Pterostilbene as a potent ESR-1 in breast cancer therapy: insights from network pharmacology, molecular docking, dynamics simulations, ADMET, and in vitro analysis.

IF 3.9 2区 化学 Q2 CHEMISTRY, APPLIED
Harneet Marwah, Hitesh Kumar Dewangan
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引用次数: 0

Abstract

This study investigated the molecular targets and pathways modulated by pterostilbene in breast cancer using network pharmacology and in vitro analysis. The structure of chemicals of pterostilbene was retrieved from PubChem, and gene targets were predicted through Swiss Target Prediction. Human-specific targets were validated using UniProtKB and breast cancer-related targets were identified using GeneCards and BioVenn. Protein-protein interaction (PPI) networks were created using STRING and visualized using Cytoscape, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses to elucidate biological functions. Molecular docking studies using AutoDock Vina were used to assess the binding interactions of pterostilbene with key nuclear receptors (PTGS2, ESR1, EGFR, and BCL2). Molecular dynamics (MD) simulations over 200 ns in GROMACS confirmed the stability of the ESR1-pterostilbene complex and highlighted significant hydrogen bonding. ADME/T was assessed using the Protox software. In vitro cytotoxicity was assessed using the MTT assay in MCF-7 cells. Sixteen key genes, including PTGS2, ESR1, EGFR, and BCL2, were identified as key targets connecting pterostilbene to breast cancer. PPI analysis identified ESR1, EGFR, and BCL2 as central nodes in the network. Molecular docking revealed robust binding of pterostilbene (below - 8.1 kcal/mol), suggesting potential modulation of estrogen receptor signaling. MD simulations confirmed the stability of the complex with favorable structural dynamics. Toxicity analysis suggested a low risk, and MTT assays revealed selective cytotoxicity of pterostilbene toward MCF-7 breast cancer cells (IC50 = 14.8 µM) with a Selectivity Index of 2.85 compared to normal HEL 299 cells. These findings highlight the potential of pterostilbene as a treatment option for breast cancer, which merits additional exploration in experimental models and human studies.

本研究利用网络药理学和体外分析方法研究了紫檀芪在乳腺癌中的分子靶点和调控通路。研究人员从 PubChem 中检索了紫檀芪的化学结构,并通过 Swiss Target Prediction 预测了紫檀芪的基因靶点。利用 UniProtKB 验证了人类特异性靶点,并利用 GeneCards 和 BioVenn 确定了乳腺癌相关靶点。使用 STRING 创建了蛋白质-蛋白质相互作用(PPI)网络,并使用 Cytoscape 将其可视化,随后进行了基因本体(GO)和京都基因组百科全书(KEGG)通路分析,以阐明生物功能。使用 AutoDock Vina 进行的分子对接研究评估了紫檀芪与关键核受体(PTGS2、ESR1、表皮生长因子受体和 BCL2)的结合相互作用。在 GROMACS 中进行了 200 ns 的分子动力学(MD)模拟,证实了 ESR1-紫檀芪复合物的稳定性,并强调了重要的氢键作用。ADME/T 采用 Protox 软件进行评估。在 MCF-7 细胞中使用 MTT 试验评估了体外细胞毒性。包括 PTGS2、ESR1、表皮生长因子受体和 BCL2 在内的 16 个关键基因被确定为连接紫檀芪与乳腺癌的关键靶点。PPI分析确定ESR1、表皮生长因子受体和BCL2是网络中的中心节点。分子对接显示紫檀芪的结合力很强(低于 - 8.1 kcal/mol),这表明它有可能调节雌激素受体的信号传导。MD 模拟证实了该复合物具有良好的结构动力学稳定性。毒性分析表明其风险较低,MTT 试验显示紫檀芪对 MCF-7 乳腺癌细胞具有选择性细胞毒性(IC50 = 14.8 µM),与正常 HEL 299 细胞相比,选择性指数为 2.85。这些发现凸显了紫檀芪作为乳腺癌治疗选择的潜力,值得在实验模型和人体研究中进一步探索。
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来源期刊
Molecular Diversity
Molecular Diversity 化学-化学综合
CiteScore
7.30
自引率
7.90%
发文量
219
审稿时长
2.7 months
期刊介绍: Molecular Diversity is a new publication forum for the rapid publication of refereed papers dedicated to describing the development, application and theory of molecular diversity and combinatorial chemistry in basic and applied research and drug discovery. The journal publishes both short and full papers, perspectives, news and reviews dealing with all aspects of the generation of molecular diversity, application of diversity for screening against alternative targets of all types (biological, biophysical, technological), analysis of results obtained and their application in various scientific disciplines/approaches including: combinatorial chemistry and parallel synthesis; small molecule libraries; microwave synthesis; flow synthesis; fluorous synthesis; diversity oriented synthesis (DOS); nanoreactors; click chemistry; multiplex technologies; fragment- and ligand-based design; structure/function/SAR; computational chemistry and molecular design; chemoinformatics; screening techniques and screening interfaces; analytical and purification methods; robotics, automation and miniaturization; targeted libraries; display libraries; peptides and peptoids; proteins; oligonucleotides; carbohydrates; natural diversity; new methods of library formulation and deconvolution; directed evolution, origin of life and recombination; search techniques, landscapes, random chemistry and more;
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