Multiscale modeling of G protein-coupled receptors

G 蛋白偶联受体的多尺度建模

• 批准号: 8502702
• 负责人: Alan Grossfield
• 金额: $ 25.35万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8502702
• 关键词: ADRB2 gene; Adrenergic Receptor; Behavior; Biophysics; Cannabinoids; Cell Signaling Process; Collaborations; Computing Methodologies; Data; Dimerization; Drug Targeting; Elements; Event; Family; G-Protein-Coupled Receptors; GTP-Binding Proteins; Goals; Human Genome; Hydration status; Integral Membrane Protein; Investments; Knowledge; Left; Ligand Binding; Ligands; Machine Learning; Membrane Proteins; Methods; Modeling; Motion; Opsin; Pattern Recognition; Pharmaceutical Preparations; Pharmacologic Substance; Play; Process; Protein Binding; Protein Family; Proteins; Receptor Activation; Research; Research Personnel; Resolution; Resources; Retinal; Rhodopsin; Role; Running; Series; Staging; Structure; Techniques; Testing; Water; Work; computerized data processing; cost; design; follow-up; inhibitor/antagonist; insight; interest; laptop; mammalian genome; metarhodopsin I; metarhodopsin II; molecular dynamics; multi-scale modeling; network models; novel; protein structure; receptor; receptor function; research study; simulation; supercomputer; therapeutic development

DESCRIPTION (provided by applicant): The G protein-coupled receptors (GPCRs) are the largest family in the mammalian genome, and are critical to a number of cell signaling processes. As a result, they are of enormous biomedical importance; by some estimates, as many as 50% of new pharmaceuticals target GPCRs. Unsurprisingly, there has been a huge research investment in understanding their biophysics. However, integral membrane proteins are challenging to work with experimentally, leaving an opportunity for computational methods to make a significant contribution. We will use multiscale modeling techniques, including all-atom molecular dynamics simulations and elastic network models, to explore the behavior of several GPCRs, including rhodopsin (and its retinal-free form, opsin) and the ¿2-adrenergic receptor (B2AR). Specifically, we will investigate the role of ligand binding in modulating GPCR function, via two separate all-atom molecular dynamics calculations. Microsecond-scale simulations of opsin will, when contrasted with our previous work on rhodopsin in the dark state and during the early stages of activation, allow us to see which interactions in rhodopsin are determined by the presence of the ligand, while the planned simulations of the full activation process will give the first atomic-level view of the structural changes involved in GPCR activation; this knowledge could be critical to the design of novel inhibitors to other GPCRs. The second goal of this proposal is to clarify the role of internal waters in the activation mechanism of GPCRs; our previous simulations described significant increases in the internal hydration of rhodopsin and B2AR. Here, we propose to pursue those observations more rigorously, using automatic pattern recognition methods to correlate hydration changes with functionally interesting protein motions in simulations of rhodopsin, B2AR, and the cannabinoid-2 receptor (CB2). The third goal of the proposal is to develop elastic network models - a simple, computationally inexpensive approach where the protein's interactions are represented as a network of springs - in order to explore larger scale problems not readily amenable to all-atom molecular dynamics, like the modulation of protein motions by G protein binding and GPCR oligomerization. A number of possible network model implementations will be considered, and the models will be carefully validated by quantitative comparison to extensive molecular dynamics simulations, including those proposed for the first aim. The fourth and final goal of the proposal is to assess the validity of a common assumption, that rhodopsin is a good template for understanding GPCR activation in general. To test this hypothesis we will apply multiple computational methods, including long timescale molecular dynamics and elastic network models, to a series of GPCRs, including rhodopsin, opsin, B2AR, and CB2. We will quantitatively correlate the fluctuations of the different GPCRs, with the hypothesis that motions conserved across multiple GPCRs are likely to be functionally significant.

描述（由申请人提供）：G蛋白偶联受体（GPCR）是哺乳动物基因组中最大的家族，对许多细胞信号传导过程至关重要。因此，它们具有巨大的生物医学重要性;据估计，多达50%的新药针对GPCR。毫不奇怪，在了解它们的生物物理学方面已经有了巨大的研究投资。然而，完整的膜蛋白是具有挑战性的实验工作，留下了一个机会，计算方法作出重大贡献。我们将使用多尺度建模技术，包括全原子分子动力学模拟和弹性网络模型，来探索几种GPCR的行为，包括视紫红质（及其无视网膜形式，视蛋白）和2-肾上腺素能受体（B2 AR）。具体来说，我们将研究配体结合在调节GPCR功能的作用，通过两个单独的全原子分子动力学计算。与我们之前在黑暗状态和激活早期阶段对视紫红质的研究相比，视蛋白的微秒级模拟将使我们能够看到视紫红质中的哪些相互作用是由配体的存在决定的，而计划中的完整激活过程的模拟将给出GPCR激活中涉及的结构变化的第一个原子级视图;这些知识对于设计其它GPCR的新抑制剂是至关重要的。该提案的第二个目标是澄清内部沃茨在GPCR激活机制中的作用;我们之前的模拟描述了视紫红质和B2 AR的内部水合作用的显着增加。在这里，我们建议更严格地追求这些观察结果，使用自动模式识别方法将水合作用变化与视紫红质，B2 AR和大麻素-2受体（CB 2）模拟中功能有趣的蛋白质运动相关联。该提案的第三个目标是开发弹性网络模型-一种简单，计算成本低廉的方法，其中蛋白质的相互作用表示为弹簧网络-以探索更大规模的问题，不容易服从全原子分子动力学，如G蛋白结合和GPCR寡聚化对蛋白质运动的调节。一些可能的网络模型的实现将被考虑，和模型将被仔细验证的定量比较，广泛的分子动力学模拟，包括那些提出的第一个目标。该提案的第四个也是最后一个目标是评估一个常见假设的有效性，即视紫红质是理解一般GPCR激活的良好模板。为了验证这一假设，我们将应用多种计算方法，包括长时间尺度分子动力学和弹性网络模型，对一系列GPCR，包括视紫红质，视蛋白，B2 AR和CB 2。我们将定量地关联不同GPCR的波动，假设在多个GPCR之间保守的运动可能具有功能意义。