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The number of protein structures being determined by cryo-electron microscopy is growing at an explosive rate. The number of protein structures being determined by cryo-electron microscopy is growing at an explosive rate.

Move over X-ray crystallography. Cryo-electron microscopy is kicking up a storm by revealing the hidden machinery of the cell.

Single-particle electron cryo-microscopy (cryo-EM) is a powerful method for solving the three-dimensional structures of biological macromolecules. The technological development of transmission electron microscopes, detectors and automated procedures in combination with user-friendly image processing software and ever-increasing computational power have made cryo-EM a successful and expanding technology over the past decade 1 . At resolutions better than 4 Å, atomic model building starts to become possible, but the direct visualization of true atomic positions in protein structure determination requires much higher (better than 1.5 Å) resolution, which so far has not been attained by cryo-EM. The direct visualization of atom positions is essential for understanding the mechanisms of protein-catalysed chemical reactions, and for studying how drugs bind to and interfere with the function of proteins 2 . Here we report a 1.25 Å-resolution structure of apoferritin obtained by cryo-EM with a newly developed electron microscope that provides, to our knowledge, unprecedented structural detail. Our apoferritin structure has almost twice the 3D information content of the current world record reconstruction (at 1.54 Å resolution 3 ). We can visualize individual atoms in a protein, see density for hydrogen atoms and image single-atom chemical modifications. Beyond the nominal improvement in resolution, we also achieve a substantial improvement in the quality of the cryo-EM density map, which is highly relevant for using cryo-EM in structure-based drug design. Advances in electron cryo-microscopy allow the structure of apoferritin to be determined at a resolution that enables the visualization of individual atoms.

Cryogenic electron microscopy (cryo-EM) is widely used to study biological macromolecules that comprise regions with disorder, flexibility or partial occupancy. For example, membrane proteins are often kept in solution with detergent micelles and lipid nanodiscs that are locally disordered. Such spatial variability negatively impacts computational three-dimensional (3D) reconstruction with existing iterative refinement algorithms that assume rigidity. We introduce non-uniform refinement, an algorithm based on cross-validation optimization, which automatically regularizes 3D density maps during refinement to account for spatial variability. Unlike common shift-invariant regularizers, non-uniform refinement systematically removes noise from disordered regions, while retaining signal useful for aligning particle images, yielding dramatically improved resolution and 3D map quality in many cases. We obtain high-resolution reconstructions for multiple membrane proteins as small as 100 kDa, demonstrating increased effectiveness of cryo-EM for this class of targets critical in structural biology and drug discovery. Non-uniform refinement is implemented in the cryoSPARC software package. Membrane proteins exhibit spatial variation in rigidity and disorder, which poses a challenge for traditional cryo-EM reconstruction algorithms. Non-uniform refinement accounts for this spatial variability, yielding improved 3D reconstruction quality even for small membrane proteins.

Cryo-electron microscopy is a popular method for the determination of protein structures; however, identifying a sufficient number of particles for analysis can take months of manual effort. Current computational approaches find many false positives and require ad hoc postprocessing, especially for unusually shaped particles. To address these shortcomings, we develop Topaz, an efficient and accurate particle-picking pipeline using neural networks trained with a general-purpose positive-unlabeled learning method. This framework enables particle detection models to be trained with few sparsely labeled particles and no labeled negatives. Topaz retrieves many more real particles than conventional picking methods while maintaining low false-positive rates, is capable of picking challenging unusually shaped proteins (for example, small, non-globular and asymmetric particles), produces more representative particle sets and does not require post hoc curation. We demonstrate the performance of Topaz on two difficult datasets and three conventional datasets. Topaz is modular, standalone, free and open source (http://topaz.csail.mit.edu).

Cryo-electron microscopy (cryo-EM) single-particle analysis has proven powerful in determining the structures of rigid macromolecules. However, many imaged protein complexes exhibit conformational and compositional heterogeneity that poses a major challenge to existing three-dimensional reconstruction methods. Here, we present cryoDRGN, an algorithm that leverages the representation power of deep neural networks to directly reconstruct continuous distributions of 3D density maps and map per-particle heterogeneity of single-particle cryo-EM datasets. Using cryoDRGN, we uncovered residual heterogeneity in high-resolution datasets of the 80S ribosome and the RAG complex, revealed a new structural state of the assembling 50S ribosome, and visualized large-scale continuous motions of a spliceosome complex. CryoDRGN contains interactive tools to visualize a dataset’s distribution of per-particle variability, generate density maps for exploratory analysis, extract particle subsets for use with other tools and generate trajectories to visualize molecular motions. CryoDRGN is open-source software freely available at http://cryodrgn.csail.mit.edu.CryoDRGN is an unsupervised machine learning algorithm that reconstructs continuous distributions of three-dimensional density maps from heterogeneous single-particle cryo-EM data.

Interpreting electron cryo-microscopy (cryo-EM) maps with atomic models requires high levels of expertise and labour-intensive manual intervention in three-dimensional computer graphics programs 1 , 2 . Here we present ModelAngelo, a machine-learning approach for automated atomic model building in cryo-EM maps. By combining information from the cryo-EM map with information from protein sequence and structure in a single graph neural network, ModelAngelo builds atomic models for proteins that are of similar quality to those generated by human experts. For nucleotides, ModelAngelo builds backbones with similar accuracy to those built by humans. By using its predicted amino acid probabilities for each residue in hidden Markov model sequence searches, ModelAngelo outperforms human experts in the identification of proteins with unknown sequences. ModelAngelo will therefore remove bottlenecks and increase objectivity in cryo-EM structure determination. ModelAngelo builds atomic models and identifies proteins with unknown sequences in cryo-EM maps.

ABCB1 detoxifies cells by exporting diverse xenobiotic compounds, thereby limiting drug disposition and contributing to multidrug resistance in cancer cells. Multiple small-molecule inhibitors and inhibitory antibodies have been developed for therapeutic applications, but the structural basis of their activity is insufficiently understood. We determined cryo-EM structures of nanodisc-reconstituted, human ABCB1 in complex with the Fab fragment of the inhibitory, monoclonal antibody MRK16 and bound to a substrate (the antitumor drug vincristine) or to the potent inhibitors elacridar, tariquidar, or zosuquidar. We found that inhibitors bound in pairs, with one molecule lodged in the central drug-binding pocket and a second extending into a phenylalanine-rich cavity that we termed the “access tunnel.” This finding explains how inhibitors can act as substrates at low concentration, but interfere with the early steps of the peristaltic extrusion mechanism at higher concentration. Our structural data will also help the development of more potent and selective ABCB1 inhibitors.

The three-dimensional positions of atoms in protein molecules define their structure and their roles in biological processes. The more precisely atomic coordinates are determined, the more chemical information can be derived and the more mechanistic insights into protein function may be inferred. Electron cryo-microscopy (cryo-EM) single-particle analysis has yielded protein structures with increasing levels of detail in recent years 1 , 2 . However, it has proved difficult to obtain cryo-EM reconstructions with sufficient resolution to visualize individual atoms in proteins. Here we use a new electron source, energy filter and camera to obtain a 1.7 Å resolution cryo-EM reconstruction for a human membrane protein, the β3 GABA A receptor homopentamer 3 . Such maps allow a detailed understanding of small-molecule coordination, visualization of solvent molecules and alternative conformations for multiple amino acids, and unambiguous building of ordered acidic side chains and glycans. Applied to mouse apoferritin, our strategy led to a 1.22 Å resolution reconstruction that offers a genuine atomic-resolution view of a protein molecule using single-particle cryo-EM. Moreover, the scattering potential from many hydrogen atoms can be visualized in difference maps, allowing a direct analysis of hydrogen-bonding networks. Our technological advances, combined with further approaches to accelerate data acquisition and improve sample quality, provide a route towards routine application of cryo-EM in high-throughput screening of small molecule modulators and structure-based drug discovery. Advances in electron cryo-microscopy hardware allow proteins to be studied at atomic resolution.

A software tool, cryoSPARC, addresses the speed bottleneck in cryo-EM image processing, enabling automated macromolecular structure determination in hours on a desktop computer without requiring a starting model. Single-particle electron cryomicroscopy (cryo-EM) is a powerful method for determining the structures of biological macromolecules. With automated microscopes, cryo-EM data can often be obtained in a few days. However, processing cryo-EM image data to reveal heterogeneity in the protein structure and to refine 3D maps to high resolution frequently becomes a severe bottleneck, requiring expert intervention, prior structural knowledge, and weeks of calculations on expensive computer clusters. Here we show that stochastic gradient descent (SGD) and branch-and-bound maximum likelihood optimization algorithms permit the major steps in cryo-EM structure determination to be performed in hours or minutes on an inexpensive desktop computer. Furthermore, SGD with Bayesian marginalization allows ab initio 3D classification, enabling automated analysis and discovery of unexpected structures without bias from a reference map. These algorithms are combined in a user-friendly computer program named cryoSPARC ( http://www.cryosparc.com ).