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

谷氨酸受体，并了解其结合特性，是基本的生物医学的重要性，因为他们介导神经元信号。本项目旨在表征和理解谷氨酸与谷氨酸受体家族成员n -甲基- d -天冬氨酸受体（NMDAr）的结合，这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一个高性能计算（HPC）问题。它需要模拟的时间尺度和系统规模远远超过任何以前进行过的。该项目将使用Blue Waters的千万亿级计算能力来研究这样一个系统，使用新的采样方法和原始的计算和数据处理技术。该项目将使用分子动力学（MD）模拟来研究这个大分子系统。然而，获取足够的复杂化学系统的构型空间样本，以准确描述重要基态的结构特性、它们的相对倾向以及它们之间可获得的转变，仍然是一个挑战。该项目建议使用一种新的软件框架，在正确的计算资源上，使我们通过MD对大分子构象空间进行采样的能力发生了一步变化。该项目将研究一种具有重大生物医学相关性的蛋白质，即n -甲基- d -天冬氨酸受体（NMDAr）的配体结合域（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、元动力学、自适应偏差方法或过渡路径采样：与其运行一个或几个长MD轨迹，不如同时模拟许多（数百或数千）短轨迹。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，而粗粒度信息-体现了理解系统所需的化学洞察力，例如近似自由能-用于改进生成进一步轨迹的方式（即我们如何采样）。对采样空间的分析结果随后在迭代过程中使用，以进一步指导对构象空间（即我们采样的地方）的搜索。该Blue Waters分配将允许该项目访问总计2.7毫秒的NMDAr LBD系统模拟。通过我们的方法允许的三个数量级（至少）的采样速度加快，该项目将能够在秒的时间尺度上绘制这种与构象动力学相关的蛋白质的构型景观，也就是说，完全表征配体结合域在NMDAr的生物学功能和机制中的作用。

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.