E-Pharmacophore Model Assisted Discovery of Novel Antagonists ofnNOS

N. R. Madhulitha, N. Pradeep, S. Swargam, eep, K. Hema, P. Chiranjeevi, Katari Sudheer Kumar, A. Umamaheswari
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引用次数: 10

Abstract

The nitric oxide (NO) synthesized by neuronal nitric oxide synthase (nNOS) acts as a neurotransmitter and plays a crucial role in a series of neurobiological functions. In diseased condition, activated nNOS induces nitrosylation as well as phosphorylation of tau protein and glycogen synthase kinase 3 beta (GSK-3β) respectively. Hyper phosphorylation of tau accelerates tau oligomerization resulting in formation of neurofibrillary tangles (NFT), ensuring the neuronal cell death in hippocampus region; a hallmark of Alzheimer’s disease (AD). Thus, designing inhibitor towards nNOS may reduce the neuronal loss caused by nNOS. Hence nNOS has been one of the revitalizing targets for AD. In the present work, one energetically optimized structure-based pharmacophore (e-pharmacophore) was generated using nNOS co-crystal structure (4D1N) to map important pharmacophoric features of nNOS. Shape based similarity screening performed using e-pharmacophore against in-house library of more than one million compounds resulted 2701 library of compounds. Rigid receptor docking (RRD) was applied and followed by molecular mechanics and generalized Born and surface area (MM-GBSA) calculation which results 22 nNOS ligands. To define the leads, dock complexes were subjected to quantum-polarized ligand docking (QPLD) followed by free energy calculations revealed 3 leads. On comparison with 1 existing inhibitor,it concealed three best leads with lower binding energy and better binding affinity. The best lead was subjected to induced fit docking (IFD) with MM-GBSA calculation and further molecular dynamics (MD) simulations for 50 ns in solvated model system. Potential energy, root mean square deviation (RMSD) and root mean square fluctuations (RMSF) results disclosed constancy of lead 1 interactions throughout 50 ns MD simulations run. Thus proposed three leads are having favorable absorption distribution metabolism excretion toxicity (ADME/T) properties and provide a scaffold for designing nNOS antagonists.
电子载体模型辅助发现新型一氧化氮合酶拮抗剂
神经元一氧化氮合酶(nNOS)合成的一氧化氮(NO)是一种神经递质,在一系列神经生物学功能中起着至关重要的作用。在患病状态下,活化的nNOS分别诱导tau蛋白和糖原合成酶激酶3β(GSK-3β)的亚硝化和磷酸化。tau的过度磷酸化加速了tau的寡聚化,导致神经原纤维缠结(NFT)的形成,确保了海马区的神经元细胞死亡;阿尔茨海默病(AD)的标志。因此,设计针对nNOS的抑制剂可以减少nNOS引起的神经元损失。因此,nNOS一直是AD的活化靶点之一。在本工作中,利用nNOS共晶结构(4D1N)生成了一个能量优化的基于结构的药效团(e-药效团),以绘制nNOS的重要药效团特征。使用电子载体对超过一百万种化合物的内部文库进行基于形状的相似性筛选,得到2701个化合物文库。应用刚性受体对接(RRD),然后进行分子力学和广义Born和表面积(MM-GBSA)计算,得到22个nNOS配体。为了确定引线,对对接复合物进行量子极化配体对接(QPLD),然后进行自由能计算,得到3个引线。与现有的1种抑制剂相比,它以较低的结合能和较好的结合亲和力隐藏了三种最佳的铅。在溶剂化模型系统中,对最佳引线进行诱导拟合对接(IFD)和MM-GBSA计算,并进一步模拟50ns的分子动力学(MD)。势能、均方根偏差(RMSD)和均方根波动(RMSF)结果揭示了在整个50ns MD模拟运行中铅1相互作用的恒定性。因此,提出的三种铅具有良好的吸收-分布-代谢-排泄毒性(ADME/T)特性,并为设计nNOS拮抗剂提供了支架。
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