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

项目摘要/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 配备了最新的直接电子探测器(DED)的多台高分辨率显微镜。这个 该设施使用自动数据采集和简化的图像处理协议，其中一些协议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。改进的摄像机和自动化数据的结合 收集现在使用户可以常规地快速收集确定次3.5？结构所需的数据， 允许在生成的密度贴图中直接构建模型的分辨率。然而，每个Cryo-EM数据集 需要数TB的存储空间和数千个CPU(中央处理器)或GPU(图形处理 在配备有大量随机存取存储器(RAM)的计算机系统上工作数小时。这使得 高吞吐量数据收集是低温电磁管道的新瓶颈。能够有效地存储、 处理和分析日益增长的海量数据集是这一过程的关键。因此，能够快速地 收集大型数据集，再加上迅速扩大的校园用户基础，意味着访问 计算资源现在是许多U-M低温电磁项目成功的主要限制因素。 密歇根大学生命科学研究所的低温电磁计算资源 (LSI)，最初部分资金来自对该大学的S10拨款，并将于 2021年5月，供应商将不再为它们提供支持。这项提案要求提供资金以取代 老化的计算和存储系统，以及集成具有以下节点的强大计算群集 足够的内存来有效地确定大粒子的原子分辨率结构(&gt；1,000像素框大小)。 升级后的计算集群将包括1,248个英特尔CPU核心、16个NVIDIA Quadro RTX 8000 GPU (图形处理器)、两个大内存节点和820 TB存储，它们将组合在一起并 与我们将保持在线的一些较新的CPU/GPU计算节点集成。安装此软件 资源对于以下方面至关重要：1)U-M冷冻-EM设施有能力在四个高分辨率 具有“即时”处理功能的低温电子显微镜；2)大型异质低温电子显微镜的图像处理 数据集(1,000,000个粒子)；以及3)具有足够内存的计算节点，以容纳结构 分析大分子复合体，如病毒和细菌分泌系统，以及大分子 用于原位冷冻分析的体积。这里提出的计算基础设施将显著 加强密歇根大学最先进的低温EM结构测定，促进科学进步 许多NIH支持调查人员，以及一批有才华的初级调查人员。

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.