As new medications are used to treat COVID-19, many studies have reported that proteins such as spike, polymerase and proteases are prone to high levels of mutation that can create resistance to therapy over time [1]. Thus, it becomes necessary to, not only target other viral proteins such as the non-structural proteins (nsp’s), but to also target the most conserved residues of these proteins. A synergistic combination of bioinformatics, computer-aided drug-design and in vitro studies can feed into better understanding of SARS-CoV-2 (SC-2) and therefore help in the development of small molecule inhibitors against the nsp’s. As part of our initial anti-viral work, a pharmacophore study on nsp15 found a hit molecule (INS316) that made interactions with Ser293, Lys344 and Leu345 residues [2] which are highly conserved across SC-2.

We have performed multiple sequence alignment studies on different datasets i.e., ~200,000, ~1 million and ~11 million sequences of SC-2 Orf1ab sequences to identify the most conserved residues. These residues were then visualized on 3D protein X-ray structures using MOE software. We found that there were known and novel binding pocket residues that were 100% conserved in our datasets. Our results indicate that these highly conserved pockets can be targeted for developing promising SC-2 inhibitors. We have also performed mutational analysis and have found different mutational hotspots across the nsp’s. Our group has recently been selected to enter two international challenges organized by the CACHE consortium to discover inhibitors of the RNA binding tunnel of SC-2 nsp13 and the Mac1 domain of SC-2 nsp3. We have used a tiered screening workflow which included the use of volume/shape information of the binding pockets (fastROCS), use of in-house pharmacophore generation software (MoPBS [3]/MOE) and performed docking in the binding pocket (FRED) to rank hits for subsequent clustering and to identify compounds that bind to these conserved pockets. Our results on nsp3 will be presented here.

References

1. Pachetti, M., Marini, B., Benedetti, F., Giudici, F., Mauro, E., Storici, P., Masciovecchio, C., Angeletti, S., Ciccozzi, M., Gallo, R. C., Zella, D., & Ippodrino, R. (2020). Emerging SARS-CoV-2 mutation hot spots include a novel RNA-dependent-RNA polymerase variant. Transl. Med., 18(1), 179. https://doi.org/10.1186/s12967-020-02344-6
2. Kandwal, S., & Fayne, D. (2022). Repurposing drugs for treatment of SARS-CoV-2 infection: computational design insights into mechanisms of action. Biomol. Struct. Dyn., 40(3), 1316–1330. https://doi.org/10.1080/07391102.2020.1825232
3. Braun, J., & Fayne, D. (2022). Mapping of Protein Binding Sites using clustering algorithms - Development of a pharmacophore based drug discovery tool. Mol. Graph. Model., 115, 108228. https://doi.org/10.1016/j.jmgm.2022.108228