article · Journal of Enzyme Inhibition and Medicinal Chemistry
SARS-CoV-2 uses an enzyme known as nsp16 to protect its genetic material by forming an RNA cap, making this protein a critical target for antiviral discovery. Computational screening was carried out using a structure of the nsp16 enzyme bound to the reference molecule Sinefungin. A three-dimensional pharmacophore model screened approximately 48 million drug-like compounds from the Zinc database, filtering the collection down to 24 candidates. Molecular docking identified four top-scoring compounds with higher predicted binding affinities than Sinefungin. Subsequent molecular dynamics simulations over 150 nanoseconds, combined with binding free energy calculations, highlighted compound 11 as the leading candidate. The molecule demonstrated superior stability and binding energy compared to the reference compound, indicating strong potential as an inhibitor of viral RNA protection.
Targeting the nsp16 enzyme disrupts the process that shields viral RNA from host defences, presenting an opportunity to stop SARS-CoV-2 replication. Using computational drug discovery enables rapid scanning of tens of millions of chemical structures, helping researchers pinpoint promising molecules for therapeutic development without initially needing extensive physical laboratory testing.
This research identifies an early-stage chemical lead for antiviral drug development against COVID-19. Potential users include pharmaceutical developers and medicinal chemistry teams seeking targets for synthesising and validating new therapeutic compounds. The work remains at an early computational stage, as identified molecules such as compound 11 require biological validation, chemical optimisation, and preclinical testing before any clinical or commercial application can be pursued.
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The novel coronavirus disease COVID-19, caused by the virus SARS CoV-2, has exerted a significant unprecedented economic and medical crisis, in addition to its impact on the daily life and health care systems all over the world. Regrettably, no vaccines or drugs are currently available for this new critical emerging human disease. Joining the global fight against COVID-19, in this study we aim at identifying a potential novel inhibitor for SARS COV-2 2'-O-methyltransferase (nsp16) which is one of the most attractive targets in the virus life cycle, responsible for the viral RNA protection <i>via</i> a cap formation process. Firstly, nsp16 enzyme bound to Sinefungin was retrieved from the protein data bank (PDB ID: 6WKQ), then, a 3D pharmacophore model was constructed to be applied to screen 48 Million drug-like compounds of the Zinc database. This resulted in only 24 compounds which were subsequently docked into the enzyme. The best four score-ordered hits from the docking outcome exhibited better scores compared to Sinefungin. Finally, three molecular dynamics (MD) simulation experiments for 150 ns were carried out as a refinement step for our proposed approach. The MD and MM-PBSA outputs revealed compound <b>11</b> as the best potential nsp16 inhibitor herein identified, as it displayed a better stability and average binding free energy for the ligand-enzyme complex compared to Sinefungin.
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DOI: 10.1080/14756366.2021.1885396
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