基于结构的虚拟筛选植物源雌激素受体β选择性配体作为预防年龄相关性神经退行性疾病的潜在疗法。

Structure-based virtual screening for plant-based ERbeta-selective ligands as potential preventative therapy against age-related neurodegenerative diseases.

作者信息

Zhao Liqin, Brinton Roberta D

机构信息

Department of Molecular Pharmacology & Toxicology and the Program in Neuroscience, School of Pharmacy, University of Southern California, Los Angeles, California 90089, USA.

出版信息

J Med Chem. 2005 May 19;48(10):3463-6. doi: 10.1021/jm0490538.

DOI:10.1021/jm0490538

PMID:15887952

Abstract

ERbeta has been associated with estrogen-induced promotion of memory function and neuronal survival. Based on the optimized complex structure of human ERbeta LBD bound with genistein, computer-aided structure-based virtual screening against a natural source chemical database was conducted to determine the occurrence of plant-based ERbeta-selective ligands. Twelve representative hits derived from database screening were assessed for their binding profiles to both ERs, three of which displayed over 100-fold binding selectivity to ERbeta over ERalpha.

摘要

雌激素受体β（ERβ）与雌激素诱导的记忆功能促进和神经元存活有关。基于与染料木黄酮结合的人ERβ配体结合域（LBD）的优化复合物结构，针对天然来源化学数据库进行了基于计算机辅助结构的虚拟筛选，以确定植物来源的ERβ选择性配体的存在情况。对数据库筛选得到的12个代表性命中物进行了它们与两种雌激素受体结合情况的评估，其中3个对ERβ的结合选择性比对ERα高100倍以上。