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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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Average energy landscapes E (i) obtained if clustering the 3765 known σ70-promoters according to their strength into four groups with approximately 900 sequences each (top: weak promoters, bottom: strong promoters).
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Figure 6: Average energy landscapes E (i) obtained if clustering the 3765 known σ70-promoters according to their strength into four groups with approximately 900 sequences each (top: weak promoters, bottom: strong promoters).

Mentions: In the next step, we investigate whether the energy landscape observed in Figure 5 is present in all σ70-promoters or only occurs in certain sets with specific promoter strength. Therefore, we split the 3765 promoters in subgroups according to their strength as measured by the sigma factor's; binding energy El(i) to the l-th promoter sequence calculated according to Equation (2) (see Algorithm section). Here, i ∈ [−38;−32] depending on the position of the l-th promoter with respect to the TSS. In order to maintain a sufficient statistical basis, we divide the promoters into four groups with approximately 900 promoters each. Figure 6 shows the resulting plots sorted from weak (top) to strong promoters (bottom). It can be seen that the characteristic energy landscape is not observed for weak promoters, whereas it becomes distinct for stronger promoters. It is generally assumed that the binding energy at the promoter itself determines the rate of detection and hereby the expression rate of the respective gene (4,7,9). However, to our knowledge, a dependence of the promoter strength on the wider surrounding has never been reported.Figure 6.


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)

Average energy landscapes E (i) obtained if clustering the 3765 known σ70-promoters according to their strength into four groups with approximately 900 sequences each (top: weak promoters, bottom: strong promoters).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 6: Average energy landscapes E (i) obtained if clustering the 3765 known σ70-promoters according to their strength into four groups with approximately 900 sequences each (top: weak promoters, bottom: strong promoters).
Mentions: In the next step, we investigate whether the energy landscape observed in Figure 5 is present in all σ70-promoters or only occurs in certain sets with specific promoter strength. Therefore, we split the 3765 promoters in subgroups according to their strength as measured by the sigma factor's; binding energy El(i) to the l-th promoter sequence calculated according to Equation (2) (see Algorithm section). Here, i ∈ [−38;−32] depending on the position of the l-th promoter with respect to the TSS. In order to maintain a sufficient statistical basis, we divide the promoters into four groups with approximately 900 promoters each. Figure 6 shows the resulting plots sorted from weak (top) to strong promoters (bottom). It can be seen that the characteristic energy landscape is not observed for weak promoters, whereas it becomes distinct for stronger promoters. It is generally assumed that the binding energy at the promoter itself determines the rate of detection and hereby the expression rate of the respective gene (4,7,9). However, to our knowledge, a dependence of the promoter strength on the wider surrounding has never been reported.Figure 6.

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