# Structural and Functional Analyses of the Full-length Insulin Receptor (IR) and Type 1 Insulin-like Growth Factor Receptor (IGF1R) in the Liganded Active State

> **NIH NIH R01** · UT SOUTHWESTERN MEDICAL CENTER · 2021 · $51,580

## Abstract

This application for supplemental grant is to request fund for purchasing a high performance GPU
cluster for single particle cryo-EM data storage and processing, which is under the parent project
that is focused on the understanding of the exact ligand induced activation mechanism of insulin
receptor and IGF1R. In the last year, we have made tremendous progress toward finishing all the
3 aims of proposed project. Nevertheless, after I submitted the original proposal, our cryo-EM
facility has been expanded significantly, currently housing 4 high-end electron microscopies,
which is two times more than that one year ago. In addition, our cryo-EM facility has recently
implemented a new data collection method with beam-image shift, which increases the
throughput of data collection by another factor of 3. As a result, we can collect approximately 6
times more data in certain period, compared with one year ago. Thus, the current two old GPU
servers in my lab would not allow us to process all newly generated cryo-EM data in a timely
manner, which would lead to delay for the proposed project. Furthermore, with the improved
data collection capability after our cryo-EM facility expands, we can generate 4-5 TB new cryo-
EM data weekly. Such large amount of data cannot be stored in my old GPU servers for long term.
By using an advanced GPU cluster that have 16 powerful GPU cards and 1000 TB storage space, I
will be able to finish the processing of one full dataset in a few days. This will also allow me to try
several recently developed 3D classification methods to distinguish the structures of full-length
receptor/ligand complexes in different conformational states, which will be the key results in this
proposed project. With the 1000 TB storage device, we could store more than 50 different
datasets in the same time, meaning that each dataset can be stored in the GPU cluster for ~1
year before transferring to a permanent storage device. This would allow me to easily reprocess
the data to improve our structures, as soon as a new method for image processing is developed.

## Key facts

- **NIH application ID:** 10386663
- **Project number:** 3R01GM136976-02S1
- **Recipient organization:** UT SOUTHWESTERN MEDICAL CENTER
- **Principal Investigator:** Xiaochen Bai
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $51,580
- **Award type:** 3
- **Project period:** 2020-03-15 → 2025-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10386663

## Citation

> US National Institutes of Health, RePORTER application 10386663, Structural and Functional Analyses of the Full-length Insulin Receptor (IR) and Type 1 Insulin-like Growth Factor Receptor (IGF1R) in the Liganded Active State (3R01GM136976-02S1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10386663. Licensed CC0.

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