Suppr超能文献

应对新冠病毒:通过结构分析、虚拟筛选、分子对接和MM-PBSA计算鉴定潜在的主要蛋白酶抑制剂

Tackling COVID-19: identification of potential main protease inhibitors via structural analysis, virtual screening, molecular docking and MM-PBSA calculations.

作者信息

Al-Shar'i Nizar A

机构信息

Faculty of Pharmacy, Department of Medicinal Chemistry and Pharmacognosy, Jordan University of Science and Technology, Irbid, Jordan.

出版信息

J Biomol Struct Dyn. 2021 Oct;39(17):6689-6704. doi: 10.1080/07391102.2020.1800514. Epub 2020 Jul 31.

Abstract

The widespread of the COVID-19 disease, caused by the novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), had severely affected the entire world. Unfortunately, no successful vaccines or antiviral drugs are currently available which leaves the scientific community under huge pressure to tackle this pandemic. Among the identified promising druggable targets, specific to this virus, is the main protease (M) enzyme, which is vital for viral replication, transcription and packaging within the host cells. In this study, selective inhibition of the M was sought via thorough analysis of its available structural data in the Protein Data Bank. To this end, COVID-19 M crystal complexes were explored and the key interacting residues (KIRs) within its active site, that are expected to be vital for effective ligand binding, were identified. Based on these KIRs, 3D pharmacophore models were generated and used in virtual screening of different databases. Retrieved hits were docked into the active site of the enzyme and their MM-PBSA based free binding energies were calculated. Finally, ADMET descriptors were calculated to aid the selection of top scoring hits with best ADMET properties. Nine compounds with different chemotypes were identified as potential M inhibitors. Further, MD simulations of a virtual complex of M with one of the promising hits revealed stable binding which is indicative of good inhibitory potential. The identified compounds in this study are expected to support the global drug discovery efforts in fighting against this highly contagious virus by narrowing the searchable chemical space for potential effective therapeutics.

摘要

由新型严重急性呼吸综合征冠状病毒(SARS-CoV-2)引起的COVID-19疾病的广泛传播,已对整个世界造成了严重影响。不幸的是,目前尚无成功的疫苗或抗病毒药物,这使得科学界在应对这一疫情时面临巨大压力。在已确定的有前景的可成药靶点中,针对这种病毒的主要蛋白酶(M)酶是其中之一,它对于病毒在宿主细胞内的复制、转录和包装至关重要。在本研究中,通过对蛋白质数据库中其可用结构数据的全面分析,寻求对M的选择性抑制。为此,对COVID-19 M晶体复合物进行了探索,并确定了其活性位点内预期对有效配体结合至关重要的关键相互作用残基(KIRs)。基于这些KIRs,生成了3D药效团模型,并用于不同数据库的虚拟筛选。检索到的命中化合物被对接至该酶的活性位点,并计算了基于MM-PBSA的自由结合能。最后,计算了ADMET描述符,以帮助选择具有最佳ADMET性质的高分命中化合物。九种具有不同化学类型的化合物被鉴定为潜在的M抑制剂。此外,对M与一种有前景的命中化合物的虚拟复合物进行的分子动力学模拟显示出稳定的结合,这表明其具有良好的抑制潜力。预计本研究中鉴定出的化合物将通过缩小潜在有效治疗药物的可搜索化学空间,支持全球抗击这种高传染性病毒的药物发现工作。

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