Jakhar Ritu, Khichi Alka, Kumar Dev, Dangi Mehak, Chhillar Anil Kumar
Centre for Bioinformatics, Maharshi Dayanand University, Rohtak 12400, India.
Centre for Biotechnology, Maharshi Dayanand University, Rohtak 124001, India.
ACS Omega. 2022 Aug 31;7(36):32665-32678. doi: 10.1021/acsomega.2c04310. eCollection 2022 Sep 13.
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 -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 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 -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 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 https://github.com/ritu225/NN-QSAR_model.git.
像DNA促旋酶这样的II型拓扑异构酶在细菌DNA中引发ATP依赖性负超螺旋。它在所有细菌中都至关重要，但在真核生物中不存在，这使其成为抗菌药物的一个显著靶点。环丙沙星是一种临床批准的药物，但其临床疗效受到革兰氏阳性菌和革兰氏阴性菌耐药性出现的影响。因此，识别能够有效抑制DNA促旋酶的新型化合物至关重要，而定量构效关系（QSAR）建模是实现这一目标的快速且经济的手段。一种基于QSAR的虚拟筛选方法被应用于从七个环丙沙星支架结构生成的29828种化合物的组合文库中识别新的促旋酶抑制剂。QSAR是使用271种化合物的数据集构建的，这些化合物是从先前研究报告的现有数据中确定为阳性和阴性抑制剂的。最佳QSAR模型是使用Orange中的5折交叉验证神经网络开发的，它基于五个PaDEL描述符，准确率和灵敏度为83%。使用最佳开发的QSAR模型对构建的组合文库进行筛选的结果是，675种化合物被确定为DNA促旋酶的潜在抑制剂。这些抑制剂进一步使用AutoDock与DNA促旋酶对接，以比较所选/筛选化合物的结合模式和得分，615种化合物的对接得分与环丙沙星相当或更低。其中，我们研究中报告的前五个类似物902b、9699f、4419f、5538f和898b的结合得分分别为-13.81、-12.95、-12.52、-12.43和-12.41 kcal/mol。这五个类似物100 ns的分子动力学模拟支持了类似物与DNA促旋酶的相互作用稳定性。QSAR模型筛选出的91%的类似物显示出比环丙沙星更好的结合能，证明了所生成模型的有效性。本手稿中提出的NN-QSAR模型可从https://github.com/ritu225/NN-QSAR_model.git下载。
