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Open Access Highly Accessed Research article

PSSM-based prediction of DNA binding sites in proteins

Shandar Ahmad12* and Akinori Sarai1

Author Affiliations

1 Department of Bioinformatics and Bioscience, Kyushu Institute of Technology, Iizuka 820 8502, Fukuoka, Japan

2 Department of Biosciences, Jamia Millia Islamia University, New Delhi-110025, India

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BMC Bioinformatics 2005, 6:33  doi:10.1186/1471-2105-6-33

Published: 19 February 2005



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.


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 webcite