Dataset Information

Mapping the Escherichia coli DnaA-binding landscape reveals a preference for binding pairs of closely spaced DNA sites.

ABSTRACT: DnaA is a widely conserved DNA-binding protein that is essential for the initiation of DNA replication in many bacterial species, including Escherichia coli. Cooperative binding of ATP-bound DnaA to multiple 9mer sites ('DnaA boxes') at the origin of replication results in local unwinding of the DNA and recruitment of the replication machinery. DnaA also functions as a transcription regulator by binding to DNA sites upstream of target genes. Previous studies have identified many sites of direct positive and negative regulation by E. coli DnaA. Here, we use a ChIP-seq to map the E. coli DnaA-binding landscape. Our data reveal a compact regulon for DnaA that coordinates the initiation of DNA replication with expression of genes associated with nucleotide synthesis, replication, DNA repair and RNA metabolism. We also show that DnaA binds preferentially to pairs of DnaA boxes spaced 2 or 3 bp apart. Mutation of either the upstream or downstream site in a pair disrupts DnaA binding, as does altering the spacing between sites. We conclude that binding of DnaA at almost all target sites requires a dimer of DnaA, with each subunit making critical contacts with a DnaA box.

SUBMITTER: Stringer AM

PROVIDER: S-EPMC11317965 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Mapping the <i>Escherichia coli</i> DnaA-binding landscape reveals a preference for binding pairs of closely spaced DNA sites.

Stringer Anne M AM Fitzgerald Devon M DM Wade Joseph T JT

Microbiology (Reading, England) 20240701 7

DnaA is a widely conserved DNA-binding protein that is essential for the initiation of DNA replication in many bacterial species, including <i>Escherichia coli</i>. Cooperative binding of ATP-bound DnaA to multiple 9mer sites ('DnaA boxes') at the origin of replication results in local unwinding of the DNA and recruitment of the replication machinery. DnaA also functions as a transcription regulator by binding to DNA sites upstream of target genes. Previous studies have identified many sites of ...[more]

PMID: 39012340

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Project description:BackgroundAs one of the most common protein post-translational modifications, glycosylation is involved in a variety of important biological processes. Computational identification of glycosylation sites in protein sequences becomes increasingly important in the post-genomic era. A new encoding scheme was employed to improve the prediction of mucin-type O-glycosylation sites in mammalian proteins.ResultsA new protein bioinformatics tool, CKSAAP_OGlySite, was developed to predict mucin-type O-glycosylation serine/threonine (S/T) sites in mammalian proteins. Using the composition of k-spaced amino acid pairs (CKSAAP) based encoding scheme, the proposed method was trained and tested in a new and stringent O-glycosylation dataset with the assistance of Support Vector Machine (SVM). When the ratio of O-glycosylation to non-glycosylation sites in training datasets was set as 1:1, 10-fold cross-validation tests showed that the proposed method yielded a high accuracy of 83.1% and 81.4% in predicting O-glycosylated S and T sites, respectively. Based on the same datasets, CKSAAP_OGlySite resulted in a higher accuracy than the conventional binary encoding based method (about +5.0%). When trained and tested in 1:5 datasets, the CKSAAP encoding showed a more significant improvement than the binary encoding. We also merged the training datasets of S and T sites and integrated the prediction of S and T sites into one single predictor (i.e. S+T predictor). Either in 1:1 or 1:5 datasets, the performance of this S+T predictor was always slightly better than those predictors where S and T sites were independently predicted, suggesting that the molecular recognition of O-glycosylated S/T sites seems to be similar and the increase of the S+T predictor's accuracy may be a result of expanded training datasets. Moreover, CKSAAP_OGlySite was also shown to have better performance when benchmarked against two existing predictors.ConclusionBecause of CKSAAP encoding's ability of reflecting characteristics of the sequences surrounding mucin-type O-glycosylation sites, CKSAAP_ OGlySite has been proved more powerful than the conventional binary encoding based method. This suggests that it can be used as a competitive mucin-type O-glycosylation site predictor to the biological community. CKSAAP_OGlySite is now available at http://bioinformatics.cau.edu.cn/zzd_lab/CKSAAP_OGlySite/.

Dataset Information

Mapping the Escherichia coli DnaA-binding landscape reveals a preference for binding pairs of closely spaced DNA sites.

Publications

Mapping the <i>Escherichia coli</i> DnaA-binding landscape reveals a preference for binding pairs of closely spaced DNA sites.

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