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We present a computational model of DNA-binding by σ70 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 σ70 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 σ70. 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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We present a biophysical model of promoter search by Escherichia coli RNA polymerase. We use an unconventional weight matrix derived from promoter strength data to extract the energy landscape common to a large set of known promoters. This exhibits a continuous strengthening of the binding energy when approaching the transcription start site from either side. During promoter search, the RNA polymerase slides along the DNA double helix (one-dimensional diffusion) after randomly binding to it. We discuss the possibility that the sliding has a sequence-dependent component, which implies that the energy landscape influences the movement with respect to speed, direction and efficiency. Based on this assumption, we relate the obtained energy landscape around the promoters to the one-dimensional diffusion of the RNA polymerase. Our analytical results suggest that the sequence-dependent random walk slows down and gets directed upon entering a region of 500 bp around the transcription start site, which significantly increases the efficiency of promoter search. These results may explain how the RNA polymerase is able to find the promoter in biologically relevant times out of a vast excess of non-target sites. Moreover, they provide evidence for a sequence-dependent component of one-dimensional diffusion.

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