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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 landscape E (i) in a wider range around the 3765 known σ70-promoters aligned to the transcription start site (TSS, position 0). E (i) = 0 again corresponds to the energy Eran of random sequences.
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Figure 5: Average energy landscape E (i) in a wider range around the 3765 known σ70-promoters aligned to the transcription start site (TSS, position 0). E (i) = 0 again corresponds to the energy Eran of random sequences.

Mentions: In order to investigate the energy landscape in the vicinity of the promoters, we apply our method to a wider range around the transcription start site. Figure 5 shows the average binding energy E (i) of all 3765 promoters in a range of 800 bp aligned to the TSS (position 0). The decline beginning at 300 bp before the promoter and the constant incline of the binding energy in the 200 bp after the promoter are remarkable. It is obvious in comparison with the energy E (i) = 0 of random sequences that the average binding energy deviates significantly from the random case in a range of ∼500 bp around the promoter. (A), (B) and (C) in Figure 5 show again the three minima at −58, −35 and −12 that were observed in Figure 3.Figure 5.


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 landscape E (i) in a wider range around the 3765 known σ70-promoters aligned to the transcription start site (TSS, position 0). E (i) = 0 again corresponds to the energy Eran of random sequences.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 5: Average energy landscape E (i) in a wider range around the 3765 known σ70-promoters aligned to the transcription start site (TSS, position 0). E (i) = 0 again corresponds to the energy Eran of random sequences.
Mentions: In order to investigate the energy landscape in the vicinity of the promoters, we apply our method to a wider range around the transcription start site. Figure 5 shows the average binding energy E (i) of all 3765 promoters in a range of 800 bp aligned to the TSS (position 0). The decline beginning at 300 bp before the promoter and the constant incline of the binding energy in the 200 bp after the promoter are remarkable. It is obvious in comparison with the energy E (i) = 0 of random sequences that the average binding energy deviates significantly from the random case in a range of ∼500 bp around the promoter. (A), (B) and (C) in Figure 5 show again the three minima at −58, −35 and −12 that were observed in Figure 3.Figure 5.

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