Dataset Information

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,

SUBMITTER: Gupta A

PROVIDER: S-EPMC7605330 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Dataset Information

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

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

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

OmicsDI is part of the ELIXIR infrastructure

Tweets

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