药效团
雌激素受体
化学
热点(计算机编程)
雌激素受体α
虚拟筛选
乳腺癌
癌症研究
计算生物学
生物化学
癌症
生物
内科学
医学
计算机科学
操作系统
作者
Rupeng Dai,Xinghua Bao,Ying Zhang,Yan Huang,Haohao Zhu,Kundi Yang,Bo Wang,Hongmei Wen,Wei Li,Jian Liu
标识
DOI:10.1021/acs.jcim.3c01503
摘要
The estrogen-receptor alfa (ERα) is considered pivotal for breast cancer treatment. Although selective estrogen-receptor degraders (SERDs) have been developed to induce ERα degradation and antagonism, their agonistic effect on the uterine tissue and poor pharmacokinetic properties limit further application of ERα; thus, discovering novel SERDs is necessary. The ligand preferentially interacts with several key residues of the protein (defined as hot-spot residues). Improving the interaction with hot-spot residues of ERα offers a promising avenue for obtaining novel SERDs. In this study, pharmacophore modeling, molecular mechanics/generalized Born surface area (MM/GBSA), and amino-acid mutation were combined to determine several hot-spot residues. Focusing on the interaction with these hot-spot residues, hit fragments A1-A3 and A9 were virtually screened from two fragment libraries. Finally, these hit fragments were linked to generate compounds B1-B3, and their biological activities were evaluated. Remarkably, compound B1 exhibited potent antitumor activity against MCF-7 cells (IC50 = 4.21 nM), favorable ERα binding affinity (Ki = 14.6 nM), and excellent ERα degradative ability (DC50 = 9.7 nM), which indicated its potential to evolve as a promising SERD for breast cancer treatment.
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