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Bioinformatics Advance Access originally published online on October 13, 2005
Bioinformatics 2005 21(24):4416-4419; doi:10.1093/bioinformatics/bti715
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oxfordjournals.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions{at}oxfordjournals.org

Improving disulfide connectivity prediction with sequential distance between oxidized cysteines

Chi-Hung Tsai 1, Bo-Juen Chen 1, Chen-hsiung Chan 1, Hsuan-Liang Liu 2 and Cheng-Yan Kao 1,3,*

1Department of Computer Science and Information Engineering, National Taiwan University Taipei, Taiwan 106
2Department of Chemical Engineering and Graduate Institute of Biotechnology, National Taipei University of Technology Taipei, Taiwan 10608
3Institute for Information Industry Taipei, Taiwan 106

*To whom correspondence should be addressed.

Summary: Predicting disulfide connectivity precisely helps towards the solution of protein structure prediction. In this study, a descriptor derived from the sequential distance between oxidized cysteines (denoted as DOC) is proposed. An approach using support vector machine (SVM) method based on weighted graph matching was further developed to predict the disulfide connectivity pattern in proteins. When DOC was applied, prediction accuracy of 63% for our SVM models could be achieved, which is significantly higher than those obtained from previous approaches. The results show that using the non-local descriptor DOC coupled with local sequence profiles significantly improves the prediction accuracy. These improvements demonstrate that DOC, with a proper scaling scheme, is an effective feature for the prediction of disulfide connectivity. The method developed in this work is available at the web server PreCys (prediction of cys–cys linkages of proteins).

Availability: http://bioinfo.csie.ntu.edu.tw:5433/Disulfide/

Contact: cykao{at}csie.ntu.edu.tw

Supplementary information: Supplementary data, detailed results, tables and information are available at http://bioinfo.csie.ntu.edu.tw:5433/Disulfide/

Received on August 19, 2005; revised on October 11, 2005; accepted on October 11, 2005

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