scholarly journals Inhibition of COVID-19 RNA-Dependent RNA Polymerase by Natural Bioactive Compounds: Molecular Docking Analysis

2020 ◽  
Author(s):  
Nourhan M. Abd El-Aziz ◽  
Mohamed G. Shehata ◽  
Olfat M. Eldin Awad ◽  
Sobhy A. El-Sohaimy
Keyword(s):  
Molecular Docking ◽  
Benzoic Acid ◽  
Rna Polymerase ◽  
Gallic Acid ◽  
Ellagic Acid ◽  
Binding Energies ◽  
Rna Dependent Rna Polymerase ◽  
Docking Analysis ◽  
Cell Defense ◽  
Pharmacokinetic Properties

Abstract Till now there is no approved treatment for COVID-19. Phenolic compounds are known to have antiviral activity against many viruses such as HCV and HIV, through their phenol rings interaction with viral proteins and/or RNA, or via its regulating MAP kinase signaling in host cell defense. The present study aimed to assess polyphenolic compounds (gallic acid, quercetin, caffeine, resveratrol, naringenin, benzoic acid, oleuropein and ellagic acid) as COVID-19 RNA-dependent RNA polymerase (PDB ID 6M71) inhibitors, using a molecular docking. Molecular docking of these polyphenols were performed using Autodock 4.0 and Chimera 1.8.1 programs. Drug likeness and polyphenols pharmacokinetic properties were calculated using SWISSADME prediction website (http://www.swissadme.ch/). Remdesivir and ribavirin were used as standard antiviral drugs for comparison. Docking analysis results, ranked by binding energy value (ΔG) of several tested ligands toward COVID-19 polymerase were; remdesivir > gallic acid > quercetin > caffeine > ribavirin > resveratrol > naringenin > benzoic acid > oleuropein > ellagic acid. The binding energies were -8.51, - 7.55, - 7.17, -6.10, - 6.01, - 5.79, - 5.69, - 5.54, - 4.94 and -4.59 kcal/mol, respectively. All tested polyphenols performed hydrogen bonds with one or two of the nucleotide triphosphate entry channel (NTP) amino acids in COVID-19 polymerase (ARG 555, ARG 555, LYS 545), except caffeine and oleuropein. Binding of polyphenols to NTP of COVID-19 polymerase may influence in the entry of the substrate and divalent cations into the central active site cavity, inhibiting the enzyme activity. It appears promising that, gallic acid and quercetin exhibited high binding affinity than ribavirin toward COVID-19 polymerase and expressed good drug likeness and pharmacokinetic properties. Therefore, gallic acid and quercetin may represent a potential treatment option for COVID-19. Further researches are urgently required to investigate the potential uses of these polyphenols in COVID-19 treatment. Additionally, resveratrol, naringenin, benzoic and ellagic acid seem to have the best potential to act as COVID-19 polymerase inhibitors.

scholarly journals Molecular docking analysis of SARS-CoV-2 linked RNA dependent RNA polymerase (RdRp) with compounds from Plectranthus amboinicus

2021 ◽  
Vol 17 (1) ◽  
pp. 167-170
Author(s):  
Jayaraman Selvaraj ◽  
Keyword(s):  
Molecular Docking ◽  
Rna Polymerase ◽  
Rosmarinic Acid ◽  
Target Protein ◽  
Coumaric Acid ◽  
Rna Dependent Rna Polymerase ◽  
Docking Analysis ◽  
Salvianolic Acid ◽  
Plectranthus Amboinicus ◽  
Molecular Docking Analysis

It is of interest to document the moelcular docking analysis of SARS-CoV-2 linked RNA dependent RNA polymerase (RdRp) with compounds from Plectranthus amboinicus. Hence, we report the binding features of rutin, Luteolin, Salvianolic acid A, Rosmarinic acid and p-Coumaric acid with the target protein SARS-CoV-2 linked RNA dependent RNA polymerase (RdRp) for further consideration.

scholarly journals The application of in silico experimental model in the assessment of ciprofloxacin and levofloxacin interaction with main SARS-CoV-2 targets: S-, E- and TMPRSS2 proteins, RNA-dependent RNA polymerase and papain-like protease (PLpro)—preliminary molecular docking analysis

2021 ◽  
Author(s):  
Krzysztof Marciniec ◽  
Artur Beberok ◽  
Stanisław Boryczka ◽  
Dorota Wrześniok
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Rna Polymerase ◽  
Stable Complex ◽  
E Proteins ◽  
Rna Dependent Rna Polymerase ◽  
Docking Analysis ◽  
Molecular Studies ◽  
Molecular Docking Analysis ◽  
Positive Controls

Abstract Background The new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified at the end of 2019. Despite growing understanding of SARS-CoV-2 in virology as well as many molecular studies, except remdesivir, no specific anti-SARS-CoV-2 drug has been officially approved. Methods In the present study molecular docking technique was applied to test binding affinity of ciprofloxacin and levofloxacin—two commercially available fluoroquinolones, to SARS-CoV-2 S-, E- and TMPRSS2 proteins, RNA-dependent RNA polymerase and papain-like protease (PLPRO). Chloroquine and dexamethasone were used as reference positive controls. Results When analyzing the molecular docking data it was noticed that ciprofloxacin and levofloxacin possess lower binding energy with S protein as compared to the references. In the case of TMPRSS2 protein and PLPRO protease the best docked ligand was levofloxacin and in the case of E proteins and RNA-dependent RNA polymerase the best docked ligands were levofloxacin and dexamethasone. Moreover, a molecular dynamics study also reveals that ciprofloxacin and levofloxacin form a stable complex with E- and TMPRSS2 proteins, RNA polymerase and papain-like protease (PLPRO). Conclusions The revealed data indicate that ciprofloxacin and levofloxacin could interact and potentially inhibit crucial SARS-CoV-2 proteins.

scholarly journals Deep learning-based computational drug discovery to inhibit the RNA Dependent RNA Polymerase: application to SARS-CoV and COVID-19

2020 ◽  
Author(s):  
Sayalee Patankar
Keyword(s):  
Deep Learning ◽  
Rna Polymerase ◽  
Short Term Memory ◽  
Comparison Group ◽  
Binding Energies ◽  
Therapeutic Options ◽  
Rna Dependent Rna Polymerase ◽  
Drug Candidates ◽  
Zinc Database ◽  
Binding Data

There is an urgency to find drugs and vaccines for the 2019 coronavirus disease (COVID-19). Therapeutic options include repurposing existing drugs or finding new ones. One approach is to target the RNA-dependent RNA polymerase (RdRp) and block viral RNA synthesis. Currently clinical trials to repurpose remdesivir, a RdRp targeting pro-drug for Ebola, to COVID-19 is under way. More such potential drugs need to be identified to efficiently find best therapeutic options. To address this need, a Long Short Term Memory (LSTM) model from literature was trained to read the SMILES fingerprint of a molecule and predict the IC50 of the molecule when binding to an RdRp. This model was trained using IC50 binding data from the PDB database. 310,000 drug-like compounds from the ZINC database were then screened using the trained LSTM model. Additionally, the 310,000 molecules with their predicted IC50s were used to train a generative Semi-Supervised Variational AutoEncoder (SSVAE) model from literature. Although not trained by actual experimental data (sufficient data are not available), the SSVAE model was used to generate 10 new molecules by sampling from the latent space to demonstrate its utility. These 10 molecules and the 1025 molecules with the lowest predicted IC50s from the LSTM model were docked onto the SARS coronavirus (a virus similar to COVID-19) RdRp using AutoDock Vina. Top four most stable inhibitors from the screened ZINC database compounds had binding energies of less than -33.89 kJ/mol. These binding energies were less than the binding energies of the comparison group consisting of prior drugs remdesivir, favipiravir, and galidesivir. Among the ten new molecules generated by the SSVAE model, the most stable new molecule had binding energy lower than the comparison group of prior drugs. The low binding energies of these molecules indicate they could potentially be good drug candidates for the SARS CoV and COVID-19. These results also show the utility of deep learning-based models in screening existing compound and generating new molecules to find drugs for COVID-19.

scholarly journals Molecular Docking Analysis of the T450A Mutation of the Gene rpoB Mycobacterium leprae from Leprosy Patients in Papua, West Papua and North Maluku, Indonesia

2021 ◽  
pp. 3578-3584
Author(s):  
Yustinus Maladan ◽  
Hana Krismawati ◽  
Tri Wahyuni ◽  
Hotma Martogi Lorensi Hutapea ◽  
Muhammad Fajri Rokhmad ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Rna Polymerase ◽  
Binding Affinity ◽  
Rpob Gene ◽  
Ramachandran Plot ◽  
Docking Analysis ◽  
West Papua ◽  
Leprosy Patients ◽  
Subunit B ◽  
Molecular Docking Analysis

Leprosy persists to be a health problem in Indonesia, especially in the provinces of North Maluku, West Papua and Papua. Early diagnosis and complete treatment with multidrug therapy (MDT) remain the key strategy for reducing the disease burden. One of the major components of MDT is rifampicin which in certain cases in several countries, M. leprae resistance to this drug issue has been reported albeit only a few. This research aimed to detect and analyze polymorphism in M. leprae rpoB gene that was isolated from leprosy patients in three provinces: North Maluku Province, West Papua Province and Papua Province, Indonesia. The identification of mutations in the M. leprae rpoB gene was carried out by aligning the results of DNA sequencing with the reference strain. The 3D structure of rpoB was derived using the Swiss Model. The T450A, S456L, and H451Y variants of RNA Polymerase B subunits were constructed using FoldX based on the wild-type structure. The structures were repaired, and protein stability was evaluated using foldX under the Yasara viewer. The QC of the rpoB M. leprae homology models was conducted with Ramachandran Plot modeling using PROCHECK. The difference in binding affinity between native protein and T450A, S456L, and H45I variants were analyzed using molecular docking. rpoB gene of M. leprae contains a mutation found in nucleotide of 1348 bp. The mutation triggered the conversion of the amino acid Threonine to Alanine in the amino acid to 450 rpoB subunit B. The structure of 3D RNA Polymerase Subunit B was constructed using rpoB Mycobacterium tuberculosis with PDB code 5UH5 as template. According to Ramachandran Plot, the percentage of residues in the most favored regions are 91.9%, and there was no significant number of residues in the disallowed regions. The results of molecular docking showed that the T450A variant had the same binding affinity with the native protein which was -8.9 kcal. Binding affinity on the S456L and H451Y variants increased by -7.3 kcal and -8.2 kcal, respectively. According to Molecular Docking analysis, T450A variant did not affect the energy binding between RNA polymerase and rifampicin.

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