tetano
Editor, Senior Moderator
PeerJ
. 2022 Oct 7;10:e14120.
doi: 10.7717/peerj.14120. eCollection 2022.
Identification of 1H-purine-2,6-dione derivative as a potential SARS-CoV-2 main protease inhibitor: molecular docking, dynamic simulations, and energy calculations
Hossam Nada[SUP] 1 2 [/SUP], Ahmed Elkamhawy[SUP] 1 3 [/SUP], Kyeong Lee[SUP] 1 [/SUP]
Affiliations
Abstract
The rapid spread of the coronavirus since its first appearance in 2019 has taken the world by surprise, challenging the global economy, and putting pressure on healthcare systems across the world. The introduction of preventive vaccines only managed to slow the rising death rates worldwide, illuminating the pressing need for developing effective antiviral therapeutics. The traditional route of drug discovery has been known to require years which the world does not currently have. In silico approaches in drug design have shown promising results over the last decade, helping to decrease the required time for drug development. One of the vital non-structural proteins that are essential to viral replication and transcription is the SARS-CoV-2 main protease (Mpro). Herein, using a test set of recently identified COVID-19 inhibitors, a pharmacophore was developed to screen 20 million drug-like compounds obtained from a freely accessible Zinc database. The generated hits were ranked using a structure based virtual screening technique (SBVS), and the top hits were subjected to in-depth molecular docking studies and MM-GBSA calculations over SARS-COV-2 Mpro. Finally, the most promising hit, compound (1), and the potent standard (III) were subjected to 100 ns molecular dynamics (MD) simulations and in silico ADME study. The result of the MD analysis as well as the in silico pharmacokinetic study reveal compound 1 to be a promising SARS-Cov-2 MPro inhibitor suitable for further development.
Keywords: E-pharmacophore modeling; Free energy calculations; Molecular dynamics (MD) simulation; SARS-Cov-2 Mpro; Structure-based virtual screening (SBVS).
. 2022 Oct 7;10:e14120.
doi: 10.7717/peerj.14120. eCollection 2022.
Identification of 1H-purine-2,6-dione derivative as a potential SARS-CoV-2 main protease inhibitor: molecular docking, dynamic simulations, and energy calculations
Hossam Nada[SUP] 1 2 [/SUP], Ahmed Elkamhawy[SUP] 1 3 [/SUP], Kyeong Lee[SUP] 1 [/SUP]
Affiliations
- PMID: 36225900
- PMCID: PMC9549888
- DOI: 10.7717/peerj.14120
Abstract
The rapid spread of the coronavirus since its first appearance in 2019 has taken the world by surprise, challenging the global economy, and putting pressure on healthcare systems across the world. The introduction of preventive vaccines only managed to slow the rising death rates worldwide, illuminating the pressing need for developing effective antiviral therapeutics. The traditional route of drug discovery has been known to require years which the world does not currently have. In silico approaches in drug design have shown promising results over the last decade, helping to decrease the required time for drug development. One of the vital non-structural proteins that are essential to viral replication and transcription is the SARS-CoV-2 main protease (Mpro). Herein, using a test set of recently identified COVID-19 inhibitors, a pharmacophore was developed to screen 20 million drug-like compounds obtained from a freely accessible Zinc database. The generated hits were ranked using a structure based virtual screening technique (SBVS), and the top hits were subjected to in-depth molecular docking studies and MM-GBSA calculations over SARS-COV-2 Mpro. Finally, the most promising hit, compound (1), and the potent standard (III) were subjected to 100 ns molecular dynamics (MD) simulations and in silico ADME study. The result of the MD analysis as well as the in silico pharmacokinetic study reveal compound 1 to be a promising SARS-Cov-2 MPro inhibitor suitable for further development.
Keywords: E-pharmacophore modeling; Free energy calculations; Molecular dynamics (MD) simulation; SARS-Cov-2 Mpro; Structure-based virtual screening (SBVS).