Natural Marine-derived Compounds as Potential Inhibitors of SARS-CoV-2 Main Protease: A Comprehensive Computational Study

Background: The COVID-19 pandemic, caused by the SARS-CoV-2 virus, highlights the urgent need for effective antiviral agents. The main protease [3-chymotrypsin-like protease (3CLpro)] of SARS-CoV-2 is crucial for viral replication and is a promising target for therapeutic intervention. Objectives: In this study, an in silico approach was employed to identify potential 3CLpro inhibitors from a library of 80 marine-derived natural compounds. Methods: Molecular docking was performed using AutoDock Vina to assess the binding affinity and interaction profiles of the compounds with the active site of 3CLpro. The top-scoring compounds were selected for molecular dynamics simulations using GROMACS to analyze the structural stability and dynamic behavior of the ligand-protease complexes. Results: Several compounds, particularly those from marine fungi and sponges, formed stable interactions with catalytic residues His41 and Cys145, maintaining conformational stability throughout 100 ns of simulation. In silico ADMET assessments were further performed to predict the pharmacokinetic properties of the docked compounds. Overall, molecular dynamics (MD) analysis showed that the dynamic properties of the protein alter significantly when it is in complex with the selected compounds. Conclusions: The findings in this study suggest that Isobutyrolactone II and Aspernolide A, marine natural products, could serve as promising lead compounds for the development of SARS-CoV-2 main protease inhibitors, warranting further experimental validation.

Authors

Publication Details

Journal
Journal of Reports in Pharmaceutical Sciences
Published
2025-08-11
DOI
https://doi.org/10.5812/jrps-159451
Citations
3
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
4.51

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Natural Marine-derived Compounds as Potential Inhibitors of SARS-CoV-2 Main Protease: A Comprehensive Computational Study

Sara Rahmani, Amin Nowroozi, Ghazal Nowroozi, Zahra Asvar et al.
3 citations
Journal of Reports in Pharmaceutical Sciences
Computational Drug Discovery Methods
4.51
article

Natural Marine-derived Compounds as Potential Inhibitors of SARS-CoV-2 Main Protease: A Comprehensive Computational Study

Sara Rahmani, Amin Nowroozi, Ghazal Nowroozi, Zahra Asvar, Parvin Jalali, Armin Zareie
article en
3 citations

Abstract

Background: The COVID-19 pandemic, caused by the SARS-CoV-2 virus, highlights the urgent need for effective antiviral agents. The main protease [3-chymotrypsin-like protease (3CLpro)] of SARS-CoV-2 is crucial for viral replication and is a promising target for therapeutic intervention. Objectives: In this study, an in silico approach was employed to identify potential 3CLpro inhibitors from a library of 80 marine-derived natural compounds. Methods: Molecular docking was performed using AutoDock Vina to assess the binding affinity and interaction profiles of the compounds with the active site of 3CLpro. The top-scoring compounds were selected for molecular dynamics simulations using GROMACS to analyze the structural stability and dynamic behavior of the ligand-protease complexes. Results: Several compounds, particularly those from marine fungi and sponges, formed stable interactions with catalytic residues His41 and Cys145, maintaining conformational stability throughout 100 ns of simulation. In silico ADMET assessments were further performed to predict the pharmacokinetic properties of the docked compounds. Overall, molecular dynamics (MD) analysis showed that the dynamic properties of the protein alter significantly when it is in complex with the selected compounds. Conclusions: The findings in this study suggest that Isobutyrolactone II and Aspernolide A, marine natural products, could serve as promising lead compounds for the development of SARS-CoV-2 main protease inhibitors, warranting further experimental validation.

Journal of Reports in Pharmaceutical SciencesVol. 13(1)
Kermanshah University of Medical Sciences
Openalex Percentile: Top 5%
Computational Drug Discovery Methods
4.51
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.