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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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Ratios of observed occurrence versus. expected occurrence of the nucleotides A (left) and T (right) in 2015 E. coli transcription factor binding sites downloaded from RegulonDB (14).
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Figure 7: Ratios of observed occurrence versus. expected occurrence of the nucleotides A (left) and T (right) in 2015 E. coli transcription factor binding sites downloaded from RegulonDB (14).

Mentions: To investigate whether the distribution of TFBSs has an influence on the nucleotide content, we determine the ratios of observed nucleotide content versus expected nucleotide content [p(A) = p(T) = 0.246, p(C) = p(G) = 0.254] in all TFBSs. To eliminate the influence of their genome surrounding, we embed the TFBSs at their correct position into random sequences. Figure 7 shows the resulting ratios in these 2015 sequences for the nucleotides A and T. The horizontal line marks a ratio of 1, i.e. the case that the observed nucleotide occurrence equals the expected occurrence. The figure shows an over-representation of A and T in TFBSs in the surrounding of the transcription start site, while G and C are under-represented (data not shown).Figure 7.


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)

Ratios of observed occurrence versus. expected occurrence of the nucleotides A (left) and T (right) in 2015 E. coli transcription factor binding sites downloaded from RegulonDB (14).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 7: Ratios of observed occurrence versus. expected occurrence of the nucleotides A (left) and T (right) in 2015 E. coli transcription factor binding sites downloaded from RegulonDB (14).
Mentions: To investigate whether the distribution of TFBSs has an influence on the nucleotide content, we determine the ratios of observed nucleotide content versus expected nucleotide content [p(A) = p(T) = 0.246, p(C) = p(G) = 0.254] in all TFBSs. To eliminate the influence of their genome surrounding, we embed the TFBSs at their correct position into random sequences. Figure 7 shows the resulting ratios in these 2015 sequences for the nucleotides A and T. The horizontal line marks a ratio of 1, i.e. the case that the observed nucleotide occurrence equals the expected occurrence. The figure shows an over-representation of A and T in TFBSs in the surrounding of the transcription start site, while G and C are under-represented (data not shown).Figure 7.

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