Estrogen compounds are suspected of disrupting endocrine functions by mimicking natural hormones, and such compounds may pose a serious threat to the health of humans and wildlife. Close attention has been paid to the...Estrogen compounds are suspected of disrupting endocrine functions by mimicking natural hormones, and such compounds may pose a serious threat to the health of humans and wildlife. Close attention has been paid to the prediction and molecular mechanisms of estrogen activity for estrogen compounds. In this article, estrogen receptor α subtype (ERα)–based comparative molecular similarity indices analysis (COMSIA) was performed on 44 estrogen compounds with structural diversity to find out the structural relationship with the activity and to predict the activity. The model with the significant correlation and the best predictive power (R2 = 0.965, Q2LOO = 0.599, R2pred = 0.825) was achieved. The COMSIA and docking results revealed the structural features for estrogen activity and key amino acid residues in binding pocket, and provided an insight into the interaction between the ligands and these amino acid residues.展开更多
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 ...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 analysis, 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 (R 2=0.961, q LOO 2 =0.671, R Pred 2 =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.展开更多
AIM: Inhibitors of catechol-O-methyltransferase (COMT) have always been administered to improve the bioavailability of L-Dopa in the treatment of Parkinson disease (PD). A new three-dimensional quantitative structure-...AIM: Inhibitors of catechol-O-methyltransferase (COMT) have always been administered to improve the bioavailability of L-Dopa in the treatment of Parkinson disease (PD). A new three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis is performed to correlate the molecular fields with percent inhibition values. METHODS: Three predictive models were derived based on 36 previously reported COMT inhibitors employing comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methodologies. RESULTS: The CoMFA model and CoMSIA model with steric and electrostatic field yielded cross-validated rcv2 0.585 and 0.528 respectively, whereas the conventional rncv2 were 0.979 and 0.891. The CoMSIA model with hydrophobic field exhibited rcv2 0.544 and rncv2 0.930. CONCLUSION: The derived models from CoMFA and CoMSIA all exhibit good prediction for both internal and external validations. The individual inspection of 3D contours generated from these models helps in understanding the possible region for structural modification of molecules to improve the inhibitory bioactivity. The 3D-QSAR models may be useful in designing and predicting novel COMT inhibitors.展开更多
基金National Natural Science Foundation of China (Grant No. 20507008)
文摘Estrogen compounds are suspected of disrupting endocrine functions by mimicking natural hormones, and such compounds may pose a serious threat to the health of humans and wildlife. Close attention has been paid to the prediction and molecular mechanisms of estrogen activity for estrogen compounds. In this article, estrogen receptor α subtype (ERα)–based comparative molecular similarity indices analysis (COMSIA) was performed on 44 estrogen compounds with structural diversity to find out the structural relationship with the activity and to predict the activity. The model with the significant correlation and the best predictive power (R2 = 0.965, Q2LOO = 0.599, R2pred = 0.825) was achieved. The COMSIA and docking results revealed the structural features for estrogen activity and key amino acid residues in binding pocket, and provided an insight into the interaction between the ligands and these amino acid residues.
基金Supported by the National Natural Science Foundation of China (Grant No. 20507008)the National Natural Science Foundation Key Project of China (Grant No. 20737001)the National Basic Research Program of China (973 Program) (Grant No. 2003CB415002)
文摘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 analysis, 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 (R 2=0.961, q LOO 2 =0.671, R Pred 2 =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.
文摘AIM: Inhibitors of catechol-O-methyltransferase (COMT) have always been administered to improve the bioavailability of L-Dopa in the treatment of Parkinson disease (PD). A new three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis is performed to correlate the molecular fields with percent inhibition values. METHODS: Three predictive models were derived based on 36 previously reported COMT inhibitors employing comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methodologies. RESULTS: The CoMFA model and CoMSIA model with steric and electrostatic field yielded cross-validated rcv2 0.585 and 0.528 respectively, whereas the conventional rncv2 were 0.979 and 0.891. The CoMSIA model with hydrophobic field exhibited rcv2 0.544 and rncv2 0.930. CONCLUSION: The derived models from CoMFA and CoMSIA all exhibit good prediction for both internal and external validations. The individual inspection of 3D contours generated from these models helps in understanding the possible region for structural modification of molecules to improve the inhibitory bioactivity. The 3D-QSAR models may be useful in designing and predicting novel COMT inhibitors.
