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

本次追加补助金申请为购买高性能GPU的资金申请 用于单粒子冷冻电镜数据存储和处理的集群，位于父项目下 重点是了解胰岛素的确切配体诱导激活机制 受体和 IGF1R。去年，我们在完成所有目标方面取得了巨大进展 3 拟议项目的目标。尽管如此，在我提交最初的提案后，我们的冷冻电镜 设施已大幅扩建，目前拥有 4 台高端电子显微镜， 这是一年前的两倍。此外，我们的冷冻电镜设施最近还 实施了一种具有光束图像移位的新数据收集方法，这增加了 数据收集的吞吐量乘以另一个因子 3。因此，我们可以收集大约 6 与一年前相比，某个时期的数据增加了数倍。由此可见，目前的两款老GPU 我实验室的服务器不允许我们及时处理所有新生成的冷冻电镜数据 方式，这将导致拟议项目的延误。此外，随着改进的 在我们的冷冻电镜设施扩建后，我们可以生成 4-5 TB 的新冷冻电镜数据收集能力 每周新兴市场数据。如此大量的数据无法长期存储在我的旧 GPU 服务器中。 通过使用拥有 16 个强大 GPU 卡和 1000 TB 存储空间的先进 GPU 集群，我 将能够在几天内完成一个完整数据集的处理。这也让我可以尝试 最近开发的几种3D分类方法来区分全长的结构 不同构象状态的受体/配体复合物，这将是该研究的关键结果 拟议的项目。使用 1000 TB 存储设备，我们可以存储 50 多个不同的 同时存储多个数据集，这意味着每个数据集可以在 GPU 集群中存储 ~1 转移到永久存储设备之前的一年。这将使我能够轻松地重新处理 一旦开发出新的图像处理方法，就可以利用数据来改进我们的结构。

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.