Molecular Dynamics Conformation Of Opioid Peptides

阿片肽的分子动力学构象

• 批准号: 7734503
• 负责人: LAWRENCE H LAZARUS
• 金额: $ 22.26万
• 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/7734503
• 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

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.

分子建模方法（分子动力学，构象搜索，蒙特卡洛）使用了来自牛变白结构（不包括细胞内和细胞外结构域）的结构的数据，这是迄今为止唯一通过结晶的哺乳动物7-反脑桥接受体，以开发出Delta-opopioider的模型，以开发出silta-opopio的模型。即，以计算机为导向的诱变，以确保视紫红质格式的序列通过交换特定的氨基酸通过交换Delta-阿片受体的序列。 基于DMT-TIC药态团的多种三角洲激动剂和拮抗剂，这些药效团由三种具有不同特异性的选择性化合物（X射线衍射分析）（三种选择性化合物（Delta-和Mu-Apioid受体的选择性和非选择性），以及与MU-Opoid受体的特定受体相结合，该型号与Delta-Offiity相结合，并具有非常低的受体，该型号的受受体构成。 口袋。 最初是从文献中获得的位置定向诱变的数据中确定的配体结合域。 配体的最小化分子模型反映了其已知的生物学活性和受体亲和力以及肽的构象变化，最初通过1-H NMR（COZY，NOESY，HOHAHA，ROESY，ROESY，DQF-COSY实验）检查了CD和温度条件。 在配体方面，芳环距离可能是一个非常重要的特征，它可以区分Mu-和delta-Apoile受体的三角洲 - 阿片受体拮抗剂和激动剂，但尽管具有肽固有的柔韧性，也提供了推定的“受体结合构象”。 正如预期的那样，Mu-Apoid受体激动剂在三角洲受体口袋区域表现不佳，证实了这种方法的应用。 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精选的侧链相互作用区分开。 数据表明，肽配体和受体的假定受体结合的构象涉及在芳香环和氢键之间堆叠，而Mu-Apopioid激动剂与对Delta配体特异性残基的相互作用很差。 此外，似乎有两个区域，在其中激动剂和拮抗剂相互作用，其中仅由这两种类型的化合物共享。 因此，三角洲 - 阿片受体拮抗剂的环内距离可能会由于其在其受体中的拟合而定位生物学差异。 具有双重受体结合特征的肽类似物或对MU-阿片受体的选择性，同样有助于在预测模式下应用分子建模。 因此，Delta受体以及我们的Delta和Mu-Apioid拮抗剂和激动剂药算术的模型将作为新有效配体设计的脚手架。 基于由含有DMT-TIC和DMT-TIC-BID衍生物的几个低能模块开发的药物归因于Delta-阿片受体类似物，使用LigandScout软件生成了用于虚拟筛查的药算池。 此外，还获得了吗啡（MU激动剂），NALT44和SNC-80（Delta激动剂）的药理，以验证药效团筛选程序。 吗啡药片产生了1,100多次命中，而NALT44和SNC-80在Derwent World Drug指数（WDI）的屏幕上都产生了一次命中。 DMT-TIC药算术的虚拟筛选鉴定出了WDI的7次命中：而其中的4个最多可检索100次命中，并在我们的阿片类药物数据库中鉴定出了SEARED DMT-TIC衍生物，但3个产生的命中具有不存在的特征，但需要阿片类药物结合。 同样，使用Chemdiverse数据库（ChemDiv）筛选了相同的4个药体，导致了3-900次命中，但大多数缺乏“阿片类药物样”特征。 但是，随着修改，一些命中可以作为阿片类药物候选者的铅。 当生物活性配体构象和受体结合位点尚不清楚时，这些方法提供了一种替代方法来识别狂喜的药物归于虚拟筛查。