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Modeling DNA-binding of Escherichia coli sigma70 exhibits a characteristic energy landscape around strong promoters.

Weindl J, Hanus P, Dawy Z, Zech J, Hagenauer J, Mueller JC - Nucleic Acids Res. (2007)

Bottom Line: These are likely to occur due to correlation between the two binding sites of sigma70.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.

View Article: PubMed Central - PubMed

Affiliation: Institute for Communications Engineering, Technische Universität München, Arcisstrasse 21, 80290 München, Germany.

ABSTRACT
We present a computational model of DNA-binding by sigma70 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 sigma70 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 sigma70. 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.

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Graphical illustration of the parameters k, s and i from Equation (2). ni+k−1 and ni+k+s−1 reference the k-th nucleotide and (k+s) -th nucleotide, respectively, of the sliding window situated at position i with respect to the transcription start site (TSS, position 0).
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Figure 2: Graphical illustration of the parameters k, s and i from Equation (2). ni+k−1 and ni+k+s−1 reference the k-th nucleotide and (k+s) -th nucleotide, respectively, of the sliding window situated at position i with respect to the transcription start site (TSS, position 0).

Mentions: We apply the weight matrix W using a sliding window that is shifted in single-nucleotide steps over the DNA. As mentioned before, the sigma factor is capable of stretching or squeezing and hereby adapting to different promoter spacings s in order to bind to the energetically most favorable site. Therefore, the binding energy E(i) at position i is obtained by minimizing the energy score E(s, i) calculated according to Equation (1) over the spacing s:2where ni+k−1 and ni+k+s−1 reference the nucleotides at positions k and k + s, respectively, of the sliding window, which is situated at position i with respect to the transcription start site (see illustration in Figure 2). We limit the spacing to s ∈ [15;19] since most of the promoters fall in this range (3).Figure 2.


Modeling DNA-binding of Escherichia coli sigma70 exhibits a characteristic energy landscape around strong promoters.

Weindl J, Hanus P, Dawy Z, Zech J, Hagenauer J, Mueller JC - Nucleic Acids Res. (2007)

Graphical illustration of the parameters k, s and i from Equation (2). ni+k−1 and ni+k+s−1 reference the k-th nucleotide and (k+s) -th nucleotide, respectively, of the sliding window situated at position i with respect to the transcription start site (TSS, position 0).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2175306&req=5

Figure 2: Graphical illustration of the parameters k, s and i from Equation (2). ni+k−1 and ni+k+s−1 reference the k-th nucleotide and (k+s) -th nucleotide, respectively, of the sliding window situated at position i with respect to the transcription start site (TSS, position 0).
Mentions: We apply the weight matrix W using a sliding window that is shifted in single-nucleotide steps over the DNA. As mentioned before, the sigma factor is capable of stretching or squeezing and hereby adapting to different promoter spacings s in order to bind to the energetically most favorable site. Therefore, the binding energy E(i) at position i is obtained by minimizing the energy score E(s, i) calculated according to Equation (1) over the spacing s:2where ni+k−1 and ni+k+s−1 reference the nucleotides at positions k and k + s, respectively, of the sliding window, which is situated at position i with respect to the transcription start site (see illustration in Figure 2). We limit the spacing to s ∈ [15;19] since most of the promoters fall in this range (3).Figure 2.

Bottom Line: These are likely to occur due to correlation between the two binding sites of sigma70.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.

View Article: PubMed Central - PubMed

Affiliation: Institute for Communications Engineering, Technische Universität München, Arcisstrasse 21, 80290 München, Germany.

ABSTRACT
We present a computational model of DNA-binding by sigma70 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 sigma70 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 sigma70. 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.

Show MeSH
Related in: MedlinePlus