Dataset Information

Information analysis of Fis binding sites.

ABSTRACT: Originally discovered in the bacteriophage Mu DNA inversion system gin, Fis (Factor for Inversion Stimulation) regulates many genetic systems. To determine the base frequency conservation required for Fis to locate its binding sites, we collected a set of 60 experimentally defined wild-type Fis DNA binding sequences. The sequence logo for Fis binding sites showed the significance and likely kinds of base contacts, and these are consistent with available experimental data. Scanning with an information theory based weight matrix within fis, nrd, tgt/sec and gin revealed Fis sites not previously identified, but for which there are published footprinting and biochemical data. DNA mobility shift experiments showed that a site predicted to be 11 bases from the proximal Salmonella typhimurium hin site and a site predicted to be 7 bases from the proximal P1 cin site are bound by Fis in vitro. Two predicted sites separated by 11 bp found within the nrd promoter region, and one in the tgt/sec promoter, were also confirmed by gel shift analysis. A sequence in aldB previously reported to be a Fis site, for which information theory predicts no site, did not shift. These results demonstrate that information analysis is useful for predicting Fis DNA binding.

SUBMITTER: Hengen PN

PROVIDER: S-EPMC147151 | biostudies-other | 1997 Dec

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Information analysis of Fis binding sites.

Hengen P N PN Bartram S L SL Stewart L E LE Schneider T D TD

Nucleic acids research 19971201 24

Originally discovered in the bacteriophage Mu DNA inversion system gin, Fis (Factor for Inversion Stimulation) regulates many genetic systems. To determine the base frequency conservation required for Fis to locate its binding sites, we collected a set of 60 experimentally defined wild-type Fis DNA binding sequences. The sequence logo for Fis binding sites showed the significance and likely kinds of base contacts, and these are consistent with available experimental data. Scanning with an inform ...[more]

PMID: 9396807

Similar Datasets

Project description:BackgroundRNA-protein interaction plays an essential role in several biological processes, such as protein synthesis, gene expression, posttranscriptional regulation and viral infectivity. Identification of RNA-binding sites in proteins provides valuable insights for biologists. However, experimental determination of RNA-protein interaction remains time-consuming and labor-intensive. Thus, computational approaches for prediction of RNA-binding sites in proteins have become highly desirable. Extensive studies of RNA-binding site prediction have led to the development of several methods. However, they could yield low sensitivities in trade-off for high specificities.ResultsWe propose a method, RNAProB, which incorporates a new smoothed position-specific scoring matrix (PSSM) encoding scheme with a support vector machine model to predict RNA-binding sites in proteins. Besides the incorporation of evolutionary information from standard PSSM profiles, the proposed smoothed PSSM encoding scheme also considers the correlation and dependency from the neighboring residues for each amino acid in a protein. Experimental results show that smoothed PSSM encoding significantly enhances the prediction performance, especially for sensitivity. Using five-fold cross-validation, our method performs better than the state-of-the-art systems by 4.90%-6.83%, 0.88%-5.33%, and 0.10-0.23 in terms of overall accuracy, specificity, and Matthew's correlation coefficient, respectively. Most notably, compared to other approaches, RNAProB significantly improves sensitivity by 7.0%-26.9% over the benchmark data sets. To prevent data over fitting, a three-way data split procedure is incorporated to estimate the prediction performance. Moreover, physicochemical properties and amino acid preferences of RNA-binding proteins are examined and analyzed.ConclusionOur results demonstrate that smoothed PSSM encoding scheme significantly enhances the performance of RNA-binding site prediction in proteins. This also supports our assumption that smoothed PSSM encoding can better resolve the ambiguity of discriminating between interacting and non-interacting residues by modelling the dependency from surrounding residues. The proposed method can be used in other research areas, such as DNA-binding site prediction, protein-protein interaction, and prediction of posttranslational modification sites.

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

Information analysis of Fis binding sites.

Publications

Information analysis of Fis binding sites.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets