Suppr超能文献

靶向严重急性呼吸综合征冠状病毒2主要蛋白酶:一种采用先进虚拟筛选、分子动力学和体外验证的综合方法

Targeting SARS-CoV-2 main protease: a comprehensive approach using advanced virtual screening, molecular dynamics, and in vitro validation.

作者信息

Gevorgyan Smbat, Khachatryan Hamlet, Shavina Anastasiya, Gharaghani Sajjad, Zakaryan Hovakim

机构信息

Laboratory of Antiviral Drug Discovery. Institute of Molecular Biology of National Academy of Sciences, 0014, Yerevan, Armenia.

Denovo Sciences Inc, 0060, Yerevan, Armenia.

出版信息

Virol J. 2024 Dec 21;21(1):330. doi: 10.1186/s12985-024-02607-4.

Abstract

The COVID-19 pandemic, driven by the SARS-CoV-2 virus, necessitates the development of effective therapeutics. The main protease of the virus, Mpro, is a key target due to its crucial role in viral replication. Our study presents a novel approach combining ligand-based pharmacophore modeling with structure-based advanced virtual screening to identify potential inhibitors of Mpro. We screened around 200 million compounds using this integrated methodology, resulting in a shortlist of promising compounds. These were further scrutinized through molecular dynamics simulations, revealing their interaction dynamics with Mpro. Subsequent in vitro assays using the Mpro enzyme identified two compounds exhibiting significant micromolar inhibitory activity. These findings provide valuable scaffolds for the development of advanced therapeutics targeting Mpro. The comprehensive nature of our approach, spanning computational predictions to experimental validations, offers a robust pathway for rapid and efficient identification of potential drug candidates against COVID-19.

摘要

由严重急性呼吸综合征冠状病毒2(SARS-CoV-2)病毒引发的2019年冠状病毒病(COVID-19)大流行,使得开发有效的治疗方法成为必要。该病毒的主要蛋白酶Mpro,因其在病毒复制中的关键作用而成为关键靶点。我们的研究提出了一种将基于配体的药效团建模与基于结构的高级虚拟筛选相结合的新方法,以鉴定Mpro的潜在抑制剂。我们使用这种综合方法筛选了约2亿种化合物,得到了一份有前景的化合物候选清单。通过分子动力学模拟对这些化合物进行了进一步研究,揭示了它们与Mpro的相互作用动力学。随后使用Mpro酶进行的体外试验鉴定出两种具有显著微摩尔抑制活性的化合物。这些发现为开发针对Mpro的先进治疗方法提供了有价值的框架。我们的方法具有全面性,涵盖从计算预测到实验验证,为快速有效地鉴定针对COVID-19的潜在药物候选物提供了一条强有力的途径。

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