Department of Chemistry and Biology, Ryerson University, Toronto, ON, Canada.
Department of Computer Science, University of Toronto, Toronto, ON, Canada.
Sci Rep. 2021 Dec 2;11(1):23315. doi: 10.1038/s41598-021-02432-7.
The COVID-19 pandemic has highlighted the urgent need for the identification of new antiviral drug therapies for a variety of diseases. COVID-19 is caused by infection with the human coronavirus SARS-CoV-2, while other related human coronaviruses cause diseases ranging from severe respiratory infections to the common cold. We developed a computational approach to identify new antiviral drug targets and repurpose clinically-relevant drug compounds for the treatment of a range of human coronavirus diseases. Our approach is based on graph convolutional networks (GCN) and involves multiscale host-virus interactome analysis coupled to off-target drug predictions. Cell-based experimental assessment reveals several clinically-relevant drug repurposing candidates predicted by the in silico analyses to have antiviral activity against human coronavirus infection. In particular, we identify the MET inhibitor capmatinib as having potent and broad antiviral activity against several coronaviruses in a MET-independent manner, as well as novel roles for host cell proteins such as IRAK1/4 in supporting human coronavirus infection, which can inform further drug discovery studies.
新冠疫情凸显了鉴定针对多种疾病的新型抗病毒药物疗法的迫切需求。新冠病毒由感染人类冠状病毒 SARS-CoV-2 引起，而其他相关的人类冠状病毒会导致从严重呼吸道感染到普通感冒等各种疾病。我们开发了一种计算方法，用于鉴定新的抗病毒药物靶点，并重新利用临床相关药物化合物来治疗一系列人类冠状病毒疾病。我们的方法基于图卷积网络（GCN），涉及多尺度宿主-病毒互作组分析以及脱靶药物预测。基于细胞的实验评估揭示了几种经计算机分析预测具有抗人类冠状病毒感染的抗病毒活性的临床相关药物再利用候选物。特别是，我们发现 MET 抑制剂卡马替尼以 MET 非依赖性方式对几种冠状病毒具有强大且广谱的抗病毒活性，以及宿主细胞蛋白（如 IRAK1/4）在支持人类冠状病毒感染中的新作用，这可以为进一步的药物发现研究提供信息。
