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.

项目摘要该补充的目的是使用8个NVIDIA A100图形获得Dell高性能计算服务器加工单元（GPU）加速发展深度学习方法以重建蛋白质来自低温电子显微镜（Cryo-EM）图像数据的结构准确，自动。冷冻EM 技术可以确定大型蛋白质复合物和组件的第四纪结构对于传统技术（例如X射线晶体学或核磁共振（NMR）确定。随着冷冻EM技术通常达到高分辨率近年来，它一直在彻底改变结构生物学领域，并广泛用于确定结构大型蛋白质复合物和组件。但是，蛋白质结构的计算重建冷冻EM图像数据仍然是一个耗时且劳动密集型的过程。高级人工智能（AI）诸如深度学习之类的方法拥有自动化过程并改善重建的关键准确性。父母R01授予此补充剂旨在开发尖端的深度学习模型，例如 2D和3D变形金刚自动化从冷冻EM数据重建蛋白质结构的关键任务：（1）在低温EM图像中挑选蛋白质颗粒（显微照片）；（2）通过蛋白质构建的冷冻EM密度图粒子图像；（3）从冷冻EM密度图中重建蛋白质结构；（4）整合方法（1），（2）和（3）作为管道，以自动从低温EM图像重建高精度蛋白结构无人干预的数据。我们在该项目的前八个月中取得了重大进展，证明了拟议的方法是完全可行的，而且高度有希望。但是，培训和测试大型深度学习大型Cryo-EM数据集上的变压器模型有效，有效地需要大量的GPU计算力量。使用当前可用的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。尽管速度可以带来重大进展，但不够快为了最大程度地提高父r01项目的尖端深度学习方法的潜力和影响。这补充剂将使我们能够购买由8个NVIDIA A100 80GB组成的高性能计算服务器 GPU大大加快了父R01项目的研究。该GPU服务器可以减少完成一个深度学习模型从大约一年到不到两个月的开发，并且因此，大大提高了开发人员的生产率，并大大加速了发布和发布该项目中开发的方法和工具。此外，每个GPU的大（80GB）内存将启用我们训练由数百万参数组成的高质量深层变压器，以最大程度地提高准确性从冷冻EM图像数据中重建蛋白质结构。