Profiling SARS-CoV-2 Main Protease (M^PRO) Binding to Repurposed Drugs Using Molecular Dynamics Simulations in Classical and Neural Network-Trained Force Fields

Abstract

The current COVID-19 pandemic caused by a novel coronavirus SARS-CoV-2 urgently calls for a working therapeutic. Here, we report a computation-based workflow for efficiently selecting a subset of FDA-approved drugs that can potentially bind to the SARS-CoV-2 main protease M PRO . The workflow started with docking (using Autodock Vina) each of 1615 FDA-approved drugs to the M PRO active site. This step selected 62 candidates with docking energies lower than −8.5 kcal/mol. Then, the 62 docked protein–drug complexes were subjected to 100 ns of molecular dynamics (MD) simulations in a molecular mechanics (MM) force field (CHARMM36). This step reduced the candidate pool to 26, based on the root-mean-square-deviations (RMSDs) of the drug molecules in the trajectories. Finally, we modeled the 26 drug molecules by a pseudoquantum mechanical (ANI) force field and ran 5 ns hybrid ANI/MM MD simulations of the 26 protein–drug complexes. ANI was trained by neural network models on quantum mechanical density functional theory (wB97X/6-31G(d)) data points. An RMSD cutoff winnowed down the pool to 12, and free energy analysis (MM/PBSA) produced the final selection of 9 drugs: dihydroergotamine, midostaurin, ziprasidone, etoposide, apixaban, fluorescein, tadalafil, rolapitant, and palbociclib. Of these, three are found to be active in literature reports of experimental studies. To provide physical insight into their mechanism of action, the interactions of the drug molecules with the protein are presented as 2D-interaction maps. These findings and mappings of drug–protein interactions may be potentially used to guide rational drug discovery against COVID-19.

Identification of Plitidepsin as Potent Inhibitor of SARS-CoV-2-Induced Cytopathic Effect After a Drug Repurposing Screen

“…The best 11 molecules, based on Glides's docking score, were selected for experimental validation. Notice, however, that top docking scores, did not exceed −9, indicating poor potential binding, in agreement with other non-productive repurposing efforts reported on SARS-CoV-2 main protease, including sophisticated in silico studies (Gupta and Zhou, 2020).…”

“…We performed Glide docking on SARS-CoV-2 3CL Main protease using the DrugBank database ( https://go.drugbank.com ), with a total of 6837 molecules containing FDA approved drugs, reported phased I/II/III drugs and experimental molecules (i.e. drugs that are at the preclinical or animal testing stage) ( Gupta and Zhou, 2020 ). For this, two different receptors were prepared, the 6LU7 pdb structure, after removing the covalently bound inhibitor, and a combination of two crystals from the Diamond collection ( https://www.diamond.ac.uk/covid-19 ).…”

A Machine Learning-Enabled Pipeline for Large-Scale Virtual Drug Screening

“…Hybrid ANI/MM MD simulations were as described in our previous work 32 . Force field settings for the protein and solvent were as stated above for classical simulations, that for ligands was ANI-2x 33 .…”

“…1) and a drug target for breast cancer 34,35 . We adapted our previous workflow 32 to produce 8 RPN11 inhibitors. In comparison, with significantly reduced computing cost, machine learningenable pipeline picked up 6 of these inhibitors.…”

DFT, molecular docking and molecular dynamics simulation studies on some newly introduced natural products for their potential use against SARS-CoV-2

“…Thus, inhibiting this enzyme may be a potential treatment of COVID-19. The extant literature contains several studies on SARS-CoV-2 main protease and its potential inhibitors [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] . Throughout the history, natural compounds have been used in the treatment of various diseases, and today they continue to be important compounds in the development of many drugs.…”