Limits...
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.

Show MeSH

Related in: MedlinePlus

Average binding energy E (i) [calculated according to Equation (2)] of 3765 known σ70-promoters aligned to the transcription start site (TSS). E (i) = 0 corresponds to the energy Eran of random sequences. E (i) shows three significant minima at positions −58 (A), −35 (B) and −12 (C).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2175306&req=5

Figure 3: Average binding energy E (i) [calculated according to Equation (2)] of 3765 known σ70-promoters aligned to the transcription start site (TSS). E (i) = 0 corresponds to the energy Eran of random sequences. E (i) shows three significant minima at positions −58 (A), −35 (B) and −12 (C).

Mentions: We download the E. coli σ70-promoters from the NCBI database (strain K 12; promoters extractable from the 400 datasets AE000x.1, x ∈ [111;510] (accessible via Genbank entry ‘u00096’), http://www.ncbi.nlm.nih.gov) that classifies promoters into computationally predicted and experimentally documented. Figure 3 shows the modeled average binding energy E (i) calculated according to Equation (2) and (3) (see Algorithm section) for all 3765 predicted and documented promoters in a range of 200 bp around the promoters aligned to the transcription start site (TSS, i = 0).Figure 3.


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 binding energy E (i) [calculated according to Equation (2)] of 3765 known σ70-promoters aligned to the transcription start site (TSS). E (i) = 0 corresponds to the energy Eran of random sequences. E (i) shows three significant minima at positions −58 (A), −35 (B) and −12 (C).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 3: Average binding energy E (i) [calculated according to Equation (2)] of 3765 known σ70-promoters aligned to the transcription start site (TSS). E (i) = 0 corresponds to the energy Eran of random sequences. E (i) shows three significant minima at positions −58 (A), −35 (B) and −12 (C).
Mentions: We download the E. coli σ70-promoters from the NCBI database (strain K 12; promoters extractable from the 400 datasets AE000x.1, x ∈ [111;510] (accessible via Genbank entry ‘u00096’), http://www.ncbi.nlm.nih.gov) that classifies promoters into computationally predicted and experimentally documented. Figure 3 shows the modeled average binding energy E (i) calculated according to Equation (2) and (3) (see Algorithm section) for all 3765 predicted and documented promoters in a range of 200 bp around the promoters aligned to the transcription start site (TSS, i = 0).Figure 3.

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