang; Yang Zhang; Hong-Bin Shen
Journal:  Bioinformatics       Date:  2015-08-07       Impact factor: 6.937

3.  Intramolecular disulphide bond arrangements in nonhomologous proteins.

Authors:  Gerald R S Hartig; Tran T Tran; Mark L Smythe
Journal:  Protein Sci       Date:  2005-02       Impact factor: 6.725

Review 4.  Redox biology: computational approaches to the investigation of functional cysteine residues.

Authors:  Stefano M Marino; Vadim N Gladyshev
Journal:  Antioxid Redox Signal       Date:  2011-04-14       Impact factor: 8.401

5.  Prediction of the disulfide-bonding state of cysteines in proteins at 88% accuracy.

Authors:  Pier Luigi Martelli; Piero Fariselli; Luca Malaguti; Rita Casadio
Journal:  Protein Sci       Date:  2002-11       Impact factor: 6.725

6.  Progressive ataxia and myoclonic epilepsy in a patient with a homozygous mutation in the FOLR1 gene.

Authors:  Belén Pérez-Dueñas; Claudio Toma; Aida Ormazábal; Jordi Muchart; Francesc Sanmartí; Georgina Bombau; Mercedes Serrano; Angels García-Cazorla; Bru Cormand; Rafael Artuch
Journal:  J Inherit Metab Dis       Date:  2010-09-21       Impact factor: 4.982

7.  DBCP: a web server for disulfide bonding connectivity pattern prediction without the prior knowledge of the bonding state of cysteines.

Authors:  Hsuan-Hung Lin; Lin-Yu Tseng
Journal:  Nucleic Acids Res       Date:  2010-06-08       Impact factor: 16.971

8.  On the relevance of sophisticated structural annotations for disulfide connectivity pattern prediction.

Authors:  Julien Becker; Francis Maes; Louis Wehenkel
Journal:  PLoS One       Date:  2013-02-15       Impact factor: 3.240

9.  Protein disulfide topology determination through the fusion of mass spectrometric analysis and sequence-based prediction using Dempster-Shafer theory.

Authors:  Rahul Singh; William Murad
Journal:  BMC Bioinformatics       Date:  2013-01-21       Impact factor: 3.169

10.  Prediction of disulfide connectivity in proteins with machine-learning methods and correlated mutations.

Authors:  Castrense Savojardo; Piero Fariselli; Pier Luigi Martelli; Rita Casadio
Journal:  BMC Bioinformatics       Date:  2013-01-14       Impact factor: 3.169

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