Design of a molecular support for cryo-EM structure determination

Edited by Fred J. Sigworth, Yale University, New Haven, CT, and approved October 13, 2016 (received for review August 2, 2016)
November 7, 2016
113 (47) E7456-E7463


As the scope of macromolecular structure determination by cryo-electron microscopy (cryo-EM) is expanding rapidly, it is becoming increasingly clear that many biological complexes are too fragile to withstand the harsh conditions involved in making cryo-EM samples. We describe an original approach to protect proteins from harmful forces during cryo-EM sample preparation by enclosing them inside a three-dimensional support structure that we designed using DNA origami techniques. By binding the transcription cofactor p53 to a specific DNA sequence, and by modifying the position of this sequence in our support structure, we also sought to control the relative orientation of individual p53:DNA complexes.


Despite the recent rapid progress in cryo-electron microscopy (cryo-EM), there still exist ample opportunities for improvement in sample preparation. Macromolecular complexes may disassociate or adopt nonrandom orientations against the extended air–water interface that exists for a short time before the sample is frozen. We designed a hollow support structure using 3D DNA origami to protect complexes from the detrimental effects of cryo-EM sample preparation. For a first proof-of-principle, we concentrated on the transcription factor p53, which binds to specific DNA sequences on double-stranded DNA. The support structures spontaneously form monolayers of preoriented particles in a thin film of water, and offer advantages in particle picking and sorting. By controlling the position of the binding sequence on a single helix that spans the hollow support structure, we also sought to control the orientation of individual p53 complexes. Although the latter did not yet yield the desired results, the support structures did provide partial information about the relative orientations of individual p53 complexes. We used this information to calculate a tomographic 3D reconstruction, and refined this structure to a final resolution of ∼15 Å. This structure settles an ongoing debate about the symmetry of the p53 tetramer bound to DNA.
Cryo-electron microscopy (cryo-EM) structure determination of biological macromolecules is undergoing rapid progress. With the advent of efficient direct electron detectors and the development of powerful algorithms for image processing, numerous structures to near-atomic resolution have been reported in the past few years (1, 2). In cryo-EM single-particle analysis, solutions of purified protein and/or nucleic acid complexes are typically applied to a thin, amorphous carbon film with micrometer-sized holes in it that is held in place by a metal grid. Excess liquid is then blotted away with filter paper, and the sample is rapidly plunged in liquid ethane (3, 4). This procedure ideally results in the formation of a film of vitreous ice that is only slightly thicker than the macromolecular complex of interest. Keeping the frozen sample at liquid nitrogen temperatures allows its insertion into the high vacuum of a transmission electron microscope and limits the effects of radiation damage by the electrons that are used for imaging (5). Images taken through the holes of the carbon film ideally contain 2D projections of many, assumedly identical copies of the macromolecular complex of interest, which are often called particles. Projections from different viewing directions can then be combined in a 3D reconstruction of the scattering potential of the molecule (6). If the resulting map approaches a resolution of 3 Å, it allows building an atomic model of the molecules, from which useful information about their function may be derived.
A major hurdle in single-particle analysis is the need to recover the relative viewing angles of the individual particles. This information is lost in the experiment because every particle adopts an uncontrolled orientation in the ice layer. The viewing angles are, therefore, determined a posteriori by image-processing algorithms that match the experimental projection of every individual particle with projections of a 3D model (7). However, the projection-matching procedure is ultimately hampered by radiation damage. Because the electrons that are used for imaging destroy the very structures of interest (see ref. 8 for a recent review), one needs to limit carefully the number of electrons used for imaging. This procedure results in high levels of experimental noise, which in turn lead to errors in the a posteriori determination of the viewing angles. These errors impose severe limitations on the 3D reconstruction, in particular for smaller complexes, because the signal-to-noise ratio in the images decreases with the size of the particles. If one could experimentally control the orientations of each particle in the ice layer, then, in principle, one could determine structures to higher resolution and of smaller complexes. This situation is illustrated by samples where the relative orientations of many molecules is set—for example, in 2D crystals or helical assemblies of protein molecules. In such cases, near-atomic-resolution reconstructions were already achieved decades ago, and from much smaller molecules than currently possible with single-particle analysis (912). Both developments that triggered the recent revolution in attainable resolution of cryo-EM single-particle analysis directly addressed this same hurdle. Better detectors led to lower levels of experimental noise, and better image-processing algorithms led to more accurate viewing angles.
Another complication of cryo-EM structure determination lies in the way the sample is prepared (see also refs. 13 and 14). The exact physics of cryo-EM sample preparation is poorly described, but several factors may negatively affect its results. First, the blotting process itself may involve strong forces in the sample that destroy fragile protein complexes. Second, during the short time between blotting and vitrification, the macromolecules are in a thin liquid film that extends for millimeters to the side, but is only a few hundred angstroms thick. Brownian motion will cause the macromolecules to collide with the air–water interface >1,000 times per second (15). Biological macromolecules may unfold when they hit the air–water interface (16), or they may adsorb to this interface in a nonrandom manner—for example, by presenting their most hydrophobic patch to it. This interaction leads to an uneven distribution of viewing angles in the cryo-EM images, and the corresponding lack of different views may hamper 3D reconstruction. Despite the numerous successes of single-particle analysis in recent years, preparing suitable cryo-EM samples therefore remains a nontrivial task. Often, establishing optimal ice thickness and particle distribution in the ice requires careful optimization of blotting and freezing conditions by an experienced experimentalist, and fragile complexes have often been observed to fall apart (17, 18).
This work describes an attempt to address both the problems of the unknown viewing angles and the unfavorable conditions of sample preparation from an experimental perspective. To this end, we set out to design a support structure that simultaneously exerts control over the orientations of individual particles, while also controlling the ice thickness and protecting the particles from the air–water interface. We chose to use 3D DNA-origami techniques to design such a support structure. DNA-origami allows flexible and customizable design of 3D structures at the nanometer scale (1921). The use of 2D arrays of DNA scaffold to bind proteins for cryo-EM sample preparation has been proposed before (22). Many proteins naturally interact with nucleic acids like DNA or RNA. In the first instance, to limit the number of technological challenges, we designed a support structure for proteins that naturally bind to double-stranded DNA in a sequence-dependent fashion. This approach includes many proteins involved in the regulation of transcription, and we chose the transcription factor p53 as a paradigm.
p53 plays a central role in the cell cycle (23) and is best known for its function in tumor suppression (24). The active form of human p53 is a homotetramer of 4 × 393 amino acids. Its domain organization consists of an intrinsically disordered N-terminal transactivation domain, a proline-rich region, a structured DNA-binding domain (DBD), a tetramerization domain connected via a flexible linker, and an intrinsically disordered C-terminal regulatory domain (25). The structures of the DBD and the tetramerization domain of human p53 have been solved by X-ray crystallography and NMR (2633), as have tetrameric complexes of the DBD with fragments of DNA (34, 35). The structures of full-length and truncated mutants of human p53 bound to DNA have been analyzed by negative-stain EM and small-angle X-ray diffraction (36, 37), and the DBDs have the same symmetry as found in the crystallographic studies. Other cryo-EM studies on mouse p53 propose a different symmetry (38, 39). Because the C terminus of p53 increases unspecific binding to DNA (40), we chose to use the human truncated p531–360 construct (with a molecular mass of ∼160 kDa for the tetramer) in our experiments. Below, we describe the rationale behind our approach, the results obtained with this p53 construct, and the opportunities that this type of support structure offer for more general cryo-EM sample preparation procedures.


Support Structure Design.

Fig. 1 illustrates the design of our proposed DNA-origami support structure. We designed a hollow pillar with a honey-combed motif of 82 parallel double-stranded DNA (dsDNA) helices with a height of 26 nm. Two parallel arrays of DNA helices create a central cavity of 13.2 × 13.6 nm and outer dimensions of 26.4 × 32.7 nm. A dsDNA helix with a central p53-specific binding sequence spans the center of the hollow space. Ten parallel helices at the periphery, which we will call the flag (Fig. 1 A and B, top left), make the structure asymmetric so that top and bottom views can be readily distinguished. Different aspects of this design aim to address four main objectives of our support structure.
Fig. 1.
Design of the support structure. (A) Perspective view of the support structure. Each dsDNA helix is shown as a white cylinder. The position of the specific binding sequence on the central dsDNA helix is shown in red; ssDNA overhangs (T10) are shown in blue. (B) Top view of the support structure. Inner and outer dimensions of the support structure are shown in gray. The dimensions of the asymmetric feature (flag) are shown in green. (C) Side view of the support structure. (D) Illustration of five different settings for the p53-specific binding sequence on the central dsDNA helix. A surface representation of the tetrameric DBD of p53 is shown in red. In each representation, the p53-binding site is shifted one base upward from left to right. Because of the helical nature of the dsDNA, this shift also results in a rotation of the p53 complex.
First, we aimed to keep the target protein in the middle of a sufficiently thin ice layer and away from the air–water interface. p53 binds preferentially to the sequence GGACATGTCCGGACATGTCC. By including this sequence in the central dsDNA helix (Fig. 1 A and B, red), our target protein would bind to the approximate center of our support structure. By designing ssDNA overhangs (T10) on both the top and bottom faces of the pillar (Fig. 1 AC, blue), we aimed to expedite a preferred orientation of the support in the ice layer. The ssDNA overhangs have exposed aromatic bases, which make them more likely to interact with the hydrophobic air–water interface than the sides of the pillar, which is more hydrophilic because of its exposed DNA backbones. This rationale was inspired by initial observations of a preferred orientation in the ice layer of a different DNA-origami object, where similar overhangs were added to prevent end-to-end polymerization of the structures (41) (Fig. S1). If both faces of the pillar would indeed interact with the air–water interface at the top and bottom of the sample, then the height of the pillar itself would determine the thickness of the ice layer. By designing a distance of ∼260 Å between the ssDNA overhangs at the top and the bottom of the pillar, the p53 complex, with an expected size of <120 Å, would then not interact with either of the air–water interfaces. In addition, by anchoring the protein in the middle of the ice layer, differences in defocus height that might lead to loss of resolution in relatively thick ice layers (42) can be prevented.
Fig. S1.
Orientation of DNA-origami structures in ice. (A) Different views of a DNA-origami object. (B) Micrograph of the DNA-origami object showing mainly front views (i.e., similar to A, Top), where the DNA helices of origami object are located perpendicular to the air–water interface.
Second, by effectively enclosing the target protein inside the support structure, we aimed to protect it from blotting forces, aggregation, or other harmful interactions. It is also conceivable that interactions of the target protein with the holey carbon film, with itself (in the form of aggregation), or with the air–water interface may all influence optimal blotting conditions. Therefore, enclosing the target protein inside a DNA-origami structure that is always the same may also have a beneficial effect on the reproducibility of optimal blotting conditions for different target proteins.
Third, even though we enclosed the target protein inside the support, we still aimed to minimize the overlap in projected densities of the target protein and the support, because this density overlap would hamper alignment of the particle. The central dsDNA helix traverses the middle of the hollow pillar, and the target protein binds to (approximately) the center of the pillar. Thereby, provided the support structure adopts the intended orientation in the ice, with its open top or bottom faces perpendicular to the electron beam, the projected density from the target protein may be separated from the projected density of the support by excising smaller subimages (also see Fig. 2).
Fig. 2.
Image-processing strategy. (Scale bars: 20 nm.) (A) Part of a typical micrograph. A template for automated particle picking is shown in the top right corner. (B) Tomographic side view of a hole showing a monolayer of support structures. Near the edge of the hole (indicated with an arrow), the ice gets slightly thicker. (C) Examples of 2D classes that are discarded. (D) Examples of 2D classes of intact structures with the flag in the top left or top-right. A mirror operation is applied to images with the flag on the top right. (E) The average of all intact particles with the correct orientation in ice, including the mirrored particles, is used as a template for their alignment. (F) Subimages (cyan) with a width and height of 20 nm are extracted from the aligned particles and submitted to 2D classification with a prior on the in-plane rotation. A circular mask that is applied during this process is shown in yellow. Three types of particles are distinguished: supports without tilt axis (F, Top), supports with tilt axis but without a density for p53 (F, Middle), and supports with tilt axis and p53 density (F, Bottom). (G) Illustration of the final selection of particles, where the angle from a realignment of the p53-only subimage is compared with the angle of the entire support structure, and particles with large differences in these angles (e.g., Lower) are discarded.
Lastly, we also sought to exert experimental control over the orientation of our target protein, p53, inside the support structure. Because of the helical character of the central dsDNA, we can induce different relative orientations of our target protein with respect to the support structure. By translating the binding sequence 1 bp upward, the p53 complex will rotate 34° along the axis of the dsDNA (Fig. 1D). Thereby, by making five different versions of our support structure, shifting the p53-binding sequence one base pair at a time, we can generate different orientations that cover the entire 180° of a tomographic tilt series (Fig. 1D). Therefore, we also refer to the central dsDNA helix as the tilt axis. The capability of rotating a single copy of our target protein complex in a controlled manner around the tilt axis turns our design from a passive support into the nanoscale equivalent of a sample holder with a tilting stage. However, as we will discuss in more detail below, in practice, it is difficult to achieve precise control over the orientation of p53 along the tilt axis.

Synthesis and Imaging of the Support Structure.

The designed DNA-origami structure was synthesized and purified with a yield between 50% and 90% by using standard procedures (Materials and Methods). We first used the support structure alone (i.e., without the target protein) to determine suitable freezing conditions for cryo-EM grid preparation. We found conditions where the support structures adopt a pseudo 2D-crystalline arrangement in large areas of the grid, where most structures adopt the intended top-bottom orientation in the ice layer (Fig. 2A). Using cryo-electron tomography, we confirmed that the ice layer is indeed as thick as the designed DNA-origami support structures (Fig. 2B and Movie S1). Interestingly, in areas where the ice appeared thinner than the designed height, we observed no support structures. In areas where the ice appeared thicker, we also observed support structures in side-view orientations (Fig. S2).
Fig. S2.
Distribution of DNA origami supports. (A) Overview image of the holes as seen when collecting data and choosing a hole to take an image in. (B) Red area shown in A. (C) Blue area shown in B. (DF) Sample micrograph showing good distribution of particles where the ice is the right thickness, random orientations where it gets too thick, and no particles where it gets too thin.
Subsequently, we made cryo-EM grids of the support structure together with p53. We made five different samples, each with a different position of the p53 binding sequence on the tilt axis. As hypothesized, we were able to use the same blotting conditions as for the empty support structures, although the grids exhibited fewer areas with the optimal, near-crystalline arrangements of the support structures. Nevertheless, we could use the observation that support structures preferentially adopt top-bottom views in regions with the desired ice thickness to select suitable areas for data acquisition from relatively low-magnification overviews in the microscope (Fig. S2). Data acquisition was performed for each tilt axis setting separately on an FEI Titan Krios microscope at 300 kV with a K2 Summit detector (Table 1).
Table 1.
Data acquisition statistics for each tilt axis setting
Tilt axis setting, bpMicrographsAutopicked particlesIntact top view supports (mirrored)Empty supportsSupports with only DNASupports with p53-like densityp53 in expected orientationp53 in 2D classes used for initial-model calculation
−260469,21436,727 (19,672)11,01815,43910,2705,8212,363
−141551,83040,824 (26,886)15,91617,0827,8264,8312,197
058261,52841,131 (19,122)18,45216,5876,0923,3731,375
+157665,94239,303 (19,526)14,42415,6659,2145,1691,795
+238524,40019,302 (8,988)6,3197,4125,5713,5041,541
Total2,562272,914177,287 (94,194)66,12972,18538,97322,6989,271

The 2D Image Preprocessing.

The incorporation of the target protein inside a larger support structure not only provides experimental information about its orientation, it also facilitates the selection of individual particles from the micrographs. We use the template-based particle selection procedure in RELION for this process (43). By using a 2D template structure that corresponds to the top view of our support structure (Fig. 2 A, Inset), we automatically selected 272,914 particles from the five different experiments and combined all of these particles into a single dataset.
To remove inadvertently picked side views, broken support structures, or other false positives (Fig. 2C) from the dataset, we selected 177,287 particles in three rounds of reference-free 2D classification. Because the support structure may adopt either a top-up or a bottom-up orientation in the ice, we applied a mirror operation to the 53% of these particles that contributed to 2D class averages with a flag on the top right side of the support structure (Fig. 2D and Table 1). The resulting set of intact support structures was aligned to a common reference (Fig. 2E), to allow extraction of the central part of the individual particle images in a smaller box. An additional round of reference-free 2D classification was used to discard particles for which no obvious p53 density was visible (Fig. 2F). In this calculation, we used a prior probability, or prior in short, on the in-plane rotations. In the empirical Bayesian approach to image processing in RELION (44), prior probabilities on orientational parameters are expressed as Gaussian functions centered on the expected value for that orientation and with a SD that expresses the uncertainty in that expectation. In this case, we centered the prior on the in-plane rotations from the initial 2D alignment of the entire support structure and used a SD of 15° to allow for errors in those assignments. At this point, 36,837 particle images were selected. A final round of particle selection was based on identifying particles that rotated too much in the p53-only realignment with respect to the support structure (Fig. 2G) and led to a final dataset of 22,698 subimages containing p53.

The 3D Reconstruction.

The 2D alignment parameters of the support structures, combined with the known setting of the tilt axis, provide information about the five parameters that define the orientation of every p53 complex. By using only 2D alignment parameters, one can therefore calculate a tomographic 3D reconstruction of p53. The two in-plane translations of the support structure, combined with the known offset of the binding sequence on the tilt axis, can be used to center the target protein. In addition, three Euler angles describe the relative orientations of all p53 particles with respect to a common 3D frame of reference. By using a reference with the dsDNA tilt axis along the y axis, the first Euler angle (“rot” in RELION) is expected to be ∼0°, because the tilt axis is designed to run perpendicular to the top–bottom axis of the support structure. The second Euler angle (“tilt”) is determined by the tilt-axis setting of the experiment. By defining the tilt angle for our central tilt axis position (0 bp) as 90°, the 1- or 2-bp shifted positions of the binding sequence on either side have expected tilt angles of 21.4° (−2 bp), 55.7° (−1 bp), 124.3° (+1 bp), and 158.6° (+2 bp). Finally, the third Euler angle (“psi”) is directly available from the in-plane rotation determined in the 2D alignment of the support structure.
To calculate the initial tomographic p53 reconstruction, we first split the selected 22,698 subimages by their original tilt axis settings and performed five separate 2D classifications using a prior on the in-plane rotations of 15°. From these five runs, we selected 35 total 2D class averages with the best protein-like features (Fig. 3A). We then used the expected Euler angles as defined above to perform a tomographic reconstruction, directly from the 2D class averages. A preliminary map without symmetry (Fig. 3B) indicated the presence of C2 symmetry, which was subsequently imposed (Fig. 3C). Apart from the twofold rotational symmetry, the reconstructed map also showed local translational pseudosymmetry along the dsDNA axis.
Fig. 3.
Initial model generation and final maps. (A) Class averages used for the initial model generation sorted by tilt axis setting. An illustration of the designed orientation is shown on the left, and the applied tilt angle is shown in front of the class averages. (B) Initial model reconstructed with the expected tilt angles in C1. The angles for rot and psi were all set to 0°. In RELION, the first rotation of the 3D reference object (rot) is around the z axis, which comes out of the xy plane of the figure; the second rotation (tilt) is around the new y axis, and the third rotation (psi) is around the new z axis. B, Inset shows a simplified explanation of these rotations from the point of view of the experimental particles. In that case, psi is the in-plane rotation, tilt is the rotation around the central DNA axis, and rot describes out-of-plane rocking. (C) The same model as in B with C2 symmetry imposed. The twofold symmetry axis is along the z axis and is indicated with an oval. (D) Map after realignment of the class averages with 5° priors on the rot and psi angles and unrestricted tilt angles. (E) Different views of the final map generated from 9,271 particles. The estimated resolution is ∼15 Å. Protein Data Bank model 4HJE (35) of the DBD in blue. (F) Histogram of the refined tilt angles for each of the tilt axis settings.
The observations that particles from different tilt-axis settings gave rise to similar 2D class averages, and that different 2D class averages were observed within a single tilt-axis dataset, indicate that the tilt axis is probably more flexible than anticipated. In principle, the statistical framework of RELION is well-suited to model deviations from the expected orientations of each individual particle through the use of Gaussian priors on the translations and the Euler angles. The SD of the Gaussian priors can be used to tune the amount of expected deviations. Larger deviations from the expected orientations (e.g., because the attachment of the target protein to the DNA-origami structure is more flexible than anticipated) will lead to less-informative priors. To some extent, deviations from the anticipated tilt angle are actually beneficial to the reconstruction process, because they will lead to a more uniform angular sampling along the tomographic tilt axis.
Therefore, we first performed a rotational and translational realignment of the 35 selected 2D class averages, where we used a prior with a SD of 10° on rot and psi, but tilt was left free. The resulting reconstruction (Fig. 3D) was then used as an initial model in a set of 3D refinements, where we used the 9,271 p53 particles that were assigned to the selected 2D classes. Without using rotational priors on any of the Euler angles, or when using only a prior on rot or psi, the reconstructions looked worse (e.g., density for the dsDNA disappeared) than when using a 5° prior on both rot and psi. As expected from the observation that our tilt axis is more flexible than anticipated, imposing a prior on tilt made the reconstruction worse (Fig. S3). When using progressively less informative (i.e., broader, priors on rot and psi), the reconstructions also became worse (Fig. S4), whereas priors with SDs <5° are too narrow for the angular sampling rate used in the refinement. Consequently, our best refinement had a SD of 5° for the priors on psi and rot and left the tilt angle unrestrained. The resulting map, at an estimated resolution of ∼15 Å, shows the same domain architecture as the known crystal structure of the DBD of p53 bound to dsDNA (35) and shows additional densities in both the front and the back of the complex (Fig. 3E). The histogram of the refined tilt angles confirms that the tilt axis is more flexible than anticipated. Some information in the tilt angle is maintained, but angles ∼90° are somehow disfavored (Fig. 3F).
Fig. S3.
Refinements with different prior schemes. Front and back views are shown of reconstructions from refinements where no priors were imposed on any angles (first column), 5° priors were imposed on either psi or rot (second and third columns, respectively), on both rot and psi (fourth column), or on both rot and psi combined with a 15° prior on tilt (fifth column). For each refinement, histograms of the resulting tilt, rot, and psi angles are shown for each of the tilt-axis settings (blue, −2 bp; cyan, −1 bp; green, 0 bp; orange, +1 bp; red, +2 bp).
Fig. S4.
Refinements with progressively loose priors on psi and rot. Front and back views are shown of reconstructions from refinements where progressively less informative (i.e., broader) priors were imposed on the rot and psi angles. The tilt angles were unrestrained in all refinements.


We present an approach to sample preparation for cryo-EM structure determination of biological macromolecules. Using 3D DNA-origami, we designed a support structure with a defined size and shape that binds specifically to a target protein of interest. The resulting structure is an artificial molecular support that exerts experimental control over the orientations of individual protein molecules and protects them from aggregation or harmful interactions with the air–water interface. In addition, the support structure may facilitate the optimization of freezing conditions and aid in the selection of suitable ice thickness for data acquisition. Nevertheless, as discussed below, this work does not yet represent a ready-to-use solution for high-resolution structure determination of a wide range of different target proteins, but should rather be considered as a proof-of-principle toward achieving this ambitious goal.
By choosing a target protein that naturally binds to dsDNA in a sequence-specific manner, our support structure was designed to act as the nanoscale equivalent of a goniometer that exerts experimental control over the orientations of individual protein complexes. By using five different positions of the p53 binding sequence on a central dsDNA helix in our structure, our structure intended to provide five different views of a p53 tetramer bound to the support structure. Although the prior information about the five different orientations could indeed be used successfully to calculate an initial tomographic reconstruction of the p53 tetramer bound to DNA, further refinement of the orientations of 2D class averages or individual particle images revealed a distribution in tilt angles that is much broader than one would expect from a rigid dsDNA helix, and somehow seems to disfavor tilt angles ∼90°. Because the complete structure of the p53 tetramer remains unknown, it could be that the support structure is too small to accommodate p53 in this orientation. Analysis of multiple 2D class average images from our data (Movie S2) revealed that the support structures are also not as rigid as one might need to exert precise orientation control. Moreover, despite previous attempts at optimizing the incorporation of the central dsDNA helix in the support structure (45), it could be that our current design (Figs. S5 and S6) still leads to incorrect attachments in a subset of the structures. Nevertheless, despite the lack of complete control over the tilt angle, the support structure still provided information about the center of the particle, its in-plane rotation (the psi angle), and its out-of-plane rocking (the rot angle), and this information could be used in statistical priors to improve the 3D reconstruction. The intended design of our support structure as a molecular goniometer that provides experimental information about the orientations of individual p53 complexes was, therefore, partially successful.
Fig. S5.
Attachment of the dsDNA tilt-axis in the support structure. Blue shows the scaffold and gray/red the tilt axis. The binding site for p53 is shown in red (central tilt axis). The sequences for the tilt axis with the binding site in the center are: 1, ATCGCGCACCAGACGACTGGGCCTCAGTGTCGGACATGTCCGGACATGTCCGAGCATGAGGCGGGCGAGCACTCCCCGCCTC; 2, CCGTTCCCGCCAGGGTTGGGGCCTCATGCTCGGACATGTCCGGACATGTCCGACACTGAGGCAGCGTCAGAATGATATTAAT; 3, …ATTAATA…TCATTCT…GACGCTATCCAGTC…GTCTGGT…GCGCGAT….; 4, …GAGGCGG…GGAGTGC…TCGCCCTTCCCAAC…CCTGGCG…GGAACGG…
Fig. S6.
Cadnano design diagram of the support structure. Scaffold strand is depicted with blue lines. Staples are colored by purpose. Gray, core structure oligos; cyan, 10xT passivation oligos; orange/red, tilt axis oligos; black, outside loops.
Our experiments also revealed several challenges that will need to be overcome in future studies. First of all, the final number of 9,271 selected p53 particle images represents a low yield from 2,562 selected micrographs. Approximately 42% of our support structures did not incorporate the tilt axis, and 77% of the remaining support structures did not bind to p53. The latter was surprising, because at a concentration of 1 mM p53 tetramers and 150 nM support structures, and with a dissociation constant of ∼50 nM for dsDNA, we had expected almost all support structures to contain p53. Possibly, steric hindrance from the support or strain in the central dsDNA affected the binding of p53. A further reduction in particle number came from our image-selection procedures: 38% of the particles that contained density for p53 had it bound in an unrealistic orientation in respect to the support structure, and from these only 41% contributed to the selected 2D classes of the subimages that showed the best protein-like features. Second, although the low particle yield may have hampered reaching higher resolution, the selected particles still represented >18,000 asymmetric units, which should probably have yielded a higher resolution reconstruction (cf. ref. 46). One possibility could be that the central cavity of the support structure was too small for the defocus used. Delocalization in the images because of defocusing may have led to a superposition of the signal from the support structure and the target protein, which may have limited the alignment of the individual target proteins, and therefore the resolution of the final reconstruction. This problem could in principle be circumvented by in-focus imaging through phase-plate technology (47). In addition, future designs of larger, more stable support structures with a more accessible protein binding site, and with better incorporation of the tilt axis may improve both particle yield and resolution. To create these larger supports with low defect rates, new assembly and purification strategies will need to be considered and adopted (see, for example, refs. 48 and 49).
Nevertheless, at a resolution of ∼15 Å, the resulting reconstruction provides useful information about how p53 binds to dsDNA. The observation that our reconstruction shows C2 symmetry with an additional twofold translational component along the DNA axis, which is similar to the crystal structure of the tetrameric core of p53 bound to dsDNA, indicates that p53 does not bind to DNA in a previously proposed arrangement with D2 symmetry (38, 39). Instead, the presence of extra density in the front and back of the complex (Fig. 3E) provides support for an alternative model where the p53 tetramerization domain binds to the opposite side of the dsDNA helix from the DBD (36).
The design of our support structure also contains several useful features that may inspire future experiments. By enclosing the target protein in a hollow structure, it is protected from interactions with copies of itself or with the hydrophobic air–water interface during cryo-EM sample preparation. This protection may prevent the proteins from aggregation, or from unfolding or adopting preferred orientations against this air–water interface. Also, it might be that the optimization of freezing conditions will depend more on the support structure than on the nature of the protein inside, so that the optimal conditions would differ less for different proteins compared with current methods. We indeed used similar conditions for the samples with only support structures and the mixture with p53, although the mixture showed fewer regions of near-crystalline arrangements of the support structures. We used approximately six times more p53 tetramers than support structure in our mixture and did not attempt to remove unbound p53 tetramers through additional purification steps. It could therefore be that interactions of unbound p53 tetramers with the air–water interface still had an effect on the freezing conditions. In addition, the observation that the support structures adopt monolayers in an ice layer with the intended thickness is potentially useful. One might, for example, try to add support structures to standard cryo-EM samples with the idea of using them as “spacers” (13) to control ice thickness. Admittedly, it remains difficult to conclude whether the support structures maintain the desired ice thickness over a given area or whether the monolayers just happen to form in those areas where the ice thickness is ideal. However, because one can easily select areas with monolayers of support structures from low-magnification overview images in the microscope, the support structures do facilitate the data acquisition process. The interactions of our support structure with the air–water interface may also have a beneficial effect on the local particle concentration. For a 300-Å-thick layer of a 150 nM solution, one would expect only a single support structure in every micrograph. Our observation of ∼100 structures per micrograph suggests that interactions of the support structure with the air–water interface, possibly both at the top and the bottom, may lead to a strong local enrichment in concentration. For proteins, a similar enrichment has been attributed to a sticky layer of denatured protein at the air–water interface (15). Because our samples without protein show a similar—or even stronger—enrichment in DNA support structures, unbound p53 tetramers are probably not required for the enrichment. It is difficult to assess whether the interactions with the air–water interface lead to unfolding of the support structures themselves. We did observe partial structures (Fig. 2 A and C), but these could also be explained by folding defects. It therefore remains unclear whether the presence of denatured DNA material at the air–water interface is important for the enrichment effect.
The experiments described here rely on the specific binding of a target protein that naturally binds to dsDNA in a sequence-specific manner. Although several transcription factors are known to do so, the design of a more broadly applicable support structure would be desirable. It would be relatively straightforward to include a DNA mismatch in the central axis, which could include target proteins from DNA-mismatch repair pathways. Alternatively, one could construct a central axis with a bubble or from DNA/RNA hybrids to include a range of different target proteins related to replication, transcription, or DNA repair. However, to design a support that could bind to an arbitrary target protein, one would probably need to combine chemical modifications of some of the DNA staples within the support with a specific tag on the target protein. For example, one could use commercially available biotin-labeled DNA on the support structure to bind a counterpart to a protein tag attached to a monovalent streptavidin (50, 51). For the study of membrane proteins, one might even consider designs where nanodiscs (52) are attached to DNA-origami support structures or where these structures interact directly with patches of membranes (53). Such more generally applicable designs probably would retain even less control over the orientation of the target protein than the design described in this work. Still, the support structure could maintain some of its other useful features, such as facilitating particle selection, preventing aggregation, and keeping proteins away from harmful interfaces. Apart from the beneficial effects on cryo-EM sample preparation, large support structures that specifically bind a target protein of interest might even play a role in protein purification. Provided that the binding of the target protein would be tight enough, their large molecular mass (∼5 MDa) would allow relatively straightforward separation of the target protein from smaller contaminants through the use of, for example, gel filtration of sucrose gradients. Alternatively, one could try to fix the DNA supports directly on the grid surface and use the grids themselves for on-grid affinity purification in a similar manner as was done with nitrilotriacetic acid-modified lipid monolayers (54, 55) or antibodies attached to carbon films (56, 57).
In summary, this work provides an original approach to exert experimental control over the orientations of individual protein complexes and protect them from harmful forces during cryo-EM sample preparation. As the field of cryo-EM structure determination keeps growing, the physics involved in its sample preparation will become clearer, and new concepts in sample preparation will continue to emerge. Our approach provides ample possibilities for further developments and may thereby contribute to tackling some of the outstanding challenges in this rapidly changing technique.

Materials and Methods

Origami Design, Synthesis, and Purification.

DNA-origami design (Dataset S1) was performed in cadnano (Version 0.2) (58). The scaffold DNA (the 7,560-nt-long version of the M13mp18 phage genome) was prepared as described (59). DNA staple oligonucleotides, prepared by solid-phase chemical synthesis, were ordered from Eurofins MWG. The DNA oligos for the tilt axis were ordered to HPLC-grade purity; all other oligos (Table S1) were ordered to high-purity, salt-free grade. The supports containing different tilt-axis settings were synthesized individually in one-pot mixtures containing 50 nM scaffold DNA, 75 nM tilt axis DNA, and 200 nM staple DNA in a 10 mM Tris buffer at pH 7.6 with 1 mM EDTA and 20 mM MgCl2. The mixture was incubated at 65 °C for 15 min, then annealed from 60 °C to 45 °C over the course of 8 h, and stored at 25 °C. Purification from excess staple oligos was performed in five rounds of molecular mass cutoff filtration by using 100-kDa Amicon filters (Millipore) in a buffer containing 20 mM Tris base and 5 mM MgCl2. The final concentration of the support structures was adjusted to ∼250 nM.

P53 Expression and Purification.

Production and purification of truncated p53 variant (residues 1–360) lacking the C-terminal regulatory domain followed published protocols (28, 60). Briefly, the proteins were produced in Escherichia coli BL21(DE3) as a fusion protein with N-terminal 6× His-tag, Bacillus stearothermophilus lipoyl domain, and tobacco etch virus protease cleavage site. They were then purified by using standard His-tag purification protocols, followed by tobacco etch virus protease cleavage, heparin affinity chromatography, and a final gel filtration step on a Superdex 200 16/60 preparative gel filtration column (GE Healthcare) in 300 mM NaCl, 20 mM Tris (pH 7.5), and 5 mM DTT. Protein samples were flash-frozen in liquid nitrogen and stored at −80 °C. The variant contained four stabilizing mutations, M133L/V203A/N239Y/N268D (28, 61, 62), in the DBD.
The purified p53 sample was mixed with the purified DNA-origami sample to final concentrations of ∼150 nM DNA origami and ∼4 µM p53 (monomer) in a 20 mM Tris buffer containing 1.5 µM DTT, 45 mM NaCl, and 4 mM MgCl2. The mixture was incubated for 20 min at 4 °C before preparing cryo-EM grids.

Electron Microscopy.

Cryo-EM grids for the support structures alone or for the support structures with p53 and the five different settings of the tilt axis were prepared separately using similar procedures. Aliquots of 3 µL of sample were incubated for 10 s on glow-discharged Quantifoil grids, blotted for 2 s, and plunge-frozen in liquid ethane by using a Vitrobot Mark 3 (FEI Company). Grids were transferred to an FEI Titan Krios microscope that was operated at 300 kV. Images were recorded on a K2 detector using a Gatan energy filter (with a slit width of 20 eV). Movies with a total electron dose of ∼38 e2 were recorded in superresolution mode and subsequently downscaled to a final pixel size of 1.76 Å in RELION. The defocus was varied between 1 and 5 μm. Cryo-electron tomography data were collected by using described procedures (63).
Electron micrographs were manually evaluated for astigmatism and drift, and 2,562 micrographs were selected for further analysis. Beam-induced motion correction was performed in UCSF MOTIONCORR (64); estimation of contrast transfer function parameter was performed in Gctf (65); and all subsequent image-processing operations were performed in RELION-1.4 (44).

Data Availability

Data deposition: The p53 reconstruction reported in this paper has been deposited in the Electron Microscopy Data Bank, (accession no. 3453).


We thank Christos Savva, Shaoxia Chen, Toby Darling, and Jake Grimmett for technical support, and Miriana Petrovich for protein purification. This work was supported by the European Molecular Biology Organisation through a long-term postdoctoral fellowship (ALTF-1229-2013 to T.G.M.) and an advanced fellowship (aALTF-778-2015 to T.A.M.B.), and by European Commission Marie Skłodowska-Curie postdoctoral fellowships (to T.G.M. and X.-c.B.). The project was further supported by European Research Council Starting Grant GA 256270 (to H.D.); by the Deutsche Forschungsgemeinschaft through grants provided within the Sonderforschungsbereich SFB863, the Center for Integrated Protein Science Munich, and the Nano Initiative Munich; and UK Medical Research Council Grants MC_UP_A024_1010 (to A.R.F.) and MC_UP_A025_1013 (to S.H.W.S.).

Supporting Information

Supporting Information (PDF)
Supporting Information


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Information & Authors


Published in

Go to Proceedings of the National Academy of Sciences
Go to Proceedings of the National Academy of Sciences
Proceedings of the National Academy of Sciences
Vol. 113 | No. 47
November 22, 2016
PubMed: 27821763


Data Availability

Data deposition: The p53 reconstruction reported in this paper has been deposited in the Electron Microscopy Data Bank, (accession no. 3453).

Submission history

Published online: November 7, 2016
Published in issue: November 22, 2016


  1. cryo-EM
  2. DNA-origami
  3. single particle analysis
  4. structural biology
  5. p53


We thank Christos Savva, Shaoxia Chen, Toby Darling, and Jake Grimmett for technical support, and Miriana Petrovich for protein purification. This work was supported by the European Molecular Biology Organisation through a long-term postdoctoral fellowship (ALTF-1229-2013 to T.G.M.) and an advanced fellowship (aALTF-778-2015 to T.A.M.B.), and by European Commission Marie Skłodowska-Curie postdoctoral fellowships (to T.G.M. and X.-c.B.). The project was further supported by European Research Council Starting Grant GA 256270 (to H.D.); by the Deutsche Forschungsgemeinschaft through grants provided within the Sonderforschungsbereich SFB863, the Center for Integrated Protein Science Munich, and the Nano Initiative Munich; and UK Medical Research Council Grants MC_UP_A024_1010 (to A.R.F.) and MC_UP_A025_1013 (to S.H.W.S.).


This article is a PNAS Direct Submission.



Thomas G. Martin
Medical Research Council Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge CB2 0QH, United Kingdom;
Tanmay A. M. Bharat
Medical Research Council Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge CB2 0QH, United Kingdom;
Sir William Dunn School of Pathology, University of Oxford, Oxford OX1 3RE, United Kingdom;
Andreas C. Joerger
Medical Research Council Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge CB2 0QH, United Kingdom;
German Cancer Consortium (DKTK), Institute of Pharmaceutical Chemistry, Johann Wolfgang Goethe University, 60438 Frankfurt am Main, Germany;
Xiao-chen Bai
Medical Research Council Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge CB2 0QH, United Kingdom;
Florian Praetorius
Physik Department, Walter Schottky Institute, Technische Universität München, 85748 Garching near Munich, Germany
Alan R. Fersht
Medical Research Council Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge CB2 0QH, United Kingdom;
Hendrik Dietz1 [email protected]
Physik Department, Walter Schottky Institute, Technische Universität München, 85748 Garching near Munich, Germany
Medical Research Council Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge CB2 0QH, United Kingdom;


To whom correspondence may be addressed. Email: [email protected] or [email protected].
Author contributions: T.G.M., A.R.F., H.D., and S.H.W.S. designed research; T.G.M., T.A.M.B., and X.-c.B. performed research; A.C.J. and F.P. contributed new reagents/analytic tools; T.G.M., T.A.M.B., X.-c.B., and S.H.W.S. analyzed data; and T.G.M., A.C.J., A.R.F., H.D., and S.H.W.S. wrote the paper.

Competing Interests

The authors declare no conflict of interest.

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    Design of a molecular support for cryo-EM structure determination
    Proceedings of the National Academy of Sciences
    • Vol. 113
    • No. 47
    • pp. 13257-E7644







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