BindN applies support vector machines (SVMs) to prediction of DNA and RNA-binding residues from sequence features, including the side chain pKa value, hydrophobicity index and molecular mass of an amino acid.  The SVM classifiers have been constructed using two curated datasets (PDNA-62 and PRINR25) from the Protein Data Bank.  For DNA-binding residues, the prediction accuracy estimated from cross-validation is about 70% with equal sensitivity and specificity.  For RNA-binding residues, the estimated accuracy is approximately 68%.  Comparable results have also been obtained using the two test datasets, TestPDB and TestSP.  To reduce the number of false positive predictions, users may choose a high specificity value and use the DNA or RNA-binding domain (if known) instead of the full-length protein sequence as the input to BindN.  Please send your comments or suggestions to ljwang@ksu.edu.