More Power to the Many: Scalable Ensemble-based Simulations and Data Analysis

为更多人提供更多力量：可扩展的基于集成的模拟和数据分析

• 批准号: 1713749
• 负责人: Shantenu Jha
• 金额: $ 2.92万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-01 至 2020-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1713749&HistoricalAwards=false
• 关键词: More; Power; Many; Scalable; Ensemble

Glutamate receptors, and understanding their binding characteristics, are of fundamental biomedical importance as they mediate neuronal signaling. This project proposes to characterize and understand glutamate binding to the N-methyl-D-aspartate receptor (NMDAr), a member of the glutamate receptor family of proteins, with potential profound consequences for neuroscience and pharmacology. However, the characterization of the configurational landscape of NMDAr is a High Performance Computing (HPC) problem. It requires simulations with timescales and system sizes well beyond any that have previously been undertaken. The project will use the petascale computing capabilities of Blue Waters to study such a system, using new sampling methods and original computing and data processing techniques.The project will use molecular dynamics (MD) simulations to study this macromolecular system. However, it remains a challenge to obtain an adequate sampling of the configurational space of complex chemical systems to accurately describe the structural properties of important substates, their relative propensities, and accessible transitions between them. The project proposes to use a novel software framework that on the right computational resource makes a step-change in our ability to sample the conformational space of macromolecules by MD. The project will study a protein of great biomedical relevance that exemplifies these issues, namely the ligand binding domain (LBD) of the N-methyl-D-aspartate receptor (NMDAr). The idea at the core of the software strategy is similar to many other multiscale methods -- such as umbrella sampling, metadynamics, adaptive biasing methods, or transition path sampling: instead of one or a few long MD trajectories being run, many (hundreds or thousands) of short trajectories may be simulated concurrently. Information is extracted from these very large datasets using sophisticated data reduction and analysis methods, and the coarse-grained information -- which embodies the chemical insight necessary to understand the system, e.g. an approximate free energy -- is used to refine the way in which further trajectories are generated (i.e., how we sample). Results from the analysis of the space sampled are then used in an iterative process to further direct the search of the conformational space (i.e., where we sample). This Blue Waters allocation will allow the project to access a total of 2.7 milliseconds of simulation of the NMDAr LBD system. With the three orders of magnitude (at least) speed-up in sampling allowed by our methodology with respect to plain MD, the project will be able to map the configurational landscape of this protein relevant for conformational dynamics up to a timescale of seconds, that is, to completely characterize the role of the ligand binding domain in the biological function and mechanism of NMDAr.

谷氨酸受体及其理解其结合特征在介导神经元信号传导时具有基本的生物医学重要性。该项目建议表征和理解谷氨酸与N-甲基-D-天冬氨酸受体（NMDAR）（NMDAR）的结合，这是蛋白质的谷氨酸受体家族的成员，对神经科学和药理学带来了潜在的深远影响。 但是，NMDAR的配置格局的表征是高性能计算（HPC）问题。 它需要使用时间尺度和系统大小的模拟，远远超出了以前进行的任何模拟。 该项目将使用蓝色水的销售计算能力来研究这种系统，并使用新的采样方法以及原始的计算和数据处理技术。该项目将使用分子动力学（MD）模拟来研究这种大分子分子系统。 然而，要对复杂化学系统的配置空间进行足够的采样，以准确描述重要取代的结构特性，它们的相对倾向以及它们之间可访问的过渡，这仍然是一个挑战。 该项目建议使用一个新颖的软件框架，该框架在正确的计算资源上使我们能够通过MD采样大分子的构象空间的逐步变化。 该项目将研究一种具有伟大生物医学相关性的蛋白质，例如，即N-甲基-D-天冬氨酸受体（NMDAR）的配体结合结构域（LBD）。 该软件策略核心的想法与许多其他多尺度方法相似，例如伞采样，元动力学，自适应偏置方法或过渡路径采样：可以同时模拟一个或几个长的MD轨迹，而是运行许多（数百或数千个）短轨迹。 使用复杂的数据减少和分析方法从这些非常大的数据集中提取信息，以及粗粒的信息 - 体现了了解系统所需的化学见解，例如近似的自由能 - 用于完善产生进一步轨迹的方式（即我们采样）。然后，在迭代过程中使用了采样空间的分析结果，以进一步指导构象空间的搜索（即我们采样的位置）。这种蓝色水域分配将使项目总共访问NMDAR LBD系统的2.7毫秒模拟。在我们的方法学对于普通MD方面允许的采样中的三个数量级（至少）加速，该项目将能够绘制该蛋白质与构象动力学相关的构型景观，直到秒的时间表，即完全表征配体结合结构域在生物学功能和机构中的作用。