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，并了解其结合特性，是基本的生物医学重要性，因为它们介导的神经信号。该项目旨在表征和了解谷氨酸与N-甲基-D-天冬氨酸受体（NMDAr）的结合，NMDAr是谷氨酸受体蛋白家族的一员，对神经科学和药理学具有潜在的深远影响。 然而，NMDAr的配置景观的表征是高性能计算（HPC）问题。 它需要模拟的时间尺度和系统规模远远超过以往任何一次。 该项目将利用Blue沃茨的千万亿次计算能力，采用新的采样方法和独创的计算和数据处理技术，利用分子动力学（MD）模拟来研究这种大分子系统。 然而，它仍然是一个挑战，以获得足够的采样的复杂化学系统的构型空间，以准确地描述重要的子状态的结构特性，它们的相对倾向，以及它们之间的可访问的转换。 该项目提出使用一种新的软件框架，在正确的计算资源上，使我们通过分子动力学对大分子构象空间进行采样的能力发生了飞跃。 该项目将研究一种具有重要生物医学意义的蛋白质，即N-甲基-D-天冬氨酸受体（NMDAr）的配体结合结构域（LBD）。 软件策略的核心思想类似于许多其他多尺度方法，如伞形采样、元自适应方法、自适应偏置方法或过渡路径采样：可以同时模拟许多（数百或数千）短轨迹，而不是运行一个或几个长MD轨迹。 使用复杂的数据简化和分析方法从这些非常大的数据集中提取信息，并且粗粒度的信息（其体现了理解系统所必需的化学洞察力，例如近似自由能）用于细化生成进一步轨迹的方式（即，如何采样）。然后在迭代过程中使用来自采样空间的分析结果以进一步指导构象空间的搜索（即，我们的样品）。此Blue沃茨分配将允许项目访问NMDAr LBD系统的总计2.7毫秒的模拟。与三个数量级（至少）的采样速度由我们的方法允许相对于平原MD，该项目将能够映射的构象动力学相关的这种蛋白质的构型景观到秒的时间尺度，也就是说，完全表征的配体结合结构域的作用在NMDAR的生物功能和机制。