Dataset Information

QSAR, homology modeling, and docking simulation on SARS-CoV-2 and pseudomonas aeruginosa inhibitors, ADMET, and molecular dynamic simulations to find a possible oral lead candidate.

ABSTRACT:

Background

In 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.

Results

The QSAR method was employed to statistically analyze the structure-activity relationships (SAR) and had conceded good statistical significance for eminent predictive model; (GA-MLR: Q²_LOO = 0.8027; R² = 0.8735; R²_ext = 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.

Conclusion

Our 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.

SUBMITTER: Edache EI

PROVIDER: S-EPMC9205150 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectiveThis work was designed to study 2,4-disubstituted 6-fluoroquinolines as antiplasmodial agents by using in silico techniques, to aid in the design of novel analogs with high potency against malaria and high inhibition of Plasmodium falciparum translation elongation factor 2 (PfeEF2), a novel drug target.MethodsQuantitative structure-activity relationships (QSAR) of 2,4-disubstituted 6-fluoroquinolines were studied with the genetic function approximation technique in Material Studio software. The 3D structure of PfeEF2 was modeled in the SWISS-MODEL workspace through homology modeling. A molecular docking study of the modeled PfeEF2 and 2,4-disubstituted 6-fluoroquinolines was conducted with Autodock Vina in Pyrx software. Furthermore, the in silico pharmacokinetic properties of selected compounds were investigated.ResultsA robust, reliable and predictive QSAR model was developed that related the chemical structures of 2,4-disubstituted 6-fluoroquinolines to their antiplasmodium activities. The model had an internal squared correlation coefficient R2 of 0.921, adjusted squared correlation coefficient R2adj of 0.878, leave-one-out cross-validation coefficient Q2cv of 0.801 and predictive squared correlation coefficient R2pred of 0.901. The antiplasmodium activity of 6-fluoroquinolines was found to depend on the n5Ring, GGI9, TDB7u, TDB8u and RDF75i physicochemical properties: n5Ring, TDB8u and RDF75i were positively associated, whereas GGI9 and TDB7u were negatively associated, with the antiplasmodium activity of the compounds. Stable complexes formed between the compounds and modeled PfeEF2, with binding affinity ranging from -8.200 to -10.700 kcal/mol. Compounds 5, 11, 16, 22 and 24 had better binding affinities than quinoline-4-carboxamide (DDD107498), as well as good pharmacokinetic properties, and therefore may be better inhibitors of this novel target.ConclusionQSAR and docking studies provided insight into designing novel 2,4-disubstituted 6-fluoroquinolines with high antiplasmodial activity and good structural properties for inhibiting a novel antimalarial drug target.

Project description:The aurora kinase is a key enzyme that is implicated in tumor growth. Research revealed that small molecules that target aurora kinase have beneficial effects as anticancer agents. In the present study, in order to identify potential antibreast cancer agents with aurora kinase inhibitory activity, we employed QSARINS software to perform the quantitative structure-activity relationship (QSAR). The statistical values resulted from the study include R2 = 0.8902, CCCtr = 0.7580, Q2 LOO = 0.7875, Q2LMO = 0.7624, CCCcv = 0.7535, R2ext = 0.8735, and CCCext = 0.8783. Among the four generated models, the two best models encompass five important variables, including PSA, EstateVSA5, MoRSEP3, MATSp5, and RDFC24. The parameters including the atomic volume, atomic charges, and Sanderson's electronegativity played an important role in designing newer lead compounds. Based on the above data, we have designed six series of compounds including 1a-e, 2a-e, 3a-e, 4a-e, 5a-e, and 6a-e. All these compounds were subjected to molecular docking studies by using AutoDock v4.2.6 against the aurora kinase protein (1MQ4). Among the above 30 compounds, the 2-amino thiazole derivatives 1a, 2a, 3e, 4d, 5d, and 6d have excellent binding interactions with the active site of 1MQ4. Compound 1a had the highest docking score (-9.67) and hence was additionally subjected to molecular dynamic simulation investigations for 100 ns. The stable binding of compound 1a with 1MQ4 was verified by RMSD, RMSF, RoG, H-bond, molecular mechanics-generalized Born surface area (MM-GBSA), free binding energy calculations, and solvent-accessible surface area (SASA) analyses. Furthermore, newly designed compound 1a exhibited excellent ADMET properties. Based on the above findings, we propose that the designed compound 1a may be utilized as the best theoretical lead for future experimental research of selective inhibition of aurora kinase, therefore assisting in the creation of new antibreast cancer drugs.