Edache Emmanuel Israel, Uzairu Adamu, Mamza Paul Andrew, Shallangwa Gideon Adamu
Department of Pure and Applied Chemistry, Faculty of Science, University of Maiduguri, P.M.B, Maiduguri, Borno State 1069 Nigeria.
Department of Chemistry, Faculty of Physical Sciences, Ahmadu Bello University, P.M.B. 1044, Zaria, Kaduna State Nigeria.
J Genet Eng Biotechnol. 2022 Jun 17;20:88. doi: 10.1186/s43141-022-00362-z. eCollection 2022 Dec.
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.
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: Q = 0.8027; = 0.8735; = 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 inhibitors. The next mathematical model work accomplished here emphasizes finding a potential drug that could aid in curing and SARS-CoV-2 as the drug targets . 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.
Our acknowledged drugs could be a proficient cure for SARS-CoV-2 and 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.
The online version contains supplementary material available at 10.1186/s43141-022-00362-z.
为了寻找曾经被评估为活性铜绿假单胞菌抑制剂的高效且无毒的亚氨基胍衍生物，对亚氨基胍衍生物进行了定量构效关系（QSAR）、同源建模、对接模拟、ADMET和分子动力学模拟的联合数学方法研究。
采用QSAR方法对构效关系（SAR）进行统计分析，得到了具有良好统计意义的显著预测模型；（遗传算法-多元线性回归：Q = 0.8027；R² = 0.8735；RMSE = 0.7536）。对预测模型的深入研究表明，以I态加权的中心Broto-Moreau自相关-滞后1和以I态加权的基于3D拓扑距离的自相关-滞后9主导着生物活性，并提供了许多有用信息，以了解开发新型高效抑制剂所需的特性。这里完成的下一个数学模型工作重点是寻找一种潜在药物，该药物可作为药物靶点用于治疗 和SARS-CoV-2。这涉及RNA聚合酶结合转录因子DksA和COVID-19主要蛋白酶受体的同源建模、对接模拟以及针对受体的命中化合物的药代动力学筛选研究，以识别可用于调节建模酶的潜在抑制剂。建模蛋白质显示出超过90%的最有利区域，最小禁止区域小于5%，并在亲水环境下进行模拟。对所有系列与构建的蛋白质模型的结合口袋进行对接模拟，以展示它们的结合方式并识别结合位点内的关键相互作用残基。通过1纳秒分子动力学模拟（MDS）运行的RMSD、RMSF和SASA分析评估了它们对建模受体的结合稳定性。
我们认可的药物可能是治疗SARS-CoV-2和进行药物发现的有效疗法，尽管如此，额外的测试（体外和体内）对于解释它们作为新型药物的潜力和作用方式至关重要。
在线版本包含可在10.1186/s43141-022-00362-z获取的补充材料。
