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通过共识对接和自由结合能计算鉴定新型 SARS-CoV-2 主要蛋白酶和非结构蛋白 13(nsp13)抑制剂。

Identification of Novel SARS-CoV-2 Main Protease and Nonstructural Protein 13 (nsp13) Inhibitors through Consensus Docking and Free Binding Energy Calculations.

机构信息

Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Medical University - Sofia, Bulgaria.

出版信息

Comb Chem High Throughput Screen. 2023;26(6):1242-1250. doi: 10.2174/1386207325666220818141112.

Abstract

BACKGROUND

A new strain of a novel disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been recently declared a pandemic by the World Health Organization (WHO). The virus results in significant mortality and morbidity across the planet; therefore, novel treatments are urgently required. Recently deposited crystallographic structures of SARS-CoV-2 proteins have ignited the interest in virtual screenings of large databases.

OBJECTIVE

In the current study, we evaluated the inhibitory capacity of the IMPPAT phytochemical database (8500 compounds) and the SuperDRUG2 dataset (4000 compounds) in SARS-CoV-2 main protease and helicase Nsp13 through consensus-based docking simulations.

METHODS

Glide and GOLD 5.3 were implemented in the in silico process. Further MM/GBSA calculations of the top 10 inhibitors in each protein were carried out to investigate the binding free energy of the complexes. An analysis of the major ligand-protein interactions was also conducted.

RESULTS

After the docking simulations, we acquired 10 prominent phytochemicals and 10 FDAapproved drugs capable of inhibiting Nsp5 and Nsp13. Delphinidin 3,5,3'-triglucoside and hirsutidin 3-O-(6-O-p-coumaroyl)glucoside demonstrated the most favorable binding free energies against Nsp5 and Nsp13, respectively.

CONCLUSION

In conclusion, the analysis of the results identified that the phytochemicals demonstrated enhanced binding capacities compared to the FDA-approved database.

摘要

背景

世界卫生组织(WHO)最近宣布,一种由严重急性呼吸系统综合征冠状病毒 2(SARS-CoV-2)引起的新型疾病的新菌株已构成大流行。该病毒在全球范围内导致了大量的死亡率和发病率;因此,急需新的治疗方法。最近提交的 SARS-CoV-2 蛋白晶体结构激发了对大型数据库进行虚拟筛选的兴趣。

目的

在本研究中,我们通过基于共识的对接模拟评估了 IMPPAT 植物化学成分数据库(8500 种化合物)和 SuperDRUG2 数据集(4000 种化合物)对 SARS-CoV-2 主蛋白酶和螺旋酶 Nsp13 的抑制能力。

方法

在计算机模拟过程中使用了 Glide 和 GOLD 5.3。进一步对每个蛋白质中排名前 10 的抑制剂进行 MM/GBSA 计算,以研究复合物的结合自由能。还对主要配体-蛋白质相互作用进行了分析。

结果

经过对接模拟,我们获得了 10 种有潜力抑制 Nsp5 和 Nsp13 的主要植物化学成分和 10 种 FDA 批准的药物。飞燕草素 3,5,3'-三葡萄糖苷和毛蕊花糖苷 3-O-(6-O-对香豆酰)葡萄糖苷分别对 Nsp5 和 Nsp13 表现出最有利的结合自由能。

结论

总之,对结果的分析表明,与 FDA 批准的数据库相比,植物化学成分显示出增强的结合能力。

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