Targeting SARS-CoV-2 endoribonuclease: a structure-based virtual screening supported by in vitro analysis

Abstract

Researchers are focused on discovering compounds that can interfere with the COVID-19 life cycle. One of the important non-structural proteins is endoribonuclease since it is responsible for processing viral RNA to evade detection of the host defense system. This work investigates a hierarchical structure-based virtual screening approach targeting NSP15. Different filtering approaches to predict the interactions of the compounds have been included in this study. Using a deep learning technique, we screened 823,821 compounds from five different databases (ZINC15, NCI, Drug Bank, Maybridge, and NCI Diversity set III). Subsequently, two docking protocols (extra precision and induced fit) were used to assess the binding affinity of the compounds, followed by molecular dynamic simulation supported by the MM-GBSA free binding energy. Interestingly, one compound (ZINC000104379474) from the ZINC15 database has been found to have a good binding affinity of - 7.68 kcal/Mol. The VERO-E6 cell line was used to investigate its therapeutic effect in vitro. Half-maximal cytotoxic concentration and Inhibitory concentration 50 were determined to be 0.9 mg/ml and 0.01 mg/ml, respectively; therefore, the selectivity index is 90. In conclusion, ZINC000104379474 was shown to be a good hit for targeting the virus that needs further investigations in vivo to be a drug candidate.

Figure 1

A workflow showing the steps performed in this study.

Figure 2

Shows the NSP15 domains. Red: The N-terminal domain. Magenta: The middle domain. Blue: The C-terminal domain which contains the active site of the protein.

Figure 3

The 2D and 3D interactions between the IF docking of ZINC000104379474 and NSP15. There are 12 interactions in total. Half of them (six interactions) are H-bonds between H235, D240, Q245, G248, K290, and Y343, followed by three salt bridges (D240, K290, and E340), then one Pi-cation (H243) interaction and two Pi–Pi stacking (Y343).

Figure 4

The molecular dynamics simulation analysis of the NSP15-ZINC000104379474 complex. (A) The radius of gyration (RoG) in Å, root-mean-square displacement (RMSD) in Å, surface accessible surface area (SASA) in Ų, and the total number of H-bonds versus the simulation time in ns. (B) The per-residue root-men-square fluctuation (RMSF) in Å.

Figure 5

the representative frames (A–E) from clustering using TTClust, and the types and number of interactions calculated and depicted using PLIP webserver for NSP15-ZINC000104379474 complex. Dashed-Grey lines: Hydrophobic interactions, blue lines: H-bond, dashed-yellow lines: salt-bridges.

Figure 6

Molecular Mechanics Generalized Born Surface Area (MM-GBSA) decomposition of NSP15-ZINC000104379474 complex. It shows the contribution of amino acids within 1 nm of the ZINC000104379474 compound.

Figure 7

Dose–response and inhibition curves for ZINC000104379474 compound on VERO-E6 cells. Half maximal cytotoxic concentration (CC₅₀) in Vero E6 cells (black) and inhibitory concentration 50% (IC₅₀) against NRC-03-nhCoV were calculated using nonlinear regression analysis of GraphPad Prism.