Integrating druggability and genetic variability of coronavirus genomes to inform drug discovery against COVID-19 and future pandemic threats

在与加拿大生物技术公司Cyclica合作并协商后，我们建议使用计算机建模来整合国际公共存储库中提供的SARS-CoV-2基因组的遗传和结构数据。利用先进的结构生物信息学工具，我们将把数千种SARS-CoV-2变体的基因突变映射到SARS-CoV-2蛋白的晶体结构上，以确定最适合开发广谱抑制剂的结合位点。然后，我们将把这一练习扩展到SARS-CoV-2以外的冠状病毒株，以确定开发泛冠状病毒抑制剂的机会。我们将分享加拿大在线门户网站上产生的所有知识，这将成为Cyclica、加拿大和世界各地专注于COVID-19危机的药物发现工作的宝贵资源。虽然拟议的工作是对COVID-19和其他冠状病毒基因组的计算分析，因此不是应用研究，但所产生的知识将为我们的联盟合作伙伴正在进行的COVID-19药物发现工作提供信息。

In partnership and after consultation with Cyclica, a Canadian biotech, we propose to use computer modelling to integrate genetic and structural data on the SARS-CoV-2 genome available from international public repositories. Using advanced structural bioinformatic tools, we will map genetic mutations from thousands of SARS-CoV-2 variants onto crystal structures of SARS-CoV-2 proteins to identify binding sites most amenable for the development of broad-spectrum inhibitors. We will then extend this exercise to coronavirus strains beyond SARS-CoV-2 to identify opportunities for the development of pan-coronavirus inhibitors. We will share all the knowledge generated on a Canadian online portal, which will become a valuable resource for drug discovery efforts at Cyclica, in Canada and around the world focused on the COVID-19 crisis. While the proposed work is a computational analysis of the COVID-19 and other coronavirus genomes, and is therefore not applied research, the knowledge generated will inform COVID-19 drug discovery efforts ongoing by our Alliance partner.