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Nucleic Acids Research 2005 33(2):546-558; doi:10.1093/nar/gki204
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Published online 26 January 2005

© The Author 2005. Published by Oxford University Press. All rights reserved
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Article

Quantitative evaluation of protein–DNA interactions using an optimized knowledge-based potential

Zhijie Liu1, Fenglou Mao1, Jun-tao Guo1, Bo Yan1, Peng Wang1, Youxing Qu1 and Ying Xu1,2,*

1 Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, University of Georgia Athens, GA 30602, USA 2 Computational Biology Institute, Oak Ridge National Laboratory Oak Ridge, TN 37831, USA

*To whom correspondence should be addressed. Tel: +1 706 542 9779; Fax: +1 706 542 9751; Email: xyn{at}bmb.uga.edu

Received November 8, 2004. Revised December 16, 2004. Accepted January 3, 2005.

Computational evaluation of protein–DNA interaction is important for the identification of DNA-binding sites and genome annotation. It could validate the predicted binding motifs by sequence-based approaches through the calculation of the binding affinity between a protein and DNA. Such an evaluation should take into account structural information to deal with the complicated effects from DNA structural deformation, distance-dependent multi-body interactions and solvation contributions. In this paper, we present a knowledge-based potential built on interactions between protein residues and DNA tri-nucleotides. The potential, which explicitly considers the distance-dependent two-body, three-body and four-body interactions between protein residues and DNA nucleotides, has been optimized in terms of a Z-score. We have applied this knowledge-based potential to evaluate the binding affinities of zinc-finger protein–DNA complexes. The predicted binding affinities are in good agreement with the experimental data (with a correlation coefficient of 0.950). On a larger test set containing 48 protein–DNA complexes with known experimental binding free energies, our potential has achieved a high correlation coefficient of 0.800, when compared with the experimental data. We have also used this potential to identify binding motifs in DNA sequences of transcription factors (TF). The TFs in 79.4% of the known TF–DNA complexes have accurately found their native binding sequences from a large pool of DNA sequences. When tested in a genome-scale search for TF-binding motifs of the cyclic AMP regulatory protein (CRP) of Escherichia coli, this potential ranks all known binding motifs of CRP in the top 15% of all candidate sequences.


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