%0 Journal Article
%T PSSM-based prediction of DNA binding sites in proteins
%A Shandar Ahmad
%A Akinori Sarai
%J BMC Bioinformatics
%D 2005
%I BioMed Central
%R 10.1186/1471-2105-6-33
%X An average of sensitivity and specificity using PSSMs is up to 8.7% better than the prediction with sequence information only. Much smaller data sets could be used to generate PSSM with minimal loss of prediction accuracy.One problem in using PSSM-derived prediction is obtaining lengthy and time-consuming alignments against large sequence databases. In order to speed up the process of generating PSSMs, we tried to use different reference data sets (sequence space) against which a target protein is scanned for PSI-BLAST iterations. We find that a very small set of proteins can actually be used as such a reference data without losing much of the prediction value. This makes the process of generating PSSMs very rapid and even amenable to be used at a genome level. A web server has been developed to provide these predictions of DNA-binding sites for any new protein from its amino acid sequence.Online predictions based on this method are available at http://www.netasa.org/dbs-pssm/ webciteThere has been a growing interest in the prediction of DNA-binding sites in proteins which play crucial roles in gene regulation [1-4]. We have previously developed a method of predicting DNA-binding sites of proteins from the sequence information [5]. We reported development of a neural network and corresponding web server to predict amino acid residues which are likely to bind DNA. The only input to the neural network in this algorithm was the identity of the amino acid residue and its two sequence neighbors on C- and N- terminals. We also developed a method to identify DNA-binding proteins using electrical moments from structural information of proteins [6]. On the other hand, several investigators have reported that the use of evolutionary information in sequence-based predictions of secondary structure and solvent accessibility can improve the prediction capacity of a neural network [7-10].Here we report the use of such evolutionary information in improving the prediction of DNA-bi
%U http://www.biomedcentral.com/1471-2105/6/33