Dataset Information

Evolutionary population genetics of promoters: predicting binding sites and functional phylogenies.

ABSTRACT: We study the evolution of transcription factor-binding sites in prokaryotes, using an empirically grounded model with point mutations and genetic drift. Selection acts on the site sequence via its binding affinity to the corresponding transcription factor. Calibrating the model with populations of functional binding sites, we verify this form of selection and show that typical sites are under substantial selection pressure for functionality: for cAMP response protein sites in Escherichia coli, the product of fitness difference and effective population size takes values 2NDeltaF of order 10. We apply this model to cross-species comparisons of binding sites in bacteria and obtain a prediction method for binding sites that uses evolutionary information in a quantitative way. At the same time, this method predicts the functional histories of orthologous sites in a phylogeny, evaluating the likelihood for conservation or loss or gain of function during evolution. We have performed, as an example, a cross-species analysis of E. coli, Salmonella typhimurium, and Yersinia pseudotuberculosis. Detailed lists of predicted sites and their functional phylogenies are available.

SUBMITTER: Mustonen V

PROVIDER: S-EPMC1276062 | biostudies-literature | 2005 Nov

REPOSITORIES: biostudies-literature

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Evolutionary population genetics of promoters: predicting binding sites and functional phylogenies.

Mustonen Ville V Lässig Michael M

Proceedings of the National Academy of Sciences of the United States of America 20051019 44

We study the evolution of transcription factor-binding sites in prokaryotes, using an empirically grounded model with point mutations and genetic drift. Selection acts on the site sequence via its binding affinity to the corresponding transcription factor. Calibrating the model with populations of functional binding sites, we verify this form of selection and show that typical sites are under substantial selection pressure for functionality: for cAMP response protein sites in Escherichia coli, t ...[more]

PMID: 16236723

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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:Transcription factor binding site (TFBS) identification plays an important role in deciphering gene regulatory codes. With comprehensive knowledge of TFBSs, one can understand molecular mechanisms of gene regulation. In the recent decades, various computational approaches have been proposed to predict TFBSs in the genome. The TFBS dataset of a TF generated by each algorithm is a ranked list of predicted TFBSs of that TF, where top ranked TFBSs are statistically significant ones. However, whether these statistically significant TFBSs are functional (i.e. biologically relevant) is still unknown. Here we develop a post-processor, called the functional propensity calculator (FPC), to assign a functional propensity to each TFBS in the existing computationally predicted TFBS datasets. It is known that functional TFBSs reveal strong positional preference towards the transcriptional start site (TSS). This motivates us to take TFBS position relative to the TSS as the key idea in building our FPC. Based on our calculated functional propensities, the TFBSs of a TF in the original TFBS dataset could be reordered, where top ranked TFBSs are now the ones with high functional propensities. To validate the biological significance of our results, we perform three published statistical tests to assess the enrichment of Gene Ontology (GO) terms, the enrichment of physical protein-protein interactions, and the tendency of being co-expressed. The top ranked TFBSs in our reordered TFBS dataset outperform the top ranked TFBSs in the original TFBS dataset, justifying the effectiveness of our post-processor in extracting functional TFBSs from the original TFBS dataset. More importantly, assigning functional propensities to putative TFBSs enables biologists to easily identify which TFBSs in the promoter of interest are likely to be biologically relevant and are good candidates to do further detailed experimental investigation. The FPC is implemented as a web tool at http://santiago.ee.ncku.edu.tw/FPC/.

Project description:Gene expression analyses have led to a better understanding of growth control of prostate cancer cells. We and others have identified the presence of several zinc finger transcription factors in the neoplastic prostate, suggesting a potential role for these genes in the regulation of the prostate cancer transcriptome. One of the transcription factors (TFs) identified in the prostate cancer epithelial cells was the Wilms tumor gene (WT1). To rapidly identify coordinately expressed prostate cancer growth control genes that may be regulated by WT1, we used an in silico approach.Evolutionary conserved transcription factor binding sites (TFBS) recognized by WT1, EGR1, SP1, SP2, AP2 and GATA1 were identified in the promoters of 24 differentially expressed prostate cancer genes from eight mammalian species. To test the relationship between sequence conservation and function, chromatin of LNCaP prostate cancer and kidney 293 cells were tested for TF binding using chromatin immunoprecipitation (ChIP). Multiple putative TFBS in gene promoters of placental mammals were found to be shared with those in human gene promoters and some were conserved between genomes that diverged about 170 million years ago (i.e., primates and marsupials), therefore implicating these sites as candidate binding sites. Among those genes coordinately expressed with WT1 was the kallikrein-related peptidase 3 (KLK3) gene commonly known as the prostate specific antigen (PSA) gene. This analysis located several potential WT1 TFBS in the PSA gene promoter and led to the rapid identification of a novel putative binding site confirmed in vivo by ChIP. Conversely for two prostate growth control genes, androgen receptor (AR) and vascular endothelial growth factor (VEGF), known to be transcriptionally regulated by WT1, regulatory sequence conservation was observed and TF binding in vivo was confirmed by ChIP.Overall, this targeted approach rapidly identified important candidate WT1-binding elements in genes coordinately expressed with WT1 in prostate cancer cells, thus enabling a more focused functional analysis of the most likely target genes in prostate cancer progression. Identifying these genes will help to better understand how gene regulation is altered in these tumor cells.

Dataset Information

Evolutionary population genetics of promoters: predicting binding sites and functional phylogenies.

Publications

Evolutionary population genetics of promoters: predicting binding sites and functional phylogenies.

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