Dataset Information

In search of RdRp and Mpro inhibitors against SARS CoV-2: Molecular docking, molecular dynamic simulations and ADMET analysis.

ABSTRACT: Corona Virus Disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome coronavirus (SARS CoV-2) has been declared a worldwide pandemic by WHO recently. The complete understanding of the complex genomic structure of SARS CoV-2 has enabled the use of computational tools in search of SARS CoV-2 inhibitors against the multiple proteins responsible for its entry and multiplication in human cells. With this endeavor, 177 natural, anti-viral chemical entities and their derivatives, selected through the critical analysis of the literatures, were studied using pharmacophore screening followed by molecular docking against RNA dependent RNA polymerase and main protease. The identified hits have been subjected to molecular dynamic simulations to study the stability of ligand-protein complexes followed by ADMET analysis and Lipinski filters to confirm their drug likeliness. It has led to an important start point in the drug discovery and development of therapeutic agents against SARS CoV-2.

SUBMITTER: Gajjar ND

PROVIDER: S-EPMC8059878 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundIn seek of potent and non-toxic iminoguanidine derivatives formerly assessed as active Pseudomonas aeruginosa inhibitors, a combined mathematical approach of quantitative structure-activity relationship (QSAR), homology modeling, docking simulation, ADMET, and molecular dynamics simulations were executed on iminoguanidine derivatives.ResultsThe QSAR method was employed to statistically analyze the structure-activity relationships (SAR) and had conceded good statistical significance for eminent predictive model; (GA-MLR: Q2LOO = 0.8027; R2 = 0.8735; R2ext = 0.7536). Thorough scrutiny of the predictive models disclosed that the Centered Broto-Moreau autocorrelation - lag 1/weighted by I-state and 3D topological distance-based autocorrelation-lag 9/weighted by I-state oversee the biological activity and rendered much useful information to realize the properties required to develop new potent Pseudomonas aeruginosa inhibitors. The next mathematical model work accomplished here emphasizes finding a potential drug that could aid in curing Pseudomonas aeruginosa and SARS-CoV-2 as the drug targets Pseudomonas aeruginosa. This involves homology modeling of RNA polymerase-binding transcription factor DksA and COVID-19 main protease receptors, docking simulations, and pharmacokinetic screening studies of hits compounds against the receptor to identify potential inhibitors that can serve to regulate the modeled enzymes. The modeled protein exhibits the most favorable regions more than 90% with a minimum disallowed region less than 5% and is simulated under a hydrophilic environment. The docking simulations of all the series to the binding pocket of the built protein model were done to demonstrate their binding style and to recognize critical interacting residues inside the binding site. Their binding constancy for the modeled receptors has been assessed through RMSD, RMSF, and SASA analysis from 1-ns molecular dynamics simulations (MDS) run.ConclusionOur acknowledged drugs could be a proficient cure for SARS-CoV-2 and Pseudomonas aeruginosa drug discovery, having said that extra testing (in vitro and in vivo) is essential to explain their latent as novel drugs and manner of action.

Dataset Information

In search of RdRp and Mpro inhibitors against SARS CoV-2: Molecular docking, molecular dynamic simulations and ADMET analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets