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在RELION-2中使用图形处理器(GPU)并行化加速冷冻电镜结构测定

Accelerated cryo-EM structure determination with parallelisation using GPUs in RELION-2.

作者信息

Kimanius Dari, Forsberg Björn O, Scheres Sjors Hw, Lindahl Erik

机构信息

Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm, Sweden.

MRC Laboratory of Molecular Biology, Cambridge, United Kingdom.

出版信息

Elife. 2016 Nov 15;5:e18722. doi: 10.7554/eLife.18722.

Abstract

By reaching near-atomic resolution for a wide range of specimens, single-particle cryo-EM structure determination is transforming structural biology. However, the necessary calculations come at large computational costs, which has introduced a bottleneck that is currently limiting throughput and the development of new methods. Here, we present an implementation of the RELION image processing software that uses graphics processors (GPUs) to address the most computationally intensive steps of its cryo-EM structure determination workflow. Both image classification and high-resolution refinement have been accelerated more than an order-of-magnitude, and template-based particle selection has been accelerated well over two orders-of-magnitude on desktop hardware. Memory requirements on GPUs have been reduced to fit widely available hardware, and we show that the use of single precision arithmetic does not adversely affect results. This enables high-resolution cryo-EM structure determination in a matter of days on a single workstation.

摘要

通过对各种样本实现近原子分辨率,单颗粒冷冻电镜结构测定正在改变结构生物学。然而,必要的计算成本巨大,这引入了一个瓶颈,目前限制了通量和新方法的开发。在这里,我们展示了RELION图像处理软件的一种实现方式,该方式使用图形处理器(GPU)来处理其冷冻电镜结构测定工作流程中计算量最大的步骤。在桌面硬件上,图像分类和高分辨率精修都加速了一个数量级以上,基于模板的颗粒选择加速超过了两个数量级。GPU上的内存需求已降低,以适应广泛可用的硬件,并且我们表明使用单精度算法不会对结果产生不利影响。这使得在单个工作站上只需几天时间就能完成高分辨率冷冻电镜结构测定。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57f2/5310839/2964efd6e5c6/elife-18722-fig1.jpg

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