Multiscale modeling of G protein-coupled receptors

G 蛋白偶联受体的多尺度建模

• 批准号: 8689100
• 负责人: Alan Grossfield
• 金额: $ 26.27万
• 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/8689100
• 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; 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; signal processing; 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（由适用提供）：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。结果，它们具有巨大的生物医学重要性。据某些估计，多达50％的新药靶向GPCR。毫不奇怪，在了解其生物物理学方面已经进行了巨大的研究投资。但是，整体膜蛋白要挑战实验合作，为计算方法提供了重大贡献的机会。我们将使用多尺度建模技术，包括全部原子分子动力学模拟和弹性网络模型，探索几种GPCR的行为，包括Rhodopsin（及其常规形式的OPSIN）和2-肾上腺素能受体（B2AR）。当与我们先前在黑暗状态下的视紫红质的工作以及在激活的早期阶段对比，对OPSIN的微秒尺度模拟将使我们可以查看Rhopopsin中的哪些相互作用是由配体的存在确定的，而全面激活过程的计划模拟将使GP CRCR的结构性更改涉及GP CRCR的第一个原子级别的视图，使得GP CR CPR激活;这些知识对于对其他GPCR的新型抑制剂的设计可能至关重要。该提案的第二个目标是阐明内部水在GPCR的激活机制中的作用。我们以前的模拟描述了视紫红质和B2AR的内部水合的显着增加。在这里，我们建议使用自动模式识别方法更严格地追求这些观察结果，以将水合作用的变化与动功能有趣的蛋白质运动相关联，在Rhodopsin，B2AR和Cannabinoid-2受体（CB2）的模拟中。该提案的第三个目标是开发弹性网络模型 - 一种简单的，计算上的廉价方法，蛋白质的相互作用表示为弹簧网络 - 以探索不容易被全部原子分子动力学的较大规模问题，例如G蛋白结合和GPCR寡聚的蛋白质运动的调节。将考虑许多可能的网络模型实现，并通过定量比较与广泛的分子动力学模拟（包括针对第一个目标提出的那些模拟）仔细验证这些模型。该提案的第四个也是最终目标是评估共同假设的有效性，即视紫红质是一般理解GPCR激活的良好模板。为了检验该假设，我们将应用多种计算方法，包括长时间分子动力学和弹性网络模型，包括一系列GPCR，包括Rhodopsin，Opsin，B2AR和CB2。我们将定量地将不同GPCR的波动与跨多个GPCR的运动的假设相关联可能在功能上很重要。