School of Chemical Sciences, Central University of Gujarat, Gandhinagar, Gujarat, India.
Department of Physics, M. S. University of Baroda, Vadodara, Gujarat, India.
J Cell Biochem. 2018 Nov;119(10):8490-8500. doi: 10.1002/jcb.27075. Epub 2018 Aug 13.
In the current study, we have constructed receptor-based pharmacophore models by exploiting the Plasmodium falciparum enoyl-acyl carrier protein reductase (PfENR) structural proteome. The derived models were subjected to a series of validation procedures to list the representative hypotheses that can be used for the screening of the Drug-like Diverse Database. A set of 739 molecules was retrieved and analyzed for the adsorption, distribution, metabolism, excretion and toxicity (ADMET) and drug-likeness attributes. The filtered drug-like molecules (64) were then subjected to molecular docking and HYDE assessment studies. The hybrid structure-based approach yielded 4 molecules, UKR1308259, ENA1096786, UKR403454, and ASI51224, as PfENR inhibitors. The stability of these inhibitors was assessed using molecular mechanics-generalized born surface area approach-based free binding energy calculations and molecular dynamics simulations. Molecular mechanics-generalized born surface area calculations and molecular dynamics simulations showed that UKR1308259, ENA1096786, and ASI51224 were more potent PfENR inhibitors. The rationale behind the current work was to identify orally available inhibitor molecules with diverse scaffolds that could serve as initial leads for the drug design against PfENR.
在本研究中,我们通过利用恶性疟原虫烯酰基载体蛋白还原酶(PfENR)结构蛋白质组构建了基于受体的药效团模型。所得到的模型经过一系列验证程序,列出了可用于筛选类似药物的多样化数据库的代表性假设。从 Drug-like Diverse Database 中检索并分析了一组 739 个分子的吸附、分布、代谢、排泄和毒性(ADMET)和药物相似性属性。然后,对过滤后的类似药物分子(64 个)进行分子对接和 HYDE 评估研究。基于混合结构的方法得到了 4 个 PfENR 抑制剂分子,即 UKR1308259、ENA1096786、UKR403454 和 ASI51224。这些抑制剂的稳定性通过基于分子力学-广义 Born 表面面积方法的自由结合能计算和分子动力学模拟进行评估。分子力学-广义 Born 表面面积计算和分子动力学模拟表明,UKR1308259、ENA1096786 和 ASI51224 是更有效的 PfENR 抑制剂。目前这项工作的基本原理是确定具有不同骨架的口服有效抑制剂分子,这些抑制剂分子可以作为针对 PfENR 的药物设计的初始先导化合物。