Automatic particle picking from electron micrographs

Robustness of signal detection in cryo-electron microscopy via a bi-objective-function approach

Background

The detection of weak signals and selection of single particles from low-contrast micrographs of frozen hydrated biomolecules by cryo-electron microscopy (cryo-EM) represents a major practical bottleneck in cryo-EM data analysis. Template-based particle picking by an objective function using fast local correlation (FLC) allows computational extraction of a large number of candidate particles from micrographs. Another independent objective function based on maximum likelihood estimates (MLE) can be used to align the images and verify the presence of a signal in the selected particles. Despite the widespread applications of the two objective functions, an optimal combination of their utilities has not been exploited. Here we propose a bi-objective function (BOF) approach that combines both FLC and MLE and explore the potential advantages and limitations of BOF in signal detection from cryo-EM data.

Results

The robustness of the BOF strategy in particle selection and verification was systematically examined with both simulated and experimental cryo-EM data. We investigated how the performance of the BOF approach is quantitatively affected by the signal-to-noise ratio (SNR) of cryo-EM data and by the choice of initialization for FLC and MLE. We quantitatively pinpointed the critical SNR (~ 0.005), at which the BOF approach starts losing its ability to select and verify particles reliably. We found that the use of a Gaussian model to initialize the MLE suppresses the adverse effects of reference dependency in the FLC function used for template-matching.

Conclusion

The BOF approach, which combines two distinct objective functions, provides a sensitive way to verify particles for downstream cryo-EM structure analysis. Importantly, reference dependency of the FLC does not necessarily transfer to the MLE, enabling the robust detection of weak signals. Our insights into the numerical behavior of the BOF approach can be used to improve automation efficiency in the cryo-EM data processing pipeline for high-resolution structural determination.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2714-8) contains supplementary material, which is available to authorized users.

Deformed alignment of super-resolution images for semi-flexible structures

Due to low labeling efficiency and structural heterogeneity in fluorescence-based single-molecule localization microscopy (SMLM), image alignment and quantitative analysis is often required to make accurate conclusions on the spatial relationships between proteins. Cryo-electron microscopy (EM) image alignment procedures have been applied to average structures taken with super-resolution microscopy. However, unlike cryo-EM, the much larger cellular structures analyzed by super-resolution microscopy are often heterogeneous, resulting in misalignment. And the light-microscopy image library is much smaller, which makes classification challenging. To overcome these two challenges, we developed a method to deform semi-flexible ring-shaped structures and then align the 3D structures without classification. These algorithms can register semi-flexible structures with an accuracy of several nanometers in short computation time and with greatly reduced memory requirements. We demonstrated our methods by aligning experimental Stochastic Optical Reconstruction Microscopy (STORM) images of ciliary distal appendages and simulated structures. Symmetries, dimensions, and locations of protein complexes in 3D are revealed by the alignment and averaging for heterogeneous, tilted, and under-labeled structures.

Robust w-Estimators for Cryo-EM Class Means

A critical step in cryogenic electron microscopy (cryo-EM) image analysis is to calculate the average of all images aligned to a projection direction. This average, called the class mean, improves the signal-to-noise ratio in single-particle reconstruction. The averaging step is often compromised because of the outlier images of ice, contaminants, and particle fragments. Outlier detection and rejection in the majority of current cryo-EM methods are done using cross-correlation with a manually determined threshold. Empirical assessment shows that the performance of these methods is very sensitive to the threshold. This paper proposes an alternative: a w-estimator of the average image, which is robust to outliers and which does not use a threshold. Various properties of the estimator, such as consistency and influence function are investigated. An extension of the estimator to images with different contrast transfer functions is also provided. Experiments with simulated and real cryo-EM images show that the proposed estimator performs quite well in the presence of outliers.

A primer to single-particle cryo-electron microscopy

Cryo-electron microscopy (cryo-EM) of single-particle specimens is used to determine the structure of proteins and macromolecular complexes without the need for crystals. Recent advances in detector technology and software algorithms now allow images of unprecedented quality to be recorded and structures to be determined at near-atomic resolution. However, compared with X-ray crystallography, cryo-EM is a young technique with distinct challenges. This primer explains the different steps and considerations involved in structure determination by single-particle cryo-EM to provide an overview for scientists wishing to understand more about this technique and the interpretation of data obtained with it, as well as a starting guide for new practitioners.

Texture indicators for segmentation of polyomavirus particles in transmission electron microscopy images

A fully automatic approach to locate polyomavirus particles in transmission electron microscopy images is presented that can localize intact particles, many damaged capsids, and an acceptable percentage of superposed ones. Performance of the approach is quantified in 25 electron micrographs containing nearly 390 particles and compared with the interpretation of the micrographs by two independent electron microscopy experts. All parameterization is based on the particle expected dimensions. This approach uses indicators calculated from the local co-occurrence matrix of gray levels to assess the textured pattern typical of polyomavirus and prune the initial set of candidates. In more complicated backgrounds, about 2-10% of the elements survive. A restricted set of the accepted points is used to evaluate the typical average and variance and to reduce the set of survivors accordingly. These intermediate points are evaluated using (i) a statistical index concerning the radiometric distribution of a circular neighborhood around the centroid of each candidate and (ii) a structural index resuming the expected morphological characteristics of eight radial intensity profiles encompassing the area of the possible particle. This hierarchical approach attains 90% efficiency in the detection of entire virus particles, tolerating a certain lack of differentiation in the borders and a certain amount of shape alterations.

Automatic virus particle selection--the entropy approach

This paper describes a fully automatic approach to locate icosahedral virus particles in transmission electron microscopy images. The initial detection of the particles takes place through automatic segmentation of the entropy-proportion image; this image is computed in particular regions of interest defined by two concentric structuring elements contained in a small overlapping window running over all the image. Morphological features help to select the candidates, as the threshold is kept low enough to avoid false negatives. The candidate points are subject to a credibility test based on features extracted from eight radial intensity profiles in each point from a texture image. A candidate is accepted if these features meet the set of acceptance conditions describing the typical intensity profiles of these kinds of particles. The set of points accepted is subjected to a last validation in a three-parameter space using a discrimination plan that is a function of the input image to separate possible outliers.

Particle-verification for single-particle, reference-based reconstruction using multivariate data analysis and classification

As collection of electron microscopy data for single-particle reconstruction becomes more efficient, due to electronic image capture, one of the principal limiting steps in a reconstruction remains particle-verification, which is especially costly in terms of user input. Recently, some algorithms have been developed to window particles automatically, but the resulting particle sets typically need to be verified manually. Here we describe a procedure to speed up verification of windowed particles using multivariate data analysis and classification. In this procedure, the particle set is subjected to multi-reference alignment before the verification. The aligned particles are first binned according to orientation and are binned further by K-means classification. Rather than selection of particles individually, an entire class of particles can be selected, with an option to remove outliers. Since particles in the same class present the same view, distinction between good and bad images becomes more straightforward. We have also developed a graphical interface, written in Python/Tkinter, to facilitate this implementation of particle-verification. For the demonstration of the particle-verification scheme presented here, electron micrographs of ribosomes are used.

SwarmPS: rapid, semi-automated single particle selection software

Single particle analysis (SPA) coupled with high-resolution electron cryo-microscopy is emerging as a powerful technique for the structure determination of membrane protein complexes and soluble macromolecular assemblies. Current estimates suggest that approximately 10(4)-10(5) particle projections are required to attain a 3A resolution 3D reconstruction (symmetry dependent). Selecting this number of molecular projections differing in size, shape and symmetry is a rate-limiting step for the automation of 3D image reconstruction. Here, we present Swarm(PS), a feature rich GUI based software package to manage large scale, semi-automated particle picking projects. The software provides cross-correlation and edge-detection algorithms. Algorithm-specific parameters are transparently and automatically determined through user interaction with the image, rather than by trial and error. Other features include multiple image handling (approximately 10(2)), local and global particle selection options, interactive image freezing, automatic particle centering, and full manual override to correct false positives and negatives. Swarm(PS) is user friendly, flexible, extensible, fast, and capable of exporting boxed out projection images, or particle coordinates, compatible with downstream image processing suites.

Object detection by correlation coefficients using azimuthally averaged reference projections

A method of computing correlation coefficients for object detection that takes advantage of using azimuthally averaged reference projections is described and compared with two alternative methods-computing a cross-correlation function or a local correlation coefficient versus the azimuthally averaged reference projections. Two examples of an application from structural biology involving the detection of projection views of biological macromolecules in electron micrographs are discussed. It is found that a novel approach to computing a local correlation coefficient versus azimuthally averaged reference projections, using a rotational correlation coefficient, outperforms using a cross-correlation function and a local correlation coefficient in object detection from simulated images with a range of levels of simulated additive noise. The three approaches perform similarly in detecting macromolecular views in electron microscope images of a globular macrolecular complex (the ribosome). The rotational correlation coefficient outperforms the other methods in detection of keyhole limpet hemocyanin macromolecular views in electron micrographs.

A refined circular template matching method for classification of human cytomegalovirus capsids in TEM images

An automatic image analysis method for describing, segmenting, and classifying human cytomegalovirus capsids in transmission electron micrograph (TEM) images of host cell nuclei has been developed. Three stages of the capsid assembly process in the host cell nucleus have been investigated. Each class is described by a radial density profile, which is the average grey-level at each radial distance from the center. A template, constructed from the profile, is used to find possible capsid locations by correlation based matching. The matching results are further refined by size and distortion analysis of each possible capsid, resulting in a final segmentation and classification.

Correlation-based methods of automatic particle detection in electron microscopy images with smoothing by anisotropic diffusion

Two methods of correlation-based automatic particle detection in electron microscopy images are compared - computing a cross-correlation function or a local correlation coefficient vs. azimuthally averaged reference projections (either from a model or from experimental particle images). The ability of smoothing images by anisotropic diffusion to improve the performance of particle detection is also considered. Anisotropic diffusion is an effective method of preprocessing that enhances the edges and overall shape of particles while reducing noise. It is found that anisotropic diffusion improves particle detection by a local correlation coefficient when projections from a high-resolution reconstruction are used as references. When references from experimental particle images are used, a cross-correlation function shows a more marked improvement in particle detection in images smoothed by anisotropic diffusion.

Image segmentation for automatic particle identification in electron micrographs based on hidden Markov random field models and expectation maximization

Three-dimensional reconstruction of large macromolecules like viruses at resolutions below 10 A requires a large set of projection images. Several automatic and semi-automatic particle detection algorithms have been developed along the years. Here we present a general technique designed to automatically identify the projection images of particles. The method is based on Markov random field modelling of the projected images and involves a pre-processing of electron micrographs followed by image segmentation and post-processing. The image is modelled as a coupling of two fields--a Markovian and a non-Markovian. The Markovian field represents the segmented image. The micrograph is the non-Markovian field. The image segmentation step involves an estimation of coupling parameters and the maximum á posteriori estimate of the realization of the Markovian field i.e, segmented image. Unlike most current methods, no bootstrapping with an initial selection of particles is required.

Wavelets filtering for classification of very noisy electron microscopic single particles images--application on structure determination of VP5-VP19C recombinant

Background

Images of frozen hydrated [vitrified] virus particles were taken close-to-focus in an electron microscope containing structural signals at high spatial frequencies. These images had very low contrast due to the high levels of noise present in the image. The low contrast made particle selection, classification and orientation determination very difficult. The final purpose of the classification is to improve the signal-to-noise ratio of the particle representing the class, which is usually the average. In this paper, the proposed method is based on wavelet filtering and multi-resolution processing for the classification and reconstruction of this very noisy data. A multivariate statistical analysis (MSA) is used for this classification.

Results

The MSA classification method is noise dependant. A set of 2600 projections from a 3D map of a herpes simplex virus -to which noise was added- was classified by MSA. The classification shows the power of wavelet filtering in enhancing the quality of class averages (used in 3D reconstruction) compared to Fourier band pass filtering. A 3D reconstruction of a recombinant virus (VP5-VP19C) is presented as an application of multi-resolution processing for classification and reconstruction.

Conclusion

The wavelet filtering and multi-resolution processing method proposed in this paper offers a new way for processing very noisy images obtained from electron cryo-microscopes. The multi-resolution and filtering improves the speed and accuracy of classification, which is vital for the 3D reconstruction of biological objects. The VP5-VP19C recombinant virus reconstruction presented here is an example, which demonstrates the power of this method. Without this processing, it is not possible to get the correct 3D map of this virus.

Automatic particle detection through efficient Hough transforms

Manual selection of single particles in images acquired using cryo-electron microscopy (cryoEM) will become a significant bottleneck when a very large number of images are required to achieve three-dimensional reconstructions at near atomic resolution. Investigation of fast, accurate approaches for automatic particle detection has become one of the current challenges in the cryoEM community. At the same time, the investigation is hampered by the fact that few benchmark particles or image datasets exist in the community. The unavailability of such data makes it difficult to evaluate newly developed algorithms and to leverage expertise from other disciplines. The paper presents our recent contribution to this effort. It also describes our newly developed computational framework for particle detection, through the application of edge detection and a sequence of ordered Hough transforms. Experimental results using keyhole limpet hemocyanin (KLH) as a model particle are very promising. In addition, it introduces a newly established web site, designed to support the investigation of automatic particle detection by providing an annotated image dataset of KLH available to the general scientific community.

Particle finding in electron micrographs using a fast local correlation algorithm

A versatile tool for selecting particles from electron micrographs, intended for single particle analysis and three-dimensional reconstruction, is presented. It is based on a local real-space correlation method. Real-space correlations calculated over a local area are suitable for finding small objects or patterns in a larger field. They provide a very sensitive measure-of-fit, partly due to local optimisation of the numerical scaling. It is equivalent to least squares with optimised scaling between the two objects being correlated. The only disadvantage of real-space methods is that they are slow to compute. A fast local correlation algorithm based on Fourier transforms has been developed, which is approximately two orders of magnitude faster than the explicit real-space formulation. The algorithm is demonstrated by application to the problem of locating images of macromolecules in transmission electron micrographs of unstained frozen hydrated specimens. This is a challenging computational problem because these images have low contrast and a low signal-to-noise ratio. Picking particles by hand is very time consuming and can be less accurate. The automated procedure gives a significant increase in speed, which is important if large numbers of particles have to be picked.

Structure of yeast RNA polymerase II in solution: implications for enzyme regulation and interaction with promoter DNA

An 18 A resolution structure of the 12-subunit yeast RNA polymerase II (RNAPII) calculated from electron microscope images of single particles preserved in amorphous ice reveals the conformation of the enzyme in solution. The Rpb4/Rpb7 polymerase subunit complex was localized and found to be ideally positioned to determine the path of the nascent RNA transcript. The RNAPII structure suggests a revised mode of interaction with promoter DNA and demonstrates that regulation of RNAPII must involve structural changes that render the enzyme competent for initiation.

Cryo-EM reveals an active role for aminoacyl-tRNA in the accommodation process

During the elongation cycle of protein biosynthesis, the specific amino acid coded for by the mRNA is delivered by a complex that is comprised of the cognate aminoacyl-tRNA, elongation factor Tu and GTP. As this ternary complex binds to the ribosome, the anticodon end of the tRNA reaches the decoding center in the 30S subunit. Here we present the cryo- electron microscopy (EM) study of an Escherichia coli 70S ribosome-bound ternary complex stalled with an antibiotic, kirromycin. In the cryo-EM map the anticodon arm of the tRNA presents a new conformation that appears to facilitate the initial codon-anticodon interaction. Furthermore, the elbow region of the tRNA is seen to contact the GTPase-associated center on the 50S subunit of the ribosome, suggesting an active role of the tRNA in the transmission of the signal prompting the GTP hydrolysis upon codon recognition.

An automatic particle pickup method using a neural network applicable to low-contrast electron micrographs

Three-dimensional reconstruction from electron micrographs requires the selection of many single-particle projection images; more than 10 000 are generally required to obtain 5- to 10-A structural resolution. Consequently, various automatic detection algorithms have been developed and successfully applied to large symmetric protein complexes. This paper presents a new automated particle recognition and pickup procedure based on the three-layer neural network that has a large application range than other automated procedures. Its use for both faint and noisy electron micrographs is demonstrated. The method requires only 200 selected particles as learning data and is able to detect images of proteins as small as 200 kDa.

Semi-automated icosahedral particle reconstruction at sub-nanometer resolution

Electron cryomicroscopy of large macromolecular complexes is becoming an increasingly powerful tool for revealing three-dimensional structures without the need for crystallization. The execution of image processing, however, requires experience and is error-prone due to the need for a human operator to carry out interactive and repetitive processes. We have designed an approach which is intended to make image processing simple and rapid, both for experts and for novice users. We demonstrate this approach using the well-established reconstruction scheme for icosahedral particles. Finally, we implement semi-automated virus reconstruction (SAVR), an expert system that integrates the most CPU intensive and iterative steps using the scripting language Python. SAVR is portable across platforms and has been parallelized to run on both shared and distributed memory platforms. SAVR also allows the incorporation of new algorithms and facilitates the management of the increasingly large data sets needed to achieve higher resolution reconstructions. The package has been successfully applied to several data sets and shown capable of generating icosahedral reconstructions to sub-nanometer resolutions (7-10 A ).

Automated identification of filaments in cryoelectron microscopy images

Since the foundation for the three-dimensional image reconstruction of helical objects from electron micrographs was laid more than 30 years ago, there have been sustained developments in specimen preparation, data acquisition, image analysis, and interpretation of results. However, the boxing of filaments in large numbers of images--one of the critical steps toward the reconstruction at high resolution--is still constrained by manual processing even though interactive interfaces have been built to aid the tedious and sometimes inaccurate boxing process. This article describes an accurate approach for automated detection of filamentous structures in low-contrast images acquired in defocus pairs using cryoelectron microscopy. The performance of the approach has been evaluated across various magnifications and at a series of defocus values using tobacco mosaic virus (TMV) preserved in vitreous ice as a test specimen. By integrating the proposed approach into our automated data acquisition and reconstruction system, we are now able to generate a three-dimensional map of TMV to approximately 10-A resolution within 24 h of inserting the specimen grid into the microscope.

Localization of L11 protein on the ribosome and elucidation of its involvement in EF-G-dependent translocation

L11 protein is located at the base of the L7/L12 stalk of the 50 S subunit of the Escherichia coli ribosome. Because of the flexible nature of the region, recent X-ray crystallographic studies of the 50 S subunit failed to locate the N-terminal domain of the protein. We have determined the position of the complete L11 protein by comparing a three-dimensional cryo-EM reconstruction of the 70 S ribosome, isolated from a mutant lacking ribosomal protein L11, with the three-dimensional map of the wild-type ribosome. Fitting of the X-ray coordinates of L11-23 S RNA complex and EF-G into the cryo-EM maps combined with molecular modeling, reveals that, following EF-G-dependent GTP hydrolysis, domain V of EF-G intrudes into the cleft between the 23 S ribosomal RNA and the N-terminal domain of L11 (where the antibiotic thiostrepton binds), causing the N-terminal domain to move and thereby inducing the formation of the arc-like connection with the G' domain of EF-G. The results provide a new insight into the mechanism of EF-G-dependent translocation.

Review: automatic particle detection in electron microscopy

Advances in cryoEM and single-particle reconstruction have led to results at increasingly high resolutions. However, to sustain continuing improvements in resolution it will be necessary to increase the number of particles included in performing the reconstructions. Manual selection of particles, even when assisted by computer preselection, is a bottleneck that will become significant as single-particle reconstructions are scaled up to achieve near-atomic resolutions. This review describes various approaches that have been developed to address the problem of automatic particle selection. The principal conclusions that have been drawn from the results so far are: (1) cross-correlation with a reference image ("matched filtering") is an effective way to identify candidate particles, but it is inherently unable to avoid also selecting false particles; (2) false positives can be eliminated efficiently on the basis of estimates of particle size, density, and texture; (3) successful application of edge detection (or contouring) to particle identification may require improvements over currently available methods; and (4) neural network techniques, while computationally expensive, must also be investigated as a technology for eliminating false particles.

Automated image acquisition for single-particle reconstruction using p97 as the biological sample

We have used Leginon, a fully automatic system capable of acquiring cryo-electron micrographs, to collect data of single particles, specifically of the AAA ATPase p97. The images were acquired under low-dose conditions and required no operator intervention other than the initial setup and periodic refilling of the cold-stage dewar. Each image was acquired at two different defocus values. Two-dimensional projection maps of p97 were calculated from these data and compared to results previously obtained using the conventional manual data collection methods to film. The results demonstrate that Leginon performs as well as an experienced microscopist for the acquisition of single-particle data. The general advantages of automation are discussed.

Reconstruction principles of icosahedral virus structure determination using electron cryomicroscopy

Electron cryomicroscopy is a useful tool for studying the three-dimensional structure of icosahedral viruses. This review is intended to provide beginners with an understanding of icosahedral virus structure determination focusing on the data processing aspects. We begin with an overview of the entire structure determination process and a brief summary of the sample preparation and imaging aspects. Next, we provide detailed descriptions of each data processing step leading to three-dimensional reconstruction, including application of image corrections, resolution assessment, and structure visualization. To aid in understanding this reconstruction process we provide a variety of illustrative examples. Last, we summarize future prospects for icosahedral virus structural studies.

Local average intensity-based method for identifying spherical particles in electron micrographs

A method is presented that reliably detects spherical viruses from a wide variety of noisy low-contrast electron micrographs. Such detection is one of the first image analysis steps in the computer-aided reconstruction of three-dimensional density distribution models of viruses. Particle detection is based on the comparison of intensity in a circular area and in the surrounding ring followed by a number of tests to validate the potential particles. The only required input from the user in addition to the micrograph is an approximate radius of the particle. The method has been implemented as program ETHAN that has been tested for several different data sets. ETHAN has also successfully been used to detect DNA-less virus particles for an actual reconstruction.

Solution structure of the E. coli 70S ribosome at 11.5 A resolution

Over 73,000 projections of the E. coli ribosome bound with formyl-methionyl initiator tRNAf(Met) were used to obtain an 11.5 A cryo-electron microscopy map of the complex. This map allows identification of RNA helices, peripheral proteins, and intersubunit bridges. Comparison of double-stranded RNA regions and positions of proteins identified in both cryo-EM and X-ray maps indicates good overall agreement but points to rearrangements of ribosomal components required for the subunit association. Fitting of known components of the 50S stalk base region into the map defines the architecture of the GTPase-associated center and reveals a major change in the orientation of the alpha-sarcin-ricin loop. Analysis of the bridging connections between the subunits provides insight into the dynamic signaling mechanism between the ribosomal subunits.

Review: electron crystallography: present excitement, a nod to the past, anticipating the future

From a modest beginning with negatively stained samples of the helical T4 bacteriophage tail, electron crystallography has emerged as a powerful tool in structural biology. High-resolution density maps, interpretable in terms of an atomic structure, can be obtained from specimens prepared as well-ordered, two-dimensional crystals, and the resolution achieved with helical specimens and icosahedral viruses is approaching the same goal. A hybrid approach to determining the molecular structure of complex biological assemblies is generating great interest, in which high-resolution structures that have been determined for individual protein components are fitted into a lower resolution envelope of the large complex. With this as background, how much more can be anticipated for the future? Considerable scope still remains to improve the quality of electron microscope images. Automation of data acquisition and data processing, together with the emergence of computational speeds of 10(12) floating point operations per second or higher, will make it possible to extend high-resolution structure determination into the realm of single-particle microscopy. As a result, computational alignment of single particles, i.e., the formation of "virtual crystals," can begin to replace biochemical crystallization. Since single-particle microscopy may remain limited to "large" structures of 200 to 300 kDa or more, however, smaller proteins will continue to be studied as helical assemblies or as two-dimensional crystals. The further development of electron crystallography is thus likely to turn increasingly to the use of single particles and small regions of ordered assemblies, emphasizing more and more the potential for faster, higher throughput.

EMAN: semiautomated software for high-resolution single-particle reconstructions

We present EMAN (Electron Micrograph ANalysis), a software package for performing semiautomated single-particle reconstructions from transmission electron micrographs. The goal of this project is to provide software capable of performing single-particle reconstructions beyond 10 A as such high-resolution data become available. A complete single-particle reconstruction algorithm is implemented. Options are available to generate an initial model for particles with no symmetry, a single axis of rotational symmetry, or icosahedral symmetry. Model refinement is an iterative process, which utilizes classification by model-based projection matching. CTF (contrast transfer function) parameters are determined using a new paradigm in which data from multiple micrographs are fit simultaneously. Amplitude and phase CTF correction is then performed automatically as part of the refinement loop. A graphical user interface is provided, so even those with little image processing experience will be able to begin performing reconstructions. Advanced users can directly use the lower level shell commands and even expand the package utilizing EMAN's extensive image-processing library. The package was written from scratch in C++ and is provided free of charge on our Web site. We present an overview of the package as well as several conformance tests with simulated data.

Location of translational initiation factor IF3 on the small ribosomal subunit

The location of translational initiation factor IF3 bound to the 30S subunit of the Thermus thermophilus ribosome has been determined by cryoelectron microscopy. Both the 30S.IF3 complex and control 30S subunit structures were determined to 27-A resolution. The difference map calculated from the two reconstructions reveals three prominent lobes of positive density. The previously solved crystal structure of IF3 fits very well into two of these lobes, whereas the third lobe probably arises from conformational changes induced in the 30S subunit as a result of IF3 binding. Our placement of IF3 on the 30S subunit allows an understanding in structural terms of the biochemical functions of this initiation factor, namely its ability to dissociate 70S ribosomes into 30S and 50S subunits and the preferential selection of initiator tRNA by IF3 during initiation.

Escherichia coli 70 S ribosome at 15 A resolution by cryo-electron microscopy: localization of fMet-tRNAfMet and fitting of L1 protein

Cryo-electron microscopy of the ribosome in different binding states with mRNA and tRNA helps unravel the different steps of protein synthesis. Using over 29,000 projections of a ribosome complex in single-particle form, a three-dimensional map of the Escherichia coli 70 S ribosome was obtained in which a single site, the P site, is occupied by fMet-tRNAfMet as directed by an AUG codon containing mRNA. The superior resolution of this three-dimensional map, 14.9 A, has made it possible to fit the tRNA X-ray crystal structure directly and unambiguously into the electron density, thus determining the locations of anticodon-codon interaction and peptidyltransferase center of the ribosome. Furthermore, at this resolution, one of the distinctly visible domains corresponding to a ribosomal protein, L1, closely matches with its X-ray structure.

Visualization of elongation factor G on the Escherichia coli 70S ribosome: the mechanism of translocation

During protein synthesis, elongation factor G (EF-G) binds to the ribosome and promotes the step of translocation, a process in which tRNA moves from the A to the P site of the ribosome and the mRNA is advanced by one codon. By using three-dimensional cryo-electron microscopy, we have visualized EF-G in a ribosome-EF-G-GDP-fusidic acid complex. Fitting the crystal structure of EF-G-GDP into the cryo density map reveals a large conformational change mainly associated with domain IV, the domain that mimics the shape of the anticodon arm of the tRNA in the structurally homologous ternary complex of Phe-tRNAPhe, EF-Tu, and a GTP analog. The tip portion of this domain is found in a position that overlaps the anticodon arm of the A-site tRNA, whose position in the ribosome is known from a study of the pretranslocational complex, implying that EF-G displaces the A-site tRNA to the P site by physical interaction with the anticodon arm.

Alignment of conduits for the nascent polypeptide chain in the ribosome-Sec61 complex

An oligomer of the Sec61 trimeric complex is thought to form the protein-conducting channel for protein transport across the endoplasmic reticulum. A purified yeast Sec61 complex bound to monomeric yeast ribosomes as an oligomer in a saturable fashion. Cryo-electron microscopy of the ribosome-Sec61 complex and a three-dimensional reconstruction showed that the Sec61 oligomer is attached to the large ribosomal subunit by a single connection. Moreover, a funnel-shaped pore in the Sec61 oligomer aligned with the exit of a tunnel traversing the large ribosomal subunit, strongly suggesting that both structures function together in the translocation of proteins across the endoplasmic reticulum membrane.