用于病毒螺旋酶原子分子动力学模拟的通用开源工作流程。

IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES
Bryan Raubenolt, Daniel Blankenberg
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引用次数: 0

摘要

病毒螺旋酶是开发抗病毒疗法的理想靶点。鉴于它们具有解开双链核酸的重要功能,抑制它们就能阻止病毒的复制周期。以往的研究已经阐明了这些螺旋酶的关键结构细节,包括底物结合位点的位置、柔性结构域以及潜在抑制剂的发现。在这里,我们介绍了一系列新的银河工具和工作流程,用于执行和分析病毒螺旋酶的分子动力学模拟。我们首先验证了这些工具和工作流程,它们再现了之前模拟的寨卡(NS3)和 SARS-CoV-2 (NSP13)螺旋酶的原型和与抑制剂复合物的数据。通过将这些银河工作流程应用于新的案例,我们进一步证明了它们的实用性和通用性,证明了它们作为一种可广泛使用的探索抗病毒活性的方法的有用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generalized open-source workflows for atomistic molecular dynamics simulations of viral helicases.

Viral helicases are promising targets for the development of antiviral therapies. Given their vital function of unwinding double-stranded nucleic acids, inhibiting them blocks the viral replication cycle. Previous studies have elucidated key structural details of these helicases, including the location of substrate binding sites, flexible domains, and the discovery of potential inhibitors. Here we present a series of new Galaxy tools and workflows for performing and analyzing molecular dynamics simulations of viral helicases. We first validate them by demonstrating recapitulation of data from previous simulations of Zika (NS3) and SARS-CoV-2 (NSP13) helicases in apo and complex with inhibitors. We further demonstrate the utility and generalizability of these Galaxy workflows by applying them to new cases, proving their usefulness as a widely accessible method for exploring antiviral activity.

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来源期刊
GigaScience
GigaScience MULTIDISCIPLINARY SCIENCES-
CiteScore
15.50
自引率
1.10%
发文量
119
审稿时长
1 weeks
期刊介绍: GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.
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