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Cryo-EM has revealed the structures of many challenging yet exciting macromolecular assemblies at near-atomic resolution (3–4.5Å), providing biological phenomena with molecular descriptions. However, at these resolutions, accurately positioning individual atoms remains challenging and error-prone. Manually refining thousands of amino acids – typical in a macromolecular assembly – is tedious and time-consuming. We present an automated method that can improve the atomic details in models that are manually built in near-atomic-resolution cryo-EM maps. Applying the method to three systems recently solved by cryo-EM, we are able to improve model geometry while maintaining the fit-to-density. Backbone placement errors are automatically detected and corrected, and the refinement shows a large radius of convergence. The results demonstrate that the method is amenable to structures with symmetry, of very large size, and containing RNA as well as covalently bound ligands. The method should streamline the cryo-EM structure determination process, providing accurate and unbiased atomic structure interpretation of such maps.

Folding of mammalian genomes into spatial domains is critical for gene regulation. The insulator protein CTCF and cohesin control domain location by folding domains into loop structures, which are widely thought to be stable. Combining genomic and biochemical approaches we show that CTCF and cohesin co-occupy the same sites and physically interact as a biochemically stable complex. However, using single-molecule imaging we find that CTCF binds chromatin much more dynamically than cohesin (~1–2 min vs. ~22 min residence time). Moreover, after unbinding, CTCF quickly rebinds another cognate site unlike cohesin for which the search process is long (~1 min vs. ~33 min). Thus, CTCF and cohesin form a rapidly exchanging 'dynamic complex' rather than a typical stable complex. Since CTCF and cohesin are required for loop domain formation, our results suggest that chromatin loops are dynamic and frequently break and reform throughout the cell cycle. A human cell contains about 2 meters of DNA tightly packed in a compartment called the nucleus. Within the space inside the nucleus, different parts of the DNA fold into distinct bundles known as domains. These domains are important for organising the genome and are crucial for regulating gene expression, by stimulating specific DNA segments to activate certain genes. Previous research has shown that DNA segments within the same domain frequently interact, whereas DNA segments in different domains rarely do. The domains are often folded into loops that are held together by a ring-shaped protein complex called cohesin, while another protein called CTCF positions cohesin and thereby sets the boundaries between the domains. Some mutations are known to disrupt these boundaries, which allows certain DNA segments to activate the wrong genes. This can lead to cancer or cause defects when embryos are developing. However, we do not currently understand how these domains are formed or maintained. In particular, it was unclear whether these loop domains are stable or dynamic structures. Hansen et al. addressed these questions in embryonic stem cells from mice and human cancer cells. It was found that cohesin and CTCF form a complex that binds to the DNA and likely holds the loops together. In further experiments, single molecules of cohesin and CTCF were tracked inside cells using super-resolution microscopy. The results showed that CTCF and cohesin bind to DNA with different dynamics: CTCF binds the DNA for about a minute, whereas cohesin binds the DNA for about 20–25 minutes. Once CTCF detaches from DNA, it quickly rebinds DNA at another site, but cohesin takes much longer. These observations suggest that rather than remaining static, chromatin domains are held together by a dynamic protein complex, with a molecular composition that exchanges over time. This results suggests that DNA loop domains, which were generally assumed to be very stable anchor points, are in fact highly dynamic structures that frequently fall apart and reform. The next challenge will be to understand how the dynamic nature of these loop domains contribute to gene regulation. This may, one day, enable us to manipulate the domains to correct faulty folding of DNA in cancer and other diseases.

By reaching near-atomic resolution for a wide range of specimens, single-particle cryo-EM structure determination is transforming structural biology. However, the necessary calculations come at large computational costs, which has introduced a bottleneck that is currently limiting throughput and the development of new methods. Here, we present an implementation of the RELION image processing software that uses graphics processors (GPUs) to address the most computationally intensive steps of its cryo-EM structure determination workflow. Both image classification and high-resolution refinement have been accelerated more than an order-of-magnitude, and template-based particle selection has been accelerated well over two orders-of-magnitude on desktop hardware. Memory requirements on GPUs have been reduced to fit widely available hardware, and we show that the use of single precision arithmetic does not adversely affect results. This enables high-resolution cryo-EM structure determination in a matter of days on a single workstation.

Imaging is used to map activity across populations of neurons. Microscopes with cellular resolution have small (<1 millimeter) fields of view and cannot simultaneously image activity distributed across multiple brain areas. Typical large field of view microscopes do not resolve single cells, especially in the axial dimension. We developed a 2-photon random access mesoscope (2p-RAM) that allows high-resolution imaging anywhere within a volume spanning multiple brain areas (∅ 5 mm x 1 mm cylinder). 2p-RAM resolution is near diffraction limited (lateral, 0.66 μm, axial 4.09 μm at the center; excitation wavelength = 970 nm; numerical aperture = 0.6) over a large range of excitation wavelengths. A fast three-dimensional scanning system allows efficient sampling of neural activity in arbitrary regions of interest across the entire imaging volume. We illustrate the use of the 2p-RAM by imaging neural activity in multiple, non-contiguous brain areas in transgenic mice expressing protein calcium sensors.

Methylation of adenosines at the N6 position (m6A) is a dynamic and abundant epitranscriptomic mark that regulates critical aspects of eukaryotic RNA metabolism in numerous biological processes. The RNA methyltransferases METTL3 and METTL14 are components of a multisubunit m6A writer complex whose enzymatic activity is substantially higher than the activities of METTL3 or METTL14 alone. The molecular mechanism underpinning this synergistic effect is poorly understood. Here we report the crystal structure of the catalytic core of the human m6A writer complex comprising METTL3 and METTL14. The structure reveals the heterodimeric architecture of the complex and donor substrate binding by METTL3. Structure-guided mutagenesis indicates that METTL3 is the catalytic subunit of the complex, whereas METTL14 has a degenerate active site and plays non-catalytic roles in maintaining complex integrity and substrate RNA binding. These studies illuminate the molecular mechanism and evolutionary history of eukaryotic m6A modification in post-transcriptional genome regulation.

Almost all critical functions in cells rely on specific protein–protein interactions. Understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack a thorough understanding of the energetics of association of proteins. Here, we introduce a new and simple approach to predict binding affinity based on functional and structural features of the biological system, namely the network of interfacial contacts. We assess its performance against a protein–protein binding affinity benchmark and show that both experimental methods used for affinity measurements and conformational changes have a strong impact on prediction accuracy. Using a subset of complexes with reliable experimental binding affinities and combining our contacts and contact-types-based model with recent observations on the role of the non-interacting surface in protein–protein interactions, we reach a high prediction accuracy for such a diverse dataset outperforming all other tested methods. Proteins help to copy DNA, transport nutrients and perform many other important roles in cells. To perform these tasks, proteins often interact with other proteins and work together. These interactions can be very complex because each protein has a three-dimensional shape that may change when it binds to other proteins. Also, two proteins may form several connections with each other. It is possible to carry out experiments to calculate how likely it is that two proteins will physically interact with each other and how strong their connections will be. However, these measurements are time consuming and costly to do. Some researchers have developed computer models to help predict the interactions between proteins, but these models are often incorrect because they leave out some of the chemical or physical properties that influence the ability of proteins to interact. With the aim of making a better model, Vangone and Bonvin examined 122 different combinations of proteins whose abilities to interact had previously been experimentally measured. Vangone and Bonvin found that the number of connections between each pair of proteins was a strong predictor of how tightly the proteins connect to each other. Particular features of the surface of the proteins—specifically, the region defined as the non-interacting surface—can also influence how strong the interaction is. Vangone and Bonvin used this information to develop a new model that predicts how tightly proteins interact with each other based on the number of connections between the two proteins and the characteristics of the non-interacting surface. The model is simple, and Vangone and Bonvin show that it is more accurate than previous models. Defects in the interactions between proteins can lead to many diseases in humans, so this model may be useful for the development of new drugs to treat these conditions.

Clustering of proteins into micrometer-sized structures at membranes is observed in many signaling pathways. Most models of clustering are specific to particular systems, and relationships between physical properties of the clusters and their molecular components are not well understood. We report biochemical reconstitution on supported lipid bilayers of protein clusters containing the adhesion receptor Nephrin and its cytoplasmic partners, Nck and N-WASP. With Nephrin attached to the bilayer, multivalent interactions enable these proteins to polymerize on the membrane surface and undergo two-dimensional phase separation, producing micrometer-sized clusters. Dynamics and thermodynamics of the clusters are modulated by the valencies and affinities of the interacting species. In the presence of the Arp2/3 complex, the clusters assemble actin filaments, suggesting that clustering of regulatory factors could promote local actin assembly at membranes. Interactions between multivalent proteins could be a general mechanism for cytoplasmic adaptor proteins to organize membrane receptors into micrometer-scale signaling zones. The membrane that surrounds a cell is made up of a mixture of lipid molecules and proteins. Membrane proteins perform a wide range of roles, including transmitting signals into, and out of, cells and helping neighboring cells to stick together. To perform these tasks, these proteins commonly need to bind to other molecules—collectively known as ligands—that are found either inside or outside the cell. Membrane proteins are able to move around within the membrane, and in many systems, ligand binding causes the membrane proteins to cluster together. Although this clustering has been seen in many different systems, no general principles that describe how clustering occurs had been found. Now, Banjade and Rosen have constructed an artificial cell membrane to investigate the clustering of a membrane protein called Nephrin, which is essential for kidneys to function correctly. When it is activated, Nephrin interacts with protein ligands called Nck and N-WASP that are found inside cells and helps filaments of a protein called actin to form. These filaments perform a number of roles including enabling cells to adhere to each other and to move. In Banjade and Rosen's artificial system, when a critical concentration of ligands was exceeded, clusters of Nephrin, Nck and N-WASP suddenly formed. This suggests that the clusters form through a physical process known as ‘phase separation’. Banjade and Rosen found that this critical concentration depends on how strongly the proteins interact and the number of sites they possess to bind each other. Within the clusters, the three proteins formed large polymer chains. The clusters were mobile and, over time, small clusters coalesced into larger clusters. Even though the clusters persisted for hours, individual proteins did not stay in a given cluster for long and instead continuously exchanged back-and-forth between the cluster and its surroundings. When actin and another protein complex that interacts with N-WASP were added to the artificial membrane system, actin filaments began to form at the protein clusters. Banjade and Rosen suggest that such clusters act as ‘signaling zones’ that coordinate the construction of the actin filaments. Regions that are also found in many other signaling proteins mediate the interactions between Nephrin, Nck and N-WASP. Banjade and Rosen therefore suggest that phase separation and protein polymer formation could explain how many different types of membrane proteins form clusters.

Piezo ion channels are activated by various types of mechanical stimuli and function as biological pressure sensors in both vertebrates and invertebrates. To date, mechanical stimuli are the only means to activate Piezo ion channels and whether other modes of activation exist is not known. In this study, we screened ∼3.25 million compounds using a cell-based fluorescence assay and identified a synthetic small molecule we termed Yoda1 that acts as an agonist for both human and mouse Piezo1. Functional studies in cells revealed that Yoda1 affects the sensitivity and the inactivation kinetics of mechanically induced responses. Characterization of Yoda1 in artificial droplet lipid bilayers showed that Yoda1 activates purified Piezo1 channels in the absence of other cellular components. Our studies demonstrate that Piezo1 is amenable to chemical activation and raise the possibility that endogenous Piezo1 agonists might exist. Yoda1 will serve as a key tool compound to study Piezo1 regulation and function. Within our bodies, cells and tissues are constantly being pushed and pulled by their surrounding environment. These mechanical forces are then transformed into electrical or chemical signals by cells. This process is crucial for many biological structures, such as blood vessels, to develop correctly, and is also a key part of our senses of touch and hearing. In 2010, researchers discovered a group of ion channels—proteins embedded in the membrane that surrounds a cell—that open up when a force is applied and allow ions such as calcium, potassium, and sodium to flow. This movement of ions generates the electrical response of the cell to the applied force. However, not much is known about how these ‘Piezo’ ion channels work. To investigate this, it is important to be able to precisely control how and when the Piezo channels open. Many other ion channels are studied by using small chemical compounds to activate them, but there were none that were known to act on Piezo proteins. Syeda et al.—including some of the researchers involved in the 2010 work—screened over three million compounds for their ability to cause calcium ions to flow into human cells to try to identify chemicals that activate the Piezo channels. This revealed one promising candidate named Yoda1, which specifically activated Piezo1: a Piezo protein that had previously been linked to a role in blood vessel development in embryos. To investigate how Yoda1 activates Piezo1, Syeda et al. placed Piezo1 in an artificial cell membrane that did not contain any other cellular components. When Yoda1 was added to this set up, the Piezo1 channels opened up. This suggests that Piezo1 and Yoda1 interact in a manner that does not require additional cellular components other than a cell membrane. Separate work by Cahalan, Lukacs et al. uses Yoda1 to reveal that Piezo1 helps to control the volume of red blood cells, showing that in the future, Yoda1 could be valuable in research that investigates the roles of Piezo1.

Cells can enter into a dormant state when faced with unfavorable conditions. However, how cells enter into and recover from this state is still poorly understood. Here, we study dormancy in different eukaryotic organisms and find it to be associated with a significant decrease in the mobility of organelles and foreign tracer particles. We show that this reduced mobility is caused by an influx of protons and a marked acidification of the cytoplasm, which leads to widespread macromolecular assembly of proteins and triggers a transition of the cytoplasm to a solid-like state with increased mechanical stability. We further demonstrate that this transition is required for cellular survival under conditions of starvation. Our findings have broad implications for understanding alternative physiological states, such as quiescence and dormancy, and create a new view of the cytoplasm as an adaptable fluid that can reversibly transition into a protective solid-like state. Most organisms live in unpredictable environments, which can often lead to nutrient shortages and other conditions that limit their ability to grow. To survive in these harsh conditions, many organisms adopt a dormant state in which their metabolism slows down to conserve vital energy. When the environmental conditions improve, the organisms can return to their normal state and continue to grow. The interior of cells is known as the cytoplasm. It is very crowded and contains many molecules and compartments called organelles that carry out a variety of vital processes. The cytoplasm has long been considered to be fluid-like in nature, but recent evidence suggests that in bacterial cells it can solidify to resemble a soft glass-type material under certain conditions. When cells become dormant they stop dividing and reorganise their cytoplasm in several ways; for example, the water content drops and many essential proteins form storage compartments. However, it was not clear how cells regulate the structure of the cytoplasm to enter into or exit from dormancy. Now, Munder et al. analyse the changes that occur in the cytoplasm when baker’s yeast cells enter a dormant state. The experiments show that when yeast cells are deprived of energy – as happens during dormancy – the cytoplasm becomes more acidic than normal. This limits the ability of molecules and organelles to move around the cytoplasm. Similar results were also seen in other types of fungi and an amoeba. Munder et al. found that this increase in acidity during dormancy causes many proteins to interact with each other and form large clumps or filament structures that result in the cytoplasm becoming stiffer. A separate study by Joyner et al. found that when yeast cells are starved of sugar, two large molecules are less able to move around the cell interior. Together, the findings of the studies suggest that the interior of cells can undergo a transition from a fluid-like to a more solid-like state to protect the cells from damage when energy is in short supply. The next challenge is to understand the molecular mechanisms that cause the physical properties of the cytoplasm to change under different conditions.