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Journal of Integrative Bioinformatics

Editor-in-Chief: Schreiber, Falk / Hofestädt, Ralf

Managing Editor: Sommer, Björn

Ed. by Baumbach, Jan / Chen, Ming / Orlov, Yuriy / Allmer, Jens

Editorial Board: Giorgetti, Alejandro / Harrison, Andrew / Kochetov, Aleksey / Krüger, Jens / Ma, Qi / Matsuno, Hiroshi / Mitra, Chanchal K. / Pauling, Josch K. / Rawlings, Chris / Fdez-Riverola, Florentino / Romano, Paolo / Röttger, Richard / Shoshi, Alban / Soares, Siomar de Castro / Taubert, Jan / Tauch, Andreas / Yousef, Malik / Weise, Stephan / Hassani-Pak, Keywan

CiteScore 2018: 0.90

SCImago Journal Rank (SJR) 2018: 0.315

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Volume 7, Issue 3


Prediction of thioredoxin and glutaredoxin target proteins by identifying reversibly oxidized cysteinyl residues

Hang-mao Lee / Karl Josef Dietz / Ralf Hofestädt
Published Online: 2016-10-18 | DOI: https://doi.org/10.1515/jib-2010-130


A significant part of cellular proteins undergo reversible thiol-dependent redox transitions which often control or switch protein functions. Thioredoxins and glutaredoxins constitute two key players in this redox regulatory protein network. Both interact with various categories of proteins containing reversibly oxidized cysteinyl residues. The identification of thioredoxin/glutaredoxin target proteins is a critical step in constructing the redox regulatory network of cells or subcellular compartments. Due to the scarcity of thioredoxin/glutaredoxin target protein records in the public database, a tool called Reversibly Oxidized Cysteine Detector (ROCD) is implemented here to identify potential thioredoxin/glutaredoxin target proteins computationally, so that the in silico construction of redox regulatory network may become feasible. ROCD was tested on 46 thioredoxin target proteins in plant mitochondrion, and the recall rate was 66.7% when 50% sequence identity was chosen for structural model selection. ROCD will be used to predict the thioredoxin/glutaredoxin target proteins in human liver mitochondrion for our redox regulatory network construction project. The ROCD will be developed further to provide prediction with more reliability and incorporated into biological network visualization tools as a node prediction component. This work will advance the capability of traditional database- or text mining-based method in the network construction.

About the article

Published Online: 2016-10-18

Published in Print: 2010-12-01

Citation Information: Journal of Integrative Bioinformatics, Volume 7, Issue 3, Pages 208–218, ISSN (Online) 1613-4516, DOI: https://doi.org/10.1515/jib-2010-130.

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© 2010 The Author(s). Published by Journal of Integrative Bioinformatics.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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