Author: Vu, Hoang; Iyengar, Suhasini; Barnsley, Kelton; Ondrechen, Mary; Beuning, Penny
Title: Drug Sites Prediction and Computational Drug Screening for SARSâ€CoVâ€2 Proteins Cord-id: m510glpa Document date: 2021_5_14
ID: m510glpa
Snippet: The COVIDâ€19 pandemic, caused by the Severe Acute Respiratory Coronavirus 2 (SARSâ€CoVâ€2) virus, first started in the Wuhan region of Hubei, China, and has quickly spread to 191 countries and territories, infecting more than 86.4 million people, and resulting in 1.87 global deaths as of January 6(th). With SARSâ€CoVâ€2’s genomic sequence and protein structures deciphered and updated rapidly, clinical treatments and vaccine developments have proceeded simultaneously as researchers attemp
Document: The COVIDâ€19 pandemic, caused by the Severe Acute Respiratory Coronavirus 2 (SARSâ€CoVâ€2) virus, first started in the Wuhan region of Hubei, China, and has quickly spread to 191 countries and territories, infecting more than 86.4 million people, and resulting in 1.87 global deaths as of January 6(th). With SARSâ€CoVâ€2’s genomic sequence and protein structures deciphered and updated rapidly, clinical treatments and vaccine developments have proceeded simultaneously as researchers attempt to learn more about the infecting mechanisms of this virus. Among these attempts, computational drug screening for SARSâ€CoVâ€2 has potential for: (1) narrowing down billions of chemical compounds into a list of possible highâ€affinity ligands for SARSâ€CoVâ€2 protein targets, (2) providing information about the activities of SARSâ€CoVâ€2 proteins, (3) offering possible treatments, and (4) assisting in scientific knowledge to fight against future coronavirus infections. In this work, computational ligand screening for SARSâ€CoVâ€2 is a combination of site prediction using machine learning technology Partial Order Optimum Likelihood (POOL) and molecular docking. Among the techniques deployed, the machine learning technology POOL was developed by us and assists in the drug screening process for SARSâ€CoVâ€2 by predicting targeted protein sites, including those that are not the obvious catalytic sites, such as exosites, allosteric sites, and other interaction sites. Results will be presented for the SARSâ€CoVâ€2 main protease, nonâ€structural protein 1 (Nsp1), nonâ€structural protein 9 (Nsp9), and nonâ€structural protein 15 (Nsp15). Compounds are taken from a variety of libraries, including the ZINC and Enamine databases. Protein structures are downloaded from the Protein Data Bank (www.rcsb.org). Molecular dynamic structure simulations are used to generate structures for ensemble docking.
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