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1.
Front Chem ; 10: 1017394, 2022.
Article in English | MEDLINE | ID: covidwho-2119653

ABSTRACT

Three protein targets from SARS-CoV-2, the viral pathogen that causes COVID-19, are studied: the main protease, the 2'-O-RNA methyltransferase, and the nucleocapsid (N) protein. For the main protease, the nucleophilicity of the catalytic cysteine C145 is enabled by coupling to three histidine residues, H163 and H164 and catalytic dyad partner H41. These electrostatic couplings enable significant population of the deprotonated state of C145. For the RNA methyltransferase, the catalytic lysine K6968 that serves as a Brønsted base has significant population of its deprotonated state via strong coupling with K6844 and Y6845. For the main protease, Partial Order Optimum Likelihood (POOL) predicts two clusters of biochemically active residues; one includes the catalytic H41 and C145 and neighboring residues. The other surrounds a second pocket adjacent to the catalytic site and includes S1 residues F140, L141, H163, E166, and H172 and also S2 residue D187. This secondary recognition site could serve as an alternative target for the design of molecular probes. From in silico screening of library compounds, ligands with predicted affinity for the secondary site are reported. For the NSP16-NSP10 complex that comprises the RNA methyltransferase, three different sites are predicted. One is the catalytic core at the conserved K-D-K-E motif that includes catalytic residues D6928, K6968, and E7001 plus K6844. The second site surrounds the catalytic core and consists of Y6845, C6849, I6866, H6867, F6868, V6894, D6895, D6897, I6926, S6927, Y6930, and K6935. The third is located at the heterodimer interface. Ligands predicted to have high affinity for the first or second sites are reported. Three sites are also predicted for the nucleocapsid protein. This work uncovers key interactions that contribute to the function of the three viral proteins and also suggests alternative sites for ligand design.

2.
The FASEB Journal ; 35(S1), 2021.
Article in English | Wiley | ID: covidwho-1233918

ABSTRACT

The virus SARS-CoV-2, the cause of the current COVID-19 pandemic, is not well understood. It is critical to understand how the viral proteins function and how their function may be modulated. Inhibitors that target these enzymes serve as potential therapeutic interventions against COVID-19. This work uses artificial intelligence methods developed by us to find sites that other methods may not find and therefore, identify potential exosites, allosteric sites, or other sites of interaction in the structures of viral proteins to serve as new targets for the development of antiviral agents. Large datasets of natural and synthetic compounds are computationally searched for molecules that fit into these alternative sites, and any compounds that fit will be targeted for experimental testing for their ability to inhibit the functions of these viral enzymes. This project uses the unique Partial Order Optimum Likelihood (POOL) machine learning method developed by us to predict multiple types of binding sites in SARS-CoV-2 proteins, including catalytic sites, allosteric sites, and other interaction sites. Molecular dynamics simulations are used to generate conformations for ensemble docking. Compounds from large molecular libraries are computationally docked into the predicted sites to identify potentially strong binding ligands. We have identified approximately 10000 potential ligands for more than 50 SARS-CoV-2 proteins to date. Candidate ligands to selected SARS-CoV-2 proteins are experimentally tested in vitro for binding affinity and the effect of the best-predicted inhibitors on catalytic activities determined by direct biochemical assays. Compound libraries for the study include selected compounds from the ZINC and Enamine databases;Chemical Abstract Service database compounds and COVID-specific libraries from Enamine and Life Chemicals.

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