Dataset Information

Discovery of Fur binding site clusters in Escherichia coli by information theory models.

ABSTRACT: Fur is a DNA binding protein that represses bacterial iron uptake systems. Eleven footprinted Escherichia coli Fur binding sites were used to create an initial information theory model of Fur binding, which was then refined by adding 13 experimentally confirmed sites. When the refined model was scanned across all available footprinted sequences, sequence walkers, which are visual depictions of predicted binding sites, frequently appeared in clusters that fit the footprints ( approximately 83% coverage). This indicated that the model can accurately predict Fur binding. Within the clusters, individual walkers were separated from their neighbors by exactly 3 or 6 bases, consistent with models in which Fur dimers bind on different faces of the DNA helix. When the E. coli genome was scanned, we found 363 unique clusters, which includes all known Fur-repressed genes that are involved in iron metabolism. In contrast, only a few of the known Fur-activated genes have predicted Fur binding sites at their promoters. These observations suggest that Fur is either a direct repressor or an indirect activator. The Pseudomonas aeruginosa and Bacillus subtilis Fur models are highly similar to the E. coli Fur model, suggesting that the Fur-DNA recognition mechanism may be conserved for even distantly related bacteria.

SUBMITTER: Chen Z

PROVIDER: S-EPMC2189734 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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Discovery of Fur binding site clusters in Escherichia coli by information theory models.

Chen Zehua Z Lewis Karen A KA Shultzaberger Ryan K RK Lyakhov Ilya G IG Zheng Ming M Doan Bernard B Storz Gisela G Schneider Thomas D TD

Nucleic acids research 20071005 20

Fur is a DNA binding protein that represses bacterial iron uptake systems. Eleven footprinted Escherichia coli Fur binding sites were used to create an initial information theory model of Fur binding, which was then refined by adding 13 experimentally confirmed sites. When the refined model was scanned across all available footprinted sequences, sequence walkers, which are visual depictions of predicted binding sites, frequently appeared in clusters that fit the footprints ( approximately 83% co ...[more]

PMID: 17921503

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Project description:We present Delila-genome, a software system for identification, visualization and analysis of protein binding sites in complete genome sequences. Binding sites are predicted by scanning genomic sequences with information theory-based (or user-defined) weight matrices. Matrices are refined by adding experimentally-defined binding sites to published binding sites. Delila-Genome was used to examine the accuracy of individual information contents of binding sites detected with refined matrices as a measure of the strengths of the corresponding protein-nucleic acid interactions. The software can then be used to predict novel sites by rescanning the genome with the refined matrices.Parameters for genome scans are entered using a Java-based GUI interface and backend scripts in Perl. Multi-processor CPU load-sharing minimized the average response time for scans of different chromosomes. Scans of human genome assemblies required 4-6 hours for transcription factor binding sites and 10-19 hours for splice sites, respectively, on 24- and 3-node Mosix and Beowulf clusters. Individual binding sites are displayed either as high-resolution sequence walkers or in low-resolution custom tracks in the UCSC genome browser. For large datasets, we applied a data reduction strategy that limited displays of binding sites exceeding a threshold information content to specific chromosomal regions within or adjacent to genes. An HTML document is produced listing binding sites ranked by binding site strength or chromosomal location hyperlinked to the UCSC custom track, other annotation databases and binding site sequences. Post-genome scan tools parse binding site annotations of selected chromosome intervals and compare the results of genome scans using different weight matrices. Comparisons of multiple genome scans can display binding sites that are unique to each scan and identify sites with significantly altered binding strengths.Delila-Genome was used to scan the human genome sequence with information weight matrices of transcription factor binding sites, including PXR/RXRalpha, AHR and NF-kappaB p50/p65, and matrices for RNA binding sites including splice donor, acceptor, and SC35 recognition sites. Comparisons of genome scans with the original and refined PXR/RXRalpha information weight matrices indicate that the refined model more accurately predicts the strengths of known binding sites and is more sensitive for detection of novel binding sites.

Dataset Information

Discovery of Fur binding site clusters in Escherichia coli by information theory models.

Publications

Discovery of Fur binding site clusters in Escherichia coli by information theory models.

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