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COVID-19 相关分子动力学模拟资源库及其在 nsp10-nsp16 抗病毒药物方面的应用。

A repository of COVID-19 related molecular dynamics simulations and utilisation in the context of nsp10-nsp16 antivirals.

机构信息

Epigenetics in Human Health and Disease Program, Baker Heart and Diabetes Institute, 75 Commercial Road, Prahran, VIC, 3004, Australia; Epigenomic Medicine Laboratory at prospED Training, Carlton, VIC, 3053, Australia; School of Science, STEM College, RMIT University, VIC, 3001, Australia.

Epigenomic Medicine Laboratory at prospED Training, Carlton, VIC, 3053, Australia; School of Science, STEM College, RMIT University, VIC, 3001, Australia.

出版信息

J Mol Graph Model. 2024 Jan;126:108666. doi: 10.1016/j.jmgm.2023.108666. Epub 2023 Nov 10.

Abstract

The coronavirus disease 2019 (COVID-19) pandemic highlighted the importance of establishing systems and infrastructure to develop vaccines, antiviral drugs, and therapeutic antibodies against emerging pathogens. Typical drug discovery processes involve targeting suitable proteins to effect pathogen replication or to attenuate host responses, by examining either large chemical databases or protein-protein interactions. Following initial screens, molecular dynamics (MD) simulations are critical for gaining further insight into molecular interactions. During the COVID-19 pandemic, many research groups made their simulations widely available, as highlighted by the comprehensive D.E. Shaw Research trajectory database. To investigate protein target sites and evaluate potential lead compounds, we performed over 300 MD simulations relating to COVID-19. We organised our simulations into a repository, which is publicly available at https://epimedlab.org/trajectories/. The trajectories cover a large part of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteome, and the majority of our MD simulations focused on the identification of potential antivirals. For example, we focused on the S-adenosyl-l-methionine binding site of the nsp10-nsp16 complex, a critical component of viral replication, revealing verbascoside as a potential lead. Moreover, we utilised MD trajectories to explore the interface between the spike protein receptor binding domain and human angiotensin-converting enzyme 2 receptor, with the ultimate aim being investigation of new variants in real-time. Overall, MD simulations are a critical component of the in silico drug discovery process and as highlighted throughout the pandemic, data sharing enables accelerated progress. We have organised our extensive collection of COVID-19 related MD trajectories into an easily accessible repository.

摘要

2019 年冠状病毒病(COVID-19)大流行凸显了建立系统和基础设施的重要性,以针对新兴病原体开发疫苗、抗病毒药物和治疗性抗体。典型的药物发现过程涉及针对合适的蛋白质,以通过检查大型化学数据库或蛋白质-蛋白质相互作用来影响病原体复制或减弱宿主反应。在初始筛选之后,分子动力学(MD)模拟对于进一步深入了解分子相互作用至关重要。在 COVID-19 大流行期间,许多研究小组广泛提供了他们的模拟,正如全面的 D.E. Shaw Research 轨迹数据库所强调的那样。为了研究蛋白质靶标位点并评估潜在的先导化合物,我们进行了 300 多次与 COVID-19 相关的 MD 模拟。我们将我们的模拟组织到一个存储库中,该存储库可在 https://epimedlab.org/trajectories/ 上公开获得。这些轨迹涵盖了严重急性呼吸综合征冠状病毒 2(SARS-CoV-2)蛋白质组的很大一部分,我们的大多数 MD 模拟都集中在确定潜在的抗病毒药物上。例如,我们专注于 nsp10-nsp16 复合物的 S-腺苷甲硫氨酸结合位点,这是病毒复制的关键组成部分,揭示了毛蕊花糖苷是一种潜在的先导化合物。此外,我们利用 MD 轨迹来探索刺突蛋白受体结合域与人血管紧张素转换酶 2 受体之间的界面,最终目标是实时研究新的变体。总体而言,MD 模拟是计算机药物发现过程的关键组成部分,正如整个大流行期间所强调的那样,数据共享可以加速进展。我们已经将我们广泛的 COVID-19 相关 MD 轨迹集合组织到一个易于访问的存储库中。

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