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从天然产物文库中发现间变性淋巴瘤激酶抑制剂:整体计算方法。

Discovery of anaplastic lymphoma kinase inhibitors from natural product library: A holistic in silico approach.

机构信息

Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India.

出版信息

Biotechnol Appl Biochem. 2021 Dec;68(6):1185-1191. doi: 10.1002/bab.2040. Epub 2020 Oct 13.

Abstract

Over the years, phytochemical compounds have shown compelling evidences in exhibiting powerful antitumor properties. Moreover, due to the lack of safety and high cost of cancer therapies, opportunities are being sought out in these compounds as an alternative treatment modality. Therefore, in the present study, 1,574 compounds from NPACT library were examined to excavate potent and nontoxic anaplastic lymphoma kinase (ALK) inhibitors. Notably, two pharmacophore hypotheses (AAAHP and DDRRR) were generated using ligand-based and energy-based techniques, respectively, to eliminate false-positive prediction in database screening. Furthermore, molecular docking and Prime MM/GBSA analysis were performed on the screened compounds to examine inhibitory activity against ALK. The analysis revealed that the two hits, namely, NPACT00018 and NPACT01077, exhibited better docking scores, binding energies, and also ensured excellent drug-likeness properties than the reference compound, crizotinib. Finally, the results were subjected to molecular dynamics studies to gain insight into the stability of these compounds in the binding pocket of ALK protein. Indeed, the useful predictions generated by the present computational models are of immense importance and could further speed up the anticancer drug development in the near future.

摘要

多年来,植物化学化合物在表现出强大的抗肿瘤特性方面已经提供了令人信服的证据。此外,由于癌症治疗的安全性和高成本问题,人们正在这些化合物中寻找机会,将其作为替代治疗方式。因此,在本研究中,从 NPACT 文库中检查了 1574 种化合物,以挖掘有效的、非毒性的间变性淋巴瘤激酶(ALK)抑制剂。值得注意的是,使用基于配体和基于能量的技术分别生成了两个药效团假说(AAAHP 和 DDRRR),以消除数据库筛选中的假阳性预测。此外,对筛选出的化合物进行分子对接和 Prime MM/GBSA 分析,以检查其对 ALK 的抑制活性。分析表明,这两个命中化合物,即 NPACT00018 和 NPACT01077,表现出更好的对接分数、结合能,并且比参考化合物克唑替尼具有更好的类药性。最后,对这些化合物进行分子动力学研究,以深入了解它们在 ALK 蛋白结合口袋中的稳定性。事实上,本计算模型产生的有用预测具有重要意义,并且可以在不久的将来进一步加速抗癌药物的开发。

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