Theoretical targets for TCDD: A bioinformatics approach

Olívero-Verbel, Jesús; Cabarcas-Montalvo, María; Ortega-Zúñiga, Carlos

doi:10.1016/j.chemosphere.2010.06.020

Cited by 10 publications

(4 citation statements)

References 61 publications

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“…Hence, it could be shown that also docking-based target prediction methods are potentially of use in practice, not only for the anticipation of ontarget effects, but also to rationalize (and potentially predict) adverse drug reactions via their association off-targets. TarFisDock on the other hand was applied to predict targets of the toxin 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), and it was suggested that also aryl hydrocarbon receptor (AhR) independent pathways might play a role in its toxicity [118].…”

Section: Applications Of Docking-based Target Prediction Methodsmentioning

confidence: 99%

From in silico target prediction to multi-target drug design: Current databases, methods and applications

Koutsoukas

Simms²,

Kirchmair

et al. 2011

Journal of Proteomics

248

182

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Section: Applications Of Docking-based Target Prediction Methodsmentioning

confidence: 99%

From in silico target prediction to multi-target drug design: Current databases, methods and applications

Koutsoukas

Simms²,

Kirchmair

et al. 2011

Journal of Proteomics

248

182

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“…S2A). One of the parameter to represent and measure the overall quality and deviation of the total energy of the protein structure is Z-score which is dependent upon the length of protein 28 . Results from PROSA web analysis showed the Z-score of the mVEGFR1 is displayed in the plot with a dark black point (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Several novel computational methods and algorithms have emerged in recent years to predict possible biological targets for ligands 22–27 . A previous study reported diverse proteins such as metallopeptidases 8 and 3, oxidosqualene cyclase and myeloperoxidase as theoretical targets for TCDD using Bioinformatics approach 28 .…”

Section: Introductionmentioning

confidence: 99%

Computational prediction and in vitro validation of VEGFR1 as a novel protein target for 2,3,7,8-tetrachlorodibenzo-p-dioxin

Chitrala

Yang

Busbee

et al. 2019

Sci Rep

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The toxic manifestations of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), an environmental contaminant, primarily depend on its ability to activate aryl hydrocarbon receptor (AhR), which is a ligand-dependent transcription factor belonging to the superfamily of basic-helix-loop-helix DNA-binding proteins. In the present study, we aimed to identify novel protein receptor targets for TCDD using computational and in vitro validation experiments. Interestingly, results from computational methods predicted that Vascular Endothelial Growth Factor Receptor 1 (VEGFR1) could be one of the potential targets for TCDD in both mouse and humans. Results from molecular docking studies showed that human VEGFR1 (hVEGFR1) has less affinity towards TCDD compared to the mouse VEGFR1 (mVEGFR1). In vitro validation results showed that TCDD can bind and phosphorylate hVEGFR1. Further, results from molecular dynamic simulation studies showed that hVEGFR1 interaction with TCDD is stable throughout the simulation time. Overall, the present study has identified VEGFR1 as a novel target for TCDD, which provides the basis for further elucidating the role of TCDD in angiogenesis.

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“…Among the dioxins, 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) a polychlorinated aromatic hydrocarbon is an ubiquitous environmental contaminant known to affect human health. It is the most toxic compound released as a product of waste incineration, herbicide overuse, paper chlorination and polyvinylchloride plastic production [2]. 6-Methyl-1,3,8-trichlorodibenzofuran (MCDF) a prototypical alkyl polychlorinated dibenzofuran on the other hand is a related compound to TCDD which is relatively nontoxic [3].…”

Section: Introductionmentioning

confidence: 99%

Computational Prediction and Analysis of Breast Cancer Targets for 6-Methyl-1, 3, 8-Trichlorodibenzofuran

Chitrala

Suneetha

2014

PLoS ONE

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Breast cancer is one of the most known cancer types caused to the women around the world. Dioxins on the other hand are a wide range of chemical compounds known to cause the effects on human health. Among them, 6-Methyl-1,3,8-trichlorodibenzofuran (MCDF) is a relatively non toxic prototypical alkyl polychlorinated dibenzofuran known to act as a highly effective agent for inhibiting hormone-responsive breast cancer growth in animal models. In this study, we have developed a multi-level computational approach to identify possible new breast cancer targets for MCDF. We used PharmMapper Server to predict breast cancer target proteins for MCDF. Search results showed crystal Structure of the Antagonist Form of Glucocorticoid Receptor with highest fit score and AutoLigand analysis showed two potential binding sites, site-A and site-B for MCDF. A molecular docking was performed on these two sites and based on binding energy site-B was selected. MD simulation studies on Glucocorticoid receptor-MCDF complex revealed that MCDF conformation was stable at site-B and the intermolecular interactions were maintained during the course of simulation. In conclusion, our approach couples reverse pharmacophore analysis, molecular docking and molecular dynamics simulations to identify possible new breast cancer targets for MCDF.

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Theoretical targets for TCDD: A bioinformatics approach

Cited by 10 publications

References 61 publications

From in silico target prediction to multi-target drug design: Current databases, methods and applications

From in silico target prediction to multi-target drug design: Current databases, methods and applications

Computational prediction and in vitro validation of VEGFR1 as a novel protein target for 2,3,7,8-tetrachlorodibenzo-p-dioxin

Computational Prediction and Analysis of Breast Cancer Targets for 6-Methyl-1, 3, 8-Trichlorodibenzofuran

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