Journal of Structural Biology xxx (2013) xxx–xxx
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Determination of protein structure at 8.5 Å resolution using cryo-electron tomography and sub-tomogram averaging Florian K.M. Schur, Wim J.H. Hagen, Alex de Marco 1, John A.G. Briggs ⇑ Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, Heidelberg, Germany
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Article history: Received 10 September 2013 Received in revised form 18 October 2013 Accepted 19 October 2013 Available online xxxx Keywords: Cryo-electron tomography Sub-tomogram averaging Contrast transfer function Retrovirus Capsid
a b s t r a c t Cryo-electron tomography combined with image processing by sub-tomogram averaging is unique in its power to resolve the structures of proteins and macromolecular complexes in situ. Limitations of the method, including the low signal to noise ratio within individual images from cryo-tomographic datasets and difficulties in determining the defocus at which the data was collected, mean that to date the very best structures obtained by sub-tomogram averaging are limited to a resolution of approximately 15 Å. Here, by optimizing data collection and defocus determination steps, we have determined the structure of assembled Mason–Pfizer monkey virus Gag protein using sub-tomogram averaging to a resolution of 8.5 Å. At this resolution alpha-helices can be directly and clearly visualized. These data demonstrate for the first time that high-resolution structural information can be obtained from cryo-electron tomograms using sub-tomogram averaging. Sub-tomogram averaging has the potential to allow detailed studies of unsolved and biologically relevant structures under biologically relevant conditions. Ó 2013 Elsevier Inc. All rights reserved.
1. Introduction Cryo-electron microscopy combined with established singleparticle image processing methods can yield near atomic-resolution reconstructions of purified macromolecular complexes in vitreous ice (Grigorieff and Harrison, 2011; Zhou, 2011). In single-particle reconstruction, multiple two-dimensional projection images of multiple copies of the complex are collected. The projection images are aligned, the orientations of the complex relative to the directionof-view are calculated, and the images are combined to generate a 3D reconstruction (Cong and Ludtke, 2010). Single-particle methods are generally not applicable when the 2D projection image of the complex is superimposed with the projections of other objects in the ice layer. In this case the confounding information from the superimposed objects prevents accurate determination of the correct alignment and orientation. This limitation prevents the general application of single-particle methods to resolve the high-resolution structures of macromolecular complexes in situ, for example within cells, or embedded in the membrane of heterogeneous viruses. This limitation can be overcome using cryo-electron tomography in combination with sub-tomogram averaging. In cryoelectron tomography the same sample is imaged from different directions (Lucic et al., 2005), and these images are used to calculate a 3D tomographic reconstruction. Objects in the ice layer ⇑ Corresponding author. 1
E-mail address: [email protected] (J.A.G. Briggs). Present address: FEI Munich, Lochhamer Schlag 21, 82166 Graefelfing, Germany.
that would be superimposed with the object of interest in single 2D projections do not superimpose in 3D. Multiple copies of the object of interest are then extracted from the tomogram, aligned and averaged in 3D to generate a higher-resolution reconstruction. This is sub-tomogram averaging (reviewed in Briggs, 2013). Subtomogram averaging can be successfully applied to obtain 3D reconstructions of complexes in situ: within cells, viruses, vesicles, and complex in vitro systems (Beck et al., 2004; Faini et al., 2012; Pfeffer et al., 2012; Pigino et al., 2011; White et al., 2010; Zuber and Unwin, 2013). This unique potential to allow in situ structural biology is leading to a rapid increase in the method’s use. Sub-tomogram averaging has also been combined with features of single-particle reconstruction to form hybrid approaches, where sub-tomogram averaging provides starting models or angular restraints for subsequent single-particle reconstruction from the same dataset. Bartesaghi and co-workers presented a ‘‘constrained single-particle tomography’’ approach, in which alignment is carried out using individual projection images extracted from tomographic tilt series, while appropriately constraining the relative tilt angles of images from the same tomogram (Bartesaghi et al., 2012). While a conventional sub-tomogram averaging reconstruction generated a map of GroEL with a measured resolution of 24.5 Å, this hybrid approach led to a reconstruction at a resolution of 8.4 Å. We have previously used a hybrid approach in which subtomogram averaging was used to determine the symmetry parameters of helical arrays of Gag protein from Mason–Pfizer monkey virus (M-PMV), which could subsequently be used for conventional helical real-space reconstructions of individual tubes from 2D
1047-8477/$ - see front matter Ó 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jsb.2013.10.015
Please cite this article in press as: Schur, F.K.M., et al. Determination of protein structure at 8.5 Å resolution using cryo-electron tomography and subtomogram averaging. J. Struct. Biol. (2013), http://dx.doi.org/10.1016/j.jsb.2013.10.015
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projections. Data from individual tubes were then combined by averaging in 3D (Bharat et al., 2012). This approach generated a reconstruction at approximately 8 Å resolution. Hybrid methods have advantages over unconstrained single-particle approaches, but they cannot be applied to the full range of samples addressable by sub-tomogram averaging methods. They are only applicable to samples where individual 2D projection images contain information appropriate for alignment, samples which are often also appropriate for single-particle processing. The highest-resolution reconstructions obtained using subtomogram averaging methods have a resolution of approximately 15 Å (Briggs et al., 2009; Chen et al., 2013; Eibauer et al., 2012). Although there appear to be no theoretical limitations preventing application of sub-tomogram averaging to obtain reconstructions at sub-nm resolution, no such structures have been previously reported. A number of possible factors may contribute to this limitation. Firstly, the total electron dose applied during collection of a tomogram is typically higher than that applied during single-particle reconstruction, leading to accumulation of electron-induced sample damage. Secondly, the need to collect tomographic tilt series makes it more time-consuming to collect very large datasets. Thirdly, the low electron dose in individual images makes it hard to visualize Thon rings, (a step that is required to determine the defocus at which the data is collected), and therefore harder to properly account for the contrast–transfer–function (CTF) of the microscope. Fourthly, two independent image alignment steps must be carried out accurately – of the individual projection images to generate the tomogram and of the sub-tomograms to generate the final reconstruction. Fifthly, increased apparent sample thickness, in particular at high-tilt angles, decreases the signal to noise ratio in the images. We reasoned that we could obtain higher resolution reconstructions using sub-tomogram averaging if we mitigated the effect of the first three limitations by collecting tomograms with reduced dose, collecting a large dataset, and by improved estimate of the CTF. We therefore revisited the tubular arrays of Mason–Pfizer monkey virus Gag protein, for which we had previously used a hybrid method to determine the structure at 8 Å resolution (Bharat et al., 2012). Using sub-tomogram averaging, we obtained a reconstruction at sub-nm resolution. This represents a substantial improvement in the resolution obtained using sub-tomogram averaging methods, and an important proof-of-principle that conventional sub-tomogram averaging can be used to visualize protein secondary structure.
2. Methods and results 2.1. Sample preparation and data collection Tubular arrays of the M-PMV DPro CANC protein were assembled with k-DNA and prepared for cryo-electron tomography exactly as described in Bharat et al. (2012). 102 tomograms were acquired on an FEI Titan Krios electron microscope operated at 200 keV, with a GIF2002 post-column energy filter (using a slit width of 20 eV) and a 2 2 K Gatan megascan 795 CCD camera. A 70 lm objective aperture was used, giving a resolution cut-off at 2.44 Å. For navigation and search purposes, low magnification montages were acquired using Serial-EM (Mastronarde, 2005), and tilt series were acquired at appropriate positions using FEI tomography software version 4 in automated batch mode. The nominal magnification was 42,000 giving a calibrated pixel size of 2.025 Å. The tilt range was from 45° to +60° in 3° steps, collecting first from 0° to 45° and then from 3° to 60°. Tilt series were collected at a range of nominal defoci between 1.5 and 3.3 lm. Several rounds of autofocusing were allowed for each
image during data collection (see below). The total dose for each entire tilt series was around 40 electrons/Å2. Even with this low dose, some blurring of features within the tubular specimens was apparent in the second half of each tilt series, suggesting a degradation of high resolution information. 2.2. Defocus stability and measurement To estimate the required accuracy of defocus determination, we simulated the resolution–attenuating effects of errors in determination and/or correction of the CTF. To do this, using MATLAB (MathWorks), we calculated 361 theoretical CTFs (under the data collection conditions used), each at a different defocus from 1.5 to 3.3 lm in 5 nm steps, according to the formula (Erickson and Klug, 1971; Wade, 1992).
CTFðf Þ ¼ A sin
pkf 2 Dz 0:5k2 f 2 cs þ B cos pkf 2 Dz 0:5k2 f 2 cs
With the variable f being the spatial frequency, Dz the defocus, k the electron wavelength and cs the spherical aberration. A is the defocus dependent envelope function and B is the fraction of the amplitude contrast (set to 0.1 in our simulations). We simulated phase-flipping of the theoretical CTFs assuming a normally distributed random error (with a sigma ranging from 50 to 200 nm) in the determined defocus values. For simplicity envelope functions were excluded from the simulation. We summed all the phase-flipped CTFs. The resulting curves approximate the phase contrast transfer in the CTF-corrected reconstruction (Fig. 1A). We then divided these curves by the sum of the correctly phase-flipped CTFs. This gives envelope functions describing the attenuating effect of the error in the defocus determination (Fig. 1B). There is 50% attenuation in the signal at 8 Å for a normally distributed error in defocus determination with a sigma of 100 nm. The low dose applied during collection of individual images in a cryo-tomographic tilt series means that it is challenging to determine the defocus of the individual images, in particular those at high tilt where the apparent sample thickness increases. For this reason we considered it important to collect the data with a very stable defocus, such that the mean defocus of the series represented a good estimate of the defocus of the individual images. To achieve this, during data collection we included a 30 s wait between tilting and focusing, and then repeated the autofocusing step for each image until the offset between measured and predicted defocus was below 100 nm. This procedure required no manual intervention. The small field of view used for image acquisition allowed the focus area to be positioned only a short distance along the tilt axis from the exposure area, limiting any non-eucentricity. We measured the defocus of individual images from 10 representative tilt series by fitting theoretical CTF-curves to averaged power spectra from 5122 tiles from each image using MATLAB (Fig. 1C). Between ±30°, Thon rings were sufficiently clear to obtain an approximate estimate of the CTF for individual tilts. At tilts beyond ±30° we found that the low signal to noise ratio prevented reliable estimation of the CTF. Measuring the defocus from single micrographs with very low signal to noise ratio may be inaccurate. We therefore also estimated the mean defocus of each tilt series by fitting theoretical CTF-curves to averaged power spectra from 5122 tiles from all images in the tilt series. These power spectra show clear Thon rings. To obtain a rough estimate of the error in defocus determination of individual images, we compared this mean defocus with the measured defocus of each individual image in the series. Considering data from 10 tomograms, at each tilt angle we calculated the root mean squared deviation of the measured defocus from the
Please cite this article in press as: Schur, F.K.M., et al. Determination of protein structure at 8.5 Å resolution using cryo-electron tomography and subtomogram averaging. J. Struct. Biol. (2013), http://dx.doi.org/10.1016/j.jsb.2013.10.015
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mean (Fig. 1D). This deviation incorporates both errors in determining the CTF and errors in the autofocus routine. These values range from 61 nm at 30° to 47 nm at 0°, up to 71 nm at +30°. Over all angles the root mean squared deviation was 54 nm. These analyses gave us confidence that both the autofocus routine and the determination of the CTF were accurate to better than the required 100 nm and therefore appropriate for high-resolution reconstruction.
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80 tilt series were selected from the total dataset of 102 tilt series based on a visual assessment of tilt series quality and were reconstructed using the IMOD software suite (Kremer et al., 1996). The dataset contained 133 individual tubular arrays of MPMV DPro CANC protein. The tubes displayed varying lengths up to several hundred nanometers and diameters ranging from 55 to 72 nm. Within the reconstructed tomograms, the hexameric Gag lattice on the surface of the tube could be clearly visualized, confirming the high quality of the acquired data (Fig. 2A). For later validation of the structure, the set of tomograms was divided into two halves containing roughly the same number of tubes and with a balanced distribution of nominal defoci. Each half of the data was subsequently processed completely independently. Sub-tomograms were extracted from all tubes at arbitrary, uniformly distributed positions along the tubular surface by marking the central axis of the tube and defining an array of extraction positions at a distance from this line corresponding to the radius of the tube. The cubic sub-tomograms had an edge length of 243 Å, and overlapped such that their centers were separated by approximately 40 Å. Sub-tomogram averaging was carried out as described in the next section: the process is essentially that described previously (Bharat et al., 2011, 2012; de Marco et al., 2010).
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tilt angle (degrees) Fig.1. Defocus stability and measurement. (A) The simulated CTF (red curve) of a sub-tomogram reconstruction generated from a set of tomograms with defocuses ranging between 1.5 and 3.3 lm without CTF correction (see Section 2.2) and without considering envelope functions. The simulated CTF assuming the defocus has been perfectly determined and the CTF has been corrected by phase flipping (black line), tends towards a value of 2/p at high frequencies. The simulated CTFs after correction assuming a normally distributed error in defocus determination with a sigma of 50 nm (blue), 100 nm (green) and 200 nm (orange) are shown. (B) Dividing the erroneously corrected CTF by the perfectly corrected CTF gives an error envelope indicating the dampening effect of inaccurate defocus determination on resolution. Colors as in A. With a normally distributed error of approximately 100 nm the information transfer at 8 Å is reduced to 50%. (C) Defocus measured on individual images from ten tilt series plotted as a function of tilt angle between 30° and +30°. Dotted lines indicate the mean defocus measured from the averaged power spectra of the respective series. (D) Root mean squared deviation of the measured defocus of individual images from the mean defocus of the series for 10 tilt series shown in C calculated at each tilt angle. The root mean squared deviation is approximately 54 nm.
The procedure outlined in this section was carried out in its entirety independently on each half dataset. Calculations were performed using MATLAB scripts derived from the TOM (Nickell et al., 2005) and AV3 (Forster et al., 2005) packages. The Dynamo software package was used for generation of masks and for FSC calculations (Castano-Diez et al., 2012). To minimize computing time, initial processing was performed on 4 binned, non-CTF corrected data. For each half dataset one tomogram with a defocus of 2.5 lm was chosen to obtain an initial structure. Extracted sub-tomograms from this tomogram were assigned initial angles based only upon the geometry of the tubes and were averaged to generate a smooth starting reference. The sub-tomograms from this tomogram were then iteratively aligned and averaged in six dimensions against the reference as described in Forster et al. (2005). Translational shifts were limited to 8 nm in each direction, and rotations were limited to values reasonable considering the tube geometry (approximately 60° in the plane of the tube surface and 40° in other angles). After twelve iterations, the structure had stabilized and was used as starting references for the alignment of the sub-tomograms from the other tomograms. At first the sub-tomograms from each individual tomogram were aligned and averaged separately for five iterations. To verify the success of this preliminary alignment we displayed the orientations and positions of the aligned subvolumes in 3D space after alignment using Amira (Visage imaging) and the EM toolbox (Pruggnaller et al., 2008). We observed the expected clear helical arrangement of the individual subunits (Fig. 2B). To reduce the dataset size, redundant sub-tomograms (those that had converged onto the same position as another sub-tomogram) were removed, as were those that had radially deviated from the tubular lattice.
Please cite this article in press as: Schur, F.K.M., et al. Determination of protein structure at 8.5 Å resolution using cryo-electron tomography and subtomogram averaging. J. Struct. Biol. (2013), http://dx.doi.org/10.1016/j.jsb.2013.10.015
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correction using the procedure within IMOD (Xiong et al., 2009) did not significantly change the final reconstruction. Unbinned sub-tomograms were extracted from the CTFcorrected tomograms. They were then aligned in 6 iterations using a final angular search increment of 0.5°. To avoid noise-based overalignment, a soft low pass filter was applied to the reference used for each iteration, which reached zero at 13 Å in the first of these iterations and 11 Å in the final iteration. The 2-fold symmetry inherent in the structure was applied only in the last 2 iterations. 2.5. Structure assessment We estimated the resolution of the determined structure using Fourier shell correlation (FSC) between the two independent reconstructions. The use of two completely independent reconstructions from independent starting models eliminates the risk of over-optimistic resolution assessment due to over-alignment or model bias. The resolution of the map was determined to be 9.6 and 8.5 Å at the 0.5 and 0.143 thresholds, respectively (Fig. 3). The total dataset size (considering both half datasets) was 121,346 sub-tomograms, equivalent to 242,692 asymmetric units when the twofold symmetry is considered. Restricting the dataset size to a half, quarter or eighth of the original dataset size resulted in a small decrease in resolution (Fig. 3). We averaged the two final structures and sharpened the average with an empirically determined negative B-factor of 1200 Å2, while filtering the data to a resolution of 8.5 Å (Rosenthal and Henderson, 2003), to obtain the structure shown in Fig. 4. (We note that typical high-resolution sub-tomogram averaging structures may prove to be subject to larger B-factors than typical single-particle structures due to additional dampening of high-frequency information by factors such as increased beam damage, increased beam-induced motion at higher tilts, and the additional steps of tomogram alignment and reconstruction.) We compared this structure with that previously determined using hybrid methods (EMD-2089), and found it to be consistent. Extended densities corresponding to alpha-helices were clearly visible in the correct positions, consistent with the measured resolution. Following the approach used by Bharat et al. (2012) we also extracted the three symmetry independent Gag dimers within the
Fig.2. Cryo-electron tomogram of M-PMV DPro CANC tubes. (A) A 8.1 Å thick slice through the xy plane of a representative tomogram showing three M-PMV DPro CANC tubes. A Gaussian filter was applied for visualization. The hexameric lattice arrangement of the proteins is clearly resolved. Scale bar 100 nm. (B) ‘‘Lattice maps’’ of tubes shown in (A). Hexamers are placed at the position and orientation of the sub-tomograms after alignment, indicating the positions and orientations of the hexameric unit cells of the M-PMV DPro CANC lattice. They are colored according to their cross-correlation with the reference from green (high) to red (low). The regular arrangement of the hexamers indicates the success of the alignment procedure. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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The latter could be excluded based on their lower cross-correlation values. The remaining sub-tomograms were then pooled and subjected to four further iterations of alignment and averaging. Based on the analysis described in the Section 2.2 we corrected each tilt series for the CTF assuming that for all images within a tilt series, the defocus on the tilt axis corresponded to the mean defocus value of the tilt series. For phase-flipping we followed the tile-based method described by Zanetti et al. (2009), setting the tile size to 512 pixels and the interpolation width (the distance between the center of two tiles) to 2 pixels. Repeating the CTF
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Resolution (Å) Fig.3. Resolution determination by Fourier shell correlation. Fourier shell correlations between the two independent reconstructions of M-PMV DPro CANC tubes. The full reconstruction (solid black line) has a measured resolution of 9.6 and 8.5 Å at the 0.5 and 0.143 criteria, respectively. To explore the dependence of resolution on dataset size, the Fourier shell correlation was also calculated after reducing the data set to a half (red line), quarter (green line) or eighth (blue line) of the full dataset size. After averaging the three symmetry independent copies of Gag within the asymmetric unit the resolution improves to 8.9 and 8.3 Å (dashed black line). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Please cite this article in press as: Schur, F.K.M., et al. Determination of protein structure at 8.5 Å resolution using cryo-electron tomography and subtomogram averaging. J. Struct. Biol. (2013), http://dx.doi.org/10.1016/j.jsb.2013.10.015
F.K.M. Schur et al. / Journal of Structural Biology xxx (2013) xxx–xxx
A
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Fig.4. Sub-tomogram averaging reconstruction of M-PMV DPro CANC tubes. (A) Isosurface representation of the reconstruction of the M-PMV DPro CANC hexameric unit generated by sub-tomogram averaging. The structure is viewed from the outside of the tube (left) and in section (right). The dotted line shown in the left panel indicates the tube axis. The dashed lines in the right panel indicate the positions of orthoslices in B. (B) Slices through the reconstruction through the Nterminal CA domain (N-CA) and the C-terminal CA domain (C-CA), along the dashed lines marked in A. Density is black. Scale bar 5 nm.
asymmetric unit, aligned and averaged them. This structure had a resolution assessed by FSC of 8.9 and 8.3 Å at the 0.5 and 0.143 criteria, from a total dataset of 728,076 Gag monomers. The mask applied during FSC measurement was a Gaussian-filtered sphere. Again we averaged the final structures from the two half datasets and sharpened the average with a negative B-factor of 1450 Å2. Fig. 5 shows a direct comparison between this structure and the previously published structure EMD-2090. For visualization purposes both structures are shown compared to PDB 4ARG, which results from fits of crystal structures into EMD2090 (Bharat et al., 2012). Visualizations of structures were performed using Chimera (Pettersen et al., 2004). The excellent correspondence between the two maps verifies that the final structure derived from sub-tomogram averaging has a resolution that is appropriate for interpretation of alpha helices, consistent with the measured resolution of approximately 8.5 Å.
3. Discussion Sub-tomogram averaging can be applied to derive the structures of macromolecular complexes in situations where conventional single-particle reconstruction methods cannot be applied, for example in intact cells or in purified systems where the complex of interest is superimposed in projection by confounding densities. To date, sub-tomogram averaging methods have only been applied in the low to medium resolution regimes, to derive structural data at resolutions up to approximately 15 Å, at which secondary structure information cannot be interpreted. Here we have demonstrated that sub-tomogram averaging can be used to derive structures at sub-nm resolution allowing the direct visualization of secondary structure elements.
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To achieve this improvement in obtained resolution we took three steps. Firstly, accurate defocus determination is a prerequisite for accurate CTF correction. We calculated that the defocus needed to be estimated with an accuracy of better than 100 nm. Extended data acquisition schemes using several focus images have been used to achieve accurate estimates of defocus (Eibauer et al., 2012). Here we instead facilitated defocus measurement by collecting data under automated conditions where the defocus remained very stable through the tilt series. This was achieved by iterating the autofocus routine during data collection, and by using a small field of view for exposure. The small field of view allows the focus area to be placed close to the exposure area and reduces the defocus gradient across the exposure image. Secondly, we have limited the dose used during data collection to 40 electrons/Å2. Significant beam damage only occurs late in the tilt series, and the early images therefore retain a sufficient amount of high-resolution information. (We note that a balance must be found between these first two steps, since reducing the dose makes it more challenging to accurately measure the defocus). Thirdly, we have collected a very large dataset. While the highest resolution structure we obtained is an average of over 700,000 asymmetric units, as shown in Fig. 3, a sub-nm resolution reconstruction (at the 0.143 criterion) was obtained by averaging approximately 30,000 asymmetric units from the complete aligned dataset. The data used in this manuscript was collected on a Titan Krios microscope equipped with an energy filter and a conventional CCD camera. The improved optics of the Titan Krios compared to other electron microscopes are unlikely to be critical in the resolution regime explored here. The high stability of the Titan Krios microscope stage, and the ability to operate the microscope continually for long time periods, allows for much more efficient collection of large datasets than would be possible using a side entry cryo-holder, but a similar throughput could be achieved on, for example, an FEI Polara microscope. It is reasonable to assume that the use of direct detection cameras, rather than the CCD camera used here, would allow similar resolutions to be obtained with much smaller datasets (Bai et al., 2013). Improved detectors will also make it easier to determine defocus, further facilitating determination of high-resolution structures. For these reasons we believe that a similar approach to high-resolution sub-tomogram averaging should be possible on other electron microscopes. There may be advantages in applying sub-tomogram averaging to samples that can also be studied by other methods. For large and slightly flexible tubular arrays, such as those studied here, subtomogram averaging has a number of advantages over helical reconstruction methods. It does not require classification of different helical symmetries, since they can be combined in the same reconstruction; it does not require precise determination of the helical symmetry parameters in advance, since each unit cell is aligned separately; it can compensate for flattening of the tubes, since the top and bottom of the tubes are considered separately; and it does not require a very large reconstruction volume, since it derives a structure for the unit cell, and not for a segment of the tube. Here we were able to obtain a high-resolution structure significantly more rapidly than we could obtain the same structure using our previous hybrid approach incorporating helical reconstruction methods (Bharat et al., 2012). We envisage that the future will bring continued improvements in microscope technology, image processing methods, and in the development of user-friendly and automated software for data collection and image processing. These developments will facilitate widespread application of sub-tomogram averaging to generate reconstructions from cryo-electron tomograms of samples that have not proved accessible to single-particle reconstruction, generating significant biological insights. The data presented here
Please cite this article in press as: Schur, F.K.M., et al. Determination of protein structure at 8.5 Å resolution using cryo-electron tomography and subtomogram averaging. J. Struct. Biol. (2013), http://dx.doi.org/10.1016/j.jsb.2013.10.015
F.K.M. Schur et al. / Journal of Structural Biology xxx (2013) xxx–xxx
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Fig. 5. Sub-tomogram averaging reconstruction after averaging the three symmetry-unrelated dimers, compared to the published structure solved using hybrid methods. The sub-tomogram averaging structure was sharpened and filtered to the resolution determined at the 0.143 FSC criterion. Both the sub-tomogram averaging structure and the previously published structure show similar strong rod-like densities corresponding to alpha-helices. (A) Isosurface representations of the M-PMV DPro CANC dimer, fitted with the model PDB 4ARG. The right-hand view is rotated by 50° around the vertical relative to the left-hand view. (B) Slices through the reconstruction at the N-CA and C-CA domains. The positions of the slices are marked by dotted lines in A. Scale bar 2 nm. (C) Isosurface representation of the structure as previously solved using 2D reconstruction methods (EMD-2090) in the same orientations as in A. (D) Slices through EMD-2090 at positions approximately equivalent to those in B.
demonstrates that such structures can be obtained at sub-nm resolution, and reveal protein secondary structure. 4. Author information The reconstructions and a representative tomogram have been deposited in the EMDB, accession numbers EMD-2487, EMD2488, and EMD-2489. Acknowledgments The M-PMV DPro CANC tubes imaged in this study were a kind gift from Pavel Ulbrich and Tomas Ruml, Institute of Chemical Technology, Prague. The cryo-EM grids were prepared by Tanmay Bharat. This study was technically supported by EMBL’s IT services unit and by Frank Thommen. We thank Martin Schorb and Svetlana Dodonova for discussions and advice; Khanh Huy Bui for advice and scripts to streamline tomogram reconstruction; and Giulia Zanetti, Tanmay Bharat, and Martin Beck for comments on the manuscript. This study was supported by Deutsche Forschungsgemeinschaft grant BR 3635/2-1 to JAGB. JAGB conceived the study; FKMS, AdM, and JAGB designed the experiments; FKMS and WJHH optimized microscope settings and collected data; FKMS performed the image processing; FKMS and JAGB analyzed the data and wrote the manuscript. References Bai, X.-c, Fernandez, I.S., McMullan, G., Scheres, S.H., 2013. Ribosome structures to near-atomic resolution from thirty thousand cryo-EM particles. eLife 2, e00461. Bartesaghi, A., Lecumberry, F., Sapiro, G., Subramaniam, S., 2012. Protein secondary structure determination by constrained single-particle cryo-electron tomography. Structure 20, 2003–2013. Beck, M., Förster, F., Ecke, M., Plitzko, J.M., Melchior, F., Gerisch, G., Baumeister, W., Medalia, O., 2004. Nuclear pore complex structure and dynamics revealed by cryo-electron tomography. Science 306, 1387–1390. Bharat, T.A., Riches, J.D., Kolesnikova, L., Welsch, S., Krahling, V., Davey, N., Parsy, M.L., Becker, S., Briggs, J.A., 2011. Cryo-electron tomography of Marburg virus
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Contents lists available at ScienceDirect
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Determination of protein structure at 8.5 Å resolution using cryo-electron tomography and sub-tomogram averaging Florian K.M. Schur, Wim J.H. Hagen, Alex de Marco 1, John A.G. Briggs ⇑ Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, Heidelberg, Germany
a r t i c l e
i n f o
Article history: Received 10 September 2013 Received in revised form 18 October 2013 Accepted 19 October 2013 Available online xxxx Keywords: Cryo-electron tomography Sub-tomogram averaging Contrast transfer function Retrovirus Capsid
a b s t r a c t Cryo-electron tomography combined with image processing by sub-tomogram averaging is unique in its power to resolve the structures of proteins and macromolecular complexes in situ. Limitations of the method, including the low signal to noise ratio within individual images from cryo-tomographic datasets and difficulties in determining the defocus at which the data was collected, mean that to date the very best structures obtained by sub-tomogram averaging are limited to a resolution of approximately 15 Å. Here, by optimizing data collection and defocus determination steps, we have determined the structure of assembled Mason–Pfizer monkey virus Gag protein using sub-tomogram averaging to a resolution of 8.5 Å. At this resolution alpha-helices can be directly and clearly visualized. These data demonstrate for the first time that high-resolution structural information can be obtained from cryo-electron tomograms using sub-tomogram averaging. Sub-tomogram averaging has the potential to allow detailed studies of unsolved and biologically relevant structures under biologically relevant conditions. Ó 2013 Elsevier Inc. All rights reserved.
1. Introduction Cryo-electron microscopy combined with established singleparticle image processing methods can yield near atomic-resolution reconstructions of purified macromolecular complexes in vitreous ice (Grigorieff and Harrison, 2011; Zhou, 2011). In single-particle reconstruction, multiple two-dimensional projection images of multiple copies of the complex are collected. The projection images are aligned, the orientations of the complex relative to the directionof-view are calculated, and the images are combined to generate a 3D reconstruction (Cong and Ludtke, 2010). Single-particle methods are generally not applicable when the 2D projection image of the complex is superimposed with the projections of other objects in the ice layer. In this case the confounding information from the superimposed objects prevents accurate determination of the correct alignment and orientation. This limitation prevents the general application of single-particle methods to resolve the high-resolution structures of macromolecular complexes in situ, for example within cells, or embedded in the membrane of heterogeneous viruses. This limitation can be overcome using cryo-electron tomography in combination with sub-tomogram averaging. In cryoelectron tomography the same sample is imaged from different directions (Lucic et al., 2005), and these images are used to calculate a 3D tomographic reconstruction. Objects in the ice layer ⇑ Corresponding author. 1
E-mail address: [email protected] (J.A.G. Briggs). Present address: FEI Munich, Lochhamer Schlag 21, 82166 Graefelfing, Germany.
that would be superimposed with the object of interest in single 2D projections do not superimpose in 3D. Multiple copies of the object of interest are then extracted from the tomogram, aligned and averaged in 3D to generate a higher-resolution reconstruction. This is sub-tomogram averaging (reviewed in Briggs, 2013). Subtomogram averaging can be successfully applied to obtain 3D reconstructions of complexes in situ: within cells, viruses, vesicles, and complex in vitro systems (Beck et al., 2004; Faini et al., 2012; Pfeffer et al., 2012; Pigino et al., 2011; White et al., 2010; Zuber and Unwin, 2013). This unique potential to allow in situ structural biology is leading to a rapid increase in the method’s use. Sub-tomogram averaging has also been combined with features of single-particle reconstruction to form hybrid approaches, where sub-tomogram averaging provides starting models or angular restraints for subsequent single-particle reconstruction from the same dataset. Bartesaghi and co-workers presented a ‘‘constrained single-particle tomography’’ approach, in which alignment is carried out using individual projection images extracted from tomographic tilt series, while appropriately constraining the relative tilt angles of images from the same tomogram (Bartesaghi et al., 2012). While a conventional sub-tomogram averaging reconstruction generated a map of GroEL with a measured resolution of 24.5 Å, this hybrid approach led to a reconstruction at a resolution of 8.4 Å. We have previously used a hybrid approach in which subtomogram averaging was used to determine the symmetry parameters of helical arrays of Gag protein from Mason–Pfizer monkey virus (M-PMV), which could subsequently be used for conventional helical real-space reconstructions of individual tubes from 2D
1047-8477/$ - see front matter Ó 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jsb.2013.10.015
Please cite this article in press as: Schur, F.K.M., et al. Determination of protein structure at 8.5 Å resolution using cryo-electron tomography and subtomogram averaging. J. Struct. Biol. (2013), http://dx.doi.org/10.1016/j.jsb.2013.10.015
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projections. Data from individual tubes were then combined by averaging in 3D (Bharat et al., 2012). This approach generated a reconstruction at approximately 8 Å resolution. Hybrid methods have advantages over unconstrained single-particle approaches, but they cannot be applied to the full range of samples addressable by sub-tomogram averaging methods. They are only applicable to samples where individual 2D projection images contain information appropriate for alignment, samples which are often also appropriate for single-particle processing. The highest-resolution reconstructions obtained using subtomogram averaging methods have a resolution of approximately 15 Å (Briggs et al., 2009; Chen et al., 2013; Eibauer et al., 2012). Although there appear to be no theoretical limitations preventing application of sub-tomogram averaging to obtain reconstructions at sub-nm resolution, no such structures have been previously reported. A number of possible factors may contribute to this limitation. Firstly, the total electron dose applied during collection of a tomogram is typically higher than that applied during single-particle reconstruction, leading to accumulation of electron-induced sample damage. Secondly, the need to collect tomographic tilt series makes it more time-consuming to collect very large datasets. Thirdly, the low electron dose in individual images makes it hard to visualize Thon rings, (a step that is required to determine the defocus at which the data is collected), and therefore harder to properly account for the contrast–transfer–function (CTF) of the microscope. Fourthly, two independent image alignment steps must be carried out accurately – of the individual projection images to generate the tomogram and of the sub-tomograms to generate the final reconstruction. Fifthly, increased apparent sample thickness, in particular at high-tilt angles, decreases the signal to noise ratio in the images. We reasoned that we could obtain higher resolution reconstructions using sub-tomogram averaging if we mitigated the effect of the first three limitations by collecting tomograms with reduced dose, collecting a large dataset, and by improved estimate of the CTF. We therefore revisited the tubular arrays of Mason–Pfizer monkey virus Gag protein, for which we had previously used a hybrid method to determine the structure at 8 Å resolution (Bharat et al., 2012). Using sub-tomogram averaging, we obtained a reconstruction at sub-nm resolution. This represents a substantial improvement in the resolution obtained using sub-tomogram averaging methods, and an important proof-of-principle that conventional sub-tomogram averaging can be used to visualize protein secondary structure.
2. Methods and results 2.1. Sample preparation and data collection Tubular arrays of the M-PMV DPro CANC protein were assembled with k-DNA and prepared for cryo-electron tomography exactly as described in Bharat et al. (2012). 102 tomograms were acquired on an FEI Titan Krios electron microscope operated at 200 keV, with a GIF2002 post-column energy filter (using a slit width of 20 eV) and a 2 2 K Gatan megascan 795 CCD camera. A 70 lm objective aperture was used, giving a resolution cut-off at 2.44 Å. For navigation and search purposes, low magnification montages were acquired using Serial-EM (Mastronarde, 2005), and tilt series were acquired at appropriate positions using FEI tomography software version 4 in automated batch mode. The nominal magnification was 42,000 giving a calibrated pixel size of 2.025 Å. The tilt range was from 45° to +60° in 3° steps, collecting first from 0° to 45° and then from 3° to 60°. Tilt series were collected at a range of nominal defoci between 1.5 and 3.3 lm. Several rounds of autofocusing were allowed for each
image during data collection (see below). The total dose for each entire tilt series was around 40 electrons/Å2. Even with this low dose, some blurring of features within the tubular specimens was apparent in the second half of each tilt series, suggesting a degradation of high resolution information. 2.2. Defocus stability and measurement To estimate the required accuracy of defocus determination, we simulated the resolution–attenuating effects of errors in determination and/or correction of the CTF. To do this, using MATLAB (MathWorks), we calculated 361 theoretical CTFs (under the data collection conditions used), each at a different defocus from 1.5 to 3.3 lm in 5 nm steps, according to the formula (Erickson and Klug, 1971; Wade, 1992).
CTFðf Þ ¼ A sin
pkf 2 Dz 0:5k2 f 2 cs þ B cos pkf 2 Dz 0:5k2 f 2 cs
With the variable f being the spatial frequency, Dz the defocus, k the electron wavelength and cs the spherical aberration. A is the defocus dependent envelope function and B is the fraction of the amplitude contrast (set to 0.1 in our simulations). We simulated phase-flipping of the theoretical CTFs assuming a normally distributed random error (with a sigma ranging from 50 to 200 nm) in the determined defocus values. For simplicity envelope functions were excluded from the simulation. We summed all the phase-flipped CTFs. The resulting curves approximate the phase contrast transfer in the CTF-corrected reconstruction (Fig. 1A). We then divided these curves by the sum of the correctly phase-flipped CTFs. This gives envelope functions describing the attenuating effect of the error in the defocus determination (Fig. 1B). There is 50% attenuation in the signal at 8 Å for a normally distributed error in defocus determination with a sigma of 100 nm. The low dose applied during collection of individual images in a cryo-tomographic tilt series means that it is challenging to determine the defocus of the individual images, in particular those at high tilt where the apparent sample thickness increases. For this reason we considered it important to collect the data with a very stable defocus, such that the mean defocus of the series represented a good estimate of the defocus of the individual images. To achieve this, during data collection we included a 30 s wait between tilting and focusing, and then repeated the autofocusing step for each image until the offset between measured and predicted defocus was below 100 nm. This procedure required no manual intervention. The small field of view used for image acquisition allowed the focus area to be positioned only a short distance along the tilt axis from the exposure area, limiting any non-eucentricity. We measured the defocus of individual images from 10 representative tilt series by fitting theoretical CTF-curves to averaged power spectra from 5122 tiles from each image using MATLAB (Fig. 1C). Between ±30°, Thon rings were sufficiently clear to obtain an approximate estimate of the CTF for individual tilts. At tilts beyond ±30° we found that the low signal to noise ratio prevented reliable estimation of the CTF. Measuring the defocus from single micrographs with very low signal to noise ratio may be inaccurate. We therefore also estimated the mean defocus of each tilt series by fitting theoretical CTF-curves to averaged power spectra from 5122 tiles from all images in the tilt series. These power spectra show clear Thon rings. To obtain a rough estimate of the error in defocus determination of individual images, we compared this mean defocus with the measured defocus of each individual image in the series. Considering data from 10 tomograms, at each tilt angle we calculated the root mean squared deviation of the measured defocus from the
Please cite this article in press as: Schur, F.K.M., et al. Determination of protein structure at 8.5 Å resolution using cryo-electron tomography and subtomogram averaging. J. Struct. Biol. (2013), http://dx.doi.org/10.1016/j.jsb.2013.10.015
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mean (Fig. 1D). This deviation incorporates both errors in determining the CTF and errors in the autofocus routine. These values range from 61 nm at 30° to 47 nm at 0°, up to 71 nm at +30°. Over all angles the root mean squared deviation was 54 nm. These analyses gave us confidence that both the autofocus routine and the determination of the CTF were accurate to better than the required 100 nm and therefore appropriate for high-resolution reconstruction.
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80 tilt series were selected from the total dataset of 102 tilt series based on a visual assessment of tilt series quality and were reconstructed using the IMOD software suite (Kremer et al., 1996). The dataset contained 133 individual tubular arrays of MPMV DPro CANC protein. The tubes displayed varying lengths up to several hundred nanometers and diameters ranging from 55 to 72 nm. Within the reconstructed tomograms, the hexameric Gag lattice on the surface of the tube could be clearly visualized, confirming the high quality of the acquired data (Fig. 2A). For later validation of the structure, the set of tomograms was divided into two halves containing roughly the same number of tubes and with a balanced distribution of nominal defoci. Each half of the data was subsequently processed completely independently. Sub-tomograms were extracted from all tubes at arbitrary, uniformly distributed positions along the tubular surface by marking the central axis of the tube and defining an array of extraction positions at a distance from this line corresponding to the radius of the tube. The cubic sub-tomograms had an edge length of 243 Å, and overlapped such that their centers were separated by approximately 40 Å. Sub-tomogram averaging was carried out as described in the next section: the process is essentially that described previously (Bharat et al., 2011, 2012; de Marco et al., 2010).
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tilt angle (degrees) Fig.1. Defocus stability and measurement. (A) The simulated CTF (red curve) of a sub-tomogram reconstruction generated from a set of tomograms with defocuses ranging between 1.5 and 3.3 lm without CTF correction (see Section 2.2) and without considering envelope functions. The simulated CTF assuming the defocus has been perfectly determined and the CTF has been corrected by phase flipping (black line), tends towards a value of 2/p at high frequencies. The simulated CTFs after correction assuming a normally distributed error in defocus determination with a sigma of 50 nm (blue), 100 nm (green) and 200 nm (orange) are shown. (B) Dividing the erroneously corrected CTF by the perfectly corrected CTF gives an error envelope indicating the dampening effect of inaccurate defocus determination on resolution. Colors as in A. With a normally distributed error of approximately 100 nm the information transfer at 8 Å is reduced to 50%. (C) Defocus measured on individual images from ten tilt series plotted as a function of tilt angle between 30° and +30°. Dotted lines indicate the mean defocus measured from the averaged power spectra of the respective series. (D) Root mean squared deviation of the measured defocus of individual images from the mean defocus of the series for 10 tilt series shown in C calculated at each tilt angle. The root mean squared deviation is approximately 54 nm.
The procedure outlined in this section was carried out in its entirety independently on each half dataset. Calculations were performed using MATLAB scripts derived from the TOM (Nickell et al., 2005) and AV3 (Forster et al., 2005) packages. The Dynamo software package was used for generation of masks and for FSC calculations (Castano-Diez et al., 2012). To minimize computing time, initial processing was performed on 4 binned, non-CTF corrected data. For each half dataset one tomogram with a defocus of 2.5 lm was chosen to obtain an initial structure. Extracted sub-tomograms from this tomogram were assigned initial angles based only upon the geometry of the tubes and were averaged to generate a smooth starting reference. The sub-tomograms from this tomogram were then iteratively aligned and averaged in six dimensions against the reference as described in Forster et al. (2005). Translational shifts were limited to 8 nm in each direction, and rotations were limited to values reasonable considering the tube geometry (approximately 60° in the plane of the tube surface and 40° in other angles). After twelve iterations, the structure had stabilized and was used as starting references for the alignment of the sub-tomograms from the other tomograms. At first the sub-tomograms from each individual tomogram were aligned and averaged separately for five iterations. To verify the success of this preliminary alignment we displayed the orientations and positions of the aligned subvolumes in 3D space after alignment using Amira (Visage imaging) and the EM toolbox (Pruggnaller et al., 2008). We observed the expected clear helical arrangement of the individual subunits (Fig. 2B). To reduce the dataset size, redundant sub-tomograms (those that had converged onto the same position as another sub-tomogram) were removed, as were those that had radially deviated from the tubular lattice.
Please cite this article in press as: Schur, F.K.M., et al. Determination of protein structure at 8.5 Å resolution using cryo-electron tomography and subtomogram averaging. J. Struct. Biol. (2013), http://dx.doi.org/10.1016/j.jsb.2013.10.015
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correction using the procedure within IMOD (Xiong et al., 2009) did not significantly change the final reconstruction. Unbinned sub-tomograms were extracted from the CTFcorrected tomograms. They were then aligned in 6 iterations using a final angular search increment of 0.5°. To avoid noise-based overalignment, a soft low pass filter was applied to the reference used for each iteration, which reached zero at 13 Å in the first of these iterations and 11 Å in the final iteration. The 2-fold symmetry inherent in the structure was applied only in the last 2 iterations. 2.5. Structure assessment We estimated the resolution of the determined structure using Fourier shell correlation (FSC) between the two independent reconstructions. The use of two completely independent reconstructions from independent starting models eliminates the risk of over-optimistic resolution assessment due to over-alignment or model bias. The resolution of the map was determined to be 9.6 and 8.5 Å at the 0.5 and 0.143 thresholds, respectively (Fig. 3). The total dataset size (considering both half datasets) was 121,346 sub-tomograms, equivalent to 242,692 asymmetric units when the twofold symmetry is considered. Restricting the dataset size to a half, quarter or eighth of the original dataset size resulted in a small decrease in resolution (Fig. 3). We averaged the two final structures and sharpened the average with an empirically determined negative B-factor of 1200 Å2, while filtering the data to a resolution of 8.5 Å (Rosenthal and Henderson, 2003), to obtain the structure shown in Fig. 4. (We note that typical high-resolution sub-tomogram averaging structures may prove to be subject to larger B-factors than typical single-particle structures due to additional dampening of high-frequency information by factors such as increased beam damage, increased beam-induced motion at higher tilts, and the additional steps of tomogram alignment and reconstruction.) We compared this structure with that previously determined using hybrid methods (EMD-2089), and found it to be consistent. Extended densities corresponding to alpha-helices were clearly visible in the correct positions, consistent with the measured resolution. Following the approach used by Bharat et al. (2012) we also extracted the three symmetry independent Gag dimers within the
Fig.2. Cryo-electron tomogram of M-PMV DPro CANC tubes. (A) A 8.1 Å thick slice through the xy plane of a representative tomogram showing three M-PMV DPro CANC tubes. A Gaussian filter was applied for visualization. The hexameric lattice arrangement of the proteins is clearly resolved. Scale bar 100 nm. (B) ‘‘Lattice maps’’ of tubes shown in (A). Hexamers are placed at the position and orientation of the sub-tomograms after alignment, indicating the positions and orientations of the hexameric unit cells of the M-PMV DPro CANC lattice. They are colored according to their cross-correlation with the reference from green (high) to red (low). The regular arrangement of the hexamers indicates the success of the alignment procedure. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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The latter could be excluded based on their lower cross-correlation values. The remaining sub-tomograms were then pooled and subjected to four further iterations of alignment and averaging. Based on the analysis described in the Section 2.2 we corrected each tilt series for the CTF assuming that for all images within a tilt series, the defocus on the tilt axis corresponded to the mean defocus value of the tilt series. For phase-flipping we followed the tile-based method described by Zanetti et al. (2009), setting the tile size to 512 pixels and the interpolation width (the distance between the center of two tiles) to 2 pixels. Repeating the CTF
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Resolution (Å) Fig.3. Resolution determination by Fourier shell correlation. Fourier shell correlations between the two independent reconstructions of M-PMV DPro CANC tubes. The full reconstruction (solid black line) has a measured resolution of 9.6 and 8.5 Å at the 0.5 and 0.143 criteria, respectively. To explore the dependence of resolution on dataset size, the Fourier shell correlation was also calculated after reducing the data set to a half (red line), quarter (green line) or eighth (blue line) of the full dataset size. After averaging the three symmetry independent copies of Gag within the asymmetric unit the resolution improves to 8.9 and 8.3 Å (dashed black line). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Please cite this article in press as: Schur, F.K.M., et al. Determination of protein structure at 8.5 Å resolution using cryo-electron tomography and subtomogram averaging. J. Struct. Biol. (2013), http://dx.doi.org/10.1016/j.jsb.2013.10.015
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Fig.4. Sub-tomogram averaging reconstruction of M-PMV DPro CANC tubes. (A) Isosurface representation of the reconstruction of the M-PMV DPro CANC hexameric unit generated by sub-tomogram averaging. The structure is viewed from the outside of the tube (left) and in section (right). The dotted line shown in the left panel indicates the tube axis. The dashed lines in the right panel indicate the positions of orthoslices in B. (B) Slices through the reconstruction through the Nterminal CA domain (N-CA) and the C-terminal CA domain (C-CA), along the dashed lines marked in A. Density is black. Scale bar 5 nm.
asymmetric unit, aligned and averaged them. This structure had a resolution assessed by FSC of 8.9 and 8.3 Å at the 0.5 and 0.143 criteria, from a total dataset of 728,076 Gag monomers. The mask applied during FSC measurement was a Gaussian-filtered sphere. Again we averaged the final structures from the two half datasets and sharpened the average with a negative B-factor of 1450 Å2. Fig. 5 shows a direct comparison between this structure and the previously published structure EMD-2090. For visualization purposes both structures are shown compared to PDB 4ARG, which results from fits of crystal structures into EMD2090 (Bharat et al., 2012). Visualizations of structures were performed using Chimera (Pettersen et al., 2004). The excellent correspondence between the two maps verifies that the final structure derived from sub-tomogram averaging has a resolution that is appropriate for interpretation of alpha helices, consistent with the measured resolution of approximately 8.5 Å.
3. Discussion Sub-tomogram averaging can be applied to derive the structures of macromolecular complexes in situations where conventional single-particle reconstruction methods cannot be applied, for example in intact cells or in purified systems where the complex of interest is superimposed in projection by confounding densities. To date, sub-tomogram averaging methods have only been applied in the low to medium resolution regimes, to derive structural data at resolutions up to approximately 15 Å, at which secondary structure information cannot be interpreted. Here we have demonstrated that sub-tomogram averaging can be used to derive structures at sub-nm resolution allowing the direct visualization of secondary structure elements.
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To achieve this improvement in obtained resolution we took three steps. Firstly, accurate defocus determination is a prerequisite for accurate CTF correction. We calculated that the defocus needed to be estimated with an accuracy of better than 100 nm. Extended data acquisition schemes using several focus images have been used to achieve accurate estimates of defocus (Eibauer et al., 2012). Here we instead facilitated defocus measurement by collecting data under automated conditions where the defocus remained very stable through the tilt series. This was achieved by iterating the autofocus routine during data collection, and by using a small field of view for exposure. The small field of view allows the focus area to be placed close to the exposure area and reduces the defocus gradient across the exposure image. Secondly, we have limited the dose used during data collection to 40 electrons/Å2. Significant beam damage only occurs late in the tilt series, and the early images therefore retain a sufficient amount of high-resolution information. (We note that a balance must be found between these first two steps, since reducing the dose makes it more challenging to accurately measure the defocus). Thirdly, we have collected a very large dataset. While the highest resolution structure we obtained is an average of over 700,000 asymmetric units, as shown in Fig. 3, a sub-nm resolution reconstruction (at the 0.143 criterion) was obtained by averaging approximately 30,000 asymmetric units from the complete aligned dataset. The data used in this manuscript was collected on a Titan Krios microscope equipped with an energy filter and a conventional CCD camera. The improved optics of the Titan Krios compared to other electron microscopes are unlikely to be critical in the resolution regime explored here. The high stability of the Titan Krios microscope stage, and the ability to operate the microscope continually for long time periods, allows for much more efficient collection of large datasets than would be possible using a side entry cryo-holder, but a similar throughput could be achieved on, for example, an FEI Polara microscope. It is reasonable to assume that the use of direct detection cameras, rather than the CCD camera used here, would allow similar resolutions to be obtained with much smaller datasets (Bai et al., 2013). Improved detectors will also make it easier to determine defocus, further facilitating determination of high-resolution structures. For these reasons we believe that a similar approach to high-resolution sub-tomogram averaging should be possible on other electron microscopes. There may be advantages in applying sub-tomogram averaging to samples that can also be studied by other methods. For large and slightly flexible tubular arrays, such as those studied here, subtomogram averaging has a number of advantages over helical reconstruction methods. It does not require classification of different helical symmetries, since they can be combined in the same reconstruction; it does not require precise determination of the helical symmetry parameters in advance, since each unit cell is aligned separately; it can compensate for flattening of the tubes, since the top and bottom of the tubes are considered separately; and it does not require a very large reconstruction volume, since it derives a structure for the unit cell, and not for a segment of the tube. Here we were able to obtain a high-resolution structure significantly more rapidly than we could obtain the same structure using our previous hybrid approach incorporating helical reconstruction methods (Bharat et al., 2012). We envisage that the future will bring continued improvements in microscope technology, image processing methods, and in the development of user-friendly and automated software for data collection and image processing. These developments will facilitate widespread application of sub-tomogram averaging to generate reconstructions from cryo-electron tomograms of samples that have not proved accessible to single-particle reconstruction, generating significant biological insights. The data presented here
Please cite this article in press as: Schur, F.K.M., et al. Determination of protein structure at 8.5 Å resolution using cryo-electron tomography and subtomogram averaging. J. Struct. Biol. (2013), http://dx.doi.org/10.1016/j.jsb.2013.10.015
F.K.M. Schur et al. / Journal of Structural Biology xxx (2013) xxx–xxx
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Fig. 5. Sub-tomogram averaging reconstruction after averaging the three symmetry-unrelated dimers, compared to the published structure solved using hybrid methods. The sub-tomogram averaging structure was sharpened and filtered to the resolution determined at the 0.143 FSC criterion. Both the sub-tomogram averaging structure and the previously published structure show similar strong rod-like densities corresponding to alpha-helices. (A) Isosurface representations of the M-PMV DPro CANC dimer, fitted with the model PDB 4ARG. The right-hand view is rotated by 50° around the vertical relative to the left-hand view. (B) Slices through the reconstruction at the N-CA and C-CA domains. The positions of the slices are marked by dotted lines in A. Scale bar 2 nm. (C) Isosurface representation of the structure as previously solved using 2D reconstruction methods (EMD-2090) in the same orientations as in A. (D) Slices through EMD-2090 at positions approximately equivalent to those in B.
demonstrates that such structures can be obtained at sub-nm resolution, and reveal protein secondary structure. 4. Author information The reconstructions and a representative tomogram have been deposited in the EMDB, accession numbers EMD-2487, EMD2488, and EMD-2489. Acknowledgments The M-PMV DPro CANC tubes imaged in this study were a kind gift from Pavel Ulbrich and Tomas Ruml, Institute of Chemical Technology, Prague. The cryo-EM grids were prepared by Tanmay Bharat. This study was technically supported by EMBL’s IT services unit and by Frank Thommen. We thank Martin Schorb and Svetlana Dodonova for discussions and advice; Khanh Huy Bui for advice and scripts to streamline tomogram reconstruction; and Giulia Zanetti, Tanmay Bharat, and Martin Beck for comments on the manuscript. This study was supported by Deutsche Forschungsgemeinschaft grant BR 3635/2-1 to JAGB. JAGB conceived the study; FKMS, AdM, and JAGB designed the experiments; FKMS and WJHH optimized microscope settings and collected data; FKMS performed the image processing; FKMS and JAGB analyzed the data and wrote the manuscript. References Bai, X.-c, Fernandez, I.S., McMullan, G., Scheres, S.H., 2013. Ribosome structures to near-atomic resolution from thirty thousand cryo-EM particles. eLife 2, e00461. Bartesaghi, A., Lecumberry, F., Sapiro, G., Subramaniam, S., 2012. Protein secondary structure determination by constrained single-particle cryo-electron tomography. Structure 20, 2003–2013. Beck, M., Förster, F., Ecke, M., Plitzko, J.M., Melchior, F., Gerisch, G., Baumeister, W., Medalia, O., 2004. Nuclear pore complex structure and dynamics revealed by cryo-electron tomography. Science 306, 1387–1390. Bharat, T.A., Riches, J.D., Kolesnikova, L., Welsch, S., Krahling, V., Davey, N., Parsy, M.L., Becker, S., Briggs, J.A., 2011. Cryo-electron tomography of Marburg virus
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