基于知识的 SARS-CoV-2 蛋白及其与潜在药物复合物的结构模型。
Knowledge-based structural models of SARS-CoV-2 proteins and their complexes with potential drugs.
机构信息
Faculty of Bioscience, Nagahama Institute of Bio-Science and Technology, Japan.
Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Japan.
出版信息
FEBS Lett. 2020 Jun;594(12):1960-1973. doi: 10.1002/1873-3468.13806. Epub 2020 May 25.
Abstract
The World Health Organization (WHO) has declared the coronavirus disease 2019 (COVID-19) caused by the novel coronavirus SARS-CoV-2 a pandemic. There is, however, no confirmed anti-COVID-19 therapeutic currently. In order to assist structure-based discovery efforts for repurposing drugs against this disease, we constructed knowledge-based models of SARS-CoV-2 proteins and compared the ligand molecules in the template structures with approved/experimental drugs and components of natural medicines. Our theoretical models suggest several drugs, such as carfilzomib, sinefungin, tecadenoson, and trabodenoson, that could be further investigated for their potential for treating COVID-19.
摘要
世界卫生组织（WHO）已宣布由新型冠状病毒 SARS-CoV-2 引起的 2019 年冠状病毒病（COVID-19）为大流行。然而，目前尚无针对 COVID-19 的经证实的治疗方法。为了协助基于结构的药物再利用发现工作，以对抗这种疾病，我们构建了 SARS-CoV-2 蛋白的基于知识的模型，并将模板结构中的配体分子与已批准/实验性药物和天然药物成分进行了比较。我们的理论模型表明，几种药物，如卡非佐米、西尼fungin、替卡地松和曲马多隆，可能具有进一步研究用于治疗 COVID-19 的潜力。
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