Proteins perform many critical tasks within cells, and to do so, they must first fold into specific shapes. Being able to visualize these shapes can help scientists to understand how proteins work, and help them create drugs that can interact with the proteins to treat diseases.

The past few years have seen the rapid development of an imaging technique called single-particle cryo-electron microscopy (or cryoEM for short), and this technique is now increasingly used to investigate protein structures. First, proteins are embedded in a thin film of non-crystalline ice by rapidly cooling to around the temperature of liquid nitrogen (below −180°C). This traps the protein in the shape it has in solution. High-energy electrons are then transmitted through the protein sample and their interaction with the atoms in the protein is recorded by a direct electron camera. The analysis of a large series of images recorded in this way can be used to determine the approximate positions of the atoms in the protein.

Previously, single-particle cryoEM techniques have not produced a detailed enough protein structure to be useful to scientists interested in drug development. By refining these techniques, Campbell, Veesler et al. have now obtained the most detailed cryoEM protein structure to date—a structure of an enzyme complex that helps get rid of proteins that are misfolded or that have become too abundant. The structure is so detailed that it reveals the shapes of some small groups of atoms that stick out from the sides of amino acids in the enzyme complex. (Amino acids are the building blocks of enzymes and all other proteins.) Moreover, the structure shows where individual water molecules are positioned around the protein.

The level of detail in the structure produced by Campbell, Veesler et al. is high enough to be useful to drug researchers. Furthermore, because only 10% of the images Campbell, Veesler et al. collected were used to produce the structure, future work will investigate whether incorporating more of the images could reveal structures in even greater detail.

Single-particle cryo-electron microscopy (cryoEM) is currently undergoing a revolution due to the recent development of a new generation of detectors using the complementary metal-oxide semiconductors (CMOS) technology (Kuhlbrandt, 2014). These cameras directly detect incoming electrons without the need for a scintillator converting the electrons into photons and are characterized by improved detective quantum efficiencies at all spatial frequencies compared to traditional charge-coupled device cameras (CCDs) or photographic films (Ruskin et al., 2013; McMullan et al., 2014). The fast read-out rate of these devices also allows for recording movies composed of multiple frames during typical image exposure times enabling correction of beam-induced sample motion and stage drift to reduce image blurring (Brilot et al., 2012; Shigematsu and Sigworth, 2013).

Several algorithms have been developed for tracking beam-induced motion and stage drift in movies recorded with direct detectors (Campbell et al., 2012; Bai et al., 2013; Li et al., 2013). These data processing strategies have been used to produce a multitude of high-resolution reconstructions of samples in the MDa mass range (Amunts et al., 2014; Fernandez et al., 2014; Voorhees et al., 2014; Wong et al., 2014; Allegretti et al., 2014) as well as of protein complexes once considered too small for single-particle cryoEM (≤300 kDa) (Cao et al., 2013; Liao et al., 2013; Lu et al., 2014). These achievements constitute a tremendous leap forward for single-particle cryoEM. This method can now compete with X-ray crystallography for determination of protein structures at near-atomic resolution while also offering the unique advantage of enabling the characterization of heterogeneous or flexible protein complexes (Scheres et al., 2007). High-resolution cryoEM can thus provide information about protein dynamics, which is key to understanding the functions of many macromolecular complexes.

We report here the structure of the Thermoplasma acidophilum 20S proteasome (T20S), determined at 2.8 Å resolution by single-particle cryoEM. This enzyme is a key component of the cell metabolism carrying out degradation of ubiquitinylated polypeptides to regulate the concentration of specific proteins as well as eliminating misfolded products. The T20S proteasome forms a 700 kDa complex comprising 14 α and 14 β subunits organized with D7 symmetry. Our reconstruction enables identification of the conformational preference of some amino-acid side chains (rotamers) and resolves ordered water molecules, in agreement with the expectations for crystal structures at similar resolutions. These outcomes constitute a significant step forward in overcoming the resolution limit achievable by single particle cryoEM, which can now provide protein structures of suitable quality for rational drug design.

Careful alignment of the microscope was performed before data collection to ensure Thon rings were visible up to ∼2.5 Å resolution in the power spectrum of micrographs collected over amorphous carbon using the same conditions as for data acquisition. Prior to beginning data collection, coma-free alignment was carried out to align the beam to the column optical axis with the assistance of a Zemlin tableau (Glaeser et al., 2011). Automated data collection was carried out using Leginon (Suloway et al., 2005) to control both the FEI Titan Krios microscope and the Gatan K2 Summit camera operated in ‘super-resolution’ mode. A subset of micrographs was automatically selected using a filter built into the Appion pipeline (Lander et al., 2009) that accepts images based on the resolution limit to which Thon rings can be confidently identified in the image power spectra. This is achieved by computing cross-correlation coefficients (CC) between the 1-D radially averaged power spectrum of each micrograph and the calculated Contrast Transfer Function (CTF). Only those micrographs showing a CC ≥ 80% at a resolution of 4 Å or better were retained for further processing. One of these micrographs and its corresponding power spectrum are shown in Figure 1. Using this selection criterion, 196 micrographs were selected, from which a total of 87,066 particles were automatically picked using FindEM (Roseman, 2004). These movies have been deposited in the EMPIAR database with the accession number EMPIAR-10025. Particle images were sorted and selected based on mean and standard deviation pixel values within Appion (Lander et al., 2009) and using Xmipp cl2d reference-free alignment and clustering (Sorzano et al., 2010) to yield a stack of 59,864 particles containing both top and side views that were then used for refinement and reconstruction.

Figure 1

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The selected substack of T20S particles was refined starting from a low-pass filtered initial model to high resolution using the gold standard procedure implemented in Relion (Scheres and Chen, 2012; Scheres, 2012b; Bai et al., 2013). This projection-matching refinement step provided a reconstruction at 2.98 Å resolution (estimated angular accuracy 1.3°) using all 38 movie frames and the full exposure of 53 e⁻/Ų. Previous measurements using catalase crystals have suggested that exposures as low as 11 e⁻/Ų might be necessary to attain 3 Å maps (Baker and Rubinstein, 2010; Baker et al., 2010). However, the loss of signal due to radiation must be balanced against the need for sufficient contrast in the images to allow for accurate alignment of the individual particles. Collecting data in movie mode using direct detectors has profoundly changed the way data is acquired and processed: many reported reconstructions have been obtained after eliminating sub-optimal frames (Li et al., 2013) or applying a frequency-dependent weighting scheme to the frames (Scheres, 2014). We emphasize that we were able to obtain a T20S reconstruction at 2.98 Å resolution using the full exposure of 53 e⁻/Ų and prior to weighting individual movie frames. We obtained similar results in determining the structure of an icosahedral virus to 3.7 Å resolution using an exposure of 38 e⁻/Ų with a microscope operated at 200 kV (Campbell et al., 2014). These results contrast with previous measurements and demonstrate that useful high-resolution information may survive much higher exposures than previously thought for single particle cryoEM.

We subsequently used the particle polishing procedure implemented in version 1.3 of the Relion software to account for individual beam-induced particle translations and to calculate a frequency-dependent weight for the contribution of individual movie frames to the reconstruction (Scheres, 2014). Plots of the relative B factor and C_f intersect, derived from the Guinier plots for each single frame reconstruction, along with the corresponding frequency-dependent weights are presented in Figure 2. The first frame of each movie featured a large movement in comparison with the following ones, which we and others interpret as the initial settling of beam-induced motion during exposure. This processing scheme improved the resolution of the reconstruction to 2.83 Å (estimated angular accuracy 1.0°), and this was supported by a significant enhancement of map quality.

Figure 2

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In a final round of refinement, we discarded particle images with the greatest angular uncertainty based on the height of the probability distributions at their maximum. An empirically determined cutoff of 0.07 was used, which gave a stack of 49,954 particle images (699,356 asymmetric units). Refinement of this smaller dataset under otherwise identical conditions produced a map with a slightly improved resolution of 2.81 Å (estimated angular accuracy 0.8°). This result suggests that analyzing the probability distributions over all orientations is a convenient way of discarding particles that are not contributing positively to the reconstruction (∼10,000 particles in this case). This map has been deposited in the EMDB with the accession number EMD-6287.

We fitted the T20S atomic coordinates obtained from a crystal structure at 1.9 Å resolution (Forster et al., 2005) into our cryoEM reconstruction. The density is continuous from residues 13 to 233 and residues 1 to 203 for α and β subunits, respectively, with most side chains being resolved (Figure 3). In line with previous studies (Allegretti et al., 2014; Bartesaghi et al., 2014), many negatively charged residues had lost their carboxylate groups, likely due to radiation sensitivity. Our T20S reconstruction shows many features supporting the resolution claim of 2.8 Å. Differences between the crystal structure and the cryoEM reconstruction are easily discernable at the level of the backbone and can be adjusted accordingly using real-space refinement procedures. The rotameric conformations of many amino-acid side chains are clearly visible in the electron potential map (Figure 4) and reveal differences with a crystal structure of the T20S determined at 3.4 Å resolution (Lowe et al., 1995). The quality of our reconstruction allows distinguishing between Phe and Tyr amino-acid residues based on the appearance of the density for their side chains (Figure 4), as shown previously (Zhang et al., 2010). The presence of a hydroxyl group para to the phenyl group of Tyr residues unambiguously establishes the presence of the Tyr residue as opposed to the Phe residue in our reconstruction. This level of resolvability is decisive for de novo building of atomic models of proteins of unknown structures.

Figure 3

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Figure 4 with 1 supplement see all

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In X-ray crystallography, when a structure is determined at a resolution significantly better than 3 Å, one expects to observe ordered water molecules interacting with amino-acid residues located at the protein surface (Carugo and Bordo, 1999). The higher the resolution the more ordered water molecules are visible. Analysis of our T20S reconstruction at 2.8 Å resolution revealed that many ordered water molecules are resolved, in agreement with the estimated resolution. They are positioned in appropriate chemical environments and within hydrogen-bonding distance (2.8–3.5 Å) of the surrounding atoms with which they interact. All the reconstructions described in this manuscript used the RELION gold standard refinement procedure (Scheres and Chen, 2012). In this protocol, two half datasets are refined completely independently throughout, and after each cycle the new reference volumes are low-pass filtered to the resolution where the Fourier shell correlation (FSC) between the two volumes drops to 0.143 (Rosenthal and Henderson, 2003), thus preventing overfitting of noise. As a result, the noise present in each independent map is not correlated. We were therefore able to cross-validate our assignment of water molecules as they are accounted for by the density in both maps derived from half datasets despite the reduced number of particle images contributing to each map (Figure 4—figure supplement 1) (DiMaio et al., 2013). Finally, the 1.9 Å crystal structure of the T20S proteasome also unequivocally supports the assignment of the water molecules identified in the cryoEM map as an independent cross-validation metric (Forster et al., 2005).

The structure presented here demonstrates that cryoEM is now capable of producing structures at better than 3 Å resolution, which is a prerequisite for structure-based drug design (Anderson, 2003). The possibility to resolve the conformational preference of some amino-acid side chains (rotamers) and identify ordered water molecules opens new horizons for cryoEM and structural biology in general. This level of information is required for drug lead design as these chemical entities mediate key biological processes such as catalysis, protein/protein and protein/substrate recognition. State-of-the-art cryoEM is now in a position to expedite structure-based drug discovery and will likley become increasingly important for pharmaceutical industries.

We believe several factors contributed to obtaining the resolution reported here. The proteasome itself is a rigid and homogenous sample, which is ideal for high-resolution reconstruction. For data collection, we used a relatively high electron dose, which may have been key to properly aligning particles. Furthermore, we used the mechanical stage instead of beam-tilt to move to targets for high magnification exposure in order to avoid introducing any phase shift in the images. Images of each square and each hole were examined by-eye for perceived ice thickness and only those which were judged to have very thin ice were selected for final high magnification exposures. Finally, we benefited from the availability of algorithmic advances (projection matching, particle polishing), which allows optimization of the doses used for alignment and final reconstruction.

As the potential of cryoEM has been steadily growing in the past few years, the prospect of achieving reconstructions at ∼2 Å resolution is very attractive. Remarkably, we used only 10% of the particle images present in the dataset discussed in this manuscript. Including more particle images could further improve the resolution of the T20S reconstruction and experiments are ongoing to determine if this is the case. Finally, 3D classification approaches have also proven useful for sorting particle images (Scheres, 2012a; Lyumkis et al., 2013) and implementing such approaches may also help to improve the final resolution of our reconstruction.

1. 1. Campbell MG
   2. Veesler D
   3. Cheng A
   4. Potter CS
   5. Carragher B

   (2015) Raw 38-frame movies, used to reconstruct the Thermoplasma acidophilum 20S proteasome to 2.8 Å resolution; Aligned 38-frame movies, used to reconstruct the Thermoplasma acidophilum 20S proteasome to 2.8 Å resolution; Averaged movies, used to reconstruct the Thermoplasma acidophilum 20S proteasome to 2.8 Å resolution;

   ID EMPIAR-10025. Publicly available at EMPIAR (http://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10025).

   http://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10025/
2. 1. Campbell MG
   2. Veesler D
   3. Cheng A
   4. Potter CS
   5. Carragher B

   (2015) 2.8 Angstrom resolution reconstruction of the T20S proteasome

   ID EMD-6287. Publicly available at EMDataBank (http://www.ebi.ac.uk/pdbe/entry/EMD-6287).

   http://www.ebi.ac.uk/pdbe/entry/EMD-6287

1. 1. Forster A
   2. Masters EI
   3. Whitby FG
   4. Robinson H
   5. Hill CP

   (2005) Structure of Archeabacterial 20S proteasome mutant D9S- PA26 complex

   ID 1YAR. Publicly available at RCSB Protein Data Bank (http://www.pdb.org/pdb/).

   http://www.pdb.org/pdb/explore/explore.do?structureId=1YAR
2. 1. Lowe J
   2. Stock D
   3. Jap B
   4. Zwickl P
   5. Baumeister W
   6. Huber R

   (1995) Proteasome from Thermoplasma Acidophilum

   ID 1PMA. Publicly available at RCSB Protein Data Bank (http://www.pdb.org/pdb/).

   http://www.pdb.org/pdb/explore/explore.do?structureId=1PMA

1. 1. Allegretti M
   2. Mills DJ
   3. McMullan G
   4. Kuhlbrandt W
   5. Vonck J

   (2014) Atomic model of the F420-reducing [NiFe] hydrogenase by electron cryo-microscopy using a direct electron detector

   eLife 3:e01963.

   https://doi.org/10.7554/eLife.01963

   • Google Scholar
2. 1. Amunts A
   2. Brown A
   3. Bai XC
   4. Llacer JL
   5. Hussain T
   6. Emsley P
   7. Long F
   8. Murshudov G
   9. Scheres SH
   10. Ramakrishnan V

   (2014) Structure of the yeast mitochondrial large ribosomal subunit

   Science 343:1485–1489.

   https://doi.org/10.1126/science.1249410

   • Google Scholar
3. 1. Anderson AC

   (2003) The process of structure-based drug design

   Chemistry & Biology 10:787–797.

   https://doi.org/10.1016/j.chembiol.2003.09.002

   • Google Scholar
4. 1. Bai XC
   2. Fernandez IS
   3. McMullan G
   4. Scheres SH

   (2013) Ribosome structures to near-atomic resolution from thirty thousand cryo-EM particles

   eLife 2:e00461.

   https://doi.org/10.7554/eLife.00461

   • Google Scholar
5. 1. Baker LA
   2. Rubinstein JL

   (2010) Radiation damage in electron cryomicroscopy

   Methods in Enzymology 481:371–388.

   https://doi.org/10.1016/S0076-6879(10)81015-8

   • Google Scholar
6. 1. Baker LA
   2. Smith EA
   3. Bueler SA
   4. Rubinstein JL

   (2010) The resolution dependence of optimal exposures in liquid nitrogen temperature electron cryomicroscopy of catalase crystals

   Journal of Structural Biology 169:431–437.

   https://doi.org/10.1016/j.jsb.2009.11.014

   • Google Scholar
7. 1. Bartesaghi A
   2. Matthies D
   3. Banerjee S
   4. Merk A
   5. Subramaniam S

   (2014) Structure of beta-galactosidase at 3.2-A resolution obtained by cryo-electron microscopy

   Proceedings of the National Academy of Sciences of USA 111:11709–11714.

   https://doi.org/10.1073/pnas.1402809111

   • Google Scholar
8. 1. Brilot AF
   2. Chen JZ
   3. Cheng A
   4. Pan J
   5. Harrison SC
   6. Potter CS
   7. Carragher B
   8. Henderson R
   9. Grigorieff N

   (2012) Beam-induced motion of vitrified specimen on holey carbon film

   Journal of Structural Biology 177:630–637.

   https://doi.org/10.1016/j.jsb.2012.02.003

   • Google Scholar
9. 1. Campbell MG
   2. Cheng A
   3. Brilot AF
   4. Moeller A
   5. Lyumkis D
   6. Veesler D
   7. Pan J
   8. Harrison SC
   9. Potter CS
   10. Carragher B
   11. Grigorieff N

   (2012) Movies of ice-embedded particles enhance resolution in electron cryo-microscopy

   Structure 20:1823–1828.

   https://doi.org/10.1016/j.str.2012.08.026

   • Google Scholar
10. 1. Campbell MG
    2. Kearney BM
    3. Cheng A
    4. Potter CS
    5. Johnson JE
    6. Carragher B
    7. Veesler D

    (2014) Near-atomic resolution reconstructions using a mid-range electron microscope operated at 200kV

    Journal of Structural Biology 188:183–187.

    https://doi.org/10.1016/j.jsb.2014.09.008

    • Google Scholar
11. 1. Cao E
    2. Liao M
    3. Cheng Y
    4. Julius D

    (2013) TRPV1 structures in distinct conformations reveal activation mechanisms

    Nature 504:113–118.

    https://doi.org/10.1038/nature12823

    • Google Scholar
12. 1. Carugo O
    2. Bordo D

    (1999) How many water molecules can be detected by protein crystallography?

    Acta Crystallographica. Section D, Biological Crystallography 55:479–483.

    https://doi.org/10.1107/S0907444998012086

    • Google Scholar
13. 1. Chen S
    2. McMullan G
    3. Faruqi AR
    4. Murshudov GN
    5. Short JM
    6. Scheres SH
    7. Henderson R

    (2013) High-resolution noise substitution to measure overfitting and validate resolution in 3D structure determination by single particle electron cryomicroscopy

    Ultramicroscopy 135:24–35.

    https://doi.org/10.1016/j.ultramic.2013.06.004

    • Google Scholar
14. 1. Chen VB
    2. Arendall WB III
    3. Headd JJ
    4. Keedy DA
    5. Immormino RM
    6. Kapral GJ
    7. Murray LW
    8. Richardson JS
    9. Richardson DC

    (2010) MolProbity: all-atom structure validation for macromolecular crystallography

    Acta Crystallographica. Section D, Biological Crystallography 66:12–21.

    https://doi.org/10.1107/S0907444909042073

    • Google Scholar
15. 1. DiMaio F
    2. Song Y
    3. Li X
    4. Brunner MJ
    5. Xu C
    6. Conticello V
    7. Egelman E
    8. Marlovits TC
    9. Cheng Y
    10. Baker D

    (2015) Atomic-accuracy models from 4.5-A cryo-electron microscopy data with density-guided iterative local refinement

    Nature Methods.

    https://doi.org/10.1038/nmeth.3286

    • Google Scholar
16. 1. DiMaio F
    2. Tyka MD
    3. Baker ML
    4. Chiu W
    5. Baker D

    (2009) Refinement of protein structures into low-resolution density maps using rosetta

    Journal of Molecular Biology 392:181–190.

    https://doi.org/10.1016/j.jmb.2009.07.008

    • Google Scholar
17. 1. DiMaio F
    2. Zhang J
    3. Chiu W
    4. Baker D

    (2013) Cryo-EM model validation using independent map reconstructions

    Protein Science 22:865–868.

    https://doi.org/10.1002/pro.2267

    • Google Scholar
18. 1. Emsley P
    2. Lohkamp B
    3. Scott WG
    4. Cowtan K

    (2010) Features and development of Coot

    Acta Crystallographica. Section D, Biological Crystallography 66:486–501.

    https://doi.org/10.1107/S0907444910007493

    • Google Scholar
19. 1. Fernandez IS
    2. Bai XC
    3. Murshudov G
    4. Scheres SH
    5. Ramakrishnan V

    (2014) Initiation of translation by cricket paralysis virus IRES requires its translocation in the ribosome

    Cell 157:823–831.

    https://doi.org/10.1016/j.cell.2014.04.015

    • Google Scholar
20. 1. Forster A
    2. Masters EI
    3. Whitby FG
    4. Robinson H
    5. Hill CP

    (2005) The 1.9 A structure of a proteasome-11S activator complex and implications for proteasome-PAN/PA700 interactions

    Molecular Cell 18:589–599.

    https://doi.org/10.1016/j.molcel.2005.04.016

    • Google Scholar
21. 1. Glaeser RM
    2. Typke D
    3. Tiemeijer PC
    4. Pulokas J
    5. Cheng A

    (2011) Precise beam-tilt alignment and collimation are required to minimize the phase error associated with coma in high-resolution cryo-EM

    Journal of Structural Biology 174:1–10.

    https://doi.org/10.1016/j.jsb.2010.12.005

    • Google Scholar
22. 1. Goddard TD
    2. Huang CC
    3. Ferrin TE

    (2007) Visualizing density maps with UCSF Chimera

    Journal of Structural Biology 157:281–287.

    https://doi.org/10.1016/j.jsb.2006.06.010

    • Google Scholar
23. 1. Kuhlbrandt W

    (2014) Biochemistry. The resolution revolution

    Science 343:1443–1444.

    https://doi.org/10.1126/science.1251652

    • Google Scholar
24. 1. Lander GC
    2. Stagg SM
    3. Voss NR
    4. Cheng A
    5. Fellmann D
    6. Pulokas J
    7. Yoshioka C
    8. Irving C
    9. Mulder A
    10. Lau PW
    11. Lyumkis D
    12. Potter CS
    13. Carragher B

    (2009) Appion: an integrated, database-driven pipeline to facilitate EM image processing

    Journal of Structural Biology 166:95–102.

    https://doi.org/10.1016/j.jsb.2009.01.002

    • Google Scholar
25. 1. Li X
    2. Mooney P
    3. Zheng S
    4. Booth CR
    5. Braunfeld MB
    6. Gubbens S
    7. Agard DA
    8. Cheng Y

    (2013) Electron counting and beam-induced motion correction enable near-atomic-resolution single-particle cryo-EM

    Nature Methods 10:584–590.

    https://doi.org/10.1038/nmeth.2472

    • Google Scholar
26. 1. Liao M
    2. Cao E
    3. Julius D
    4. Cheng Y

    (2013) Structure of the TRPV1 ion channel determined by electron cryo-microscopy

    Nature 504:107–112.

    https://doi.org/10.1038/nature12822

    • Google Scholar
27. 1. Lowe J
    2. Stock D
    3. Jap B
    4. Zwickl P
    5. Baumeister W
    6. Huber R

    (1995) Crystal structure of the 20S proteasome from the archaeon T. acidophilum at 3.4 A resolution

    Science 268:533–539.

    https://doi.org/10.1126/science.7725097

    • Google Scholar
28. 1. Lu P
    2. Bai XC
    3. Ma D
    4. Xie T
    5. Yan C
    6. Sun L
    7. Yang G
    8. Zhao Y
    9. Zhou R
    10. Scheres SH
    11. Shi Y

    (2014) Three-dimensional structure of human gamma-secretase

    Nature 512:166–170.

    https://doi.org/10.1038/nature13567

    • Google Scholar
29. 1. Lyumkis D
    2. Brilot AF
    3. Theobald DL
    4. Grigorieff N

    (2013) Likelihood-based classification of cryo-EM images using FREALIGN

    Journal of Structural Biology 183:377–388.

    https://doi.org/10.1016/j.jsb.2013.07.005

    • Google Scholar
30. 1. McMullan G
    2. Faruqi AR
    3. Clare D
    4. Henderson R

    (2014) Comparison of optimal performance at 300keV of three direct electron detectors for use in low dose electron microscopy

    Ultramicroscopy 147:156–163.

    https://doi.org/10.1016/j.ultramic.2014.08.002

    • Google Scholar
31. 1. Mindell JA
    2. Grigorieff N

    (2003) Accurate determination of local defocus and specimen tilt in electron microscopy

    Journal of Structural Biology 142:334–347.

    https://doi.org/10.1016/S1047-8477(03)00069-8

    • Google Scholar
32. 1. Rabl J
    2. Smith DM
    3. Yu Y
    4. Chang SC
    5. Goldberg AL
    6. Cheng Y

    (2008) Mechanism of gate opening in the 20S proteasome by the proteasomal ATPases

    Molecular Cell 30:360–368.

    https://doi.org/10.1016/j.molcel.2008.03.004

    • Google Scholar
33. 1. Roseman AM

    (2004) FindEM–a fast, efficient program for automatic selection of particles from electron micrographs

    Journal of Structural Biology 145:91–99.

    https://doi.org/10.1016/j.jsb.2003.11.007

    • Google Scholar
34. 1. Rosenthal PB
    2. Henderson R

    (2003) Optimal determination of particle orientation, absolute hand, and contrast loss in single-particle electron cryomicroscopy

    Journal of Molecular Biology 333:721–745.

    https://doi.org/10.1016/j.jmb.2003.07.013

    • Google Scholar
35. 1. Ruskin RS
    2. Yu Z
    3. Grigorieff N

    (2013) Quantitative characterization of electron detectors for transmission electron microscopy

    Journal of Structural Biology 184:385–393.

    https://doi.org/10.1016/j.jsb.2013.10.016

    • Google Scholar
36. 1. Scheres SH

    (2012a) A Bayesian view on cryo-EM structure determination

    Journal of Molecular Biology 415:406–418.

    https://doi.org/10.1016/j.jmb.2011.11.010

    • Google Scholar
37. 1. Scheres SH

    (2014) Beam-induced motion correction for sub-megadalton cryo-EM particles

    eLife 3:e03665.

    https://doi.org/10.7554/eLife.03665

    • Google Scholar
38. 1. Scheres SH

    (2012b) RELION: implementation of a Bayesian approach to cryo-EM structure determination

    Journal of Structural Biology 180:519–530.

    https://doi.org/10.1016/j.jsb.2012.09.006

    • Google Scholar
39. 1. Scheres SH
    2. Chen S

    (2012) Prevention of overfitting in cryo-EM structure determination

    Nature Methods 9:853–854.

    https://doi.org/10.1038/nmeth.2115

    • Google Scholar
40. 1. Scheres SH
    2. Gao H
    3. Valle M
    4. Herman GT
    5. Eggermont PP
    6. Frank J
    7. Carazo JM

    (2007) Disentangling conformational states of macromolecules in 3D-EM through likelihood optimization

    Nature Methods 4:27–29.

    https://doi.org/10.1038/nmeth992

    • Google Scholar
41. 1. Shigematsu H
    2. Sigworth FJ

    (2013) Noise models and cryo-EM drift correction with a direct-electron camera

    Ultramicroscopy 131:61–69.

    https://doi.org/10.1016/j.ultramic.2013.04.001

    • Google Scholar
42. 1. Sorzano CO
    2. Bilbao-Castro JR
    3. Shkolnisky Y
    4. Alcorlo M
    5. Melero R
    6. Caffarena-Fernandez G
    7. Li M
    8. Xu G
    9. Marabini R
    10. Carazo JM

    (2010) A clustering approach to multireference alignment of single-particle projections in electron microscopy

    Journal of Structural Biology 171:197–206.

    https://doi.org/10.1016/j.jsb.2010.03.011

    • Google Scholar
43. 1. Suloway C
    2. Pulokas J
    3. Fellmann D
    4. Cheng A
    5. Guerra F
    6. Quispe J
    7. Stagg S
    8. Potter CS
    9. Carragher B

    (2005) Automated molecular microscopy: the new Leginon system

    Journal of Structural Biology 151:41–60.

    https://doi.org/10.1016/j.jsb.2005.03.010

    • Google Scholar
44. 1. Voorhees RM
    2. Fernandez IS
    3. Scheres SH
    4. Hegde RS

    (2014) Structure of the Mammalian ribosome-sec61 complex to 3.4 a resolution

    Cell 157:1632–1643.

    https://doi.org/10.1016/j.cell.2014.05.024

    • Google Scholar
45. 1. Wang RY
    2. Kudryashev M
    3. Li X
    4. Egelman EH
    5. Basler M
    6. Cheng Y
    7. Baker D
    8. DiMaio F

    (2015) De novo protein structure determination from near-atomic-resolution cryo-EM maps

    Nature Methods.

    https://doi.org/10.1038/nmeth.3287

    • Google Scholar
46. 1. Wong W
    2. Bai XC
    3. Brown A
    4. Fernandez IS
    5. Hanssen E
    6. Condron M
    7. Tan YH
    8. Baum J
    9. Scheres SH

    (2014) Cryo-EM structure of the Plasmodium falciparum 80S ribosome bound to the anti-protozoan drug emetine

    eLife 3:e03080.

    https://doi.org/10.7554/eLife.03080

    • Google Scholar
47. 1. Yu Y
    2. Smith DM
    3. Kim HM
    4. Rodriguez V
    5. Goldberg AL
    6. Cheng Y

    (2010) Interactions of PAN's C-termini with archaeal 20S proteasome and implications for the eukaryotic proteasome-ATPase interactions

    The EMBO Journal 29:692–702.

    https://doi.org/10.1038/emboj.2009.382

    • Google Scholar
48. 1. Zhang X
    2. Jin L
    3. Fang Q
    4. Hui WH
    5. Zhou ZH

    (2010) 3.3 A cryo-EM structure of a nonenveloped virus reveals a priming mechanism for cell entry

    Cell 141:472–482.

    https://doi.org/10.1016/j.cell.2010.03.041

    • Google Scholar

Author details

1. Melody G Campbell

   1. National Resource for Automated Molecular Microscopy, The Scripps Research Institute, La Jolla, United States
   2. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, United States

   Contribution

   MGC, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   David Veesler

   Competing interests

   The authors declare that no competing interests exist.

2. David Veesler

   1. National Resource for Automated Molecular Microscopy, The Scripps Research Institute, La Jolla, United States
   2. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, United States
   3. Department of Biochemistry, University of Washington, Seattle, United States

   Contribution

   DV, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Melody G Campbell

   Competing interests

   The authors declare that no competing interests exist.

3. Anchi Cheng

   1. National Resource for Automated Molecular Microscopy, The Scripps Research Institute, La Jolla, United States
   2. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, United States
   3. New York Structural Biology Center, New York, United States

   Contribution

   AC, Conception and design, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

4. Clinton S Potter

   1. National Resource for Automated Molecular Microscopy, The Scripps Research Institute, La Jolla, United States
   2. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, United States
   3. New York Structural Biology Center, New York, United States

   Contribution

   CSP, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

5. Bridget Carragher

   1. National Resource for Automated Molecular Microscopy, The Scripps Research Institute, La Jolla, United States
   2. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, United States
   3. New York Structural Biology Center, New York, United States

   Contribution

   BC, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   bcarr@nysbc.org

   Competing interests

   The authors declare that no competing interests exist.

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