Multiscale modeling of G protein-coupled receptors

G 蛋白偶联受体的多尺度建模

• 批准号: 8324207
• 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/8324207
• 关键词: ADRB2 gene; Adrenergic Receptor; Behavior; Biophysics; Cannabinoids; Cell Signaling Process; Collaborations; Computing Methodologies; Data; Dimerization; Drug Delivery Systems; 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%的新药以gpcr为靶点。不出所料，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，整体膜蛋白在实验上是具有挑战性的，这为计算方法做出重大贡献留下了机会。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（B2AR）。具体来说，我们将通过两个单独的全原子分子动力学计算来研究配体结合在调节GPCR功能中的作用。微秒级的视蛋白模拟将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形成对比，使我们能够看到视紫红质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模拟将提供涉及GPCR激活的结构变化的第一个原子水平视图；这一知识对于设计其他gpcr的新型抑制剂至关重要。本提案的第二个目标是阐明内水在gpcr激活机制中的作用；我们之前的模拟描述了视紫红质和B2AR内部水化的显著增加。在这里，我们建议更严格地追求这些观察结果，在模拟视紫红质、B2AR和大麻素-2受体（CB2）时，使用自动模式识别方法将水合变化与功能上有趣的蛋白质运动联系起来。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被表示为一个弹簧网络——以探索更大规模的问题，这些问题不容易适用于全原子分子动力学，如通过G蛋白结合和GPCR寡聚化调节蛋白质运动。将考虑许多可能的网络模型实现，并且将通过与广泛的分子动力学模拟进行定量比较来仔细验证模型，包括为第一个目标提出的模型。该提案的第四个也是最后一个目标是评估一个共同假设的有效性，即视紫红质是理解GPCR激活的一个很好的模板。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，以一系列gpcr，包括视紫红质，视蛋白，B2AR和CB2。我们将定量地关联不同gpcr的波动，假设在多个gpcr之间守恒的运动可能在功能上很重要。