SARS-CoV-2 RNA dependent RNA polymerase (RdRp) targeting: an in silico perspective

Abstract

New treatment against SARS-CoV-2 now is a must. Nowadays, the world encounters a huge health crisis by the COVID-19 viral infection. Nucleotide inhibitors gave a lot of promising results in terms of its efficacy against different viral infections. In this work, molecular modeling, docking, and dynamics simulations are used to build a model for the viral protein RNA-dependent RNA polymerase (RdRp) and test its binding affinity to some clinically approved drugs and drug candidates. Molecular dynamics is used to equilibrate the system upon binding calculations to ensure the successful reproduction of previous results, to include the dynamics of the RdRp, and to understand how it affects the binding. The results show the effectiveness of Sofosbuvir, Ribavirin, Galidesivir, Remdesivir, Favipiravir, Cefuroxime, Tenofovir, and Hydroxychloroquine, in binding to SARS-CoV-2 RdRp. Additionally, Setrobuvir, YAK, and IDX-184, show better results, while four novel IDX-184 derivatives show promising results in attaching to the SARS-CoV-2 RdRp. There is an urgent need to specify drugs that can selectively bind and subsequently inhibit SARS-CoV-2 proteins. The availability of a punch of FDA-approved anti-viral drugs can help us in this mission, aiming to reduce the danger of COVID-19. The compounds 2 and 3 may tightly bind to the SARS-CoV-2 RdRp and so may be successful in the treatment of COVID-19.

Keywords: COVID-19; RdRp; SARS-CoV-2; drug repurposing; molecular docking; molecular dynamics simulation.

Figures

Graphical abstract

Figure 1.

(A) 2 D structures of the FDA approved anti-viral drugs; Sofosbuvir, Ribavirin, Galidesivir, Remdesivir, Favipiravir, and Tenofovir. (B) 2 D structures of the guanosine derivative IDX-184 and its modifications; compound 1 ((2-hydroxyphenyl)oxidanyl), compound 2 ((3,5-dihydroxyphenyl)oxidanyl), compound 3 ((3-hydroxyphenyl)oxidanyl), and compound 4 ((3-sulfanylphenyl)oxidanyl).

Figure 2.

MDS analysis (A) Root Mean Square Deviation (RMSD) (blue line), Radius of Gyration (RoG) (orange line), and Surface Accessible Surface Area (SASA) (gray line) versus time of the simulation. (B) The per residue Root Mean Square Fluctuation (RMSF) (blue line) during the 51 ns of the MDS. The structure of SARS-CoV-2 RdRp shown by surface (left) and cartoon (right) representations. The N (blue) and C (red) terminals are shown in the balls representations, while the active site aspartates are depicted in black. The most movable parts of the protein are colored in orange (K712-D716), yellow (A731-V739), and wheat (Y775-M790), while the entire protein is in green.

Figure 3.

(A) Bar graph representing the average binding energies (in kcal/mol) calculated by AutoDock Vina software for NTPs (sky blue column), Sofosbuvir (blue), Ribavirin (blue), Galidesivir (blue), Remdesivir (blue), Favipiravir (violet), Cefuroxime (cyan), Tenofovir (blue), and Hydroxychloroquine (green). (B) Bar graph representing the average binding energies for the nucleotides (GTP, CTP, UTP, and ATP), and other compounds. The compounds IDX-184, YAK, Setrobuvir (green column) are the best based on its binding affinities to SARS-CoV-2 RdRp. The negative control compounds (Cinnamaldehyde and Thymoquinone) are depicted in red columns, while other compounds with moderate affinities in yellow columns. (C) A bar graph shows the average binding energies for the IDX-184 (orange column) and its derivative compounds (1-4) (red columns). Error bars represent the standard deviation, while orange circles represent the binding affinities of the compounds to the solved SARS-CoV-2 RdRp structure (PDB ID: 7BTF).