Molecular Dynamics Conformation Of Opioid Peptides

阿片肽的分子动力学构象

• 批准号: 7593970
• 负责人: LAWRENCE H LAZARUS
• 金额: $ 29.75万
• 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/7593970
• 关键词: Affinity; Agonist; Amino Acids; Binding; Binding Sites; Biological; Book Chapters; Bos taurus; COSY; Cattle; Characteristics; Chemical Structure; Computer Simulation; Computer software; Computers; Condition; Data; Databases; Development; Docking; Ensure; Evaluation; Exhibits; Extracellular Domain; Goals; Hydrogen Bonding; Imagery; Ligand Binding Domain; Ligands; Literature; Methodology; Methods; Mining; Modeling; Modification; Molecular Conformation; Morphine; Mutagenesis; NOESY; Narcotic Antagonists; Opioid; Opioid Peptide; Peptide Conformation; Peptides; Pharmaceutical Preparations; Pliability; Process; Property; ROESY; Rate; Reader; Rhodopsin; SNC-80; Screening procedure; Side; Site-Directed Mutagenesis; Solvents; Specificity; Structure; Temperature; Terminology; X ray diffraction analysis; X-Ray Diffraction; analog; base; combinatorial chemistry; concept; data mining; delta opioid receptor; design; drug development; drug discovery; indexing; interest; molecular dynamics; molecular modeling; mu opioid receptors; peptide analog; pharmacophore; receptor; receptor binding; research study; scaffold; tool; virtual

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.

总结：分子建模方法（分子动力学、构象搜索、蒙特卡罗）使用来自牛视紫红质的结晶结构（不包括细胞内和细胞外结构域）的数据，其是迄今为止结晶的唯一哺乳动物7-跨膜受体，以便通过计算机模拟方法开发δ-阿片受体的模型;即，计算机定向诱变以确保视紫红质形式的序列通过交换特定氨基酸与δ-阿片受体的序列一致。 将基于Dmt-Tic药效团的多种δ激动剂和拮抗剂对接到所提出的结合口袋中，所述药效团来源于具有不同特异性（δ-和μ-阿片受体选择性和非选择性）的三种选择性化合物的X射线衍射分析，以及特异性μ-阿片受体激动剂，其应当与δ-阿片受体具有非常低的亲和力。 配体结合结构域最初从文献中获得的定点诱变数据确定。 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，肽中的构象变化最初通过1-H NMR（COSY、NOESY、HOHAHA、ROESY、DQF-COSY实验）、CD在不同溶剂和温度条件下进行检查。 就配体而言，芳环距离可能是区分μ-和δ-阿片受体两者的δ-阿片受体拮抗剂和激动剂的非常重要的特征，其提供了假定的“受体结合构象”，尽管肽具有固有的柔性。 正如预期的，μ-阿片受体激动剂在δ受体口袋区域中表现出较差的拟合，证实了该方法的应用。 用Dmt-Tic药效团观察到的地形特征将其与所有其他肽及其与受体口袋中的选择侧链的相互作用区分开来。 这些数据表明，假定的受体结合构象的肽配体和受体涉及芳环和氢键之间的堆叠和μ-阿片样物质激动剂与δ配体特异性的那些残基的相互作用很差。 此外，似乎存在激动剂和拮抗剂相互作用的两个区域，这两种类型的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生物学差异，因为它适合其受体。 具有双重受体结合特征或μ-阿片受体选择性的肽类似物同样有助于预测模式中的分子建模应用。 因此，δ受体的模型和我们的δ-和μ-阿片样物质拮抗剂和激动剂药效团将在新的有效配体的设计中充当支架。 基于由含有Dmt-Tic的δ-阿片受体类似物和几种Dmt-Tic-Bid衍生物的低能模型形成的药效团，使用LigandScout软件生成药效团用于虚拟筛选。 此外，获得吗啡（μ激动剂）、Nalt 44和SNC-80（δ激动剂）的药效团以验证药效团筛选程序。 吗啡药效团产生了超过1,100个命中，而Nalt 44和SNC-80在德温特世界药物指数（WDI）的筛选中各产生了一个命中。 Dmt-Tic药效团的虚拟筛选从WDI中鉴定出7个命中：其中4个检索到多达100个命中，并在我们的阿片类药物数据库中鉴定出几种Dmt-Tic衍生物，3个产生的命中具有不存在但阿片类药物结合所需的特征。 类似地，使用ChemDiverse数据库（ChemDiv）筛选相同的4个药效团，得到3-900个命中，但大多数缺乏“阿片样”特征。 然而，经过修改，一些命中可以作为阿片类药物候选人的线索。 这些方法提供了一种替代方法，以确定相关药效团的虚拟筛选时，生物活性配体的构象和受体结合位点是未知的。