Natural product-derived ALK inhibitors for treating ALK-driven lung cancers: an in silico study.

IF 3.9 2区 化学 Q2 CHEMISTRY, APPLIED Molecular Diversity Pub Date : 2024-08-08 DOI:10.1007/s11030-024-10953-2
Saud O Alshammari, Qamar A Alshammari
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Abstract

Anaplastic lymphoma kinase (ALK)-driven lung cancer represents a critical therapeutic target, demanding innovative approaches for the identification of effective inhibitors. Anaplastic lymphoma kinase (ALK), a key protein involved in the pathogenesis of ALK-driven lung cancers, has been the focus of extensive drug discovery efforts. This study employed a comprehensive computational drug discovery approach, integrating virtual screening with the Lipinski filter, re-docking, molecular dynamics (MD) simulations, and free energy calculations to identify potential inhibitors from a natural compound library. Utilizing the MTiOpenScreen web server, we screened for compounds that exhibit favorable interactions with ALK, resulting in 1227 compounds with virtual screening scores ranging from - 10.2 to - 3.7 kcal/mol. Subsequent re-docking of three selected compounds (ZINC000059779788, ZINC000043552589, and ZINC000003594862) and one reference compound against ALK yielded docking scores - 10.4, - 10.2, - 10.2, and - 10.1 kcal/mol, respectively. These compounds demonstrated promising interactions with ALK, suggesting potential inhibitory effects. Advanced analyses, including MD simulation and binding free energy calculations, further supported the potential efficacy of these compounds. MD simulations, particularly the root mean square deviation (RMSD) and root mean square fluctuation (RMSF) analyses, revealed that compounds ZINC000059779788 and ZINC000003594862 achieved better stability compared to compound ZINC000043552589. These stable conformations suggest effective binding over time. Free energy calculations using the MM/GBSA method showed that ZINC000059779788 had the most favorable binding energy, indicating a strong and stable interaction with the ALK protein. The promising computational findings from this study emphasize the necessity for additional experimental testing to verify the therapeutic efficacy of these natural compounds for treating lung cancers.

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用于治疗 ALK 驱动型肺癌的天然产物衍生 ALK 抑制剂:一项硅学研究。
无性淋巴瘤激酶(ALK)驱动的肺癌是一个关键的治疗靶点,需要创新的方法来确定有效的抑制剂。无性淋巴瘤激酶(ALK)是参与 ALK 驱动型肺癌发病机制的关键蛋白,一直是广泛药物发现工作的重点。这项研究采用了一种全面的计算药物发现方法,将虚拟筛选与利宾斯基过滤器、再对接、分子动力学(MD)模拟和自由能计算结合起来,从天然化合物库中找出潜在的抑制剂。利用 MTiOpenScreen 网络服务器,我们筛选出了与 ALK 有良好相互作用的化合物,结果发现了 1227 种化合物,其虚拟筛选得分在 - 10.2 到 - 3.7 kcal/mol 之间。随后对三个选定化合物(ZINC000059779788、ZINC000043552589 和 ZINC000003594862)和一个参考化合物与 ALK 进行了重新对接,对接得分分别为 - 10.4、- 10.2、- 10.2 和 - 10.1 kcal/mol。这些化合物表现出与 ALK 的良好相互作用,表明它们具有潜在的抑制作用。包括 MD 模拟和结合自由能计算在内的高级分析进一步证实了这些化合物的潜在功效。MD 模拟,特别是均方根偏差(RMSD)和均方根波动(RMSF)分析表明,与化合物 ZINC000043552589 相比,化合物 ZINC000059779788 和 ZINC000003594862 具有更好的稳定性。这些稳定的构象表明它们能长期有效地结合在一起。使用 MM/GBSA 方法进行的自由能计算显示,ZINC000059779788 的结合能最高,表明其与 ALK 蛋白的相互作用强烈而稳定。这项研究的计算结果前景广阔,因此有必要进行更多的实验测试,以验证这些天然化合物对治疗肺癌的疗效。
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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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