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10.2174/0929867328666210329094111 http://scihub22266oqcxt.onion/10.2174/0929867328666210329094111 33781188!ä!33781188 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Curr+Med+Chem 2021 ; 28 (37): 7614-7633 Nephropedia Template TP {{tp|p=33781188|t=2021. Protein-Ligand Docking Simulations with AutoDock4 Focused on the Main Protease of SARS-CoV-2 |pdf=|usr=}}{{33781188}} gab.com Text Protein-Ligand Docking Simulations with AutoDock4 Focused on the Main Protease of SARS-CoV-2 JO: Curr Med Chem http://www.kidney.de/pget.php?&tw=1&pmid=33781188 #FOAvip BACKGROUND: The main protease of SARS-CoV-2 (M(pro)) is one of the targets identified in SARS-CoV-2, the causative agent of COVID-19. The application of X-ray diffraction crystallography made available the three-dimensional structure of this protein target in complex with ligands, which paved the way for docking studies. OBJECTIVE: Our goal here is to review recent efforts in the application of docking simulations to identify inhibitors of the M(pro) using the program AutoDock4. METHODS: We searched PubMed to identify studies that applied AutoDock4 for docking against this protein target. We used the structures available for M(pro) to analyze intermolecular interactions and reviewed the methods used to search for inhibitors. RESULTS: The application of docking against the structures available for the M(pro) found ligands with an estimated inhibition in the nanomolar range. Such computational approaches focused on the crystal structures revealed potential inhibitors of M(pro) that might exhibit pharmacological activity against SARS-CoV-2. Nevertheless, most of these studies lack the proper validation of the docking protocol. Also, they all ignored the potential use of machine learning to predict affinity. CONCLUSION: The combination of structural data with computational approaches opened the possibility to accelerate the search for drugs to treat COVID-19. Several studies used AutoDock4 to search for inhibitors of M(pro). Most of them did not employ a validated docking protocol, which lends support to critics of their computational methodology. Furthermore, one of these studies reported the binding of chloroquine and hydroxychloroquine to M(pro). This study ignores the scientific evidence against the use of these antimalarial drugs to treat COVID-19. Twit Text FOAVip Protein-Ligand Docking Simulations with AutoDock4 Focused on the Main Protease of SARS-CoV-2 ????Curr Med Chem http://www.kidney.de/pget.php?&tw=1&pmid=33781188 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33781188 #FOAvip English Wikipedia <ref>{{Cite pmid|33781188}}</ref> Protein-Ligand Docking Simulations with AutoDock4 Focused on the Main Protease of SARS-CoV-2 #MMPMID33781188 de Azevedo Junior WF; Bitencourt-Ferreira G; Godoy JR; Adriano HMA; Dos Santos Bezerra WA; Dos Santos Soares AM Curr Med Chem 2021[]; 28 (37): 7614-7633 PMID33781188show ga BACKGROUND: The main protease of SARS-CoV-2 (M(pro)) is one of the targets identified in SARS-CoV-2, the causative agent of COVID-19. The application of X-ray diffraction crystallography made available the three-dimensional structure of this protein target in complex with ligands, which paved the way for docking studies. OBJECTIVE: Our goal here is to review recent efforts in the application of docking simulations to identify inhibitors of the M(pro) using the program AutoDock4. METHODS: We searched PubMed to identify studies that applied AutoDock4 for docking against this protein target. We used the structures available for M(pro) to analyze intermolecular interactions and reviewed the methods used to search for inhibitors. RESULTS: The application of docking against the structures available for the M(pro) found ligands with an estimated inhibition in the nanomolar range. Such computational approaches focused on the crystal structures revealed potential inhibitors of M(pro) that might exhibit pharmacological activity against SARS-CoV-2. Nevertheless, most of these studies lack the proper validation of the docking protocol. Also, they all ignored the potential use of machine learning to predict affinity. CONCLUSION: The combination of structural data with computational approaches opened the possibility to accelerate the search for drugs to treat COVID-19. Several studies used AutoDock4 to search for inhibitors of M(pro). Most of them did not employ a validated docking protocol, which lends support to critics of their computational methodology. Furthermore, one of these studies reported the binding of chloroquine and hydroxychloroquine to M(pro). This study ignores the scientific evidence against the use of these antimalarial drugs to treat COVID-19. |*SARS-CoV-2/drug effects[MESH] |Antiviral Agents/*pharmacology[MESH] |COVID-19[MESH] |Coronavirus 3C Proteases/*antagonists & inhibitors[MESH] |Ligands[MESH] |Molecular Docking Simulation[MESH] |Molecular Dynamics Simulation[MESH] |Peptide Hydrolases[MESH]Protein-Ligand Docking Simulations with AutoDock4 Focused on the Main (epmc).pdf DeepDyve Pubget Overpricing