To inform drug design for COVID-19: Computational determination of interactions between COVID-19 gene products and structurally diverse natural products and clinical drugs
To inform drug design for COVID-19: Computational determination of interactions between COVID-19 gene products and structurally diverse natural products and clinical drugs
批准号:
550135-2020
负责人:
Jakeman, David
金额:
$3.64万
依托单位:
依托单位国家:
加拿大
项目类别:
Alliance Grants
财政年份:
2020
资助国家:
加拿大
项目状态:
已结题
起止时间:
2020-01-01 至 2021-12-31
中文摘要
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英文摘要
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.
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Carbohydrates, enzymes and natural products
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