Acquiring a GPU server to accelerate developing deep learning methods to reconstruct protein structures from cryo-EM data

购买 GPU 服务器以加速开发深度学习方法，以从冷冻电镜数据重建蛋白质结构

基本信息

批准号：
10795465
负责人：
Jianlin Cheng
金额：
$ 16.72万
依托单位：
UNIVERSITY OF MISSOURI-COLUMBIA
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-20 至 2026-05-31
项目状态：
未结题

项目摘要

Project Summary The goal of this supplement is to acquire a Dell high-performance computing server with 8 Nvidia A100 Graphics Processing Units (GPUs) to accelerate the development of deep learning methods to reconstruct protein structures from cryogenic electron microscopy (cryo-EM) image data accurately and automatically. The cryo-EM technology can determine the quaternary structure of large protein complexes and assemblies consisting of many chains that are difficult or even impossible for traditional techniques such as X-ray crystallography or nuclear magnetic resonance (NMR) to determine. As the cryo-EM technology routinely reached high resolution in recent years, it has been revolutionizing the field of structural biology and widely used to determine structures of large protein complexes and assemblies. However, the computational reconstruction of protein structures from cryo-EM image data is still a time-consuming and labor-intensive process. The advanced artificial intelligence (AI) methods such as deep learning hold the key to automate the process and improve the reconstruction accuracy. The parent R01 grant of this supplement aims to develop cutting-edge deep learning models such as 2D and 3D transformers to automate the key tasks of reconstructing protein structures from cryo-EM data: (1) picking protein particles in cryo-EM images (micrographs); (2) denoising cryo-EM density maps built from protein particle images; (3) reconstructing protein structures from cryo-EM density maps; and (4) integrating the methods of (1), (2) and (3) as a pipeline to automatically reconstruct high-accuracy protein structures from cryo-EM image data without human intervention. Our substantial progress in the first eight months of this project has demonstrated that the proposed methods are fully feasible and highly promising. However, training and testing the large deep learning transformer models on big cryo-EM datasets efficiently and effectively need a large amount of GPU computing power. Using the current GPU resource available to us, it takes about one year for a developer to complete the development of one deep learning method. Although the speed can yield significant progress, it is not fast enough to maximize the potential and impact of the cutting-edge deep learning methods of the parent R01 project. This supplement will enable us to acquire a high-performance computing server consisting of 8 Nvidia A100 80GB GPUs to drastically speed up the research in the parent R01 project. This GPU servers can reduce the time of completing the development of one deep learning model from about one year to less than two months, and therefore drastically improve the productivity of the developers and greatly accelerate publishing and releasing the methods and tools developed in this project. Moreover, the large (80GB) memory of each GPU will enable us to train high-quality deep transformers consisting of millions of parameters to maximize the accuracy of reconstructing protein structures from cryo-EM image data.

项目概要本补充的目标是购买一台配备 8 个 Nvidia A100 显卡的戴尔高性能计算服务器处理单元 (GPU) 加速开发重建蛋白质的深度学习方法准确、自动地从低温电子显微镜 (cryo-EM) 图像数据中获取结构。冷冻电镜技术可以确定大型蛋白质复合物和组件的四级结构，其中包括许多链对于 X 射线晶体学或 X 射线晶体学等传统技术来说是困难甚至不可能的核磁共振（NMR）来测定。由于冷冻电镜技术通常达到高分辨率近年来，它彻底改变了结构生物学领域，并广泛用于确定结构大型蛋白质复合物和组装体。然而，蛋白质结构的计算重建冷冻电镜图像数据仍然是一个耗时且劳动密集型的过程。先进的人工智能深度学习等人工智能方法是实现流程自动化和改进重建的关键准确性。该补充的母 R01 资助旨在开发尖端的深度学习模型，例如 2D 和 3D 转换器可自动执行从冷冻电镜数据重建蛋白质结构的关键任务：(1) 在冷冻电镜图像（显微照片）中挑选蛋白质颗粒； (2) 由蛋白质构建的去噪冷冻电镜密度图粒子图像； (3) 从冷冻电镜密度图重建蛋白质结构； (4) 整合方法 (1)、(2) 和 (3) 作为从冷冻电镜图像自动重建高精度蛋白质结构的管道无需人工干预的数据。我们在该项目的前八个月取得的实质性进展表明，拟议的方法完全可行且前景广阔。然而，训练和测试大型深度学习大型冷冻电镜数据集上的 Transformer 模型高效且有效地需要大量 GPU 计算力量。使用我们当前可用的 GPU 资源，开发人员大约需要一年的时间才能完成开发一种深度学习方法。虽然速度可以带来显着的进步，但还不够快最大限度地发挥母公司 R01 项目的尖端深度学习方法的潜力和影响。这补充将使我们能够获得由8个Nvidia A100 80GB组成的高性能计算服务器 GPU 可大幅加快父 R01 项目的研究速度。该GPU服务器可以减少从大约一年到不到两个月的时间完成一个深度学习模型的开发，并且因此极大地提高了开发人员的生产力并大大加快了发布和发布的速度该项目中开发的方法和工具。此外，每个 GPU 的大内存 (80GB) 将使我们训练由数百万个参数组成的高质量深度变压器，以最大限度地提高从冷冻电镜图像数据重建蛋白质结构。