Virtual high throughput screening: Potential inhibitors for SARS-CoV-2 PLPRO and 3CLPRO proteases

Abstract

The pandemic, COVID-19, has spread worldwide and affected millions of people. There is an urgent need, therefore, to find a proper treatment for the novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), the causative agent. This paper focuses on identifying inhibitors that target SARS-CoV-2 proteases, PL^PRO and 3CL^PRO, which control the duplication and manages the life cycle of SARS-CoV-2. We have carried out detailed in silico Virtual high-throughput screening using Food and Drug Administration (FDA) approved drugs from the Zinc database, COVID-19 clinical trial compounds from Pubchem database, Natural compounds from Natural Product Activity and Species Source (NPASS) database and Maybridge database against PL^PRO and 3CL^PRO proteases. After thoroughly analyzing the screening results, we found five compounds, Bemcentinib, Pacritinib, Ergotamine, MFCD00832476, and MFCD02180753 inhibit PL^PRO and six compounds, Bemcentinib, Clofazimine, Abivertinib, Dasabuvir, MFCD00832476, Leuconicine F inhibit the 3CL^PRO. These compounds are stable within the protease proteins' active sites at 20ns MD simulation. The stability is revealed by hydrogen bond formations, hydrophobic interactions, and salt bridge interactions. Our study results also reveal that the selected five compounds against PL^PRO and the six compounds against 3CL^PRO bind to their active sites with good binding free energy. These compounds that inhibit the activity of PL^PRO and 3CL^PRO may, therefore, be used for treating COVID-19 infection.

Keywords: 3CL(PRO); COVID-19; Drug repositioning; PL(PRO); SARS-CoV-2.

Graphical abstract

Fig. 1

SARS-CoV-2 genome containing 16 non-structural proteins (Nsp) organized in individual with polyprotein 1ab (PP1ab). Red (PL^PRO) and Blue (3CL^PRO) triangles show the cleavage sites of the protease.

Fig. 2

Crystal structures of SARS-CoV-2. (A) PL^PRO and (B) 3CL^PRO.

Fig. 3

Titration Curves for PL^PRO and 3CL^PRO protease structures with different Internal Dielectric is 4, 10, and 20.

Fig. 4

Top active site of protease enzyme: (A) PL^PRO and (B) 3CL^PRO.

Fig. 5

Calculation of Average Chemical Fingerprint (CF), Tanimoto 2D Similarity Tc value. (A) Average CF Tanimoto value for NPASS database, (B) Average CF Tanimoto value for Maybridge database. The Red line shows the threshold value.

Fig. 6

The plot of ADMET_PSA_2D vs. AlogP98 (the 95 and 99% confidence limit ellipses corresponding to the HIA and BBB models for ligands). (A) NPASS database, (B) Maybridge database.

Fig. 7

BE for virtual hit compounds. The violet line shows the cut-off range for the compounds selected. (A) FDA approved compounds, (B) PubChem COVID-19 clinical trial compounds, (C) NPASS database compounds and (D) Maybridge database compounds.

Fig. 8

(A) The ten docked potential inhibitors against SARS-CoV-2 PL^PRO from the FDA approved compounds, (B) The 10 potential inhibitors bind to PL^PRO, (C) The surface protein with ligands bound to the pocket.

Fig. 9

(A) The ten docked potential inhibitors against SARS-CoV-2 PL^PRO from PubChem COVID-19 clinical trial compounds, (B) Top 10 potential inhibitors bind to PL^PRO protein, (C) The surface protein with ligand bound to the pocket.

Fig. 10

(A) The ten docked compounds from NPASS database are potential inhibitors against SARS-CoV-2 PL^PRO, (B) Top 10 potential inhibitor binds to PL^PRO protein in the Catalytic triad region, (C) The surface protein with ligand bound to the pocket.

Fig. 11

(A) The ten docked potential inhibitors agents against SARS-CoV-2 PL^PRO from the Maybridge database, (B) Top 10 potential inhibitor binds to PL^PRO protein in the Catalytic triad region, (C) The surface protein with ligand bound to the pocket.

Fig. 12

BE for the virtual hit compounds. The violet line shows the cut-off range for compound selection, (A) FDA approved compounds, (B) PubChem COVID-19 clinical trial compounds, (C) NPASS Database compounds, and (D) Maybridge Database compounds.

Fig. 13

(A) The ten potential inhibitors against SARS-CoV-2 3CL^PRO from FDA approved compounds, (B) Top 10 potential inhibitors bind to 3CL^PRO protein near the long loop connected by domain-I and domain-II, (C) The surface protein with ligand bound to the pocket.

Fig. 14

(A) The ten docked against SARS-CoV-2 3CL^PRO from PubChem COVID-19 clinical trial compounds, (B) Top 10 potential inhibitor bind to 3CL^PRO protein near the long loop connected by domain-I and domain-II, (C) The surface protein with ligand bound to the pocket.

Fig. 15

(A) The ten docked potential inhibitors against SARS-CoV-2 3CL^PRO from NPASS database compounds (B) Top 10 potential inhibitors bind to the 3CL^PRO protein near the long loop connected by domain-I domain-II, (C) The surface protein with ligand bound to the pocket.

Fig. 16

(A) The ten potential inhibitors against SARS-CoV-2 3CL^PRO from Maybridge database compounds, (B) Top 10 potential inhibitors bind to 3CL^PRO protein near to the long loop connected by domain-I and domain-II, (C) The surface protein with ligand bound to the pocket.

Fig. 17

(A) 10 Clusters found in PL^PRO and (B) 8 Clusters found in 3CL^PRO based on the Hierarchical Clustering method.

Fig. 18

MD trajectory analysis for the selected top five compound-PL^PRO complexes. (A) RMSD, (B) RMSF, (C) Rg, (D) Hydrogen bonds interaction between protein and solvent, (E) Hydrogen bond interaction between PL^PRO and compounds.

Fig. 19

MD trajectory analysis for selected top six compound-3CL^PROcomplexes. (A) RMSD, (B) RMSF, (C) Rg, (D) Hydrogen bonds formation between protein-solvent, and (E) Hydrogen bond formation between the 3CL^PRO and compounds in complexes.