Discovery of Novel Inhibitors of Bacterial DNA Gyrase Using a QSAR-Based Approach

Abstract

Type II topoisomerases like DNA gyrase initiate ATP-dependent negative supercoils in bacterial DNA. It is critical in all of the bacteria but is missing from eukaryotes, making it a striking target for antibacterials. Ciprofloxacin is a clinically approved drug, but its clinical effectiveness is affected by the emergence of resistance in both Gram-positive and Gram-negative bacteria. Thus, it is vital to identify novel compounds that can efficiently inhibit DNA gyrase, and quantitative structure–activity relationship (QSAR) modeling is a quick and economical means to do so. A QSAR-based virtual screening approach was applied to identify new gyrase inhibitors using an in-house -generated combinatorial library of 29828 compounds from seven ciprofloxacin scaffold structures. QSAR was built using a data set of 271 compounds, which were identified as positive and negative inhibitors from existing data reported in in vitro studies. The best QSAR model was developed using the 5-fold cross-validation Neural Network in Orange, and it was based on five PaDEL descriptors with an accuracy and sensitivity of 83%. As a result of screening of an in-house -built combinatorial library with the best-developed QSAR model, 675 compounds were identified as potential inhibitors of DNA gyrase. These inhibitors were further docked with DNA gyrase using AutoDock to compare the binding mode and score of the selected/screened compounds, and 615 compounds exhibited a docking score comparable to or lower than that of ciprofloxacin. Out of these, the top five analogues 902b, 9699f, 4419f, 5538f, and 898b reported in our study have binding scores of −13.81, −12.95, −12.52, −12.43, and −12.41 kcal/mol, respectively. The MD simulations of these five analogues for 100 ns supported the interaction stability of analogues with Escherichia coli DNA gyrase. Ninety-one per cent of the analogues screened by the QSAR model displayed better binding energy than ciprofloxacin, demonstrating the efficacy of the generated model. The NN-QSAR model proposed in this manuscript can be downloaded from .

Cyclohexane-1,3-dione Derivatives as Future Therapeutic Agents for NSCLC: QSAR Modeling, In Silico ADME-Tox Properties, and Structure-Based Drug Designing Approach

“…Moreover, the summary MD quality simulation was analyzed by calculating the variation of total energy ( E ), potential energy (EP), temperature ( T ), pressure ( P ), and volume ( V ) along the 100 ns of the simulation period, and these analyses were done by subjecting the studied systems to simulation quality analysis. 108 The plots of the thermodynamic properties calculated along 100 ns of MD simulation time for 3LQ8 free and complexed with ligands are presented in Figure 16 . Through the thermodynamic plots obtained, we can notice that the energy parameters of the six investigated systems are very close and showed perfect stability throughout the MD simulation time.…”

Synthesis, molecular docking, molecular dynamic simulation studies, and anti-tubercular activity evaluation of substituted benzimidazole derivatives

“…Low RMSD is a reliable indicator of a more stable system, as demonstrated by the simulation results. A wide range of oscillations in the RMSD graph, on the other hand, indicates that ligand binding to the target protein structure is unstable [41], [42].…”

Identification of Potential DNA Gyrase Inhibitors: Virtual Screening, Extra-Precision Docking and Molecular Dynamics Simulation Study

“…Recently, several authors have employed computational tools to identify novel inhibitors of various targets (Kumar, Rai, Rathi, Agarwal, & Kini, 2020; Meena et al, 2021; Mukherjee et al, 2022; Rathi, Kumar, & Kini, 2020). Jakhar et al ., have employed quantitative structure-activity relationship (QSAR) modelling to identify a few novel DNA gyrase inhibitors (Jakhar, Khichi, Kumar, Dangi, & Kumar Chhillar, 2022). Hasan et al ., have carried out in silico analysis of ciprofloxacin derivatives as potential DNA gyrase inhibitors (Hasan et al, 2021).…”