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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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Comparison between the average binding energies around 3765 promoters as observed in Figure 5 (dotted line) and the average binding energies of 3800 random sequences generated based on the nucleotide biases caused by TFBSs as calculated from Figure 7 (solid line).
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Figure 8: Comparison between the average binding energies around 3765 promoters as observed in Figure 5 (dotted line) and the average binding energies of 3800 random sequences generated based on the nucleotide biases caused by TFBSs as calculated from Figure 7 (solid line).

Mentions: In the next step, we have to evaluate the influence of the observed nucleotide bias in TFBSs on the binding energies calculated through our algorithm. For this purpose, we generate 3800 random sequences using the calculated nucleotide probabilities (depicted in Figure 7) for each position. Afterwards, we apply our weight matrix W and the vector ε to these 3800 sequences as described in the Algorithm section. Figure 8 shows a comparison between the average landscape observed around promoters and the average landscape of the 3800 generated sequences. It can be seen that the nucleotide bias caused by the TFBSs is in part responsible for the characteristic landscape, however, does neither explain all of its decrease in energy at the transcription start site nor the wide range of low energies (500 bp).Figure 8.


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)

Comparison between the average binding energies around 3765 promoters as observed in Figure 5 (dotted line) and the average binding energies of 3800 random sequences generated based on the nucleotide biases caused by TFBSs as calculated from Figure 7 (solid line).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 8: Comparison between the average binding energies around 3765 promoters as observed in Figure 5 (dotted line) and the average binding energies of 3800 random sequences generated based on the nucleotide biases caused by TFBSs as calculated from Figure 7 (solid line).
Mentions: In the next step, we have to evaluate the influence of the observed nucleotide bias in TFBSs on the binding energies calculated through our algorithm. For this purpose, we generate 3800 random sequences using the calculated nucleotide probabilities (depicted in Figure 7) for each position. Afterwards, we apply our weight matrix W and the vector ε to these 3800 sequences as described in the Algorithm section. Figure 8 shows a comparison between the average landscape observed around promoters and the average landscape of the 3800 generated sequences. It can be seen that the nucleotide bias caused by the TFBSs is in part responsible for the characteristic landscape, however, does neither explain all of its decrease in energy at the transcription start site nor the wide range of low energies (500 bp).Figure 8.

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