Structural Basis for Gating and Activation of RyR1.

The type-1 ryanodine receptor (RyR1) is an intracellular calcium (Ca(2+)) release channel required for skeletal muscle contraction. Here, we present cryo-EM reconstructions of RyR1 in multiple functional states revealing the structural basis of channel gating and ligand-dependent activation. Binding sites for the channel activators Ca(2+), ATP, and caffeine were identified at interdomain interfaces of the C-terminal domain. Either ATP or Ca(2+) alone induces conformational changes in the cytoplasmic assembly ("priming"), without pore dilation. In contrast, in the presence of all three activating ligands, high-resolution reconstructions of open and closed states of RyR1 were obtained from the same sample, enabling analyses of conformational changes associated with gating. Gating involves global conformational changes in the cytosolic assembly accompanied by local changes in the transmembrane domain, which include bending of the S6 transmembrane segment and consequent pore dilation, displacement, and deformation of the S4-S5 linker and conformational changes in the pseudo-voltage-sensor domain.

Double-headed binding of myosin II to F-actin shows the effect of strain on head structure.

Force production in muscle is achieved through the interaction of myosin and actin. Strong binding states in active muscle are associated with Mg·ADP bound to the active site; release of Mg·ADP allows rebinding of ATP and dissociation from actin. Thus, Mg·ADP binding is positioned for adaptation as a force sensor. Mechanical loads on the lever arm can affect the ability of myosin to release Mg·ADP but exactly how this is done is poorly defined. Here we use F-actin decorated with double-headed smooth muscle myosin fragments in the presence of Mg·ADP to visualize the effect of internally supplied tension on the paired lever arms using cryoEM. The interaction of the paired heads with two adjacent actin subunits is predicted to place one lever arm under positive and the other under negative strain. The converter domain is believed to be the most flexible domain within myosin head. Our results, instead, point to the segment of heavy chain between the essential and regulatory light chains as the location of the largest structural change. Moreover, our results suggest no large changes in the myosin coiled coil tail as the locus of strain relief when both heads bind F-actin. The method would be adaptable to double-headed members of the myosin family. We anticipate that the study of actin-myosin interaction using double-headed fragments enables visualization of domains that are typically noisy in decoration with single-headed fragments.

CryoEM single particle reconstruction with a complex-valued particle stack.

Single particle reconstruction (SPR) in cryoEM is an image processing task with an elaborate hierarchy that starts with many very noisy multi-frame images. Efficient representation of the intermediary image structures is critical for keeping the calculations manageable. One such intermediary structure is called a particle stack and contains cut-out images of particles in square boxes of predefined size. The micrograph that is the source of the boxed images is usually corrected for motion between frames prior to particle stack creation. However, the contrast transfer function (CTF) or its Fourier Transform point spread function (PSF) are not considered at this step. Historically, the particle stack was intended for large particles and for a tighter PSF, which is characteristic of lower resolution data. The field now performs analyses of smaller particles and to higher resolution, and these conditions result in a broader PSF that requires larger padding and slower calculations to integrate information for each particle. Consequently, the approach to handling structures such as the particle stack should be reexamined to optimize data processing. Here we propose to use as a source image for the particle stack a complex-valued image, in which CTF correction is implicitly applied as a real component of the image. We can achieve it by applying an initial CTF correction to the entire micrograph first and perform box cutouts as a subsequent step. The final CTF correction that we refine and apply later has a very narrow PSF, and so cutting out particles from micrographs that were approximately corrected for CTF does not require extended buffering, i.e. the boxes during the analysis only have to be large enough to encompass the particle. The Fourier Transform of an exit-wave reconstruction creates an image that has complex values. This is a complex value image considered in real space, opposed to standard SPR data processing where complex numbers appear only in Fourier space. This extension of the micrograph concept provides multiple advantages because the particle box size can be small and calculations crucial for high resolution reconstruction such as Ewald sphere correction, aberration refinement, and particle-specific defocus refinement can be performed on the small box data.

Cryo-EM structure of a eukaryotic zinc transporter at a low pH suggests its Zn2+-releasing mechanism.

Zinc transporter 8 (ZnT8) is mainly expressed in pancreatic islet ß cells and is responsible for H+-coupled uptake (antiport) of Zn2+ into the lumen of insulin secretory granules. Structures of human ZnT8 and its prokaryotic homolog YiiP have provided structural basis for constructing a plausible transport cycle for Zn2+. However, the mechanistic role that protons play in the transport process remains unclear. Here we present a lumen-facing cryo-EM structure of ZnT8 from Xenopus tropicalis (xtZnT8) in the presence of Zn2+ at a luminal pH (5.5). Compared to a Zn2+-bound xtZnT8 structure at a cytosolic pH (7.5), the low-pH structure displays an empty transmembrane Zn2+-binding site with a disrupted coordination geometry. Combined with a Zn2+-binding assay our data suggest that protons may disrupt Zn2+ coordination at the transmembrane Zn2+-binding site in the lumen-facing state, thus facilitating Zn2+ release from ZnT8 into the lumen.

G protein-coupled receptor interactions with arrestins and GPCR kinases: The unresolved issue of signal bias.

G protein-coupled receptor (GPCR) kinases (GRKs) and arrestins interact with agonist-bound GPCRs to promote receptor desensitization and downregulation. They also trigger signaling cascades distinct from those of heterotrimeric G proteins. Biased agonists for GPCRs that favor either heterotrimeric G protein or GRK/arrestin signaling are of profound pharmacological interest because they could usher in a new generation of drugs with greatly reduced side effects. One mechanism by which biased agonism might occur is by stabilizing receptor conformations that preferentially bind to GRKs and/or arrestins. In this review, we explore this idea by comparing structures of GPCRs bound to heterotrimeric G proteins with those of the same GPCRs in complex with arrestins and GRKs. The arrestin and GRK complexes all exhibit high conformational heterogeneity, which is likely a consequence of their unusual ability to adapt and bind to hundreds of different GPCRs. This dynamic behavior, along with the experimental tactics required to stabilize GPCR complexes for biophysical analysis, confounds these comparisons, but some possible molecular mechanisms of bias are beginning to emerge. We also examine if and how the recent structures advance our understanding of how arrestins parse the "phosphorylation barcodes" installed in the intracellular loops and tails of GPCRs by GRKs. In the future, structural analyses of arrestins in complex with intact receptors that have well-defined native phosphorylation barcodes, such as those installed by the two nonvisual subfamilies of GRKs, will be particularly illuminating.

General and robust covalently linked graphene oxide affinity grids for high-resolution cryo-EM.

Affinity grids have great potential to facilitate rapid preparation of even quite impure samples in single-particle cryo-electron microscopy (EM). Yet despite the promising advances of affinity grids over the past decades, no single strategy has demonstrated general utility. Here we chemically functionalize cryo-EM grids coated with mostly one or two layers of graphene oxide to facilitate affinity capture. The protein of interest is tagged using a system that rapidly forms a highly specific covalent bond to its cognate catcher linked to the grid via a polyethylene glycol (PEG) spacer. Importantly, the spacer keeps particles away from both the air-water interface and the graphene oxide surface, protecting them from potential denaturation and rendering them sufficiently flexible to avoid preferential sample orientation concerns. Furthermore, the PEG spacer successfully reduces nonspecific binding, enabling high-resolution reconstructions from a much cruder lysate sample.

An Unsupervised Classification Algorithm for Heterogeneous Cryo-EM Projection Images Based on Autoencoders.

Heterogeneous three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is an important but very challenging technique for recovering the conformational heterogeneity of flexible biological macromolecules such as proteins in different functional states. Heterogeneous projection image classification is a feasible solution to solve the structural heterogeneity problem in single-particle cryo-EM. The majority of heterogeneous projection image classification methods are developed using supervised learning technology or require a large amount of a priori knowledge, such as the orientations or common lines of the projection images, which leads to certain limitations in their practical applications. In this paper, an unsupervised heterogeneous cryo-EM projection image classification algorithm based on autoencoders is proposed, which only needs to know the number of heterogeneous 3D structures in the dataset and does not require any labeling information of the projection images or other a priori knowledge. A simple autoencoder with multi-layer perceptrons trained in iterative mode and a complex autoencoder with residual networks trained in one-pass learning mode are implemented to convert heterogeneous projection images into latent variables. The extracted high-dimensional features are reduced to two dimensions using the uniform manifold approximation and projection dimensionality reduction algorithm, and then clustered using the spectral clustering algorithm. The proposed algorithm is applied to two heterogeneous cryo-EM datasets for heterogeneous 3D reconstruction. Experimental results show that the proposed algorithm can effectively extract category features of heterogeneous projection images and achieve high classification and reconstruction accuracy, indicating that the proposed algorithm is effective for heterogeneous 3D reconstruction in single-particle cryo-EM.

ASOCEM: Automatic Segmentation Of Contaminations in cryo-EM.

Particle picking is currently a critical step in the cryo-electron microscopy single particle reconstruction pipeline. Contaminations in the acquired micrographs severely degrade the performance of particle pickers, resulting in many "non-particles" in the collected stack of particles. In this paper, we present ASOCEM (Automatic Segmentation Of Contaminations in cryo-EM), an automatic method to detect and segment contaminations, which requires as an input only the approximate particle size. In particular, it does not require any parameter tuning nor manual intervention. Our method is based on the observation that the statistical distribution of contaminated regions is different from that of the rest of the micrograph. This nonrestrictive assumption allows to automatically detect various types of contaminations, from the carbon edges of the supporting grid to high contrast blobs of different sizes. We demonstrate the efficiency of our algorithm using various experimental data sets containing various types of contaminations. ASOCEM is integrated as part of the KLT picker (Eldar et al., 2020) and is available at https://github.com/ShkolniskyLab/kltpicker2.

DRPnet: automated particle picking in cryo-electron micrographs using deep regression.

BACKGROUND: Identification and selection of protein particles in cryo-electron micrographs is an important step in single particle analysis. In this study, we developed a deep learning-based particle picking network to automatically detect particle centers from cryoEM micrographs. This is a challenging task due to the nature of cryoEM data, having low signal-to-noise ratios with variable particle sizes, shapes, distributions, grayscale variations as well as other undesirable artifacts. RESULTS: We propose a double convolutional neural network (CNN) cascade for automated detection of particles in cryo-electron micrographs. This approach, entitled Deep Regression Picker Network or "DRPnet", is simple but very effective in recognizing different particle sizes, shapes, distributions and grayscale patterns corresponding to 2D views of 3D particles. Particles are detected by the first network, a fully convolutional regression network (FCRN), which maps the particle image to a continuous distance map that acts like a probability density function of particle centers. Particles identified by FCRN are further refined to reduce false particle detections by the second classification CNN. DRPnet's first CNN pretrained with only a single cryoEM dataset can be used to detect particles from different datasets without retraining. Compared to RELION template-based autopicking, DRPnet results in better particle picking performance with drastically reduced user interactions and processing time. DRPnet also outperforms the state-of-the-art particle picking networks in terms of the supervised detection evaluation metrics recall, precision, and F-measure. To further highlight quality of the picked particle sets, we compute and present additional performance metrics assessing the resulting 3D reconstructions such as number of 2D class averages, efficiency/angular coverage, Rosenthal-Henderson plots and local/global 3D reconstruction resolution. CONCLUSION: DRPnet shows greatly improved time-savings to generate an initial particle dataset compared to manual picking, followed by template-based autopicking. Compared to other networks, DRPnet has equivalent or better performance. DRPnet excels on cryoEM datasets that have low contrast or clumped particles. Evaluating other performance metrics, DRPnet is useful for higher resolution 3D reconstructions with decreased particle numbers or unknown symmetry, detecting particles with better angular orientation coverage.

A Fast Image Alignment Approach for 2D Classification of Cryo-EM Images Using Spectral Clustering.

Three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is a significant technique for recovering the 3D structure of proteins or other biological macromolecules from their two-dimensional (2D) noisy projection images taken from unknown random directions. Class averaging in single-particle cryo-EM is an important procedure for producing high-quality initial 3D structures, where image alignment is a fundamental step. In this paper, an efficient image alignment algorithm using 2D interpolation in the frequency domain of images is proposed to improve the estimation accuracy of alignment parameters of rotation angles and translational shifts between the two projection images, which can obtain subpixel and subangle accuracy. The proposed algorithm firstly uses the Fourier transform of two projection images to calculate a discrete cross-correlation matrix and then performs the 2D interpolation around the maximum value in the cross-correlation matrix. The alignment parameters are directly determined according to the position of the maximum value in the cross-correlation matrix after interpolation. Furthermore, the proposed image alignment algorithm and a spectral clustering algorithm are used to compute class averages for single-particle 3D reconstruction. The proposed image alignment algorithm is firstly tested on a Lena image and two cryo-EM datasets. Results show that the proposed image alignment algorithm can estimate the alignment parameters accurately and efficiently. The proposed method is also used to reconstruct preliminary 3D structures from a simulated cryo-EM dataset and a real cryo-EM dataset and to compare them with RELION. Experimental results show that the proposed method can obtain more high-quality class averages than RELION and can obtain higher reconstruction resolution than RELION even without iteration.

KLT picker: Particle picking using data-driven optimal templates.

Particle picking is currently a critical step in the cryo-EM single particle reconstruction pipeline. Despite extensive work on this problem, for many data sets it is still challenging, especially for low SNR micrographs. We present the KLT (Karhunen Loeve Transform) picker, which is fully automatic and requires as an input only the approximated particle size. In particular, it does not require any manual picking. Our method is designed especially to handle low SNR micrographs. It is based on learning a set of optimal templates through the use of multi-variate statistical analysis via the Karhunen Loeve Transform. We evaluate the KLT picker on publicly available data sets and present high-quality results with minimal manual effort.

Imaging Three-Dimensional Elemental Inhomogeneity in Pt-Ni Nanoparticles Using Spectroscopic Single Particle Reconstruction.

The properties of nanoparticles are known to critically depend on their local chemistry but characterizing three-dimensional (3D) elemental segregation at the nanometer scale is highly challenging. Scanning transmission electron microscope (STEM) tomographic imaging is one of the few techniques able to measure local chemistry for inorganic nanoparticles but conventional methodologies often fail due to the high electron dose imparted. Here, we demonstrate realization of a new spectroscopic single particle reconstruction approach built on a method developed by structural biologists. We apply this technique to the imaging of PtNi nanocatalysts and find new evidence of a complex inhomogeneous alloying with a Pt-rich core, a Ni-rich hollow octahedral intermediate shell and a Pt-rich rhombic dodecahedral skeleton framework with less Pt at ⟨100⟩ vertices. The ability to gain evidence of local surface enrichment that varies with the crystallographic orientation of facets and vertices is expected to provide significant insight toward the development of nanoparticles for sensing, medical imaging, and catalysis.

Processing apoferritin with the Appion pipeline.

The 3DEM map challenge provided an opportunity to test different algorithms and workflows for processing single particle cryo-EM data. We were interested in testing whether we could use the standard Appion workflow with minimal manual intervention to achieve similar or better resolution than other challengers. Another question we were interested in testing was what the influence of particle sorting and elimination would be on the resolution and quality of 3D reconstructions. Since apoferritin is historically a challenging particle for single particle reconstruction and the authors of the original map challenge data used only a fraction of the particles present in the dataset, we focused on the apoferritin dataset for our entry. We submitted a 3.7 Šmap from 25,844 particles and a 3.6 Šmap from 53,334 particles and after assessment were among the best of the apoferritin maps that were submitted. Here we present the details of our reconstruction strategy and compare our strategy to that of another high-scoring apoferritin map. Altogether, our results suggest that for a relatively conformationally homogeneous particle like apoferritin, including as many particles as possible after elimination of junk leads to the highest resolution, and the choice of parameters for custom mask creation can lead to subtle but significant changes in the resolution of 3D reconstructions.

APPLE picker: Automatic particle picking, a low-effort cryo-EM framework.

Particle picking is a crucial first step in the computational pipeline of single-particle cryo-electron microscopy (cryo-EM). Selecting particles from the micrographs is difficult especially for small particles with low contrast. As high-resolution reconstruction typically requires hundreds of thousands of particles, manually picking that many particles is often too time-consuming. While template-based particle picking is currently a popular approach, it may suffer from introducing manual bias into the selection process. In addition, this approach is still somewhat time-consuming. This paper presents the APPLE (Automatic Particle Picking with Low user Effort) picker, a simple and novel approach for fast, accurate, and template-free particle picking. This approach is evaluated on publicly available datasets containing micrographs of ß-galactosidase, T20S proteasome, 70S ribosome and keyhole limpet hemocyanin projections.

Structural studies of the endogenous spliceosome - The supraspliceosome.

Pre-mRNA splicing is executed in mammalian cell nuclei within a huge (21MDa) and highly dynamic molecular machine - the supraspliceosome - that individually package pre-mRNA transcripts of different sizes and number of introns into complexes of a unique structure, indicating their universal nature. Detailed structural analysis of this huge and complex structure requires a stepwise approach using hybrid methods. Structural studies of the supraspliceosome by room temperature electron tomography, cryo-electron tomography, and scanning transmission electron microscope mass measurements revealed that it is composed of four native spliceosomes, each resembling an in vitro assembled spliceosome, which are connected by the pre-mRNA. It also elucidated the arrangement of the native spliceosomes within the intact supraspliceosome. Native spliceosomes and supraspliceosomes contain all five spliceosomal U snRNPs together with other splicing factors, and are active in splicing. The structure of the native spliceosome, at a resolution of 20Å, was determined by cryo-electron microscopy, and a unique spatial arrangement of the spliceosomal U snRNPs within the native spliceosome emerged from in silico studies. The supraspliceosome also harbor components for all pre-mRNA processing activities. Thus the supraspliceosome - the endogenous spliceosome - is a stand-alone complete macromolecular machine capable of performing splicing, alternative splicing, and encompass all nuclear pre-mRNA processing activities that the pre-mRNA has to undergo before it can exit from the nucleus to the cytoplasm to encode for protein. Further high-resolution cryo-electron microscopy studies of the endogenous spliceosome are required to decipher the regulation of alternative splicing, and elucidate the network of processing activities within it.

A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy.

BACKGROUND: Single-particle cryo-electron microscopy (cryo-EM) has become a mainstream tool for the structural determination of biological macromolecular complexes. However, high-resolution cryo-EM reconstruction often requires hundreds of thousands of single-particle images. Particle extraction from experimental micrographs thus can be laborious and presents a major practical bottleneck in cryo-EM structural determination. Existing computational methods for particle picking often use low-resolution templates for particle matching, making them susceptible to reference-dependent bias. It is critical to develop a highly efficient template-free method for the automatic recognition of particle images from cryo-EM micrographs. RESULTS: We developed a deep learning-based algorithmic framework, DeepEM, for single-particle recognition from noisy cryo-EM micrographs, enabling automated particle picking, selection and verification in an integrated fashion. The kernel of DeepEM is built upon a convolutional neural network (CNN) composed of eight layers, which can be recursively trained to be highly "knowledgeable". Our approach exhibits an improved performance and accuracy when tested on the standard KLH dataset. Application of DeepEM to several challenging experimental cryo-EM datasets demonstrated its ability to avoid the selection of un-wanted particles and non-particles even when true particles contain fewer features. CONCLUSIONS: The DeepEM methodology, derived from a deep CNN, allows automated particle extraction from raw cryo-EM micrographs in the absence of a template. It demonstrates an improved performance, objectivity and accuracy. Application of this novel method is expected to free the labor involved in single-particle verification, significantly improving the efficiency of cryo-EM data processing.

Common lines modeling for reference free Ab-initio reconstruction in cryo-EM.

We consider the problem of estimating an unbiased and reference-free ab initio model for non-symmetric molecules from images generated by single-particle cryo-electron microscopy. The proposed algorithm finds the globally optimal assignment of orientations that simultaneously respects all common lines between all images. The contribution of each common line to the estimated orientations is weighted according to a statistical model for common lines' detection errors. The key property of the proposed algorithm is that it finds the global optimum for the orientations given the common lines. In particular, any local optima in the common lines energy landscape do not affect the proposed algorithm. As a result, it is applicable to thousands of images at once, very robust to noise, completely reference free, and not biased towards any initial model. A byproduct of the algorithm is a set of measures that allow to asses the reliability of the obtained ab initio model. We demonstrate the algorithm using class averages from two experimental data sets, resulting in ab initio models with resolutions of 20Å or better, even from class averages consisting of as few as three raw images per class.

Ultrastable gold substrates: Properties of a support for high-resolution electron cryomicroscopy of biological specimens.

Electron cryomicroscopy (cryo-EM) allows structure determination of a wide range of biological molecules and specimens. All-gold supports improve cryo-EM images by reducing radiation-induced motion and image blurring. Here we compare the mechanical and electrical properties of all-gold supports to amorphous carbon foils. Gold supports are more conductive, and have suspended foils that are not compressed by differential contraction when cooled to liquid nitrogen temperatures. These measurements show how the choice of support material and geometry can reduce specimen movement by more than an order of magnitude during low-dose imaging. We provide methods for fabrication of all-gold supports and preparation of vitrified specimens. We also analyse illumination geometry for optimal collection of high resolution, low-dose data. Together, the support structures and methods herein can improve the resolution and quality of images from any electron cryomicroscope.

Statistical modeling and removal of lipid membrane projections for cryo-EM structure determination of reconstituted membrane proteins.

This paper describes steps in the single-particle cryo-EM 3D structure determination of membrane proteins in their membrane environment. Using images of the Kv1.2 potassium-channel complex reconstituted into lipid vesicles, we describe procedures for the merging of focal-pairs of exposures and the removal of the vesicle-membrane signal from the micrographs. These steps allow 3D reconstruction to be performed from the protein particle images. We construct a 2D statistical model of the vesicle structure based on higher-order singular value decomposition (HOSVD), by taking into account the structural symmetries of the vesicles in polar coordinates. Non-roundness in the vesicle structure is handled with a non-linear shape alignment to a reference, which ensures a compact model representation. The results show that the learned model is an accurate representation of the imaged vesicle structures. Precise removal of the strong membrane signals allows better alignment and classification of images of small membrane-protein particles, and allows higher-resolution 3D reconstruction.

Introduction to high-resolution cryo-electron microscopy.

For many years two techniques have dominated structural biology - X-ray crystallography and NMR spectroscopy. Traditional cryo-electron microscopy of biological macromolecules produced macromolecular reconstructions at resolution limited to 6-10 Å. Recent development of transmission electron microscopes, in particular the development of direct electron detectors, and continuous improvements in the available software, have led to the "resolution revolution" in cryo-EM. It is now possible to routinely obtain near-atomic-resolution 3D maps of intact biological macromolecules as small as ~100 kDa. Thus, cryo-EM is now becoming the method of choice for structural analysis of many complex assemblies that are unsuitable for structure determination by other methods.