3D QSAR studies of hydroxylated polychlorinated biphenyls as potential xenoestrogens.
Mono-hydroxylated polychlorinated biphenyls (OH-PCBs) are found in human biological samples and lack of data on their potential estrogenic activity has been a source of concern. We have extended our previous in silico 2D QSAR study through the application of advance techniques such as docking and 3D QSAR to gain insights into their estrogen receptor (ERα) binding. The results support our earlier findings that the hydroxyl group is the most important feature on the compounds; its position, orientation and surroundings in the structure are influential for the binding of OH-PCBs to ERα. This study has also revealed the following additional interactions that influence estrogenicity of these chemicals (a) the aromatic interactions of the biphenyl moieties with the receptor, (b) hydrogen bonding interactions of the p-hydroxyl group with key amino acids ARG394 and GLU353, (c) low or no electronegative substitution at para-positions of the p-hydroxyl group, (d) enhanced electrostatic interactions at the meta position on the B ring, and (e) co-planarity of the hydroxyl group on the A ring. In combination the 2D and 3D QSAR approaches have led us to the support conclusion that the hydroxyl group is the most important feature on the OH-PCB influencing the binding to estrogen receptors, and have enhanced our understanding of the mechanistic details of estrogenicity of this class of chemicals. Such in silico computational methods could serve as useful tools in risk assessment of chemicals.
['Ruiz|Patricia|P|', 'Ingale|Kundan|K|', 'Wheeler|John S|JS|', 'Mumtaz|Moiz|M|']
D001665:Binding Sites D003198:Computer Simulation D030562:Databases, Protein D047628:Estrogen Receptor alpha D004968:Estrogens, Non-Steroidal D017930:Genes, Reporter D006801:Humans D006860:Hydrogen Bonding D006900:Hydroxylation D008024:Ligands D062105:Molecular Docking Simulation D011078:Polychlorinated Biphenyls D011485:Protein Binding D021281:Quantitative Structure-Activity Relationship D012441:Saccharomyces cerevisiae D020798:Two-Hybrid System Techniques D015262:Xenobiotics
3D QSAR OH-PCBs 2D QSAR In silico modeling kNN MFA