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10.1021/acscombsci.0c00140 http://scihub22266oqcxt.onion/10.1021/acscombsci.0c00140 33119257!7605330!33119257     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 286.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 286.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       ACS+Comb+Sci 2020 ; 22 (12): 826-832 Nephropedia Template TP {{tp|p=33119257|t=2020. Profiling SARS-CoV-2 Main Protease (M(PRO)) Binding to Repurposed Drugs Using Molecular Dynamics Simulations in Classical and Neural Network-Trained Force Fields |pdf=|usr=}}{{33119257}} gab.com Text Profiling SARS-CoV-2 Main Protease (M(PRO)) Binding to Repurposed Drugs Using Molecular Dynamics Simulations in Classical and Neural Network-Trained Force Fields JO: ACS Comb Sci http://www.kidney.de/pget.php?&tw=1&pmid=33119257 #FOAvip 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. Twit Text FOAVip Profiling SARS-CoV-2 Main Protease (M(PRO)) Binding to Repurposed Drugs Using Molecular Dynamics Simulations in Classical and Neural Network-Trained Force Fields ????ACS Comb Sci http://www.kidney.de/pget.php?&tw=1&pmid=33119257 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33119257 #FOAvip English Wikipedia <ref>{{Cite pmid|33119257}}</ref> Profiling SARS-CoV-2 Main Protease (M(PRO)) Binding to Repurposed Drugs Using Molecular Dynamics Simulations in Classical and Neural Network-Trained Force Fields #MMPMID33119257 Gupta A; Zhou HX ACS Comb Sci 2020[Dec]; 22 (12): 826-832 PMID33119257show ga 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. |*Drug Repositioning[MESH] |*Neural Networks, Computer[MESH] |Antiviral Agents/*chemistry/pharmacology[MESH] |Catalytic Domain[MESH] |Coronavirus 3C Proteases/antagonists & inhibitors/*chemistry[MESH] |Coronavirus Protease Inhibitors/*chemistry/pharmacology[MESH] |Drug Discovery/*methods[MESH] |Molecular Dynamics Simulation[MESH] |Protein Binding[MESH]Profiling SARS-CoV-2 Main Protease (M(PRO)) Binding to Repurposed Drug(epmc).pdf DeepDyve Pubget Overpricing