Identification of the most potent bioactive natural compound as main protease inhibitor of SARS-CoV-2: Molecular docking, molecular dynamics simulations and MM-PBSA studies

Emergence of COVID-19 and thereafter intensive research on bioactive natural compounds against SARS-CoV-2, identified a large number of phytochemicals (i.e., plants-derived) and mycochemicals (i.e., fungi-derived) as potential inhibitors with proven antiviral properties against SARS-CoV-2, but there are no comparative study on the reported compounds. A comparative study among the previously identified/reported main protease (Mpro) inhibitors of SARS-CoV-2 can lead to the most potent compound that eventually helps to make an effective drug lead against SARS-CoV-2. Through manual literature curation, we selected 57 potential bioactive compounds and screened them against Mpro protein of SARS-CoV-2. A series of in silico screening such as binding affinity, drug-like properties, pharmacokinetic, physicochemical, and ADMET studies identified top ten compounds as potential Mpro inhibitors. Further, docking studies prioritized the top two compounds namely Norquinadoline A and Quinadoline B, based on their predicted affinity for the target protein. Binding free energy calculations further emphasized them as top candidates for effective Mpro inhibitors that hold promise for drug development against COVID-19. In-depth molecular dynamics studies and MM/PBSA analysis culminated in the recognition of Norquinadoline A as the most potent Mpro inhibitor of SARS-CoV-2. Thus, Norquinadoline A can be used as lead compound in further drug discovery process after in vitro and in vivo experimental studies.

Institutions

Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2024-12-01
Primary Topic
Cultural Heritage Materials Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Identification of the most potent bioactive natural compound as main protease inhibitor of SARS-CoV-2: Molecular docking, molecular dynamics simulations and MM-PBSA studies

SHILAP Revista de lepidopterología
Cultural Heritage Materials Analysis
article

Identification of the most potent bioactive natural compound as main protease inhibitor of SARS-CoV-2: Molecular docking, molecular dynamics simulations and MM-PBSA studies

article en

Abstract

Emergence of COVID-19 and thereafter intensive research on bioactive natural compounds against SARS-CoV-2, identified a large number of phytochemicals (i.e., plants-derived) and mycochemicals (i.e., fungi-derived) as potential inhibitors with proven antiviral properties against SARS-CoV-2, but there are no comparative study on the reported compounds. A comparative study among the previously identified/reported main protease (Mpro) inhibitors of SARS-CoV-2 can lead to the most potent compound that eventually helps to make an effective drug lead against SARS-CoV-2. Through manual literature curation, we selected 57 potential bioactive compounds and screened them against Mpro protein of SARS-CoV-2. A series of in silico screening such as binding affinity, drug-like properties, pharmacokinetic, physicochemical, and ADMET studies identified top ten compounds as potential Mpro inhibitors. Further, docking studies prioritized the top two compounds namely Norquinadoline A and Quinadoline B, based on their predicted affinity for the target protein. Binding free energy calculations further emphasized them as top candidates for effective Mpro inhibitors that hold promise for drug development against COVID-19. In-depth molecular dynamics studies and MM/PBSA analysis culminated in the recognition of Norquinadoline A as the most potent Mpro inhibitor of SARS-CoV-2. Thus, Norquinadoline A can be used as lead compound in further drug discovery process after in vitro and in vivo experimental studies.

SHILAP Revista de lepidopterología
University of Allahabad (IN), Banaras Hindu University (IN)
Openalex Percentile: Top 28%
Cultural Heritage Materials Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.