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Bioinformatics
Exploring the mangrove based phytochemicals as potential viral RNA helicase inhibitors by in silico docking and molecular dynamics simulation method
R. Satpathy, S. Acharya School of Biotechnology, Gangadhar Meher University, Amruta Vihar, Sambalpur, Odisha, India
Abstract:
The high molecular diversity of plant-derived compounds in mangroves has drawn attention to the discovery of their antiviral capacity against several pathogenic viruses. Therefore, screening for effective antiviral compounds with fewer harmful side effects is needed. This study aimed to screen several bioactive compounds from mangrove plants that could be appropriately used as an RNA helicase inhibitor against pathogenic viruses. Fifty-nine compounds were selected from literature and databases for initial study and screening according to Lipinski's rule of five. The chosen compounds obtained were subjected to another series of screening by molecular docking study with five different RNA helicase enzymes of the pathogenic virus using the Autodock Vina tool, followed by ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis. In addition, the best compound-bound helicase RNA complexes were included in a 50 nanosecond molecular dynamics simulation using the Gromacs 5.1.1 software, followed by Molecular Mechanics Poisson–Boltzmann Surface Area (MM-PBSA) analysis. This comparative study predicts that phytochemical gedunin is an excellent inhibitor of the RNA helicase enzyme of SARS-CoV-2, followed by the Japanese encephalitis virus and hepatitis C virus (HCV). The results of the study may lead to the development of antiviral compounds against RNA helicase enzymes of pathogenic viruses.
Key words:
pathogenic viruses, RNA helicase, molecular docking, molecular dynamics simulation, mangrove phytochemicals.
Received 30.07.2023, 31.10.2023, Published 19.11.2023
Citation:
R. Satpathy, S. Acharya, “Exploring the mangrove based phytochemicals as potential viral RNA helicase inhibitors by in silico docking and molecular dynamics simulation method”, Mat. Biolog. Bioinform., 18:2 (2023), 405–417
Linking options:
https://www.mathnet.ru/eng/mbb526 https://www.mathnet.ru/eng/mbb/v18/i2/p405
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Abstract page: | 34 | Full-text PDF : | 20 | References: | 13 |
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