Nucleic Acids Research Advance Access originally published online on October 16, 2007
Nucleic Acids Research 2007 35(20):7003-7010; doi:10.1093/nar/gkm720
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Nucleic Acids Research, 2007, Vol. 35, No. 20 7003-7010
© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Computational Biology |
Modeling DNA-binding of Escherichia coli 
70 exhibits a characteristic energy landscape around strong promoters
1Institute for Communications Engineering, Technische Universität München, Arcisstrasse 21, 80290 München, Germany, 2Department of Electrical and Computer Engineering, American University of Beirut, P.O.Box 11-0236, Riad El-Solh, Beirut, Lebanon, 3Institute for Medical Statistics and Epidemiology, Technische Universität München, Ismaninger Strasse 22, 81675 München and 4Department of Behavioural Ecology and Evolutionary Genetics, Max Planck Institute for Ornithology, 82305 Starnberg, Germany
*To whom correspondence should be addressed. Tel: +0049 8157 932 312; Fax: +0049 8157 932 400; Email: mueller{at}orn.mpg.de
Received May 4, 2007. Revised August 14, 2007. Accepted August 31, 2007.
We present a computational model of DNA-binding by 
70 in Escherichia coli which allows us to extract the functional characteristics of the wider promoter environment. Our model is based on a measure for the binding energy of 70 to the DNA, which is derived from promoter strength data and used to build up a non-standard weight matrix. Opposed to conventional approaches, we apply the matrix to the environment of 3765 known promoters and consider the average matrix scores to extract the common features. In addition to the expected minimum of the average binding energy at the exact promoter site, we detect two minima shortly upstream and downstream of the promoter. These are likely to occur due to correlation between the two binding sites of 70. Moreover, we observe a characteristic energy landscape in the 500 bp surrounding the transcription start sites, which is more pronounced in groups of strong promoters than in groups of weak promoters. Our subsequent analysis suggests that the characteristic energy landscape is more likely an influence on target search by the RNA polymerase than a result of nucleotide biases in transcription factor binding sites.