Suppr超能文献

基于结构的虚拟筛选方法揭示了天然多靶化合物,可用于开发抗疟药物以对抗耐药性。

Structure-based virtual screening approach reveals natural multi-target compounds for the development of antimalarial drugs to combat drug resistance.

机构信息

Department of Biochemistry, School of Life Sciences, Central University of Rajasthan, Ajmer, India.

School of Basic Sciences, Indian Institute of Technology Mandi, Kamand, India.

出版信息

J Biomol Struct Dyn. 2024 Sep;42(14):7384-7408. doi: 10.1080/07391102.2023.2240415. Epub 2023 Aug 1.

Abstract

Compared to the previous year, there has been an increase of nearly 2 million malaria cases in 2021. The emergence of drug-resistant strains of , the most deadly malaria parasite, has led to a decline in the effectiveness of existing antimalarial drugs. To address this problem, the present study aimed to identify natural compounds with the potential to inhibit multiple validated antimalarial drug targets. The natural compounds from the Natural Product Activity and Species Source (NPASS) database were screened against ten validated drug targets of using a structure-based molecular docking method. Twenty compounds, with targets ranging from three to five, were determined as the top hits. The molecular dynamics simulations of the top six complexes (NPC246162 in complex with AdSS, GDH, and NMT; NPC271270 in complex with CK, GDH, and dUTPase) confirmed their stable binding affinity in the dynamic environment. The Tanimoto coefficient and distance matrix score analysis show the structural divergence of all the hit compounds from known antimalarials, indicating minimum chances of cross-resistance. Thus, we propose further investigating these compounds in biochemical and parasite inhibition studies to reveal the real therapeutic potential. If found successful, these compounds may be a new avenue for future drug discovery efforts to combat existing antimalarial drug resistance.Communicated by Ramaswamy H. Sarma.

摘要

与前一年相比,2021 年疟疾病例增加了近 200 万例。最致命的疟原虫耐药株的出现,导致现有抗疟药物的效果下降。为了解决这个问题,本研究旨在确定具有抑制多种已验证的抗疟药物靶点潜力的天然化合物。使用基于结构的分子对接方法,从天然产物活性和物种来源(NPASS)数据库中筛选天然化合物,针对 中的十个已验证的药物靶点进行筛选。确定了 20 种化合物作为顶级命中物,其靶点数从 3 到 5 不等。前六个复合物(NPC246162 与 AdSS、GDH 和 NMT 复合物;NPC271270 与 CK、GDH 和 dUTPase 复合物)的分子动力学模拟证实了它们在动态环境中的稳定结合亲和力。Tanimoto 系数和距离矩阵得分分析表明,所有命中化合物与已知抗疟药物的结构差异较大,表明交叉耐药的可能性最小。因此,我们建议进一步研究这些化合物在生化和寄生虫抑制研究中的作用,以揭示其真正的治疗潜力。如果成功,这些化合物可能为未来发现抗疟药物的努力提供新途径,以应对现有的抗疟药物耐药性。由 Ramaswamy H. Sarma 交流。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验