Literature DB >> 15110765

Prediction of the disulfide-bonding state of cysteines in proteins based on dipeptide composition.

Jiang-Ning Song1, Ming-Lei Wang, Wei-Jiang Li, Wen-Bo Xu.   

Abstract

In this paper, a novel approach has been introduced to predict the disulfide-bonding state of cysteines in proteins by means of a linear discriminator based on their dipeptide composition. The prediction is performed with a newly enlarged dataset with 8114 cysteine-containing segments extracted from 1856 non-homologous proteins of well-resolved three-dimensional structures. The oxidation of cysteines exhibits obvious cooperativity: almost all cysteines in disulfide-bond-containing proteins are in the oxidized form. This cooperativity can be well described by protein's dipeptide composition, based on which the prediction accuracy of the oxidation form of cysteines scores as high as 89.1% and 85.2%, when measured on cysteine and protein basis using the rigorous jack-knife procedure, respectively. The result demonstrates the applicability of this new relatively simple method and provides superior prediction performance compared with existing methods for the prediction of the oxidation states of cysteines in proteins.

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Year:  2004        PMID: 15110765     DOI: 10.1016/j.bbrc.2004.03.189

Source DB:  PubMed          Journal:  Biochem Biophys Res Commun        ISSN: 0006-291X            Impact factor:   3.575


  6 in total

1.  Detecting native folds in mixtures of proteins that contain disulfide bonds.

Authors:  Mahesh Narayan; Ervin Welker; Huili Zhai; Xuemei Han; Guoqiang Xu; Fred W McLafferty; Harold A Scheraga
Journal:  Nat Biotechnol       Date:  2008-02-17       Impact factor: 54.908

Review 2.  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

3.  DISULFIND: a disulfide bonding state and cysteine connectivity prediction server.

Authors:  Alessio Ceroni; Andrea Passerini; Alessandro Vullo; Paolo Frasconi
Journal:  Nucleic Acids Res       Date:  2006-07-01       Impact factor: 16.971

4.  Analysis on conservation of disulphide bonds and their structural features in homologous protein domain families.

Authors:  Ratna R Thangudu; Malini Manoharan; N Srinivasan; Frédéric Cadet; R Sowdhamini; Bernard Offmann
Journal:  BMC Struct Biol       Date:  2008-12-26

5.  A simplified approach to disulfide connectivity prediction from protein sequences.

Authors:  Marc Vincent; Andrea Passerini; Matthieu Labbé; Paolo Frasconi
Journal:  BMC Bioinformatics       Date:  2008-01-14       Impact factor: 3.169

6.  Dinosolve: a protein disulfide bonding prediction server using context-based features to enhance prediction accuracy.

Authors:  Ashraf Yaseen; Yaohang Li
Journal:  BMC Bioinformatics       Date:  2013-10-01       Impact factor: 3.169

  6 in total

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