Molecular Dynamics Conformation Of Opioid Peptides

阿片肽的分子动力学构象

• 批准号: 7328899
• 负责人: LAWRENCE H LAZARUS
• 金额: --
• 依托单位: NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7328899
• 关键词: Molecular; Dynamics; Conformation; Opioid; Peptides

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）使用牛视紫红质结晶结构的数据（不包括细胞内和细胞外结构域），这是迄今为止唯一结晶的哺乳动物 7 次跨膜受体。通过计算机方法建立 δ-阿片受体模型；即，计算机定向诱变，通过交换特定氨基酸来确保视紫红质形式的序列与δ-阿片受体的序列一致。基于 Dmt-Tic 药效团的多种 δ 激动剂和拮抗剂，源自对具有不同特异性（δ- 和 mu-阿片受体选择性和非选择性）的三种选择性化合物以及特定 mu- 的 X 射线衍射分析。阿片受体激动剂与δ-阿片受体的亲和力非常低，被对接到所提出的结合袋中。配体结合结构域最初是根据文献中获得的定点诱变数据确定的。配体的最小化分子模型反映了它们已知的生物活性和受体亲和力，并且肽中的构象变化最初通过 1-H NMR（COSY、NOESY、HOHAHA、ROESY、DQF-COSY 实验）、不同溶剂和温度下的 CD 进行检查状况。就配体而言，芳环距离可能是一个非常重要的特征，它区分μ-和δ-阿片受体的δ-阿片受体拮抗剂和激动剂，尽管具有固有的灵活性，但提供了假定的“受体结合构象”肽。正如预期的那样，μ-阿片受体激动剂在 δ 受体口袋区域表现出较差的拟合度，证实了该方法的应用。通过 Dmt-Tic 药效团观察到​​的拓扑特征将其与所有其他肽及其与受体口袋中选定侧链的相互作用区分开来。数据表明，肽配体和受体的假定受体结合构象涉及芳香环和氢键之间的堆积，并且μ-阿片类激动剂与δ配体特异的那些残基相互作用较差。此外，似乎存在两个激动剂和拮抗剂相互作用的区域，而这两类化合物仅共享其中一个区域。因此，δ-阿片受体拮抗剂的环内距离可能预示着由于其与受体的配合而导致的生物学差异。具有双重受体结合特性或对 mu-阿片受体选择性的肽类似物同样有助于分子模型在预测模式中的应用。因此，δ受体模型以及我们的δ-和μ-阿片拮抗剂和激动剂药效团将作为新的有效配体设计的支架。 基于含有 Dmt-Tic 的 δ-阿片受体类似物和 Dmt-Tic-Bid 衍生物的几种低能量模型开发的药效团，使用 LigandScout 软件生成用于虚拟筛选的药效团。此外，还获得了吗啡（μ 激动剂）、Nalt44 和 SNC-80（δ 激动剂）的药效团，以验证药效团筛选程序。吗啡药效基团产生超过 1,100 个命中，而 Nalt44 和 SNC-80 在德温特世界药物索引 (WDI) 的屏幕中各产生一个命中。 Dmt-Tic 药效团的虚拟筛选从 WDI 中识别出 7 个匹配项：其中 4 个检索到了多达 100 个匹配项并在我们的阿片类药物数据库中鉴定了一些 Dmt-Tic 衍生物，而 3 个产生的匹配项缺少阿片类药物结合所需的特征。同样，使用 ChemDiverse 数据库 (ChemDiv) 筛选了相同的 4 个药效团，结果为 3-900 个命中，但大多数缺乏“类阿片”特征。然而，经过修改，一些热门产品可以作为阿片类药物候选药物的先导。当生物活性配体构象和受体结合位点未知时，这些方法提供了另一种方法来识别相关药效团以进行虚拟筛选。