References of "Vasconcelos, Ana T."
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See detailPrediction of TF target sites based on atomistic models of protein-DNA complexes.
Espinosa Angarica, Vladimir UL; Perez, Abel Gonzalez; Vasconcelos, Ana T. et al

in BMC bioinformatics (2008), 9

BACKGROUND: The specific recognition of genomic cis-regulatory elements by transcription factors (TFs) plays an essential role in the regulation of coordinated gene expression. Studying the mechanisms ... [more ▼]

BACKGROUND: The specific recognition of genomic cis-regulatory elements by transcription factors (TFs) plays an essential role in the regulation of coordinated gene expression. Studying the mechanisms determining binding specificity in protein-DNA interactions is thus an important goal. Most current approaches for modeling TF specific recognition rely on the knowledge of large sets of cognate target sites and consider only the information contained in their primary sequence. RESULTS: Here we describe a structure-based methodology for predicting sequence motifs starting from the coordinates of a TF-DNA complex. Our algorithm combines information regarding the direct and indirect readout of DNA into an atomistic statistical model, which is used to estimate the interaction potential. We first measure the ability of our method to correctly estimate the binding specificities of eight prokaryotic and eukaryotic TFs that belong to different structural superfamilies. Secondly, the method is applied to two homology models, finding that sampling of interface side-chain rotamers remarkably improves the results. Thirdly, the algorithm is compared with a reference structural method based on contact counts, obtaining comparable predictions for the experimental complexes and more accurate sequence motifs for the homology models. CONCLUSION: Our results demonstrate that atomic-detail structural information can be feasibly used to predict TF binding sites. The computational method presented here is universal and might be applied to other systems involving protein-DNA recognition. [less ▲]

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See detailComplementing computationally predicted regulatory sites in Tractor_DB using a pattern matching approach.
Guia, Marylens Hernandez; Perez, Abel Gonzalez; Espinosa Angarica, Vladimir UL et al

in In silico biology (2005), 5(2), 209-19

Prokaryotic genomes annotation has focused on genes location and function. The lack of regulatory information has limited the knowledge on cellular transcriptional regulatory networks. However, as more ... [more ▼]

Prokaryotic genomes annotation has focused on genes location and function. The lack of regulatory information has limited the knowledge on cellular transcriptional regulatory networks. However, as more phylogenetically close genomes are sequenced and annotated, the implementation of phylogenetic footprinting strategies for the recognition of regulators and their regulons becomes more important. In this paper we describe a comparative genomics approach to the prediction of new gamma-proteobacterial regulon members. We take advantage of the phylogenetic proximity of Escherichia coli and other 16 organisms of this subdivision and the intensive search of the space sequence provided by a pattern-matching strategy. Using this approach we complement predictions of regulatory sites made using statistical models currently stored in Tractor_DB, and increase the number of transcriptional regulators with predicted binding sites up to 86. All these computational predictions may be reached at Tractor_DB (www.bioinfo.cu/Tractor_DB, www.tractor.lncc.br, www.ccg.unam.mx/Computational_Genomics/tractorDB/). We also take a first step in this paper towards the assessment of the conservation of the architecture of the regulatory network in the gamma-proteobacteria through evaluating the conservation of the overall connectivity of the network. [less ▲]

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See detailComparative studies of transcriptional regulation mechanisms in a group of eight gamma-proteobacterial genomes.
Espinosa Angarica, Vladimir UL; Gonzalez, Abel D.; Vasconcelos, Ana T. et al

in Journal of molecular biology (2005), 354(1), 184-99

Experimental data on the Escherichia coli transcriptional regulation has enabled the construction of statistical models to predict new regulatory elements within its genome. Far less is known about the ... [more ▼]

Experimental data on the Escherichia coli transcriptional regulation has enabled the construction of statistical models to predict new regulatory elements within its genome. Far less is known about the transcriptional regulatory elements in other gamma-proteobacteria with sequenced genomes, so it is of great interest to conduct comparative genomic studies oriented to extracting biologically relevant information about transcriptional regulation in these less studied organisms using the knowledge from E. coli. In this work, we use the information stored in the TRACTOR_DB database to conduct a comparative study on the mechanisms of transcriptional regulation in eight gamma-proteobacteria and 38 regulons. We assess the conservation of transcription factors binding specificity across all the eight genomes and show a correlation between the conservation of a regulatory site and the structure of the transcription unit it regulates. We also find a marked conservation of site-promoter distances across the eight organisms and a correspondence of the statistical significance of co-occurrence of pairs of transcription factor binding sites in the regulatory regions, which is probably related to a conserved architecture of higher-order regulatory complexes in the organisms studied. The results obtained in this study using the information on transcriptional regulation in E. coli enable us to conclude that not only transcription factor-binding sites are conserved across related species but also several of the transcriptional regulatory mechanisms previously identified in E. coli. [less ▲]

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See detailTRACTOR_DB: a database of regulatory networks in gamma-proteobacterial genomes.
Gonzalez, Abel D.; Espinosa Angarica, Vladimir UL; Vasconcelos, Ana T. et al

in Nucleic acids research (2005), 33(Database issue), 98-102

Experimental data on the Escherichia coli transcriptional regulatory system has been used in the past years to predict new regulatory elements (promoters, transcription factors (TFs), TFs' binding sites ... [more ▼]

Experimental data on the Escherichia coli transcriptional regulatory system has been used in the past years to predict new regulatory elements (promoters, transcription factors (TFs), TFs' binding sites and operons) within its genome. As more genomes of gamma-proteobacteria are being sequenced, the prediction of these elements in a growing number of organisms has become more feasible, as a step towards the study of how different bacteria respond to environmental changes at the level of transcriptional regulation. In this work, we present TRACTOR_DB (TRAnscription FaCTORs' predicted binding sites in prokaryotic genomes), a relational database that contains computational predictions of new members of 74 regulons in 17 gamma-proteobacterial genomes. For these predictions we used a comparative genomics approach regarding which several proof-of-principle articles for large regulons have been published. TRACTOR_DB may be currently accessed at http://www.bioinfo.cu/Tractor_DB, http://www.tractor.lncc.br/ or at http://www.cifn.unam.mx/Computational_Genomics/tractorDB. Contact Email id is tractor@cifn.unam.mx. [less ▲]

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