Compute Cluster for in vitro and in situ Analysis of Molecular Machines

用于分子机器体外和原位分析的计算集群

• 批准号: 10175676
• 负责人: Melanie Diane Ohi
• 金额: $ 60万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-15 至 2023-04-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10175676
• 关键词: Aging; Biological; Biological Sciences; Computer Systems; Cryo-electron tomography; Cryoelectron Microscopy; Data; Data Collection; Data Set; Electron Microscope; Electrons; Funding; Hour; In Situ; In Vitro; Institutes; Macromolecular Complexes; Maps; Memory; Michigan; Microscope; Molecular Analysis; Molecular Machines; Process; Protocols documentation; Ramp; Request for Proposals; Research Personnel; Resolution; Resources; S10 grant; Structure; System; Talents; United States National Institutes of Health; Universities; Vendor; Virus; base; cluster computing; cohort; computer infrastructure; computing resources; data acquisition; density; detector; end of life; image processing; improved; large datasets; model building; particle; success; terabyte

PROJECT SUMMARY/ABSTRACT The University of Michigan (U-M) has established a cutting-edge cryo-electron microscopy (cryo-EM) facility with multiple high-resolution microscopes equipped with the newest direct electron detectors (DEDs). The facility uses automated data acquisition and streamlined image processing protocols, some developed at U-M, which allows for high-throughput structural determination of biological molecules using both single-particle and cryo-electron tomography (cryo-ET) strategies. The combination of improved cameras and automated data collection now makes it routine for users to rapidly collect the data required to determine sub-3.5 Å structures, resolutions that allow for direct model building in the resulting density maps. However, each cryo-EM dataset requiring terabytes of storage and thousands of CPU (Central Processing Unit) or GPU (Graphics Processing Unit) hours on computer systems equipped with large amounts of random-access memory (RAM). This makes high-throughput data collection a new bottleneck in the cryo-EM pipeline. The ability to effectively store, process, and analyze the increasingly massive datasets is essential for this process. Thus, the ability to quickly collect large datasets, combined with a rapidly expanding campus-wide user base, means that access to computational resources now represents a major limiting factor for the success of many U-M cryo-EM projects. The computational resources for cryo-EM at the University of Michigan, housed at the Life Sciences Institute (LSI), were originally funded in part through an S10 grant to the university and will reach their end of life by May 2021 when they will no longer be supported by the vendor. This proposal requests funds to replace the aging computation and storage system, as well as integrate a powerful compute cluster that has nodes with enough memory to efficiently determine atomic resolution structures of large particles (> 1,000 pixel box sizes). This upgraded computation cluster will include 1,248 Intel CPU cores, 16 NVIDIA Quadro RTX 8000 GPUs (Graphic Processing Units), two large memory nodes, and 820 TB of storage, which will be combined and integrated with some of our newer CPU/GPU compute nodes that will remain online. Installation of this resource is essential for: 1) the U-M cryo-EM facility ability to ramp up data collection on four high-resolution cryo-electron microscopes with “on-the-fly” processing; 2) image processing of large heterogeneous cryo-EM datasets (+1,000,000 particles); and 3) compute nodes with enough memory to accommodate the structural analysis of large macromolecular complexes, such as viruses and bacterial secretion systems, and the large volumes used for in situ cryo-ET analysis. The computational infrastructure proposed here will significantly enhance state-of-the-art cryo-EM structure determination at U-M, facilitating the scientific progress of numerous NIH supported investigators, as well as a cohort of talented junior investigators.

项目概要/摘要 密歇根大学 (U-M) 建立了尖端的冷冻电子显微镜 (cryo-EM) 设施 配备多个配备最新直接电子探测器 (DED) 的高分辨率显微镜。 设施使用自动数据采集和简化的图像处理协议，其中一些是在密歇根大学开发的， 它允许使用单粒子和 冷冻电子断层扫描（cryo-ET）策略结合了改进的相机和自动化数据。 现在，用户可以快速收集确定亚 3.5 Å 结构所需的数据， 然而，每个冷冻电镜数据集都允许在生成的密度图中直接建立模型。 需要 TB 的存储空间和数千个 CPU（中央处理单元）或 GPU（图形处理单元） 配备大量随机存取存储器 (RAM) 的计算机系统上的单位）小时。 高通量数据收集是冷冻电镜管道中的新瓶颈。 处理和分析日益庞大的数据集对于这个过程至关重要。因此，快速处理的能力。 收集大型数据集，再加上快速扩大的校园范围内的用户群，意味着可以访问 现在，计算资源是许多密歇根大学冷冻电镜项目成功的主要限制因素。 密歇根大学生命科学研究所的冷冻电镜计算资源 (LSI)，最初的部分资金来自大学的 S10 拨款，并将于 2021 年 5 月，供应商将不再支持此提案，要求提供资金来更换。 老化的计算和存储系统，以及集成强大的计算集群，其节点具有 足够的内存来有效确定大颗粒的原子分辨率结构（> 1,000 像素框大小）。 升级后的计算集群将包括 1,248 个 Intel CPU 核心、16 个 NVIDIA Quadro RTX 8000 GPU （图形处理单元）、两个大内存节点和 820 TB 存储，这些将被合并并 与我们的一些较新的 CPU/GPU 计算节点集成，这些节点的安装将保持在线状态。 资源对于以下方面至关重要：1) 密歇根大学冷冻电镜设施能够提高四个高分辨率的数据收集能力 具有“即时”处理功能的冷冻电子显微镜；2）大型异质冷冻电镜图像处理； 数据集（+1,000,000 个粒子）；3) 具有足够内存来容纳结构的计算节点 分析大分子复合物，例如病毒和细菌分泌系统，以及大分子复合物 用于原位冷冻 ET 分析的体积将显着增加此处提出的计算基础设施。 增强 U-M 测定中最先进的冷冻电镜结构，促进科学进步 许多国家卫生研究院支持研究人员，以及一批才华横溢的初级研究人员。

项目成果

Autoinhibited kinesin-1 adopts a hierarchical folding pattern.

自抑制驱动蛋白-1采用分层折叠模式。

• DOI: 10.1101/2023.01.26.525761
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Tan,Zhenyu;Yue,Yang;daVeigaLeprevost,Felipe;Haynes,SarahE;Basrur,Venkatesha;Nesvizhskii,AlexeyI;Verhey,KristenJ;Cianfrocco,MichaelA
• 通讯作者: Cianfrocco,MichaelA