To inform drug design for COVID-19: Computational determination of interactions between COVID-19 gene products and structurally diverse natural products and clinical drugs

疫苗是防治包括病毒在内的传染病的首选公共卫生解决方案。然而，仍然有强烈的需要开发小分子疗法作为治疗病毒感染的正交方法。这项提议旨在为新冠肺炎提供对潜在药物线索的洞察。世界各地正在解析新冠肺炎冠状病毒基因组产生的蛋白质和酶的X射线晶体结构，并将其存入公开可用的数据库。其中一些三维结构包括结合到活性部位、变构部位或蛋白质-蛋白质相互作用部位的小分子。这一见解为发现铅分子提供了一个起点，这些分子将被开发为治疗新冠肺炎感染患者的抗病毒药物。然而，与通过X射线结晶学鉴定的少数支架相比，化学多样性要丰富得多。在NSERC联盟的这项提案中，我们将使用计算方法筛选已发表的新冠肺炎病毒基因产物的X射线结构，以及结构不同的分子(数据库)，以确定对接相互作用。因此，我们将根据分子的计算结合亲和力和结构姿势对分子进行排名。这将提供一个多样化的结构数据集，作为药物发现项目的起点，作为药物先导进行评估。这里包括的初步结果表明，计算对接预测和重现了最近发表的有效抑制剂与SARS-CoV-2主要蛋白酶的X射线结构结合，该酶负责激活新冠肺炎多肽。这些数据表明，计算方法是识别铅分子的有意义的一步。将及时公布研究成果，确保研究成果迅速传播。此外，还将与伙伴组织(IntelliSyn，蒙特利尔)分享结果，这是一个合同研究组织和药物发现团队，在加拿大境内独一无二地准备在这些结果的基础上再接再厉。

Vaccines are the preferred public health solution to combating infectious disease, including viruses. However, there remains a strong need to develop small molecule therapeutics as an orthogonal approach to treat viral infections. This proposal aims to provide insight into potential drug leads for COVID-19. X-ray crystal structures of the proteins and enzymes produced from the COVID-19 coronavirus genome are being solved around the world and deposited into publicly available databases. Some of these three-dimensional structures include small molecules bound to active sites, allosteric sites or sites of protein-protein interactions. This insight offers a starting point for the discovery of lead molecules to be developed as antivirals to treat COVID-19 infected patients. However, chemical diversity is much richer and more varied than the few scaffolds identified through X-ray crystallography. In this NSERC Alliance proposal, we will screen, using computational approaches, published X-ray structures of the gene products from the COVID-19 virus alongside structurally diverse molecules (databases) in order to determine docking interactions. We will thus rank the molecules in terms of their computational binding affinity and structural pose. This will provide a diverse dataset of structures to be evaluated as drug leads as a starting point for drug discovery projects. Preliminary results included herein indicate that computational docking predicts and reproduces the very recently published X-ray structural binding of potent inhibitors to the SARS-CoV-2 main protease, the protease responsible for activating the COVID-19 polypeptide. These data demonstrate that computational approaches are a meaningful step towards identification of lead molecules. The results will be published in a timely manner to ensure rapid dissemination of the research results. Furthermore, the results will be shared with the partner organization (IntelliSyn, Montreal) a contract research organization and drug discovery team uniquely poised within Canada to build upon these results.