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Close attention has been paid to estrogen compounds because these chemicals may pose a serious threat to the health of humans and wildlife. Estrogen receptor (ER) exists as two subtypes, ERα and ERβ. The difference in amino acids sequence of the binding sites of ERα and ERβ might lead to a result that some synthetic estrogens and naturally occurring steroidal ligands have different relative affinities and binding modes for ERα and ERβ. In this investigation, comparative molecular similarity indices analysis (CoMSIA) was performed on 50 estrogen compounds binding ERβ to find out the structural relationship with the activities. We also compared two alignment schemes employed in CoMSIA analy-sis, namely, atom-fit and receptor-based alignment, with respect to the predictive capability of their respective models for structurally diverse data sets. The model with the significant correlation and the best predictive power (R2=0.961, qL 2OO=0.671, RP 2red=0.722) was achieved. The CoMSIA and docking results revealed the structural features related to an activity and provided an insight into molecular mechanisms of estrogenic activities for estrogen compounds.
Close attention has been paid to estrogen compounds because these chemicals may pose a serious threat to the health of humans and wildlife. Estrogen receptor (ER) exists as two subtypes, ERα and ERβ. The difference in amino acids sequence of the binding sites of ERα and ERβ might lead to a result that some synthetic estrogens and naturally occurring steroidal ligands have different relative affinities and binding modes for ERα and ERβ. In this investigation, a comparative molecular similarity indices analysis (CoMSIA) was performed on 50 estrogen compounds binding ERβ to find out the structural relationship with the activities. We also compared two alignment schemes employed in CoMSIA analy-sis, namely, atom-fit and receptor-based alignment, with respect to the predictive capability of their respective models for structurally diverse data sets. The model with the significant correlation and the best predictive power (R2 = 0.961, qL2OO = 0.671, RP2red = 0.722) was achieved. The CoMSIA and docking results revealed the structural features related to an activity and provided an insight into molecular mechanisms of estrogenic activities for estrogen compounds.