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.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目和 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 TeraGrid 平台上 30,000 个 SU 的开发分配 (DAC) 请求基于 NIH 支持的研究项目（R01 DA020032；PI：Marta Filizola 博士）。该项目的目标是使用迭代组合计算和实验方法，在受体模型的结构背景中确定负责 δ- 和 mu-阿片受体（同聚体和异聚体）寡聚化的分子决定因素。这一资源请求将专门用于获得无活性的δ-和μ-阿片受体单体的新的精制分子模型，该模型将作为构建这些受体的二聚体或更高阶寡聚体的基础。最近发表的β2-肾上腺素受体晶体结构（Cherezov et al., 2007）及其与视紫红质相比与阿片受体更高的序列同源性，促使我们重新构建跨膜（TM）区域的三维模型使用β2-肾上腺素能作为结构模板的δ-和μ-阿片受体。我们现在将使用这些新的 TM 模型，通过从头开始的方法添加最佳的细胞外和细胞内环区域，该方法最初是由康奈尔大学威尔医学院 Ernest Mehler 博士的实验室开发的，目前正在优化由 Mehler 和 Filizola 实验室共同努力。简而言之，该方法采用从完全扩展的结构开始对环段进行模拟退火蒙特卡罗（MC）模拟，并结合专门设计用于完成段闭合的偏置缩放集体变量（SCV）蒙特卡罗技术。由于用于研究肽和蛋白质构象偏好的精确力场必须考虑溶剂的疏水性和静电效应，因此该方法使用基于筛选库仑势的经过验证的连续静电模型，该模型也已针对小分子进行了广泛验证作为蛋白质（Hassan 等人，2000a；Hassan 等人，2000b；Hassan 和 Mehler，2002）。尽管 MC-SCV-MC 方法已被证明可以可靠地预测 GPCR 环区域的构象（Kortagere 等人，2006；Mehler 等人，2006），与其他从头开始环预测方法一样，其性能随着次数的增加而下降。环长度（超过 10 个残基）。因此，为了改善 GPCR 长环的结构表征，我们最近一直在研究将两种或多种当前可用的从头开始环预测方法与 MC-SCV-MC 协议相结合的建模方法（Bandhyopadhyay 等，2008）。我们的结果表明，将基于旋转异构体库（SCAP（Xiang 和 Honig，2001））的侧链预测策略集成到标准 MC-SCV-MC 协议中可显着改善仅通过 MC-SCV-MC 获得的环结构预测或其他相当可靠且快速的球状蛋白循环预测算法（例如 LOOPY（Jacobson 等人，2004）、MODLOOP（Fiser 和 Sali， 2003）等）。因此，我们将应用修改后的 MC-SCV-MC 方案来预测 delta-和 mu-阿片受体的环。由此产生的构象将用于：1）通过应用我们最近在视紫红质上测试的最先进的基于知识的距离约束方法来构建阿片受体的激动剂结合模型（Niv 等人， 2006）； 2）在明确的脂水环境中对非活性和激动剂结合的阿片受体进行长期分子动力学模拟。将使用 GROMACS（Van Der Spoel 等人，2005）使用我们最近在视紫红质单体和二聚体上测试的协议（Filizola 等人，2006）进行模拟。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，帮助我们更完整地表征阿片受体功能。参考文献 Bandhyopadhyay, D.、Bortolato, A.、Filizola, M. 和 Mehler, E. L.：改进 G 蛋白偶联受体环的预测。第 52 届生物物理学会年会和第 16 届 IUPAB 国际生物物理学大会（加利福尼亚州长滩），2008 年。 Cherezov, V.、Rosenbaum, D. M.、Hanson, M. A.、Rasmussen, S. G.、Thian, F. S.、Kobilka, T. S.、Choi, H. J. ，库恩，P.，韦斯，W.I.， Kobilka, B. K. 和 Stevens, R. C.：工程化人类 β2-肾上腺素 G 蛋白偶联受体的高分辨率晶体结构。 Science 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：来自分子动力学模拟的视紫红质二聚体不对称性的迹象POPC双层。 J Comput Aided Mol 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.：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.：基于筛选库仑势的溶剂效应的一般处理。 。 J. Phys。化学。 104：6478，2000b。 Hassan, S. A. 和 Mehler, E. L.：连续静电学的批判性分析：筛选的库仑势 - 隐式溶剂模型以及丙氨酸二肽的研究和蛋白质错误折叠结构的辨别。 Proteins 47 (1): 45-61, 2002. Jacobson, M. P.、Pincus, D. L.、Rapp, C. S.、Day, T. J.、Honig, B.、Shaw, D. E. 和 Friesner, R. A.：全原子蛋白质的分层方法循环预测。 Proteins 55 (2): 351-67, 2004。Kortagere, S.、Roy, ​​A. 和 Mehler, E. L.：G 蛋白偶联受体中长环的从头算计算模型。 J Comput Aided Mol Des 20 (7-8): 427-36, 2006。Mehler, E. L.、Hassan, S. A.、Kortagere, S. 和 Weinstein, H.：G 蛋白偶联受体环的从头算计算模型：来自视紫红质晶体结构的教训。蛋白质 64 (3): 673-90, 2006。Niv, M. Y.、Skrabanek, L.、Filizola, M. 和 Weinstein, H.：GPCR 激活状态建模：视紫红质模板。 J Comput Aided Mol Des 20 (7-8): 437-48, 2006。Van Der Spoel, D.、Lindahl, E.、Hess, B.、Groenhof, G.、Mark, A. E. 和 Berendsen, H. J.：GROMACS ：快速、灵活且免费。 J Comput Chem 26 (16): 1701-18, 2005。Xiang, Z. 和 Honig, B.：扩展侧链构象预测的准确性限制。分子生物学杂志 311 (2): 421-30, 2001。