title: Use of computational modeling approaches in studying the binding interactions of compounds with human estrogen receptors.
abstract: 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.
authors.affiliation: Department of Pharmacology, Toxicology and Therapeutics, School of Medicine, University of Kansas Medical Center, Kansas City, KS 66160, USA; Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China.
authors.forename: Pan
authors.initials: P
authors.lastname: Wang
authors.affiliation: Department of Chemistry, South University of Science and Technology of China, Shenzhen, Guangdong 518055, China.
authors.forename: Li
authors.initials: L
authors.lastname: Dang
authors.affiliation: Department of Pharmacology, Toxicology and Therapeutics, School of Medicine, University of Kansas Medical Center, Kansas City, KS 66160, USA; Department of Biology, South University of Science and Technology of China, Shenzhen, Guangdong 518055, China. Electronic address: BTZhu@SUSTC.edu.cn.
authors.forename: Bao-Ting
authors.initials: BT
authors.lastname: Zhu
mesh_terms: D020847:Estrogen Receptor Modulators / Q000378:metabolism; D004967:Estrogens / Q000737:chemistry / Q000378:metabolism; D006801:Humans; D008958:Models, Molecular; D011485:Protein Binding; D021281:Quantitative Structure-Activity Relationship; D011960:Receptors, Estrogen / Q000737:chemistry / Q000378:metabolism*
chemical_list: D020847:Estrogen Receptor Modulators; D004967:Estrogens; D011960:Receptors, Estrogen
keywords: Estrogen; Estrogen receptor; Estrogen-binding protein; Molecular docking; QSAR; Receptor binding affinity