利用e -药效团模型、QSAR和分子动力学模拟鉴定针对SARS-CoV-2的3c样蛋白酶的大型天然产物文库中的先导化合物

In Silico Pharmacology Pub Date : 2021-08-07 eCollection Date: 2021-01-01 DOI:10.1007/s40203-021-00109-7
Olusola Olalekan Elekofehinti, Opeyemi Iwaloye, Olorunfemi R Molehin, Courage D Famusiwa
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引用次数: 9

摘要

COVID-19是由SARS-CoV-2引起的新型疾病,对全球经济造成了灾难性影响。事实上,目前还没有FDA正式批准的药物来减轻SARS-CoV-2对人类健康的负面影响。已经发现了许多中和冠状病毒感染的药物靶点,其中3-chymotrypsin-like-protease (3CLpro)是本研究选择的一种负责病毒复制的病毒蛋白酶。本研究旨在利用计算方法从自然文库中寻找新的SARS-CoV-2 3c样蛋白酶抑制剂。从天然产物库中筛选了69,000个化合物,以匹配5个位点的e-药效团模型中的至少3个特征。通过Glide对接算法进行分子对接研究,筛选出适合度评分为1.00及以上的化合物。Qikprop还能预测化合物的药物相似度和药代动力学特征;此外,以径向为二元指纹图谱,利用KPLS分析建立的QSAR模型预测化合物对sars - cov - 23c样蛋白酶的抑制性能。采用GROMACS软件进行50 ns分子动力学(MD)模拟,了解其结合动力学。从天然产物文库中发现了9个先导化合物;根据结合能,发现其中7种比洛匹那韦更有效。与实验药物洛匹那韦相比,对接评分为-9.295 kcal/mol的STOCK1N-98687对sars - cov - 23c样蛋白酶的预测生物活性为4.427µM,具有较好的药物样特性。对接后MM-GBSA分析证实了STOCK1N-98687结合3CLpro晶体结构的稳定性。用3CLpro对STOCKIN-98687进行50 ns的MD模拟,结果表明该配合物稳定性高,波动小。本研究发现化合物STOCK1N-98687是潜在的3CLpro抑制剂;因此,值得通过湿法实验来证实STOCK1N-98687作为抗病毒药物的治疗潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Identification of lead compounds from large natural product library targeting 3C-like protease of SARS-CoV-2 using E-pharmacophore modelling, QSAR and molecular dynamics simulation.

Identification of lead compounds from large natural product library targeting 3C-like protease of SARS-CoV-2 using E-pharmacophore modelling, QSAR and molecular dynamics simulation.

Identification of lead compounds from large natural product library targeting 3C-like protease of SARS-CoV-2 using E-pharmacophore modelling, QSAR and molecular dynamics simulation.

Identification of lead compounds from large natural product library targeting 3C-like protease of SARS-CoV-2 using E-pharmacophore modelling, QSAR and molecular dynamics simulation.

COVID-19 is a novel disease caused by SARS-CoV-2 and has made a catastrophic impact on the global economy. As it is, there is no officially FDA approved drug to alleviate the negative impact of SARS-CoV-2 on human health. Numerous drug targets for neutralizing coronavirus infection have been identified, among them is 3-chymotrypsin-like-protease (3CLpro), a viral protease responsible for the viral replication is chosen for this study. This study aimed at finding novel inhibitors of SARS-CoV-2 3C-like protease from the natural library using computational approaches. A total of 69,000 compounds from natural product library were screened to match a minimum of 3 features from the five sites e-pharmacophore model. Compounds with fitness score of 1.00 and above were consequently filtered by executing molecular docking studies via Glide docking algorithm. Qikprop also predicted the compounds drug-likeness and pharmacokinetic features; besides, the QSAR model built from KPLS analysis with radial as binary fingerprint was used to predict the compounds inhibition properties against SARS-CoV-2 3C-like protease. Fifty ns molecular dynamics (MD) simulation was carried out using GROMACS software to understand the dynamics of binding. Nine (9) lead compounds from the natural products library were discovered; seven among them were found to be more potent than lopinavir based on energies of binding. STOCK1N-98687 with docking score of -9.295 kcal/mol had considerable predicted bioactivity (4.427 µM) against SARS-CoV-2 3C-like protease and satisfactory drug-like features than the experimental drug lopinavir. Post-docking analysis by MM-GBSA confirmed the stability of STOCK1N-98687 bound 3CLpro crystal structure. MD simulation of STOCKIN-98687 with 3CLpro at 50 ns showed high stability and low fluctuation of the complex. This study revealed compound STOCK1N-98687 as potential 3CLpro inhibitor; therefore, a wet experiment is worth exploring to confirm the therapeutic potential of STOCK1N-98687 as an antiviral agent.

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