Identification of high-affinity inhibitors of SARS-CoV-2 main protease: Towards the development of effective COVID-19 therapy

Abstract

Coronavirus disease 2019 (COVID-19) is an infectious disease, caused by a newly emerged highly pathogenic virus called novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Targeting the main protease (M^pro, 3CL^pro) of SARS-CoV-2 is an appealing approach for drug development because this enzyme plays a significant role in the viral replication and transcription. The available crystal structures of SARS-CoV-2 M^pro determined in the presence of different ligands and inhibitor-like compounds provide a platform for the quick development of selective inhibitors of SARS-CoV-2 M^pro. In this study, we utilized the structural information of co-crystallized SARS-CoV-2 M^pro for the structure-guided drug discovery of high-affinity inhibitors from the PubChem database. The screened compounds were selected on the basis of their physicochemical properties, drug-likeliness, and strength of affinity to the SARS-CoV-2 M^pro. Finally, we have identified 6-Deaminosinefungin (PubChem ID: 10428963) and UNII-O9H5KY11SV (PubChem ID: 71481120) as potential inhibitors of SARS-CoV-2 M^pro which may be further exploited in drug development to address SARS-CoV-2 pathogenesis. Both compounds are structural analogs of known antivirals, having considerable protease inhibitory potential with improved pharmacological properties. All-atom molecular dynamics simulations suggested SARS-CoV-2 M^pro in complex with these compounds is stable during the simulation period with minimal structural changes. This work provides enough evidence for further implementation of the identified compounds in the development of effective therapeutics of COVID-19.

Keywords: Coronavirus disease 2019; Drug discovery; Main protease; Molecular dynamics simulations; SARS-CoV-2; Virtual screening.

Graphical abstract

Fig. 1

The workflow demonstrates the process of virtual high-throughput screening used in this study. RO5, Lipinski's rule of five; ADMET, Absorption, Distribution, Metabolism, Excretion, and Toxicity.

Fig. 2

Binding pattern of the selected compounds and standard inhibitor K36 with SARS-CoV-2 M^pro. (A) Structural representation of the protein in-complexed with all the compounds making significant interactions with the functionally important residues of the SARS-CoV-2 M^pro binding pocket. (B) The potential surface of SARS-CoV-2 M^pro showing the binding pocket occupancy by the compounds.

Fig. 3

2D plots of the SARS-CoV-2 M^pro substrate-binding pocket residues and their interactions with compound (A) 57789333 (B) 5481231 (C) 71481120 (D) 54592323 (E) 10428963 and (F) K36.

Fig. 4

Structural dynamics of SARS-CoV-2 M^pro as a function of time. (A) Time evolution of the RMSD of M^pro before and after compounds binding. (B) Residual fluctuations plot of M^pro before and after 10428963 and 71481120 bindings. (C) Plot showing the radius of gyration of M^pro before and after 10428963 and 71481120 bindings. (D) SASA plot of M^pro of M^pro before and after 10428963 and 71481120 bindings.

Fig. 5

Structural compactness of M^pro before and after 10428963 and 71481120 binding. (A) Time evolution of hydrogen bonds formed intramolecular within M^pro (B) The PDF of intramolecular Hydrogen bonds.

Fig. 6

Principal component analysis. (A) 2D projections of trajectories on eigenvectors (EVs) showing conformational projections of M^pro (B) The projections of trajectories on both EVs with respect to time (C) Residual fluctuations of M^pro on eigenvector 1.

Fig. 7

The Gibbs energy landscapes for (A) free M^pro (B) M^pro-and 10428963 (C) M^pro-71481120.