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PMID: 27845625 Published · epublish English Journal Article Research Support, Non-U.S. Gov't

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

eLife ·Vol. 5 ·2016-00-15

Kimanius D, Forsberg BO, Scheres SH, Lindahl E

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.

Keywords
GPU biophysics classification cryo-EM image reconstruction micrograph none refinement structural biology
MeSH Terms
Computational Biology/methods Cryoelectron Microscopy/methods Image Processing, Computer-Assisted/methods Time Factors
Authors & Affiliations
4 authors, click to expand affiliations / ORCID
Kimanius Dari
Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm, Sweden.
Forsberg Björn O
Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm, Sweden.
Scheres Sjors Hw ORCID
MRC Laboratory of Molecular Biology, Cambridge, United Kingdom.
Lindahl Erik ORCID
Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm, Sweden. | Swedish e-Science Research Center, KTH Royal Institute of Technology, Stockholm, Sweden.
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Article Info
Journal
eLife
Abbr.
Elife
ISSN
2050-084X
Published
2016-00-15
Epub
2016-00-15
Language
English
Region
England
NLM ID
101579614
PMCID
PMC5310839
Subset
IM
Grants
Medical Research Council · MC_UP_A025_1013 · United Kingdom
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