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

项目摘要 此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），以加速深度学习方法的开发，以重建蛋白质 从低温电子显微镜（cryo-EM）图像数据中准确、自动地提取结构。冷冻EM 技术可以确定大型蛋白质复合物和组装体的四级结构， 许多链是困难的，甚至是不可能的传统技术，如X射线晶体学或 核磁共振（NMR）测定。由于冷冻电镜技术通常达到高分辨率， 近年来，它已成为结构生物学领域的一场革命，并广泛用于确定结构 蛋白质复合体和组装体的能力。然而，蛋白质结构的计算重建， 冷冻EM图像数据仍然是一个耗时和劳动密集型的过程。先进的人工智能 (AI)深度学习等方法是自动化过程和改进重建的关键 精度该补充的父R 01赠款旨在开发尖端的深度学习模型，例如 2D和3D转换器，用于自动执行从冷冻EM数据重建蛋白质结构的关键任务：（1） 在cryo-EM图像（显微照片）中拾取蛋白质颗粒;（2）从蛋白质构建的去噪cryo-EM密度图 粒子图像;（3）从冷冻EM密度图重建蛋白质结构;（4）整合这些方法 （1）、（2）和（3）作为流水线从冷冻EM图像自动重建高精度蛋白质结构 没有人为干预的数据。 我们在这个项目的头八个月取得的重大进展表明， 这些方法是完全可行的，非常有前途。然而，训练和测试大型深度学习 在大型冷冻EM数据集上高效地建立Transformer模型需要大量的GPU计算 动力.使用我们现有的GPU资源，开发人员需要大约一年的时间才能完成 开发一种深度学习方法。虽然速度可以产生重大进展，但还不够快 最大限度地发挥父R 01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一个高性能计算服务器，包括8个Nvidia A100 80 GB GPU将大大加快父R 01项目的研究速度。这种GPU服务器可以减少 从大约一年到不到两个月完成一个深度学习模型的开发， 因此，大大提高了开发人员的生产力，大大加快了发布和发布的速度 在这个项目中开发的方法和工具。此外，每个GPU的大内存（80 GB）将使 我们训练高品质的深变压器组成的数百万参数，以最大限度地提高精度， 从冷冻EM图像数据重建蛋白质结构。

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.