使用超大型虚拟筛选鉴定的雄激素受体DNA结合域的新型抑制剂。

IF 2.8 4区 医学 Q3 CHEMISTRY, MEDICINAL Molecular Informatics Pub Date : 2023-08-01 Epub Date: 2023-07-19 DOI:10.1002/minf.202300026
Mariia Radaeva, Helene Morin, Mohit Pandey, Fuqiang Ban, Maria Guo, Eric LeBlanc, Nada Lallous, Artem Cherkasov
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引用次数: 0

摘要

抑制雄激素受体(AR)仍然是对抗癌症(PC)进展的主要策略。然而,所有临床使用的AR抑制剂都靶向配体结合结构域(LBD),该结构域极易通过剪接或突变进行截短,从而产生耐药性。因此,迫切需要具有新作用模式的AR抑制剂。因此,我们启动了一个超大型化学文库的虚拟筛选,以在两个位点:蛋白质-DNA界面(P-box)和二聚化位点(D-box)找到AR-DNA结合域(DBD)的新型抑制剂。然后通过严格的计算过滤选择的化合物进行了实验验证。我们鉴定了几种有效抑制AR及其剪接变异体V7转录活性的新型化学型。已鉴定的化合物代表了以前未探索的化学支架,其作用机制避开了通过LBD突变表现出的传统耐药性。此外,我们描述了在P-盒和D-盒靶位点抑制AR-DBD所需的结合特征。
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Novel Inhibitors of androgen receptor's DNA binding domain identified using an ultra-large virtual screening.

Androgen receptor (AR) inhibition remains the primary strategy to combat the progression of prostate cancer (PC). However, all clinically used AR inhibitors target the ligand-binding domain (LBD), which is highly susceptible to truncations through splicing or mutations that confer drug resistance. Thus, there exists an urgent need for AR inhibitors with novel modes of action. We thus launched a virtual screening of an ultra-large chemical library to find novel inhibitors of the AR DNA-binding domain (DBD) at two sites: protein-DNA interface (P-box) and dimerization site (D-box). The compounds selected through vigorous computational filtering were then experimentally validated. We identified several novel chemotypes that effectively suppress transcriptional activity of AR and its splice variant V7. The identified compounds represent previously unexplored chemical scaffolds with a mechanism of action that evades the conventional drug resistance manifested through LBD mutations. Additionally, we describe the binding features required to inhibit AR DBD at both P-box and D-box target sites.

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来源期刊
Molecular Informatics
Molecular Informatics CHEMISTRY, MEDICINAL-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.30
自引率
2.80%
发文量
70
审稿时长
3 months
期刊介绍: Molecular Informatics is a peer-reviewed, international forum for publication of high-quality, interdisciplinary research on all molecular aspects of bio/cheminformatics and computer-assisted molecular design. Molecular Informatics succeeded QSAR & Combinatorial Science in 2010. Molecular Informatics presents methodological innovations that will lead to a deeper understanding of ligand-receptor interactions, macromolecular complexes, molecular networks, design concepts and processes that demonstrate how ideas and design concepts lead to molecules with a desired structure or function, preferably including experimental validation. The journal''s scope includes but is not limited to the fields of drug discovery and chemical biology, protein and nucleic acid engineering and design, the design of nanomolecular structures, strategies for modeling of macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, computer-assisted screening strategies, as well as novel technologies for the de novo design of biologically active molecules. As a unique feature Molecular Informatics publishes so-called "Methods Corner" review-type articles which feature important technological concepts and advances within the scope of the journal.
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