In silico predication of nuclear hormone receptors for organic pollutants by homology modeling and molecular docking.

Homology modeling and molecular docking were used to in silico predict the rat nuclear hormone receptors of different organic pollutants. Rat aryl hydrocarbon receptor (rAhR), constitutive androstane receptor (rCAR) and pregnane X receptor (rPXR) were chosen as the target nuclear receptors. 3D models of ligand binding domains of rAhR, rCAR and rPXR were constructed by MODELLER 9V6 and assessed by the Procheck and Prosa 2003. Surflex-Dock program was applied to bind the different organic pollutants into the three receptors to predict their affinities. The results of docking experiments demonstrated that three polybrominated dibenzofurans (PBDFs, including TretaBDF, PentaBDF and HexaBDF) and 3,3',4,4',5'-pentachlorobiphenyl (PCB126) would be better categorized by rAhR-dependent mechanism, but four polybrominated diphenyl ethers (PBDEs, including BDE47, BDE80, BDE99 and BDE153) and 2,2',4,4',5,5'-hexachlorobiphenyl (PCB153) by rCAR and rPXR-dependent mechanism. For benzo(a)pyrene and pyrene, they have high affinities with the three target receptors, which suggests that "crosstalk" among the receptors might occur during the receptor induction. The results of this study are consistent with those of animal experiments reported by previous literatures, which suggest that homology modeling and molecular docking would have the potential to predict the nuclear hormone receptors of environmental pollutants.