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J Theor Biol. 2009 May 21;258(2):289-93. doi: 10.1016/j.jtbi.2009.01.024. Epub 2009 Feb 6.

Predicting DNA- and RNA-binding proteins from sequences with kernel methods.

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College of Science, China Agricultural University, Beijing 100083, China.


In this paper, support vector machines (SVMs) are applied to predict the nucleic-acid-binding proteins. We constructed two classifiers to differentiate DNA/RNA-binding proteins from non-nucleic-acid-binding proteins by using a conjoint triad feature which extract information directly from amino acids sequence of protein. Both self-consistency and jackknife tests show promising results on the protein datasets in which the sequences identity is less than 25%. In the self-consistency test, the predictive accuracy is 90.37% for DNA-binding proteins and 89.70% for RNA-binding proteins. In the jackknife test, the predictive accuracies are 78.93% and 76.75%, respectively. Comparison results show that our method is very competitive by outperforming other previously published sequence-based prediction methods.

[Indexed for MEDLINE]

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