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

> **NIH NIH S10** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2021 · $600,000

## Abstract

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 junio...

## Key facts

- **NIH application ID:** 10175676
- **Project number:** 1S10OD030275-01
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Melanie Diane Ohi
- **Activity code:** S10 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $600,000
- **Award type:** 1
- **Project period:** 2021-04-15 → 2023-04-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10175676

## Citation

> US National Institutes of Health, RePORTER application 10175676, Compute Cluster for in vitro and in situ Analysis of Molecular Machines (1S10OD030275-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10175676. Licensed CC0.

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