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.

项目摘要/摘要 密歇根大学（U-M）建立了一个尖端的低温电子显微镜（Cryo-EM）设施 具有多个高分辨率显微镜配备了最新的直接电子探测器（DEDS）。 设施使用自动数据采集和简化图像处理协议，其中一些是在U-M上开发的 这允许使用单粒子和 冷冻电子层析成像（冷冻-ET）策略。改进的相机和自动数据的组合 现在，收集使用户可以迅速收集确定低3.5Å结构所需的数据，这是常规的。 可以在产生的密度图中直接构建模型的决议。但是，每个Cryo-EM数据集 需要存储的terabyt和成千上万的CPU（中央处理单元）或GPU（图形处理） 单位）在配备大量随机存储器（RAM）的计算机系统上的小时。这使得 高通量数据收集在冷冻管道中的新瓶颈。有效存储的能力， 过程，并分析日益庞大的数据集对于此过程至关重要。那，能够快速 收集大型数据集，并结合迅速扩展的校园用户群，这意味着访问 现在，计算资源代表了许多U-M Cryo-EM项目成功的主要限制因素。 密歇根大学的低温EM计算资源，位于生命科学学院 （LSI）最初是通过S10授予大学的部分资助的，并将通过 2021年5月，他们将不再得到供应商的支持。该建议要求资金替换 老化的计算和存储系统，并集成了一个有力的计算集群，该群集具有带有节点的 足够的记忆力可以有效地确定大颗粒的原子分辨率结构（> 1,000像素盒尺寸）。 此升级的计算群集将包括1,248个英特尔CPU内核，16 Nvidia Quadro RTX 8000 GPU （图形处理单元），两个大存储节点和820 TB的存储，它们将组合在一起 与我们的一些较新的CPU/GPU计算节点集成在一起，这些节点将保持在线。安装此 资源对于：1）U-M Cryo-EM设施能够提高四个高分辨率的数据收集的能力 带有“即时”处理的冷冻电子显微镜； 2）大型低温EM的图像处理 数据集（+1,000,000个粒子）； 3）计算有足够内存的节点以适应结构 分析大型大分子复合物，例如病毒和细菌分泌系统，以及大型 用于原位冷冻分析的体积。这里提出的计算基础架构将大大显着 增强U-M处的最新冷冻EM结构确定，支持科学进步 许多NIH支持调查人员，以及一系列才华横溢的初级调查人员。

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