Identifying SARS-CoV-2 main protease inhibitors by applying the computer screening of a large database of molecules. Issue 5 (4th May 2022)

• Record Type: Journal Article
• Authors: Sepehri, B.; Ghavami, R.; Mahmoudi, F.; Irani, M.; Ahmadi, R.; Moradi, D.
• Is Part Of: SAR and QSAR in environmental research. Volume 33:Issue 5(2022)
• Page Start: 341
• Page End: 356
• Publication Date: 2022-05-04
• Subjects: COVID-19 -- SARS-CoV-2 -- virtual screening -- similarity search -- molecular docking -- molecular dynamics simulations; Structure-activity relationships (Biochemistry) -- Periodicals; QSAR (Biochemistry) -- Periodicals; 572.4
• DOI: 10.1080/1062936X.2022.2050424
• Languages: English
• ISSNs: 1062-936X

Abstract

ABSTRACT: The outbreak of coronavirus disease 2019 (COVID-19) at the end of 2019 affected global health. Its infection agent was called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Wearing a mask, maintaining social distance, and vaccination are effective ways to prevent infection of SARS-CoV-2, but none of them help infected people. Targeting the enzymes of SARS-CoV-2 is an effective way to stop the replication of the virus in infected people and treat COVID-19 patients. SARS-CoV-2 main protease is a therapeutic target which the inhibition of its enzymatic activity prevents from the replication of SARS-CoV-2. A large database of molecules has been searched to identify new inhibitors for SARS-CoV-2 main protease enzyme. At the first step, ligand screening based on similarity search was used to select similar compounds to known SARS-CoV-2 main protease inhibitors. Then molecules with better predicted pharmacokinetic properties were selected. Structure-based virtual screening based on the application of molecular docking and molecular dynamics simulation methods was used to select more effective inhibitors among selected molecules in previous step. Finally two compounds were considered as SARS-CoV-2 main protease inhibitors.