BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences
By Liangjiang Wang, Susan J. Brown. Nucleic Acids Res., 2006
424 citations recorded by the source corpus.
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.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/180305402b0b80f5ccb3d1ba87d0cc5ff19f9150
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- Lukasz Kurgan
- Zhenling Peng
- Vladimir N Uversky
- Fanchi Meng
- Zsuzsanna Dosztányi
- Gábor Erdös
- Bálint Mészáros
- Yaoqi Zhou
Citations in the corpus
- High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder (2015, background): “edu/services/disis (inactive) BindN (Wang and Brown, 2006) 2006 N N Y Y http://bioinfo.”