NEW DYNAMIC MODELS OF OPIOID RECEPTORS

阿片受体的新动态模型

• 批准号: 7956268
• 负责人: ANDREA BORTOLATO
• 金额: $ 0.08万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7956268
• 关键词: Accounting; Adrenergic Agents; Adrenergic Receptor; Agonist; Alanine; Algorithms; Arts; Binding; Bioinformatics; Biomedical Research; Biophysics; Computer Retrieval of Information on Scientific Projects Database; Computer Simulation; Congresses; Development; Dipeptides; Discrimination; Electrostatics; Funding; G-Protein-Coupled Receptors; Goals; Grant; High Performance Computing; Homo; Human Engineering; Hydrogen Bonding; Institution; International; Length; Libraries; Ligand Binding; Lipids; Methods; Modeling; Molecular; Molecular Conformation; Molecular Models; Opioid Receptor; Opioid Receptor Binding; Peptides; Performance; Proteins; Protocols documentation; Publications; Receptor Activation; Research; Research Personnel; Research Project Grants; Resolution; Resources; Rhodopsin; Science; Sequence Homology; Side; Signal Transduction; Simulate; Societies; Solvents; Source; Structure; Study models; Techniques; Testing; Transmembrane Domain; United States National Institutes of Health; Universities; adrenergic; base; design; dimer; field study; flexibility; globular protein; improved; knowledge base; medical schools; meetings; molecular dynamics; molecular modeling; monomer; mu opioid receptors; preference; protein structure; rat Ran 2 protein; receptor; receptor function; simulation; small molecule; three-dimensional modeling; water environment

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. This request for a Development Allocation (DAC) of 30,000 SUs on TeraGrid platforms is based on a NIH supported research project (R01 DA020032; PI: Dr. Marta Filizola). The goal of this project is to identify the molecular determinants responsible for the oligomerization of delta- and mu-opioid receptors (both homo- and heteromers) in a structural context of receptor models, using an iterative combined computational and experimental approach. This request for resources will specifically be used to obtain new refined molecular models of inactive delta- and mu-opioid receptor monomers, which will serve as a basis for the construction of dimers or higher-order oligomers of these receptors. The recent publication of the crystal structure of beta2-adrenergic receptor(Cherezov et al., 2007), and its higher sequence homology with opioid receptors compared to rhodopsin, prompted us to re-build three-dimensional models of the transmembrane (TM) regions of delta- and mu-opioid receptors using beta2-adrenergic as a structural template. We will now use these new TM models to add optimal extra- and intra-cellular loop regions using an ab-initio approach that was originally developed in the lab of Dr. Ernest Mehler at Weill Medical College of Cornell University, and is currently being optimized in a collaborative effort by the Mehler and Filizola labs. Briefly, this method employs simulated annealing Monte Carlo (MC) simulations carried out on the loop segment starting from a completely extended structure, combined with a biased scaled collective variables (SCV) Monte Carlo technique especially designed to complete the closure of the segment. Since an accurate force field for the study of peptide and protein conformational preferences must account for the hydrophobic and electrostatic effects of the solvent, the method uses a validated continuum electrostatic model based on screened Coulomb potentials, which has extensively been validated for small molecules as well as proteins (Hassan et al., 2000a; Hassan et al., 2000b; Hassan and Mehler, 2002). Alhough the MC-SCV-MC approach has been shown to predict reliably the conformations of loop regions of GPCRs (Kortagere et al., 2006; Mehler et al., 2006), like other ab initio loop prediction methods, its performance deteriorates with increasing loop lengths (more than 10 residues). Thus, to improve structural characterization of long loops of GPCRs, we have recently been studying modeling approaches that combine two or more of the currently available ab initio loop prediction methods with the MC-SCV-MC protocol (Bandhyopadhyay et al., 2008). Our results show that the integrated use of a side-chain prediction strategy based on rotamer libraries (SCAP (Xiang and Honig, 2001)) into the standard MC-SCV-MC protocol considerably improves loop structure predictions obtained by MC-SCV-MC alone or other fairly reliable and fast loop prediction algorithms for globular proteins (e.g., LOOPY (Jacobson et al., 2004), MODLOOP (Fiser and Sali, 2003), etc.). Thus, we will apply our modified MC-SCV-MC protocol to predict loops of the delta- and mu-opioid receptors. The resulting conformations will then be used to: 1) build agonist-bound models of the opioid receptors through the application of a state-of-the-art knowledge-based distance constraint approach that we recently tested on rhodopsin (Niv et al., 2006); and 2) run long-scale molecular dynamics simulations of inactive and agonist-bound opioid receptors in an explicit lipid-water environment. Simulations will be performed with GROMACS (Van Der Spoel et al., 2005) using the protocol we recently tested on a rhodopsin monomer and dimer (Filizola et al., 2006). These studies will help us achieve a more complete representation of the opioid receptor function by identifying quantitatively the intermolecular mode of receptor activation, and adding it to current models of ligand-binding and signal transduction. REFERENCES Bandhyopadhyay, D., Bortolato, A., Filizola, M., and Mehler, E. L.: Improving Prediction of G-Protein Coupled Receptor Loops. 52nd Annual Meeting of the Biophysical Society and 16th IUPAB International Biophysics Congress (Long Beach, CA), 2008. Cherezov, V., Rosenbaum, D. M., Hanson, M. A., Rasmussen, S. G., Thian, F. S., Kobilka, T. S., Choi, H. J., Kuhn, P., Weis, W. I., Kobilka, B. K., and Stevens, R. C.: High-resolution crystal structure of an engineered human beta2-adrenergic G protein-coupled receptor. Science 318 (5854): 1258-65, 2007. Filizola, M., Wang, S. X., and Weinstein, H.: Dynamic models of G-protein coupled receptor dimers: indications of asymmetry in the rhodopsin dimer from molecular dynamics simulations in a POPC bilayer. J Comput Aided Mol Des 20 (7-8): 405-16, 2006. Fiser, A., and Sali, A.: ModLoop: automated modeling of loops in protein structures. Bioinformatics 19 (18): 2500-1, 2003. Hassan, S., Guarnieri, F., and Mehler, E.: Characterization of Hydrogen Bonding in a Continuum Solvent Model. . J. Phys. Chem. 104: 6490, 2000a. Hassan, S., Guarnieri, F., and Mehler, E.: A General Treatment for Solvent Effects Based on Screened Coulomb Potentials. . J. Phys. Chem. 104: 6478, 2000b. Hassan, S. A., and Mehler, E. L.: A critical analysis of continuum electrostatics: the screened Coulomb potential--implicit solvent model and the study of the alanine dipeptide and discrimination of misfolded structures of proteins. Proteins 47 (1): 45-61, 2002. Jacobson, M. P., Pincus, D. L., Rapp, C. S., Day, T. J., Honig, B., Shaw, D. E., and Friesner, R. A.: A hierarchical approach to all-atom protein loop prediction. Proteins 55 (2): 351-67, 2004. Kortagere, S., Roy, A., and Mehler, E. L.: Ab initio computational modeling of long loops in G-protein coupled receptors. J Comput Aided Mol Des 20 (7-8): 427-36, 2006. Mehler, E. L., Hassan, S. A., Kortagere, S., and Weinstein, H.: Ab initio computational modeling of loops in G-protein-coupled receptors: lessons from the crystal structure of rhodopsin. Proteins 64 (3): 673-90, 2006. Niv, M. Y., Skrabanek, L., Filizola, M., and Weinstein, H.: Modeling activated states of GPCRs: the rhodopsin template. J Comput Aided Mol Des 20 (7-8): 437-48, 2006. Van Der Spoel, D., Lindahl, E., Hess, B., Groenhof, G., Mark, A. E., and Berendsen, H. J.: GROMACS: fast, flexible, and free. J Comput Chem 26 (16): 1701-18, 2005. Xiang, Z., and Honig, B.: Extending the accuracy limits of prediction for side-chain conformations. J Mol Biol 311 (2): 421-30, 2001.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 此要求在TeraGRID平台上进行30,000个SU的开发分配请求基于NIH支持的研究项目（R01 DA020032； PI：Marta Filizola博士）。该项目的目的是使用迭代组合的计算和实验方法来确定在受体模型的结构背景下，在受体模型的结构环境中识别导致三角洲和阿片受体（均和异源体）的寡聚化的分子决定因素。该资源请求将专门用于获得非活性三角洲和阿片受体单体的新的精制分子模型，这些模型将作为构建这些受体的二聚体或高阶低聚物的基础。 The recent publication of the crystal structure of beta2-adrenergic receptor(Cherezov et al., 2007), and its higher sequence homology with opioid receptors compared to rhodopsin, prompted us to re-build three-dimensional models of the transmembrane (TM) regions of delta- and mu-opioid receptors using beta2-adrenergic as a structural template.现在，我们将使用这些新的TM模型使用AB-Initio方法来添加最佳的外部和细胞内环区域，该方法最初是在康奈尔大学Weill Medical College的Ernest Mehler博士实验室中开发的，目前正在Mehler和Filizola Labs的协作工作中进行了优化。简而言之，该方法采用模拟退火蒙特卡洛（MC）模拟，从完全扩展的结构开始在循环段上进行，并结合了偏置缩放的集体变量（SCV）Monte Carlo技术，旨在完成该段的关闭。 Since an accurate force field for the study of peptide and protein conformational preferences must account for the hydrophobic and electrostatic effects of the solvent, the method uses a validated continuum electrostatic model based on screened Coulomb potentials, which has extensively been validated for small molecules as well as proteins (Hassan et al., 2000a; Hassan et al., 2000b; Hassan and Mehler, 2002).已经证明，MC-SCV-MC方法可以可靠地预测GPCR的循环区域的构象（Kortagere等，2006; Mehler等，2006），与其他较不开始的循环预测方法一样，其性能会随着循环长度的增加（超过10个残基）。因此，为了改善GPCR长循环的结构表征，我们最近一直在研究将两种或多种当前可用的从头算循环预测方法与MC-SCV-MC方案结合使用的建模方法（Bandhyopadhyay等，2008）。我们的结果表明，基于Rotamer库（SCAP（Xiang and Honig，2001））中的侧链预测策略的综合使用大大改善了MC-SCV-MC单独获得的循环结构预测，或其他公平的可靠和其他公平的循环预测算法（E.GAC），E。loape，all loapy，al al al al al al alllo。 （Fiser and Sali，2003）等）。因此，我们将应用我们的改进的MC-SCV-MC方案来预测三角洲和阿片受体的循环。然后，由此产生的构象将用于以下：1）通过应用基于最新的知识距离约束方法来构建阿片受体的激动剂结合模型，我们最近在Rhodopsin上测试了该方法（Niv等，2006）； 2）在显式脂质 - 水环境中，运行对无活性和激动剂结合的阿片类药物受体的长期分子动力学模拟。模拟将使用我们最近在视紫红质单体和二聚体上测试的方案使用gromacs（van der Spoel等，2005）进行（Filizola等，2006）。这些研究将通过定量识别受体激活的分子间模式，并将其添加到当前的配体结合和信号转导模型中，从而帮助我们实现阿片受体功能的更完整表示。参考文献Bandhyopadhyay，D.，Bortolato，A.，Filizola，M。和Mehler，E。L。：改善G蛋白偶联受体环的预测。第52届生物物理学会和IUPAB国际生物物理学大会（长滩，加利福尼亚州），2008年。Cherezov，V.，Rosenbaum，D.M. Stevens，R。C。：设计的人beta2-肾上腺素G蛋白偶联受体的高分辨率晶体结构。科学318（5854）：1258-65，2007。Filizola，M.，Wang，S。X.和Weinstein，H。：G蛋白偶联受体二聚体的动态模型：popc bilayer中的分子动力学模拟的Rhodopsin Dimer中不对称的指示。 J计算摩尔DES 20（7-8）：405-16，2006。Fiser，A。和Sali，A。：Modloop：蛋白质结构中环的自动建模。生物信息学19（18）：2500-1，2003。Hassan，S.，Guarnieri，F。和Mehler，E。：在连续溶剂模型中氢键的表征。 。 J. Phys。化学104：6490，2000a。 Hassan，S.，Guarnieri，F。和Mehler，E。：基于筛选的库仑电位的溶剂效应的一般处理。 。 J. Phys。化学104：6478，2000b。 Hassan，S。A.和Mehler，E。L。：连续静脉静电学的批判性分析：筛选的库仑电位 - 极限溶剂模型以及对丙氨酸二肽的研究以及对蛋白质错误折叠结构的歧视。蛋白质47（1）：45-61，2002。Jacobson，M。P.，Pincus，D。L.，Rapp，C.S.，Day，T.J.，Honig，B.，Shaw，D。E.蛋白质55（2）：351-67，2004。Kortagere，S.，Roy，A。和Mehler，E。L。：G蛋白耦合受体中长环的始于从头算计算建模。 J计算摩尔DES 20（7-8）：427-36，2006。Mehler，E。L.，Hassan，S。A.，S.A.，Kortagere，S。和Weinstein，H。：从g-蛋白耦合受体中环的初始计算模型：旋接晶体结构的sermon。蛋白质64（3）：673-90，2006。Niv，M.Y.，Skrabanek，L.，Filizola，M。和Weinstein，H。：建模GPCR的活化状态：Rhodopsin模板。 J计算摩尔DES 20（7-8）：437-48，2006。Van der Spoel，D.，Lindahl，E.，Hess，B.，Groenhof，G.，Mark，A。E. J Comput Chem 26（16）：1701-18，2005。Xiang，Z。和Honig，b。 J Mol Biol 311（2）：421-30，2001。