emClarity: software for high-resolution cryo-electron tomography and subtomogram averaging

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Macromolecular complexes are intrinsically flexible and often challenging to purify for structure determination by single-particle cryo-electron microscopy (cryo-EM). Such complexes can be studied by cryo-electron tomography (cryo-ET) combined with subtomogram alignment and classification, which in exceptional cases achieves subnanometer resolution, yielding insight into structure–function relationships. However, it remains challenging to apply this approach to specimens that exhibit conformational or compositional heterogeneity or are present in low abundance. To address this, we developed emClarity (https://github.com/bHimes/emClarity/wiki), a GPU-accelerated image-processing package featuring an iterative tomographic tilt-series refinement algorithm that uses subtomograms as fiducial markers and a 3D-sampling-function-compensated, multi-scale principal component analysis classification method. We demonstrate that our approach offers substantial improvement in the resolution of maps and in the separation of different functional states of macromolecular complexes compared with current state-of-the-art software.

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Fig. 1: The emClarity workflow for subtomogram averaging and classification.
Fig. 2: Improvement in resolution of subtomogram averaging with emClarity.
Fig. 3: Classification of yeast 80S ribosome (EMPIAR-10045) with 3D-CTF-compensated missing wedge and multi-scale PCA.
Fig. 4: Classification of translating mammalian 80S ribosome with 3D-CTF-compensated missing wedge and multi-scale PCA.

Data availability

Cryo-EM structural data have been deposited in the Electron Microscopy Data Bank under accession codes EMD-8799 for the yeast 80S ribosome; EMD-8802, EMD-8803, EMD-8804, EMD-8805, and EMD-8806 for rabbit 80S ribosome classes I–V, respectively; and EMD-8986 for the HIV-1 Gag data.


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We thank J. Frank and W. Li for very helpful discussions; D. Bevan for technical assistance with computer clusters; S. Loerch for help with Phenix real-space refinement and COOT; and T. Brosenitsch, F.J. Alvarez, and J.P. Rickgauer for reading the manuscript. We thank F. Schur and J. Briggs for the dataset on HIV-1 immature Gag, and X. Fu for testing the emClarity software. This work was supported by the National Institutes of Health (grants GM085043 and GM082251-6869 to P.Z.) and the UK Wellcome Trust Investigator Award (206422/Z/17/Z to P.Z.).

Author information

P.Z. and B.A.H. conceived and designed the study. B.A.H. developed and tested the code for emClarity. B.A.H. and P.Z. analyzed the results and wrote the paper.

Correspondence to Peijun Zhang.

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Integrated supplementary information

Supplementary Figure 1 Benchmarking of emClarity performance on GPU cards.

(a) Relative comparison of emClarity run times among GPU cards. The run times are normalized to a V100 card (13 h 40 min). The other system components on each machine are very similar to those listed in Supplementary Table 2. (b) Comparison of speed-up factor with four processes per GPU when disk i/o is a limiting factor. These trend with the Nvidia GPU architecture Volta > Pascal > Maxwell. Interestingly, the 1080 Ti, which has Pascal architecture, does not handle multiple processes as well as the Titan line of consumer cards. These experiments were repeated independently three times and the average result is presented.

Supplementary Figure 2 TomoCPR, constrained projection refinement.

(a) Schematic overview for reference generation in the tomoCPR. (b-c) Cartoons illustrating overlapping information in the projections arising from other particles, components in the specimen, and variable defocus as a function of tilt. (d-e) Examples of non-tilted (d) and tilted (e) projections used to generate references for the yeast 80S tomoCPR. Overlapping particles and features due to contaminants (white arrows) and the carbon edge (white chevron) are shown. Scale bars, 30 nm.

Supplementary Figure 3 Illustration of the effect and compensation of the missing wedge at multiple length scales.

Far left, the total average filtered by Gaussian kernels of variable width to correlate voxels over the given length scales. Center, eigen-images composed of the average plus the eigenvector, sorted from the most variance explained (1) to least (21). Black and white arrows show examples where es27 density is absent or present in the L1 position. Right three columns show 3D-variance maps in red overlaid with the average: without missing-wedge compensation (ac), with wedge-marked differences (WMDs) (df) and with 3D-sampling function in emClarity (gi). Positions for L1 (green arrow), es27 (orange arrow), e-Site tRNA, and the mRNA channel entrance (blue arrow) are labeled.

Supplementary Figure 4 Gold-standard FSC between half-sets and angular distribution of yeast 80S ribosomes from EMPIAR-10045.

(a) Plots show FSC calculated over 38 cones (dashed lines) with the overall average FSC in solid black. (b) Angular distribution plots for emClarity and (c) RELION. The microscope reference frame indicated with the y-axis corresponding to the tilt axis and in a right-handed convention.

Supplementary Figure 5 emClarity processing of HIV-1 immature Gag particle.

Gold-standard FSC between subtomogram averages of half-sets of HIV-1 Gag (EMPIAR-10164) by emClarity. Red, fsc “true” tight mask with correction via phase randomization. Blue, soft mask with solvent corrected fsc, with an estimated solvent:protein volume ratio of 0.31. Both approaches indicated a resolution of 3.1 Å at the 0.143 cutoff (dashed line).

Supplementary information

Supplementary Information

Supplementary Figures 1–5 and Supplementary Tables 1–4

Reporting Summary

Supplementary Software

emClarity software

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Himes, B.A., Zhang, P. emClarity: software for high-resolution cryo-electron tomography and subtomogram averaging. Nat Methods 15, 955–961 (2018) doi:10.1038/s41592-018-0167-z

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