BIGDATA: IA: Collaborative Research: In Situ Data Analytics for Next Generation Molecular Dynamics Workflows

BIGDATA：IA：协作研究：下一代分子动力学工作流程的原位数据分析

• 批准号: 1841758
• 负责人: Michela Taufer
• 金额: $ 98万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1841758&HistoricalAwards=false
• 关键词: BIGDATA; IA; Collaborative; Research; Situ

Molecular dynamics simulations studying the classical time evolution of a molecular system at atomic resolution are widely recognized in the fields of chemistry, material sciences, molecular biology and drug design; these simulations are one of the most common simulations on supercomputers. Next-generation supercomputers will have dramatically higher performance than do current systems, generating more data that needs to be analyzed (i.e., in terms of number and length of molecular dynamics trajectories). The coordination of data generation and analysis cannot rely on manual, centralized approaches as it does now. This interdisciplinary project integrates research from various areas across programs such as computer science, structural molecular biosciences, and high performance computing to transform the centralized nature of the molecular dynamics analysis into a distributed approach that is predominantly performed in situ. Specifically, this effort combines machine learning and data analytics approaches, workflow management methods, and high performance computing techniques to analyze molecular dynamics data as it is generated, save to disk only what is really needed for future analysis, and annotate molecular dynamics trajectories to drive the next steps in increasingly complex simulations' workflows. The investigators tackle the data challenge of data analysis of molecular dynamics simulations on the next-generation supercomputers by (1) creating new in situ methods to trace molecular events such as conformational changes, phase transitions, or binding events in molecular dynamics simulations at runtime by locally reducing knowledge on high-dimensional molecular organization into a set of relevant structural molecular properties; (2) designing new data representations and extend unsupervised machine learning techniques to accurately and efficiently build an explicit global organization of structural and temporal molecular properties; (3) integrating simulation and analytics into complex workflows for runtime detection of changes in structural and temporal molecular properties; and (4) developing new curriculum material, online courses, and online training material targeting data analytics. The project's harnessed knowledge of molecular structures' transformations at runtime can be used to steer simulations to more promising areas of the simulation space, identify the data that should be written to congested parallel file systems, and index generated data for retrieval and post-simulation analysis. Supported by this knowledge, molecular dynamics workflows such as replica exchange simulations, Markov state models, and the string method with swarms of trajectories can be executed ?from the outside? (i.e., without reengineering the molecular dynamics code).

分子动力学模拟以原子分辨率研究分子系统的经典时间演化，在化学、材料科学、分子生物学和药物设计领域得到广泛认可;这些模拟是超级计算机上最常见的模拟之一。 下一代超级计算机将具有比当前系统更高的性能，产生更多需要分析的数据（即，在分子动力学轨迹的数量和长度方面）。数据生成和分析的协调不能像现在这样依赖人工集中的方法。 这个跨学科的项目整合了计算机科学，结构分子生物科学和高性能计算等各个领域的研究，将分子动力学分析的集中性质转变为主要在原位进行的分布式方法。具体来说，这项工作结合了机器学习和数据分析方法，工作流程管理方法和高性能计算技术，以分析生成的分子动力学数据，仅将未来分析真正需要的数据保存到磁盘，并注释分子动力学轨迹，以推动日益复杂的模拟工作流程的下一步。研究人员通过以下方式解决了下一代超级计算机上分子动力学模拟数据分析的数据挑战：（1）创建新的原位方法，通过将高维分子组织的知识局部简化为一组相关的结构分子特性，来跟踪分子动力学模拟中运行时的分子事件，如构象变化，相变或结合事件;（2）设计新的数据表示并扩展无监督机器学习技术，以准确有效地构建结构和时间分子特性的显式全局组织;（3）将模拟和分析集成到复杂的工作流程中，用于结构和时间分子特性变化的运行时检测;以及（4）开发针对数据分析的新课程材料、在线课程和在线培训材料。该项目利用的分子结构在运行时的转换知识可用于将模拟引导到模拟空间中更有前途的领域，识别应写入拥挤的并行文件系统的数据，并为检索和模拟后分析编制索引。在这些知识的支持下，分子动力学的工作流程，如副本交换模拟，马尔可夫状态模型，和字符串方法与成群的轨迹可以执行？从外面？(i.e.,而不重新设计分子动力学代码）。

项目成果

Identifying Structural Properties of Proteins from X-ray Free Electron Laser Diffraction Patterns

从 X 射线自由电子激光衍射图识别蛋白质的结构特性

• DOI: 10.1109/escience55777.2022.00017
• 发表时间: 2022
• 期刊: 18th IEEE International Conference on e-Science (eScience
• 作者: Olaya, Paula;Caino-Lores, Silvina;Lama, Vanessa;Patel, Ria;Rorabaugh, Ariel Keller;Miyashita, Osamu;Tama, Florence;Taufer, Michela
• 通讯作者: Taufer, Michela

Modeling the Performance of Scientific Workflow Executions on HPC Platforms with Burst Buffers

使用突发缓冲区对 HPC 平台上科学工作流程执行的性能进行建模

• DOI: 10.1109/cluster49012.2020.00019
• 发表时间: 2020
• 期刊: 2020 IEEE International Conference on Cluster Computing (CLUSTER
• 作者: Pottier, Loic;Ferreira da Silva, Rafael;Casanova, Henri;Deelman, Ewa
• 通讯作者: Deelman, Ewa

Accelerating Scientific Workflows on HPC Platforms with In Situ Processing

通过原位处理加速 HPC 平台上的科学工作流程

• DOI: 10.1109/ccgrid54584.2022.00009
• 发表时间: 2022
• 期刊: Cloud and Internet Computing (CCGrid
• 作者: Do, Tu Mai;Pottier, Loic;Yildiz, Orcun;Vahi, Karan;Krawczuk, Patrycja;Peterka, Tom;Deelman, Ewa
• 通讯作者: Deelman, Ewa

Molecular determinants of pH sensing in the proton-activated chloride channel.

• DOI: 10.1073/pnas.2200727119
• 发表时间: 2022-08-02
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1

Allosteric pathways of pH-sensitivity in a proton activated chloride channel

质子激活氯通道中 pH 敏感性的变构途径

• DOI: 10.1016/j.bpj.2021.11.505
• 发表时间: 2022
• 期刊: Biophysical Journal
• 影响因子: 3.4
• 作者: Kots, Ekaterina D.;Osei-Owusu, James;Qiu, Zhaozhu;Weinstein, Harel
• 通讯作者: Weinstein, Harel