Computational mechanistic insight of fungal metabolites for novel acetylcholinesterase inhibitors.

IF 3.9 2区 化学 Q2 CHEMISTRY, APPLIED
Hadeel Alyenbaawi, Mohammed Alsaweed, Qazi Mohammad Sajid Jamal, Mohammad Rehan Asad, Syed Mohd Danish Rizvi, Fuzail Ahmad, Mehnaz Kamal, Danish Iqbal
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引用次数: 0

Abstract

Activity of acetylcholinesterase (AChE) enzyme elevation has been frequently observed in Alzheimer's disease (AD) and plays a key role in disease progression. Therefore, its inhibition is considered a crucial therapeutic step in the management of cognitive defects associated with AD. In this study, we screened a library of fungal metabolites using molecular docking, molecular dynamics, and PCA to identify metabolic compounds that effectively worked against AChE. An extensive database of 19,667 fungal metabolites was methodically filtered to identify compounds with drug-like properties that are suitable for neurological disorders. Of all metabolites, only four compounds inhibited AChE better than donepezil. Mangrovamide F was the most effective against AChE, followed by Libertellenone M, Tricholopardin A, and Aspeterreurone A (ΔG: -12.6 ± 0.2, -12.3 ± 0.2, -12.2 ± 0.2, -11.8 ± 0.1 kcal/mol, respectively). Aspeterreurone A had the highest LD50 dose (39,800 mg/kg), followed by Tricholopardin A (8350 mg/kg), Mangrovamide F (707 mg/kg), and Libertellenone M (190 mg/kg). Over the course of the 200-ns simulation, the protein in the AChE-fungal metabolite complexes stabilized and fluctuated within the permissible range. The most important residue, TRP86, in the AChE protein often interacts with all the best-hit ligands primarily through hydrophobic interactions, for the longest period with Libertellenone M, followed by Tricholopardin A, Mangrovamide F, Donepezil, and Aspeterreurone A. According to our PCA data, Mangrovamide F (44.61%) had the highest eigenvalue rank, followed by Libertellenone M (27.49%), Aspeterreurone A (23%), and Tricholopardin A (20.02%). Mangrovamide F and Tricholopardin A were found to be the best inhibitors of AChE enzyme with acceptable LD50 and have less toxicity. Further in vitro and in vivo works regarding the therapeutic effects of these fungal compounds could elaborate our findings.

新型乙酰胆碱酯酶抑制剂真菌代谢物的计算机制研究。
乙酰胆碱酯酶(AChE)活性升高在阿尔茨海默病(AD)中经常观察到,并在疾病进展中起关键作用。因此,它的抑制被认为是管理AD相关认知缺陷的关键治疗步骤。在这项研究中,我们使用分子对接、分子动力学和PCA筛选真菌代谢产物库,以确定有效对抗AChE的代谢化合物。系统地过滤了19,667种真菌代谢物的广泛数据库,以确定具有类似药物特性的化合物,适用于神经系统疾病。在所有代谢物中,只有四种化合物比多奈哌齐更好地抑制乙酰胆碱酯酶。芒果酰胺F对乙酰胆碱酯乙酰胆碱酯的抑制作用最强,其次为Libertellenone M、Tricholopardin A和Aspeterreurone A(分别为ΔG: -12.6±0.2、-12.3±0.2、-12.2±0.2、-11.8±0.1 kcal/mol)。芦丁烯酮A的LD50剂量最高(39,800 mg/kg),其次是Tricholopardin A (8350 mg/kg)、Mangrovamide F (707 mg/kg)和Libertellenone M (190 mg/kg)。在200-ns的模拟过程中,疼痛-真菌代谢物复合物中的蛋白质稳定并在允许范围内波动。AChE蛋白中最重要的残基TRP86通常主要通过疏水相互作用与所有最佳命中配体相互作用,与Libertellenone M的相互作用时间最长,其次是Tricholopardin A、Mangrovamide F、Donepezil和Aspeterreurone A。根据我们的PCA数据,Mangrovamide F(44.61%)具有最高的特征值排序,其次是Libertellenone M(27.49%)、Aspeterreurone A(23%)和Tricholopardin A(20.02%)。红木酰胺F和Tricholopardin A是乙酰胆碱酯酶的最佳抑制剂,LD50可接受,毒性较小。关于这些真菌化合物的治疗作用的进一步体外和体内研究可以进一步阐明我们的发现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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