Structure-based drug designing of naphthalene based SARS-CoV PLpro inhibitors for the treatment of COVID-19

Bhati, Shipra

doi:10.1016/j.heliyon.2020.e05558

Cited by 17 publications

(9 citation statements)

References 38 publications

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“…MD simulation of 50 ns was performed at 300 K and 1 atm for two fs under the NPT ensemble. Hydrogen and covalent bonds were constrained using the SHAKE algorithm [ 83 ], whereas system temperature was controlled through Langevin dynamics [ 84 ]. The initial structure was used as a reference, and CPPTRAJ [ 85 ] of AMBER was run to generate a root-mean-square deviation (RMSD) plot to check the system MD simulation convergence [ 81 ].…”

Section: Methodsmentioning

confidence: 99%

Computational Determination of Potential Multiprotein Targeting Natural Compounds for Rational Drug Design Against SARS-COV-2

et al. 2021

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SARS-CoV-2 caused the current COVID-19 pandemic and there is an urgent need to explore effective therapeutics that can inhibit enzymes that are imperative in virus reproduction. To this end, we computationally investigated the MPD3 phytochemical database along with the pool of reported natural antiviral compounds with potential to be used as anti-SARS-CoV-2. The docking results demonstrated glycyrrhizin followed by azadirachtanin, mycophenolic acid, kushenol-w and 6-azauridine, as potential candidates. Glycyrrhizin depicted very stable binding mode to the active pocket of the Mpro (binding energy, −8.7 kcal/mol), PLpro (binding energy, −7.9 kcal/mol), and Nucleocapsid (binding energy, −7.9 kcal/mol) enzymes. This compound showed binding with several key residues that are critical to natural substrate binding and functionality to all the receptors. To test docking prediction, the compound with each receptor was subjected to molecular dynamics simulation to characterize the molecule stability and decipher its possible mechanism of binding. Each complex concludes that the receptor dynamics are stable (Mpro (mean RMSD, 0.93 Å), PLpro (mean RMSD, 0.96 Å), and Nucleocapsid (mean RMSD, 3.48 Å)). Moreover, binding free energy analyses such as MMGB/PBSA and WaterSwap were run over selected trajectory snapshots to affirm intermolecular affinity in the complexes. Glycyrrhizin was rescored to form strong affinity complexes with the virus enzymes: Mpro (MMGBSA, −24.42 kcal/mol and MMPBSA, −10.80 kcal/mol), PLpro (MMGBSA, −48.69 kcal/mol and MMPBSA, −38.17 kcal/mol) and Nucleocapsid (MMGBSA, −30.05 kcal/mol and MMPBSA, −25.95 kcal/mol), were dominated mainly by vigorous van der Waals energy. Further affirmation was achieved by WaterSwap absolute binding free energy that concluded all the complexes in good equilibrium and stability (Mpro (mean, −22.44 kcal/mol), PLpro (mean, −25.46 kcal/mol), and Nucleocapsid (mean, −23.30 kcal/mol)). These promising findings substantially advance our understanding of how natural compounds could be shaped to counter SARS-CoV-2 infection.

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Section: Methodsmentioning

confidence: 99%

Computational Determination of Potential Multiprotein Targeting Natural Compounds for Rational Drug Design Against SARS-COV-2

et al. 2021

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“…The strategy of repurposing the drug does not offer a permanent solution, and further clinical trials are required to test the effectiveness of these drugs to treat COVID-19. The approach of rational drug designing based on the pathogenic mechanism of COVID-19 and target protein structure of SARS-CoV-2 has been suggested to fight against COVID-19 [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Rational design of flavonoid based potential inhibitors targeting SARS-CoV 3CL protease for the treatment of COVID-19

Bhati

Kaushik

Singh

2021

Journal of Molecular Structure

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The current outbreak of Coronavirus Disease 2019 (COVID-19) pandemic has reported thousands of deaths worldwide due to the rapid transmission rate and the lack of antiviral drugs and vaccinations. There is an urgent need to develop potential antiviral drug candidates for the prevention of COVID-19 infection. In the present study, a series of potential inhibitors targeting SARS-CoV 3CL protease were rationally designed by incorporating gamma lactam ring, and various fluoro substituted heterocyclic ring systems to the flavonoid scaffold. The prediction of drug-likeness, oral bioavailability, toxicity, synthetic accessibility, and ADMET properties was made by computational means. Quercetin was used as standard. The binding affinity of the ligands towards the 3CL protease target was examined using docking simulations. The designed ligands possess favourable pharmacokinetic and pharmacodynamic properties. Ligand L4, L8, and L14 appeared to be the lead compounds in the series and can be considered for further in-vivo and in-vitro validation.

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“…Inhibition of SARS-CoV-2 PL \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$^{pro}$\end{document} with GRL-0617 has three main tasks: impair the virus-induced cytopathogenic effect, maintain the antiviral interferon pathway and reduce viral replication in infected cells [ 127 ]. A recent investigation attempted to design potential SARS-CoV PL \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$^{pro}$\end{document} inhibitors containing naphthalene and 3,4-dihydro-2H-pyran moieties connected via-NHCO-linker [ 89 ].…”

Section: Drug Design Strategies For Covid-19mentioning

confidence: 99%

Computational anti-COVID-19 drug design: progress and challenges

Wang

Zhang

Nie

et al. 2021

Briefings in Bioinformatics

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Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the development of robust therapeutic approaches, such as anti-COVID-19 drug design, could aid in managing the pandemic more efficiently. Some drug design strategies have been successfully applied during the COVID-19 pandemic to create and validate related lead drugs. The computational drug design methods used for COVID-19 can be roughly divided into (i) structure-based approaches and (ii) artificial intelligence (AI)-based approaches. Structure-based approaches investigate different molecular fragments and functional groups through lead drugs and apply relevant tools to produce antiviral drugs. AI-based approaches usually use end-to-end learning to explore a larger biochemical space to design antiviral drugs. This review provides an overview of the two design strategies of anti-COVID-19 drugs, the advantages and disadvantages of these strategies and discussions of future developments.

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Structure-based drug designing of naphthalene based SARS-CoV PLpro inhibitors for the treatment of COVID-19

Cited by 17 publications

References 38 publications

Computational Determination of Potential Multiprotein Targeting Natural Compounds for Rational Drug Design Against SARS-COV-2

Computational Determination of Potential Multiprotein Targeting Natural Compounds for Rational Drug Design Against SARS-COV-2

Rational design of flavonoid based potential inhibitors targeting SARS-CoV 3CL protease for the treatment of COVID-19

Computational anti-COVID-19 drug design: progress and challenges

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