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Discovery of Fur binding site clusters in Escherichia coli by information theory models.
Chen, Zehua; Lewis, Karen A; Shultzaberger, Ryan K; Lyakhov, Ilya G; Zheng, Ming; Doan, Bernard; Storz, Gisela; Schneider, Thomas D.
Afiliação
  • Chen Z; National Cancer Institute at Frederick, Center for Cancer Research Nanobiology Program, Basic Research Program, SAIC-Frederick, Inc., Frederick, MD 21702-1201, USA.
Nucleic Acids Res ; 35(20): 6762-77, 2007.
Article em En | MEDLINE | ID: mdl-17921503
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2007 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2007 Tipo de documento: Article