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Single‐Particle Reconstruction of Biological Molecules—Story in a Sample (Nobel Lecture)

2018; Wiley; Volume: 57; Issue: 34 Linguagem: Inglês

10.1002/anie.201802770

1521-3773

Joachim Frank,

RNA modifications and cancer

Pictures tell a thousand words: The development of single-particle cryo-electron microscopy set the stage for high-resolution structure determination of biological molecules. In his Nobel lecture, J. Frank describes the ground-breaking discoveries that have enabled the development of cryo-EM. The method has taken biochemistry into a new era. I developed an interest in electron optics when I worked with Ernst Kinder on my master thesis project in Physics at the University of Munich. The subject of my thesis was backscattering of electrons on the surface of liquid gold, an ambitious undertaking that forced me to construct a vacuum chamber, a crucible to heat up the gold, a detector, and an electron gun. In 1943, working with the electron microscope, Kinder had studied butterfly wings, which as he realized gained their brilliant colors from interference of light on gratings formed by tiny scales, arranged in regular order. I signed on to a graduate project with Walter Hoppe at the Max-Planck Institute in Munich (Figure 1), an X-ray crystallographer whose interest had turned to electron microscopy (EM) as a means to study biomolecules. He viewed the electron microscope as a diffractometer that, unlike the one employed in X-ray crystallography, could record not just amplitudes of diffracted electrons, but their phases, as well. This was a fancy way of saying electron microscopes were able to form images. Walter Hoppe (1917–1986) with the Siemens Elmiskope 102. Photograph from archives of the Max-Planck Society, Berlin. It is necessary at this point to look back at the state of the art of molecular EM in the late 60s and early 70s. For the beginning years, from the 1930s to the 1950s, the contributions of EM to biology had been confined mainly to the investigation of tissue at relatively low magnification. Serious forays into the quantitative visualization of molecular structure did not commence until the 1960s and were concentrated in three groups: Aaron Klug's at the Laboratory of Molecular Biology of the MRC in Cambridge, my mentor Walter Hoppe's at the Max-Planck Institute in Munich and Edward Kellenberger's at the Biozentrum in Basel. Unless symmetries are present, three-dimensional reconstruction of an object requires the combination of its projections from a wide angular range. First pioneering achievements in molecular structure research with the electron microscope were the three-dimensional (3D) reconstruction of the bacteriophage tail with helical symmetry in 1968 by DeRosier and Klug1 and the first reconstruction of an icosahedral virus in 1970 by Tony Crowther.2, 3 At that time biological molecules could not be imaged in a close to native state. Negative staining—which amounts to embedding the molecule in a puddle of heavy metal salt as it is air-dried from solution—was the only means available to produce contrast. On the other hand, biological molecules were known to be quite fragile, and maintenance of their integrity would require a fully hydrated environment. As I started my work as graduate student under Walter Hoppe, in 1967, I was exposed to discussions in a Workshop in Hirschegg in the Tyrol Alps, co-organized by Walter Hoppe and Max Perutz in 1968, later to be continued in meetings in Hirschegg in 1970 and Alpbach in 1976. These were the first meetings that brought together protein crystallographers and people working in EM.4 In my thesis project I analyzed electron micrographs with the optical diffractometer and explained patterns observed in case of drift in terms of Thon rings5 modulated by a cosine function, visible as so-called Young's fringes.6 I also examined the statistical properties of digitized micrographs. For digitization I used a densitometer built in house, which rendered the images on punched tape to be fed into the computer. In my first applications of digital image processing in EM, I explored the use of correlation functions for alignment of images.7 Another topic of my dissertation was the distortion of information by the contrast transfer function (CTF), caused by the lens aberrations of the electron microscope8 and its recovery by CTF correction.9 After finishing my Ph.D., in 1970, I went to the United States for two years under a Harkness Fellowship. The visit to three labs I chose was an eye-opener in several regards. The Jet Propulsion Lab (JPL) in Pasadena, at the time, was arguably the most advanced place in image processing hardware and software. In a project aimed to correct the contrast transfer function from a defocus series, I used their scanner to digitize micrographs of negatively stained DNA that were given to me by Walter Stoeckenius at UCSF, and I adapted my programs to interface with JPL's VICAR system. VICAR, used to process images from the Jupiter fly-by mission, was a modular image processing system that would later serve as a model for the development of SPIDER. The second lab I visited was the Donner Lab in Berkeley, where Robert M. Glaeser studied the effects of radiation damage on biological molecules under the electron beam.10 He also started developing techniques to render molecules frozen-hydrated in the EM.11 The third lab was Benjamin Siegel's at Clark Hall, Cornell University, where an experimental microscope in the mid-voltage (600 kV) range was being built. It was here that I first met Ken Downing, who worked on optical methods of information retrieval such as single-side band holography, and William Goldfarb, who would later join me in Albany. The numerous problems faced by people attempting to image biological molecules in the EM were discussed at a Workshop organized by Edward Kellenberger in Gais, in the Swiss Alps in 1973. The state of the art at the time was reflected in the title of a proceedings paper12 as "high resolution" was equated with any results at better than 30 Å. Paramount at the Workshop was the search for a method that would keep the molecule fully hydrated while exposed to the electron beam. In addition, following the pioneering studies by Glaeser10—just at the time I visited his lab as a Harkness Fellow—radiation damage was recognized as a major obstacle in the strife toward high resolution. Averaging over a large number of repeats of a structure exposed to very low dose was seen as a general solution to this problem. Thus this meeting set the stage for a ground-breaking study by Richard Henderson and Nigel Unwin:13 the reconstruction of bacteriodhopsin from the purple membrane of Halobacter embedded in glucose under near-native conditions. The confluence of novel approaches to three areas, namely sample preparation, data collection at extremely low electron dose ( 3/[Contrast2 × Resolution (as length) × Critical Electron Dose] A detailed examination of this dependency was later to be undertaken by Richard Henderson.19b My appointment in 1975 as Senior Research Scientist at the Division of Laboratories and Research of the New York State Department of Health (DLR, later named Wadsworth Center) in Albany, New York offered me the opportunity to explore this idea with practical applications. (I had been asked to start an image processing group at DLR by Donald Parsons, a Roswell Park, Buffalo research scientist who was in the process of moving the Albany and setting up a high-voltage EM facility there). With the help of micrographs provided by David Eisenberg, Tim Baker, Peter Zingsheim and Miloslav Boublik I was able to demonstrate the feasibility of obtaining two-dimensional averages showing enhanced features of molecules with images of glutamine synthetase (Figure 5),18, 20 acetylcholine receptor (Figure 6),21 and 40S ribosomal subunits from HeLa cells (Figure 7).22 Single-particle averages obtained from images of negatively stained glutamine synthetase. Left: gallery of particles selected from the micrograph and aligned. Right: averages with and without six-fold symmetrization. (Reproduced from Ref. 20.) Single-particle averages obtained from images of negatively stained acetylcholine receptor of Torpedo marmorata. Top: examples for images selected from micrographs. Bottom: two half-averages and one full average (right). The average shows distinct departure from 5-fold symmetry deduced from low-resolution 2D crystals averages by other groups. (Reproduced with permission from Ref. 21.) Single-particle averages obtained from images of 40S ribosomal subunits of HeLa cells. Top: micrograph showing 40S subunits in two views, left-facing (L) and right-facing (R). Bottom, from left to right: two half-averages, variance map, and full average of 81 L-view particles. (Reproduced with permission from Ref. 22.) Among these, the 40S subunit averages were arguably the most striking in showing the potential of the single-particle averaging technique, results that proved instrumental for gaining funding by the National Institutes of Health. Nonetheless, presentations of the results for glutamine synthetase, acetylcholine receptor and ribosome by myself and two of my collaborators, Martin Kessel and Peter Zingsheim, at the meeting organized by Wolfgang Baumeister in Burg Gemen (1979) were greeted with a great deal of skepticism. One issue to be addressed, as mentioned before, was the fact that due to the absence of crystal order, the average of aligned molecule images shows no diffraction spots in its Fourier transform, and therefore lacks an inherent measure of resolution. Without such a measure, progress in quality could not be tracked and compared among different groups. From the earlier study, during my dissertation, on the effects of drift on an electron micrograph,6 I realized that signal bandwidth is reflected by the extent of reproducible information in Fourier space.23 This extent of reproducible information is apparent from the extent of Young's fringes that show up in the optical diffraction pattern when two successive micrographs of the same specimen field are superimposed with a slight shift (Figure 8). Reproducibility of the signal content in two successive electron micrographs of carbon film, demonstrated for three different defocus settings. Upper row: optical diffraction pattern of one of the micrographs, showing Thon patterns. Lower row: Young's fringes obtained by first aligning the micrograph pairs and then translating them relative to each other by a slight amount. (Reproduced from Ref. 17.) How could this idea be translated into a quantitative measure? The extent of reproducibility in Fourier space can be quantified computationally by dividing the data going into an average randomly in half, then comparing the Fourier transforms of half-averages over rings in Fourier space. Resolution is then defined as the Fourier ring radius where a measure of comparison, such as phase residual, or R-factor,22 or cross-correlation ("Fourier ring correlation"),24, 25 passes a critical threshold (Figure 9). The same measures, computed over shells, would later prove important in estimating resolution of 3D reconstructions, as well.26 Resolution of single-particle averages defined by reproducibility of half-averages in Fourier space. Shown is the differential phase residual as a function of Fourier ring radius for the half-averages of 40S ribosomal subunits of HeLa cells. Resolution is then defined by the ring radius where the phase residual first exceeds 45°. (Reproduced with permission from Ref. 22.) These first studies of image averaging immediately brought up the problem of heterogeneity—only those molecule images could be reasonably combined in an average if they originated from molecules of identical structure and presented the same view. At that time, one of Ernst van Bruggen's students, Marin van Heel, visited my lab bringing with him images of Limulus polyphemus hemocyanin—an oligomer with distinct architecture showing multiple preferred views when negatively stained and imaged in the electron microscope (Figure 10 a). These images therefore presented a perfect example of heterogeneity. Before attempting to average those images, they had to be sorted, or classified into their subsets. The solution to this problem27 came from the insight that images, once aligned with one another, may be regarded as vectors in a space of N dimensions, where N is the number of pixels. Groups of images that are similar will then show up as clusters of vectors in that space. Equivalent problems of finding clusters in high-dimensional space had been encountered in many fields of science, and gave rise to multivariate statistical analysis, a procedure which determines a compact low-dimensional subspace tailored to the problem. With the help of Jean-Pierre Bretaudiere, a Wadsworth Center scientist working in Laboratory Medicine, we were able to use a program meant to sort blood samples to sort images instead (see Ref. 28, where this episode is recounted). Application to hemocyanin proved an immediate success (Figure 10 b,c). Sorting of hemocyanin images by Correspondence Analysis, a branch of multivariate statistical analysis. Top: Makeup of the dodecameric molecule of Limulus polyphemus hemocyanin. The slightly rhombic, twisted arrangement of the subunits creates a nonplanar architecture, reflected by the rocking of the molecule on the grid. Middle: Micrograph of negatively stained molecules showing them in different three-dimensional positions, related by flipping and rocking. Bottom: Factorial map, obtained by multivariate data analysis of the aligned molecule images, separates the images into four clusters. (Reproduced with permission from Ref. 27.) Early on, as I set out on the single-particle approach to recovering structure, it became clear to me that in order to make systematic progress in the development of algorithms and computer programs with ever-changing and expanding goals required a workbench with a large set of tools. To this end I developed a modular image processing system called SPIDER (for System for Processing of Image Data in Electron microscopy and Related fields),20, 29 which made it possible to design complex programs from pre-coded building blocks using a simple script language. For example, the command WI would invoke a routine for extracting a rectangular portion of an image, FT would invoke Fourier transformation, and AC would compute the autocorrelation of an image. Hundreds of commands were implemented over the course of the next few years. All programs were coded in FORTRAN, the most advanced language at the time. In most of the initial programming I was assisted by Helen Dowse, a SUNY Albany student of Computer Science, and Brian Shimkin, an undergraduate. As the functionality of SPIDER expanded, its script language became literally the lingua franca in my lab and, as the suite was disseminated to other labs, within a growing community of users. As noted earlier, I trace the idea underlying the SPIDER system and its modular design back to my stay at the Jet Propulsion Lab in 1970 under the Harkness Fellowship, where I became familiar with JPL's own VICAR image processing system. For computing the 3D structure of an object from its projections, one requires a fairly even coverage of the whole view range, and the angles of each projection must be known. Thus, in the single-particle approach, determination of the angles of randomly oriented molecules recorded in a micrograph was the most important yet most difficult problem to be solved. The solution came from the insight that two micrographs, one of a field of untilted particles, one of the same field tilted by a large angle, contained all the information required to assign Eulerian angles to each tilted particle.20, 30, 31 In this geometry (Figure 11 a), the angles of the tilted projections lie on a cone with random azimuths (Figure 11 b), a feature which would later give rise to the term "random-conical tilt reconstruction." Random-conical tilt data collection geometry. Top: Concept. Untilted grid is shown with molecules attached with the same face but different azimuths. Tilting of the grid by a large angle results in a unique direction of projection for each molecule. In Fourier space these correspond to intersecting central sections. (Frank, 1979; hand-drawn sketch on an overhead transparency, unpublished). Bottom: Illustration of data collection, and equivalent conical geometry. (Reproduced with permission from Ref. 32.) In 1982 I was joined by physicist Michael Radermacher, also a student of Walter Hoppe, who had worked in his dissertation project on algorithms for 3D reconstruction from projections arranged in a regular conical geometry. Thus he had the perfect background required to develop computer programs that implemented the concept of the random-conical tilt reconstruction. One important step was still missing, though: the generalization of the 3D reconstruction algorithm, which assumed regularly spaced conical tilting, to the general case of random angles. Once this had been accomplished, as reported in a short communication in 1986,30 we obtained the first single-particle reconstruction using the random-conical tilt method: the 50S subunit of the E. coli ribosome (Figure 12).33 It is now on permanent exhibit in the Nobel Museum in Stockholm in the form of a transparent contour stack mounted in a wooden frame. First single-particle reconstruction of an asymmetric molecule: the 50S subunit of the E. coli ribosome, prepared by negative staining.33a Scale bar is 100 Å. The panels depict the molecule with increasing density threshold, using a then-novel surface representation technique.33b This reconstruction was limited in quality by two factors: one was the missing cone of information in the 3D Fourier transform, the source of unidirectional artifacts in the 3D density map, and the other were the artifacts due to the preparation of the sample by air-drying and negative staining. Both limitations were readily overcome within a short period of time: the missing cone problem was solved by merging datasets obtained with three or more different zero-degree views,34 and the preparation of the sample with negative staining was replaced by cryo-embedding in vitreous ice, following the spectacular success of Jacques Dubochet's vitrification method by plunge-freezing into liquid ethane35 in the application to viruses (Figure 13).36 Schematic of single-particle data collection for molecules randomly oriented and embedded in vitreous ice. The molecules are fully hydrated in an aqueous medium and, in contrast to the preparation with negative staining and air-drying shown in Figure 2, they exhibit no shrinkage in the direction normal to the grid plane. Yet another important problem to be addressed, which affected the quality of all reconstructions from EM data, was the modulation of the image transform by the CTF. I had first worked on this problem during my dissertation, then through contributions to specific issues.37-39 This problem and its resolution-limiting effects on the reconstructed density maps were eventually overcome through the merging of data obtained with different defocus settings using a Wiener filtering algorithm (Figure 14).40, 41 Contrast transfer function correction using Wiener filtering. The Fourier transform of the corrected image is obtained by a weighted sum of Fourier transforms of the defocus series. The weights Wn are given by the Wiener filter, which is proportional to the CTF Hn weighted by the signal-to-noise ratio SNRn. (Damping due to partial coherence is not shown here for simplicity.) (Reproduced with permission from Ref. 41.) The first molecules we visualized by cryo-EM and reconstructed in three dimensions with the single-particle methods described above were the E. coli ribosome,34, 42, 43 hemocyanin,44 and calcium release channel (Figure 15).45, 46 Cryo-EM reconstructions of three molecules obtained with the matured single-particle reconstruction method: a) E. coli ribosome,43 b) Octopus hemocyanin,44 c) calcium release channel/ryanodine receptor.46 A cryo-EM reconstruction of the Haloarcula marismortui ribosome (Figure 16) proved to be helpful in the phasing of the first X-ray structure of the large ribosomal subunit.47-49 Cryo-EM reconstruction of the large ribosomal subunit from Haloarcula marismortui, used for solving a phasing ambiguity in solving the X-ray structure. (Reproduced with permission from Ref. 47.) The quality of final reconstructions benefits from iterative angular refinement that starts out with the first rough reconstruction. In any project dealing with a molecule whose structure is unknown, it is practical to make a distinction between two phases, a bootstrap phase and a refinement phase. In the bootstrap phase, a first rough reconstruction is obtained—either by the random-conical tilt method, or by an alternative method, developed by Marin van Heel and others, in which common lines in Fourier space are employed.50-53 In the refinement phase,34, 54 an existing reconstruction (i.e., the density map obtained by a bootstrap reconstruction) is used to generate a library of even-spaced projections with which each of the experimental projections is compared to assign refined angles to it for the next round of reconstruction (Figure 17). Iterative angular refinement scheme by projection matching, a scheme that underlies practically all cryo-EM reconstructions. (Reproduced with permission from Ref. 55.) The mid-90s marked the time when a methodology of 3D reconstruction in EM could first be discerned in outline, as best documented in various contributions to the Proceedings of the 15th Pfefferkorn Conference (1997). At about that time I compiled a book for the first time summarizing computational methods of single-particle 3D EM.56 From the time where the first refined, CTF-corrected reconstructions were obtained, in the mid-90s, more than 15 years had to go by before the "resolution revolution" brought us to the resolution, 3–4 Å, where atomic modeling becomes possible. The interpretation of the many low-resolution cryo-EM reconstructions obtained during that time was often dismissed as "blobology," a characterization that was unjust and unfair in most instances. In the following I would like to make this point by showing a few examples just from the area I am most familiar with—the structural basis of protein biosynthesis. These examples demonstrate that well before it reached the present state of perfection, cryo-EM gave us important insights into pivotal processes of translation by the ribosome. This happened on the level of resolution that allowed the constellations and movements of entire domains to be described. In the late 90s, one of my postdocs, Rajendra Agrawal, prepared a sample containing elongation factor G (EF-G) bound to the ribosome as it catalyzes translocation of mRNA and tRNAs.57, 58 He used a GTP analog to arrest the factor at the point where GTP hydrolysis is normally triggered. Comparison of the cryo-EM reconstruction with that of the unbound ribosome showed a dramatic change: the small subunit had rotated by 7° with respect to the large subunit (Figure 20).59, 60 This finding of the "ratchet-like" motion provided first clues on the mechanism of mRNA-tRNA translocation (Figure 18). Ratchet-like motion of the E. coli ribosome during mRNA-tRNA translocation. Upon binding of EF-G, the small subunit (yellow) is seen to rotate relative to the large subunit (blue). (Reproduced with permission from Ref. 60.) In 2002, Mikel Valle, another of my postdocs, found that during the decoding process, aminoacyl-tRNA (aa-tRNA) enters the ribosome in complex with the protein factor EF-Tu in a strongly distorted form, in the so-called A/T state (Figure 19).61, 62 In this case the antibiotic kirromycin was used to keep the factor from leaving t