Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1080/07391102.2020.1800514 http://scihub22266oqcxt.onion/10.1080/07391102.2020.1800514 32734828!ä!32734828 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 276.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Biomol+Struct+Dyn 2021 ; 39 (17): 6689-6704 Nephropedia Template TP {{tp|p=32734828|t=2021. Tackling COVID-19: identification of potential main protease inhibitors via structural analysis, virtual screening, molecular docking and MM-PBSA calculations |pdf=|usr=}}{{32734828}} gab.com Text Tackling COVID-19: identification of potential main protease inhibitors via structural analysis, virtual screening, molecular docking and MM-PBSA calculations JO: J Biomol Struct Dyn http://www.kidney.de/pget.php?&tw=1&pmid=32734828 #FOAvip The widespread of the COVID-19 disease, caused by the novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), had severely affected the entire world. Unfortunately, no successful vaccines or antiviral drugs are currently available which leaves the scientific community under huge pressure to tackle this pandemic. Among the identified promising druggable targets, specific to this virus, is the main protease (M(pro)) enzyme, which is vital for viral replication, transcription and packaging within the host cells. In this study, selective inhibition of the M(pro) was sought via thorough analysis of its available structural data in the Protein Data Bank. To this end, COVID-19 M(pro) crystal complexes were explored and the key interacting residues (KIRs) within its active site, that are expected to be vital for effective ligand binding, were identified. Based on these KIRs, 3D pharmacophore models were generated and used in virtual screening of different databases. Retrieved hits were docked into the active site of the enzyme and their MM-PBSA based free binding energies were calculated. Finally, ADMET descriptors were calculated to aid the selection of top scoring hits with best ADMET properties. Nine compounds with different chemotypes were identified as potential M(pro) inhibitors. Further, MD simulations of a virtual complex of M(pro) with one of the promising hits revealed stable binding which is indicative of good inhibitory potential. The identified compounds in this study are expected to support the global drug discovery efforts in fighting against this highly contagious virus by narrowing the searchable chemical space for potential effective therapeutics. Twit Text FOAVip Tackling COVID-19: identification of potential main protease inhibitors via structural analysis, virtual screening, molecular docking and MM-PBSA calculations ????J Biomol Struct Dyn http://www.kidney.de/pget.php?&tw=1&pmid=32734828 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32734828 #FOAvip English Wikipedia <ref>{{Cite pmid|32734828}}</ref> Tackling COVID-19: identification of potential main protease inhibitors via structural analysis, virtual screening, molecular docking and MM-PBSA calculations #MMPMID32734828 Al-Shar'i NA J Biomol Struct Dyn 2021[Oct]; 39 (17): 6689-6704 PMID32734828show ga The widespread of the COVID-19 disease, caused by the novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), had severely affected the entire world. Unfortunately, no successful vaccines or antiviral drugs are currently available which leaves the scientific community under huge pressure to tackle this pandemic. Among the identified promising druggable targets, specific to this virus, is the main protease (M(pro)) enzyme, which is vital for viral replication, transcription and packaging within the host cells. In this study, selective inhibition of the M(pro) was sought via thorough analysis of its available structural data in the Protein Data Bank. To this end, COVID-19 M(pro) crystal complexes were explored and the key interacting residues (KIRs) within its active site, that are expected to be vital for effective ligand binding, were identified. Based on these KIRs, 3D pharmacophore models were generated and used in virtual screening of different databases. Retrieved hits were docked into the active site of the enzyme and their MM-PBSA based free binding energies were calculated. Finally, ADMET descriptors were calculated to aid the selection of top scoring hits with best ADMET properties. Nine compounds with different chemotypes were identified as potential M(pro) inhibitors. Further, MD simulations of a virtual complex of M(pro) with one of the promising hits revealed stable binding which is indicative of good inhibitory potential. The identified compounds in this study are expected to support the global drug discovery efforts in fighting against this highly contagious virus by narrowing the searchable chemical space for potential effective therapeutics. |*COVID-19[MESH] |*Protease Inhibitors/pharmacology[MESH] |Antiviral Agents/pharmacology/therapeutic use[MESH] |Humans[MESH] |Molecular Docking Simulation[MESH]Tackling COVID-19: identification of potential main protease inhibitor(epmc).pdf DeepDyve Pubget Overpricing