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bioRxiv · 10.1101/2020.06.02.130849

A Deep Dive into the SARS-Cov-2 Polymerase Assembly: Identifying Novel Allosteric Sites and Analyzing the Hydrogen Bond Networks and Correlated Dynamics

Barakat, K.·Ahmed, M.·Tabana, Y.·Ha, M.
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Abstract

Replication of the SARS-CoV-2 genome is a fundamental step in the virus life cycle and inhibiting the SARS-CoV2 replicase machinery has been proven recently as a promising approach in combating the virus. Despite this recent success, there are still several aspects related to the structure, function and dynamics of the CoV-2 polymerase that still need to be addressed. This includes understanding the dynamicity of the various polymerase subdomains, analyzing the hydrogen bond networks at the active site and at the template entry in the presence of water, studying the binding modes of the nucleotides at the active site, highlighting positions for acceptable nucleotides substitutions that can be tolerated at different positions within the nascent RNA strand, identifying possible allosteric sites within the polymerase structure and studying their correlated dynamics relative to the catalytic site. Here, we combined various cutting-edge modelling tools with the recently resolved SARS-CoV-2 cryo-EM polymerase structures to fill this gap in knowledge. Our findings provide a detailed analysis of the hydrogen bond networks at various parts of the polymerase structure and suggest possible nucleotides substitutions that can be tolerated by the polymerase complex. We also report here three "druggable" allosteric sites within the nsp12 RdRp that can be targeted by small molecule inhibitors. Our correlated motion analysis shows that the dynamics within one of the newly identified sites are linked to the active site, indicating that targeting this site can significantly impact the catalytic activity of the SARS-CoV-2 polymerase.

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Barakat, K., Ahmed, M., Tabana, Y., Ha, M.. 2020-06-03. A Deep Dive into the SARS-Cov-2 Polymerase Assembly: Identifying Novel Allosteric Sites and Analyzing the Hydrogen Bond Networks and Correlated Dynamics. https://doi.org/10.1101/2020.06.02.130849

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