title: BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences.
abstract: 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 pK(a) 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.
authors.affiliation: Bioinformatics Center, Division of Biology, Kansas State University, Manhattan, Kansas 66506, USA. ljwang@ksu.edu
authors.forename: Liangjiang
authors.initials: L
authors.lastname: Wang
authors.forename: Susan J
authors.initials: SJ
authors.lastname: Brown
mesh_terms: D000596:Amino Acids / Q000737:chemistry; D001185:Artificial Intelligence; D001665:Binding Sites; D004247:DNA / Q000737:chemistry / Q000378:metabolism; D004268:DNA-Binding Proteins / Q000737:chemistry* / Q000378:metabolism; D020407:Internet; D008958:Models, Molecular; D016601:RNA-Binding Proteins / Q000737:chemistry* / Q000378:metabolism; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software; D014584:User-Computer Interface
chemical_list: D000596:Amino Acids; D004268:DNA-Binding Proteins; D016601:RNA-Binding Proteins; D004247:DNA