In Silico Prediction of Estrogen Receptor Subtype Binding Affinity and Selectivity Using Statistical Methods and Molecular Docking with 2-Arylnaphthalenes and 2-ArylquinolinesReportar como inadecuado




In Silico Prediction of Estrogen Receptor Subtype Binding Affinity and Selectivity Using Statistical Methods and Molecular Docking with 2-Arylnaphthalenes and 2-Arylquinolines - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

1

Center of Bioinformatics, Northwest A&F University, Yangling, Shaanxi, China

2

School of Chemical Engineering, Dalian University of Technology, Dalian, Liaoning, China

3

Pharmaceutical Resource Discovery, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning, China





*

Author to whom correspondence should be addressed.



Abstract Over the years development of selective estrogen receptor ER ligands has been of great concern to researchers involved in the chemistry and pharmacology of anticancer drugs, resulting in numerous synthesized selective ER subtype inhibitors. In this work, a data set of 82 ER ligands with ERα and ERβ inhibitory activities was built, and quantitative structure-activity relationship QSAR methods based on the two linear multiple linear regression, MLR, partial least squares regression, PLSR and a nonlinear statistical method Bayesian regularized neural network, BRNN were applied to investigate the potential relationship of molecular structural features related to the activity and selectivity of these ligands. For ERα and ERβ, the performances of the MLR and PLSR models are superior to the BRNN model, giving more reasonable statistical properties ERα: for MLR, Rtr2 = 0.72, Qte2 = 0.63; for PLSR, Rtr2 = 0.92, Qte2 = 0.84. ERβ: for MLR, Rtr2 = 0.75, Qte2 = 0.75; for PLSR, Rtr2 = 0.98, Qte2 = 0.80. The MLR method is also more powerful than other two methods for generating the subtype selectivity models, resulting in Rtr2 = 0.74 and Qte2 = 0.80. In addition, the molecular docking method was also used to explore the possible binding modes of the ligands and a relationship between the 3D-binding modes and the 2D-molecular structural features of ligands was further explored. The results show that the binding affinity strength for both ERα and ERβ is more correlated with the atom fragment type, polarity, electronegativites and hydrophobicity. The substitutent in position 8 of the naphthalene or the quinoline plane and the space orientation of these two planes contribute the most to the subtype selectivity on the basis of similar hydrogen bond interactions between binding ligands and both ER subtypes. The QSAR models built together with the docking procedure should be of great advantage for screening and designing ER ligands with improved affinity and subtype selectivity property. View Full-Text

Keywords: receptor; selectivity; QSAR; docking receptor; selectivity; QSAR; docking





Autor: Zhizhong Wang 1, Yan Li 2, Chunzhi Ai 3 and Yonghua Wang 1,*

Fuente: http://mdpi.com/



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