Use of computational modeling approaches in studying the binding interactions of compounds with human estrogen receptors

• Published in: Steroids 2016-01, Vol.105, p.26-41
• Main Authors: Wang, Pan, Dang, Li, Zhu, Bao-Ting
• Format: Article
• Language: English
• Subjects: Estrogen; Estrogen receptor; Estrogen Receptor Modulators - metabolism; Estrogen-binding protein; Estrogens - chemistry; Estrogens - metabolism; Humans; Models, Molecular; Molecular docking; Protein Binding; QSAR; Quantitative Structure-Activity Relationship; Receptor binding affinity; Receptors, Estrogen - chemistry; Receptors, Estrogen - metabolism
• ISSN: 0039-128X; 1878-5867
• DOI: 10.1016/j.steroids.2015.11.001

•Different estrogens interact with ERs in a sterically subtle but distinct manner.•Molecular modeling can precisely predict the 3D interaction of estrogens with ERs.•Molecular modeling is a viable tool for identifying new compounds with ER activities. Estrogens have a whole host of physiological functions in many human organs and systems, including the reproductive, cardiovascular, and central nervous systems. Many naturally-occurring compounds with estrogenic or antiestrogenic activity are present in our environment and food sources. Synthetic estrogens and antiestrogens are also important therapeutic agents. At the molecular level, estrogen receptors (ERs) mediate most of the well-known actions of estrogens. Given recent advances in computational modeling tools, it is now highly practical to use these tools to study the interaction of human ERs with various types of ligands. There are two common categories of modeling techniques: one is the quantitative structure activity relationship (QSAR) analysis, which uses the structural information of the interacting ligands to predict the binding site properties of a macromolecule, and the other one is molecular docking-based computational analysis, which uses the 3-dimensional structural information of both the ligands and the receptor to predict the binding interaction. In this review, we discuss recent results that employed these and other related computational modeling approaches to characterize the binding interaction of various estrogens and antiestrogens with the human ERs. These examples clearly demonstrate that the computational modeling approaches, when used in combination with other experimental methods, are powerful tools that can precisely predict the binding interaction of various estrogenic ligands and their derivatives with the human ERs.