Suppr超能文献

计算研究表明,人体代谢物对 SARS-CoV-2 主蛋白酶具有有效性。

Computational studies indicated the effectiveness of human metabolites against SARS-Cov-2 main protease.

机构信息

Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, 453552, India.

Department of Pharmacy, Shri G. S. Institute of Technology and Science, Indore, Madhya Pradesh, 452003, India.

出版信息

Mol Divers. 2023 Aug;27(4):1587-1602. doi: 10.1007/s11030-022-10513-6. Epub 2022 Aug 18.

Abstract

To fight against the devastating coronavirus disease 2019 (COVID-19), identifying robust anti-SARS-CoV-2 therapeutics from all possible directions is necessary. To contribute to this effort, we selected a human metabolites database containing waters and lipid-soluble metabolites to screen against the 3-chymotrypsin-like proteases (3CL) protein of SARS-CoV-2. The top 8 hits from virtual screening displayed a docking score varying between ~ - 11 and ~ - 14 kcal/mol. Molecular dynamics simulations complement the virtual screening study in conjunction with the molecular mechanics generalized Born surface area (MM/GBSA) scheme. Our analyses revealed that (HMDB0132640) has the best glide docking score, - 14.06 kcal/mol, and MM-GBSA binding free energy, - 18.08 kcal/mol. The other three lead molecules are also selected along with the top molecule through a critical inspection of their pharmacokinetic properties. HMDB0132640 displayed a better binding affinity than the other three compounds (HMDB0127868, HMDB0134119, and HMDB0125821) due to increased favorable contributions from the intermolecular electrostatic and van der Waals interactions. Further, we have investigated the ligand-induced structural dynamics of the main protease. Overall, we have identified new compounds that can serve as potential leads for developing novel antiviral drugs against SARS-CoV-2 and elucidated molecular mechanisms of their binding to the main protease. Identification of probable hits from human metabolites against SARS-CoV-2 using integrated computational approaches-Missed against MS.

摘要

为了对抗破坏性极强的 2019 年冠状病毒病(COVID-19),有必要从各个可能的方向寻找强大的抗 SARS-CoV-2 疗法。为了为此项工作做出贡献,我们选择了一个包含水和脂溶性代谢物的人类代谢物数据库,对 SARS-CoV-2 的 3-糜蛋白酶样蛋白酶(3CL)蛋白进行筛选。虚拟筛选的前 8 个命中物显示出的对接分数在-11 到-14 kcal/mol 之间。分子动力学模拟与分子力学广义 Born 表面面积(MM/GBSA)方案相结合,对虚拟筛选研究进行了补充。我们的分析表明,(HMDB0132640)具有最佳的滑行对接分数,为-14.06 kcal/mol,以及 MM-GBSA 结合自由能,为-18.08 kcal/mol。通过对其药代动力学特性的严格检查,还选择了另外三个先导分子和最佳分子。由于分子间静电和范德华相互作用的有利贡献增加,HMDB0132640 显示出比其他三个化合物(HMDB0127868、HMDB0134119 和 HMDB0125821)更好的结合亲和力。此外,我们还研究了配体诱导的主要蛋白酶结构动力学。总体而言,我们已经确定了一些新的化合物,它们可以作为针对 SARS-CoV-2 开发新型抗病毒药物的潜在先导化合物,并阐明了它们与主要蛋白酶结合的分子机制。使用综合计算方法从人类代谢物中识别针对 SARS-CoV-2 的可能命中物-Missed against MS。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/56c4/9385416/858e2168ea4c/11030_2022_10513_Fig1_HTML.jpg

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