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Nucleic Acids Research 2006 34(Web Server issue):W243-W248; doi:10.1093/nar/gkl298
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© The Author 2006. Published by Oxford University Press. All rights reserved
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@oxfordjournals.org

Article

BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences

Liangjiang Wang* and Susan J. Brown

Bioinformatics Center, Division of Biology, Kansas State University Manhattan, Kansas 66506, USA

*To whom correspondence should be addressed. Tel: +1 785 532 6347; Fax: +1 785 532 6653; Email: ljwang{at}ksu.edu

Received February 12, 2006. Revised March 1, 2006. Accepted April 7, 2006.

BindN (http://bioinformatics.ksu.edu/bindn/) takes an amino acid sequence as input and predicts potential DNA or RNA-binding residues with support vector machines (SVMs). Protein datasets with known DNA or RNA-binding residues were selected from the Protein Data Bank (PDB), and SVM models were constructed using data instances encoded with three sequence features, including the side chain pKa value, hydrophobicity index and molecular mass of an amino acid. The results suggest that DNA-binding residues can be predicted at 69.40% sensitivity and 70.47% specificity, while prediction of RNA-binding residues achieves 66.28% sensitivity and 69.84% specificity. When compared with previous studies, the SVM models appear to be more accurate and more efficient for online predictions. BindN provides a useful tool for understanding the function of DNA and RNA-binding proteins based on primary sequence data.


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