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Discovery of Drug-like Ligands for the Mac1 Domain of SARS-CoV-2 Nsp3

Authors: Rajdeep S. Virdi; Robert V. Bavisotto; Nicholas C. Hopper; Nemanja Vuksanovic; Trevor R. Melkonian; Nicholas R. Silvaggi; David N. Frick;

Discovery of Drug-like Ligands for the Mac1 Domain of SARS-CoV-2 Nsp3

Abstract

ABSTRACTSmall molecules that bind the SARS-CoV-2 non-structural protein 3 Mac1 domain in place of ADP-ribose could be useful as molecular probes or scaffolds for COVID-19 antiviral drug discovery because Mac1 has been linked to coronavirus’ ability to evade cellular detection. A high-throughput assay based on differential scanning fluorimetry (DSF) was therefore optimized and used to identify possible Mac1 ligands in small libraries of drugs and drug-like compounds. Numerous promising compounds included nucleotides, steroids, beta-lactams, and benzimidazoles. The main drawback to this approach was that a high percentage of compounds in some libraries were found to influence the observed Mac1 melting temperature. To prioritize DSF screening hits, the shapes of the observed melting curves and initial assay fluorescence were examined, and the results were compared with virtual screens performed using Autodock VINA. The molecular basis for alternate ligand binding was also examined by determining a structure of one of the hits, cyclic adenosine monophosphate, with atomic resolution.

Keywords

Models, Molecular, Binding Sites, Protein Conformation, SARS-CoV-2, coronavirus, COVID-19, 500, Coronavirus Papain-Like Proteases, antiviral drug target, macrodomain, 540, Ligands, Antiviral Agents, High-Throughput Screening Assays, Molecular Docking Simulation, Protein Domains, Cyclic AMP, thermal shift, Original Research

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
40
Top 10%
Top 10%
Top 1%
Green
gold