Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/10066
Title: Detection of the TCDD binding-fingerprint within the Ah receptor ligand binding domain by structurally driven mutagenesis and functional analysis
Authors: Pandini, A
Soshilov, AA
Song, Y
Zhao, J
Bonati, L
Denison, MS
Keywords: Aryl hydrocarbon receptor (AhR);Helix-loop-helix Per-Arnt-Sim (PAS)
Issue Date: 2009
Citation: Biochemistry, 2009, 48 (25), pp. 5972 - 5983
Abstract: The aryl hydrocarbon receptor (AhR) is a ligand-dependent, basic helix-loop-helix Per-Arnt-Sim (PAS)-containing transcription factor that can bind and be activated by structurally diverse chemicals, including the toxic environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Our previous three-dimensional homology model of the mouse AhR (mAhR) PAS B ligand binding domain allowed identification of the binding site and its experimental validation. We have extended this analysis by conducting comparative structural modeling studies of the ligand binding domains of six additional highaffinity mammalian AhRs. These results, coupled with site-directed mutagenesis and AhR functional analysis, have allowed detection of the "TCDD binding-fingerprint" of conserved residues within the ligand binding cavity necessary for high-affinity TCDD binding and TCDD-dependent AhR transformation DNA binding. The essential role of selected residues was further evaluated using molecular docking simulations of TCDD with both wild-type and mutant mAhRs. Taken together, our results dramatically improve our understanding of the molecular determinants of TCDD binding and provide a basis for future studies directed toward rationalizing the observed species differences in AhR sensitivity to TCDD and understanding the mechanistic basis for the dramatic diversity in AhR ligand structure. © 2009 American Chemical Society.
URI: http://pubs.acs.org/doi/abs/10.1021/bi900259z
http://bura.brunel.ac.uk/handle/2438/10066
DOI: http://dx.doi.org/10.1021/bi900259z
ISSN: 0006-2960
Appears in Collections:Dept of Computer Science Research Papers

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