title: PSSM-based prediction of DNA binding sites in proteins.
abstract: BACKGROUND
  Detection of DNA-binding sites in proteins is of enormous interest for technologies targeting gene regulation and manipulation. We have previously shown that a residue and its sequence neighbor information can be used to predict DNA-binding candidates in a protein sequence. This sequence-based prediction method is applicable even if no sequence homology with a previously known DNA-binding protein is observed. Here we implement a neural network based algorithm to utilize evolutionary information of amino acid sequences in terms of their position specific scoring matrices (PSSMs) for a better prediction of DNA-binding sites.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
  
  
  BACKGROUND
  Online predictions based on this method are available at http://www.netasa.org/dbs-pssm/
authors.affiliation: Department of Bioinformatics and Bioscience, Kyushu Institute of Technology, Iizuka 820 8502, Fukuoka, Japan. shandar@bse.kyutech.ac.jp
authors.forename: Shandar
authors.initials: S
authors.lastname: Ahmad
authors.forename: Akinori
authors.initials: A
authors.lastname: Sarai
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001483:Base Sequence; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003201:Computers; D004247:DNA / Q000737:chemistry*; D030562:Databases, Protein; D016678:Genome; D020407:Internet; D008958:Models, Molecular; D016571:Neural Networks, Computer; D011485:Protein Binding; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D012372:ROC Curve; D012680:Sensitivity and Specificity; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein; D012984:Software; D013997:Time Factors
chemical_list: D011506:Proteins; D004247:DNA
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