Molecular Dynamics Conformation Of Opioid Peptides
阿片肽的分子动力学构象
基本信息
- 批准号:7328899
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- 依托单位国家:美国
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- 资助国家:美国
- 起止时间:至
- 项目状态:未结题
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项目摘要
Summary: Molecular modeling methodologies (molecular dynamics, conformational searching, Monte Carlo) used data from the crystallized structure of bovine rhodopsin (excluding the intracellular and extracellular domains), which is the only mammalian 7-transmembrane receptor crystallized to date, in order to develop a model of the delta-opioid receptor by in silico methods; i.e., computer-directed mutagenesis to ensure that the sequence of the rhodopsin format coincided with that of the delta-opioid receptor by exchanging specific amino acids. A variety of delta agonists and antagonists based on the Dmt-Tic pharmacophore derived from X-ray diffraction analyses of three selective compounds with different specificities (delta- and mu-opioid receptor selective, and non-selective), as well as specific mu-opioid receptor agonists, which should have very low affinity with the delta-opioid receptor, were docked into the proposed binding pocket. The ligand-binding domain was initially determined from data on site-directed mutagenesis obtained from the literature. The minimized molecular models of the ligands reflected their known biological activities and receptor affinities and conformational changes in the peptides were initially examined by 1-H NMR (COSY, NOESY, HOHAHA, ROESY, DQF-COSY experiments), CD under varying solvent and temperature conditions. In terms of the ligands, the aromatic ring distance may be a singularly important characteristic which distinguishes delta-opioid receptor antagonists and agonists for both mu- and delta-opioid receptors providing a presumptive "receptor-bound conformation" in spite of the inherent flexibility of the peptide. As anticipated, mu-opioid receptor agonists exhibited a poor fit in the delta receptor pocket region, confirming the application of this methodology. The topographical features observed with the Dmt-Tic pharmacophore differentiate it from all other peptides and its interaction with select side-chains in the receptor pocket. The data suggest that the presumed receptor-bound conformation of the peptide ligand and receptor involves stacking between aromatic rings and hydrogen bonding and that mu-opioid agonists poorly interacted with those residues specific for delta ligands. Furthermore, there appeared to be two regions in which agonists and antagonists interact, only one of which is shared by these two types of compounds. Thus, intra-ring distance of delta-opioid receptor antagonists may portend biological differences due to its fit within its receptor. Peptide analogues with dual receptor binding characteristics or selectivity for the mu-opioid receptor equally assisted in the application of molecular modeling in a predictive mode. Thus, model of the delta receptor and our delta- and mu-opioid antagonist and agonist pharmacophores will serve as scaffolds in the design of new potent ligands.
Based on pharmacophores developed by delta-opioid receptor analogues containing Dmt-Tic and several low energy modles of Dmt-Tic-Bid derivatives, pharmacophores were generated for virtual screening using LigandScout software. Furthermore, pharmacophores were obtained for morphine (mu agonist), Nalt44 and SNC-80 (delta agonists) to validate the pharmacophore screening procedure. The morphine pharmacophore produced more than 1,100 hits, whereas Nalt44 and SNC-80 each generated a single hit in a screen of the Derwent World Drug Index (WDI). Virtual screens of the Dmt-Tic pharmacophores identified 7 hits from WDI: while 4 of these retrieved up to 100 hits and identified seeral Dmt-Tic derivatives in our opioid database, 3 produced hits with features absent but required for opioid binding. Similarly, the same 4 pharmacophores were screened using the ChemDiverse database (ChemDiv) resulting in 3-900 hit, but most lacked "opioid-like" features. However, with modifications, some hits could serve as leads for opioid drug candidates. These methods offer an alternative approach to identify revelant pharmacophores for virtual screening when bioactive ligand conformations and the receptor binding site are unknown.
摘要:分子建模方法(分子动力学、构象搜索、蒙特卡罗)利用迄今唯一结晶的哺乳动物7-跨膜受体牛视紫质的结晶结构(不包括细胞内和细胞外结构域)的数据,通过计算机控制的突变方法,通过交换特定的氨基酸来确保视紫红质的序列与三角洲-阿片受体的序列一致。基于DMT-Tic药效团的各种Delta激动剂和拮抗剂,以及与Delta-阿片受体亲和力非常低的特定mu阿片受体激动剂,被对接到拟议的结合口袋中,所述DMT-Tic药效基团来自于三种不同特异性的选择性化合物(Delta和u阿片受体选择性和非选择性)。配基结合结构域最初是从文献中获得的定点突变数据确定的。通过1-H核磁共振(COSY、NOESY、HOHAHA、ROESY、DQF-COSY实验)、CD等方法初步考察了不同溶剂和温度条件下多肽的构象变化。就配基而言,芳环距离可能是一个非常重要的特征,它区分了Delta-阿片受体拮抗剂和Mu-和Delta-阿片受体激动剂,尽管多肽具有固有的灵活性,但它提供了一种假定的“受体结合构象”。正如预期的那样,Mu-阿片受体激动剂在Delta受体口袋区域表现出较差的适合性,证实了这一方法的应用。用DMT-Tic药效团观察到的地形特征使其有别于所有其他多肽,以及它与受体口袋中选定的侧链的相互作用。这些数据表明,多肽配体和受体的假定受体结合构象涉及芳环和氢键之间的堆积,并且Mu-阿片激动剂与那些针对Delta配体的残基相互作用很差。此外,似乎存在两个激动剂和拮抗剂相互作用的区域,这两种类型的化合物只共享其中一个区域。因此,β-阿片受体拮抗剂的环内距离可能预示着生物差异,因为它与其受体相匹配。具有双受体结合特性或对Mu-阿片受体具有选择性的多肽类似物同样有助于以预测模式应用分子建模。因此,Delta受体的模型和我们的Delta-和Mu-阿片拮抗剂和激动剂药效载体将作为设计新的有效配体的支架。
基于含有DMT-Tic的增量阿片受体类似物和几种低能模型的DMT-Tic-Bid衍生物开发的药效团,利用LigandScout软件生成用于虚拟筛选的药效团。此外,还获得了吗啡(Mu激动剂)、Nalt44和SNC-80(Delta激动剂)的药效团,以验证药效团筛选程序。吗啡药效团产生了超过1100次点击,而Nalt44和SNC-80分别在德温特世界药物指数(WDI)的屏幕上产生了一次点击。DMT-Tic药效团的虚拟屏幕确定了WDI的7个匹配项:其中4个检索到100个匹配项,并在我们的阿片数据库中确定了几个DMT-Tic衍生物,3个匹配物没有特征,但阿片类药物结合所需。同样,使用ChemDiverse数据库(ChemDiv)筛选了同样的4个药效团,结果为3-900次,但大多数缺乏“阿片类药物”特征。然而,经过修改,一些命中可以作为阿片类药物候选药物的线索。这些方法为在生物活性配体构象和受体结合位点未知的情况下进行虚拟筛选提供了一种识别相关药效团的替代方法。
项目成果
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LAWRENCE H LAZARUS其他文献
LAWRENCE H LAZARUS的其他文献
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