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cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination


Single-particle electron cryomicroscopy (cryo-EM) is a powerful method for determining the structures of biological macromolecules. With automated microscopes, cryo-EM data can often be obtained in a few days. However, processing cryo-EM image data to reveal heterogeneity in the protein structure and to refine 3D maps to high resolution frequently becomes a severe bottleneck, requiring expert intervention, prior structural knowledge, and weeks of calculations on expensive computer clusters. Here we show that stochastic gradient descent (SGD) and branch-and-bound maximum likelihood optimization algorithms permit the major steps in cryo-EM structure determination to be performed in hours or minutes on an inexpensive desktop computer. Furthermore, SGD with Bayesian marginalization allows ab initio 3D classification, enabling automated analysis and discovery of unexpected structures without bias from a reference map. These algorithms are combined in a user-friendly computer program named cryoSPARC (

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Figure 1: Stochastic gradient descent for cryo-EM map calculation.
Figure 2: Evolution of 3D cryo-EM maps as computation progresses using the SGD algorithm and branch-and-bound refinement.
Figure 3: The branch-and-bound approach to high-resolution cryo-EM map refinement.
Figure 4: High-resolution structures from branch-and-bound refinement.

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We thank S. Dawood for construction of the GUI front end and members of the Rubinstein laboratory for testing cryoSPARC. A.P. was supported by a scholarship from the Natural Sciences and Engineering Research Council (NSERC), J.L.R. was supported by the Canada Research Chairs program, and D.J.F. was supported in part by the Learning in Machines and Brains program of the Canadian Institute for Advanced Research. This research was also supported by NSERC Discovery Grants (RGPIN 2015-05630 (D.J.F.) and 401724-12 (J.L.R.)) and an NVIDIA Academic Hardware Grant (M.A.B. and A.P.). Part of this work was performed while M.A.B. was a postdoctoral fellow at the University of Toronto.

Author information

Authors and Affiliations



A.P. and M.A.B. designed algorithms and implemented software. A.P., M.A.B. and J.L.R. performed experimental work. J.L.R., D.J.F., and M.A.B. contributed expertise and supervision. All authors contributed to manuscript preparation.

Corresponding authors

Correspondence to Ali Punjani or Marcus A Brubaker.

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Competing interests

All authors are engaged in a venture to commercially support cryoSPARC for industrial use through Structura Biotechnology Inc.

Supplementary information

Supplementary Text and Figures

Supplementary Notes 1–2 (PDF 249 kb)

Supplementary Text and Figures

Supplementary Protocol 1 (PDF 12295 kb)

Supplementary Data 1

3-D Maps from CryoSPARC Refinement (ZIP 60796 kb)

Supplementary Data 2

3-D Maps from CryoSPARC Refinement (ZIP 97403 kb)

Supplementary Data 3

3-D Maps from CryoSPARC Refinement (ZIP 171053 kb)

Supplementary Data 4

3-D Maps from CryoSPARC Refinement (ZIP 98933 kb)

CryoSPARC Software and Interface

CryoSPARC (cryo-EM single particle ab initio reconstruction and classification) is a software package implementing the algorithms described in this work, along with a user-friendly web browser based interface that can be used over the internet or through local installation of the software. CryoSPARC uses graphics processing using (GPU) acceleration with self-compiling code and automatic dependency management, so the package is very simple to install. The cryoSPARC web interface can be accessed from any computer on the same network as the computer running cryoSPARC, meaning that a single desktop computer or rackmount server can provide reconstruction and classification service for an entire group of cryo-EM users who each access the software from their own computers. Remote access is also simple, for instance, using a VPN or SSH. This video shows a brief overview of the cryoSPARC web interface, demonstrating example use of the program on a real dataset, along with the dataset and experiment organization functions. Parameters are set automatically or have prepopulated defaults in the program, meaning that a user can simply select their dataset and begin a reconstruction experiment with no expertise and minimal training. (MP4 37489 kb)

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Punjani, A., Rubinstein, J., Fleet, D. et al. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat Methods 14, 290–296 (2017).

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