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Oxidative Medicine and Cellular Longevity
Volume 2018, Article ID 6040149, 11 pages
https://doi.org/10.1155/2018/6040149
Research Article

Identification of Estrogen Receptor α Antagonists from Natural Products via In Vitro and In Silico Approaches

1Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Xian Nong Tan Street, Beijing 100050, China
2Beijing Key Laboratory of Drug Target Research and Drug Screening, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
3State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China

Correspondence should be addressed to Ai-Lin Liu; nc.ca.mmi@niliauil and Guan-Hua Du; nc.ca.mmi@hgud

Xiaocong Pang and Weiqi Fu contributed equally to this work.

Received 15 February 2018; Revised 1 April 2018; Accepted 11 April 2018; Published 10 May 2018

Academic Editor: Valentina Pallottini

Copyright © 2018 Xiaocong Pang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Estrogen receptor α (ERα) is a successful target for ER-positive breast cancer and also reported to be relevant in many other diseases. Selective estrogen receptor modulators (SERMs) make a good therapeutic effect in clinic. Because of the drug resistance and side effects of current SERMs, the discovery of new SERMs is given more and more attention. Virtual screening is a validated method to high effectively to identify novel bioactive small molecules. Ligand-based machine learning methods and structure-based molecular docking were first performed for identification of ERα antagonist from in-house natural product library. Naive Bayesian and recursive partitioning models with two kinds of descriptors were built and validated based on training set, test set, and external test set and then were utilized for distinction of active and inactive compounds. Totally, 162 compounds were predicted as ER antagonists and were further evaluated by molecular docking. According to docking score, we selected 8 representative compounds for both ERα competitor assay and luciferase reporter gene assay. Genistein, daidzein, phloretin, ellagic acid, ursolic acid, (−)-epigallocatechin-3-gallate, kaempferol, and naringenin exhibited different levels for antagonistic activity against ERα. These studies validated the feasibility of machine learning methods for predicting bioactivities of ligands and provided better insight into the natural products acting as estrogen receptor modulator, which are important lead compounds for future new drug design.