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The μ-opioid receptor (μOR) is a G-protein-coupled receptor (GPCR) and the target of most clinically and recreationally used opioids. The induced positive effects of analgesia and euphoria are mediated by μOR signalling through the adenylyl cyclase-inhibiting heterotrimeric G protein G i . Here we present the 3.5 Å resolution cryo-electron microscopy structure of the μOR bound to the agonist peptide DAMGO and nucleotide-free G i . DAMGO occupies the morphinan ligand pocket, with its N terminus interacting with conserved receptor residues and its C terminus engaging regions important for opioid-ligand selectivity. Comparison of the μOR–G i complex to previously determined structures of other GPCRs bound to the stimulatory G protein G s reveals differences in the position of transmembrane receptor helix 6 and in the interactions between the G protein α-subunit and the receptor core. Together, these results shed light on the structural features that contribute to the G i protein-coupling specificity of the µOR. A cryo-electron structure of the µ-opioid receptor in complex with the peptide agonist DAMGO and the inhibitory G protein G i reveals structural determinants of its G protein-binding specificity.

Fast inhibitory neurotransmission in the brain is principally mediated by the neurotransmitter GABA (γ-aminobutyric acid) and its synaptic target, the type A GABA receptor (GABA A receptor). Dysfunction of this receptor results in neurological disorders and mental illnesses including epilepsy, anxiety and insomnia. The GABA A receptor is also a prolific target for therapeutic, illicit and recreational drugs, including benzodiazepines, barbiturates, anaesthetics and ethanol. Here we present high-resolution cryo-electron microscopy structures of the human α1β2γ2 GABA A receptor, the predominant isoform in the adult brain, in complex with GABA and the benzodiazepine site antagonist flumazenil, the first-line clinical treatment for benzodiazepine overdose. The receptor architecture reveals unique heteromeric interactions for this important class of inhibitory neurotransmitter receptor. This work provides a template for understanding receptor modulation by GABA and benzodiazepines, and will assist rational approaches to therapeutic targeting of this receptor for neurological disorders and mental illness. The cryo-electron microscopy structure of the type A GABA receptor bound to GABA and the benzodiazepine site antagonist flumazenil reveals structural mechanisms that underlie intersubunit interactions and ligand selectivity of the receptor.

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.

There has been considerable recent progress in designing new proteins using deep-learning methods 1 – 9 . Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models 10 , 11 have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence–structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications. Fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks yields a generative model for protein design that achieves outstanding performance on a wide range of protein structure and function design challenges.

Type A γ-aminobutyric acid (GABA A ) receptors are pentameric ligand-gated ion channels and the main drivers of fast inhibitory neurotransmission in the vertebrate nervous system 1 , 2 . Their dysfunction is implicated in a range of neurological disorders, including depression, epilepsy and schizophrenia 3 , 4 . Among the numerous assemblies that are theoretically possible, the most prevalent in the brain are the α1β2/3γ2 GABA A receptors 5 . The β3 subunit has an important role in maintaining inhibitory tone, and the expression of this subunit alone is sufficient to rescue inhibitory synaptic transmission in β1–β3 triple knockout neurons 6 . So far, efforts to generate accurate structural models for heteromeric GABA A receptors have been hampered by the use of engineered receptors and the presence of detergents 7 – 9 . Notably, some recent cryo-electron microscopy reconstructions have reported ‘collapsed’ conformations 8 , 9 ; however, these disagree with the structure of the prototypical pentameric ligand-gated ion channel the Torpedo nicotinic acetylcholine receptor 10 , 11 , the large body of structural work on homologous homopentameric receptor variants 12 and the logic of an ion-channel architecture. Here we present a high-resolution cryo-electron microscopy structure of the full-length human α1β3γ2L—a major synaptic GABA A receptor isoform—that is functionally reconstituted in lipid nanodiscs. The receptor is bound to a positive allosteric modulator ‘megabody’ and is in a desensitized conformation. Each GABA A receptor pentamer contains two phosphatidylinositol-4,5-bisphosphate molecules, the head groups of which occupy positively charged pockets in the intracellular juxtamembrane regions of α1 subunits. Beyond this level, the intracellular M3–M4 loops are largely disordered, possibly because interacting post-synaptic proteins are not present. This structure illustrates the molecular principles of heteromeric GABA A receptor organization and provides a reference framework for future mechanistic investigations of GABAergic signalling and pharmacology. A high-resolution cryo-electron microscopy structure is reported for the full-length human α1β3γ2L GABA A receptor, functionally reconstituted in lipid nanodiscs.

Cryo-electron microscopy (cryoEM) is becoming the preferred method for resolving protein structures. Low signal-to-noise ratio (SNR) in cryoEM images reduces the confidence and throughput of structure determination during several steps of data processing, resulting in impediments such as missing particle orientations. Denoising cryoEM images can not only improve downstream analysis but also accelerate the time-consuming data collection process by allowing lower electron dose micrographs to be used for analysis. Here, we present Topaz-Denoise, a deep learning method for reliably and rapidly increasing the SNR of cryoEM images and cryoET tomograms. By training on a dataset composed of thousands of micrographs collected across a wide range of imaging conditions, we are able to learn models capturing the complexity of the cryoEM image formation process. The general model we present is able to denoise new datasets without additional training. Denoising with this model improves micrograph interpretability and allows us to solve 3D single particle structures of clustered protocadherin, an elongated particle with previously elusive views. We then show that low dose collection, enabled by Topaz-Denoise, improves downstream analysis in addition to reducing data collection time. We also present a general 3D denoising model for cryoET. Topaz-Denoise and pre-trained general models are now included in Topaz. We expect that Topaz-Denoise will be of broad utility to the cryoEM community for improving micrograph and tomogram interpretability and accelerating analysis. The low signal-to-noise ratio (SNR) in cryoEM images can make the first steps in cryoEM structure determination challenging, particularly for non-globular and small proteins. Here, the authors present Topaz-Denoise, a deep learning based method for micrograph denoising that significantly increases the SNR of cryoEM images and cryoET tomograms, which helps to accelerate the cryoEM pipeline.

The fruit fly Drosophila melanogaster has emerged as a key model organism in neuroscience, in large part due to the concentration of collaboratively generated molecular, genetic and digital resources available for it. Here we complement the approximately 140,000 neuron FlyWire whole-brain connectome 1 with a systematic and hierarchical annotation of neuronal classes, cell types and developmental units (hemilineages). Of 8,453 annotated cell types, 3,643 were previously proposed in the partial hemibrain connectome 2 , and 4,581 are new types, mostly from brain regions outside the hemibrain subvolume. Although nearly all hemibrain neurons could be matched morphologically in FlyWire, about one-third of cell types proposed for the hemibrain could not be reliably reidentified. We therefore propose a new definition of cell type as groups of cells that are each quantitatively more similar to cells in a different brain than to any other cell in the same brain, and we validate this definition through joint analysis of FlyWire and hemibrain connectomes. Further analysis defined simple heuristics for the reliability of connections between brains, revealed broad stereotypy and occasional variability in neuron count and connectivity, and provided evidence for functional homeostasis in the mushroom body through adjustments of the absolute amount of excitatory input while maintaining the excitation/inhibition ratio. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open-source toolchain for brain-scale comparative connectomics. A consensus cell type atlas for the fly brain provides both an intellectual framework and open-source toolchains for brain-scale comparative connectomics.

The class A adenosine A 1 receptor (A 1 R) is a G-protein-coupled receptor that preferentially couples to inhibitory G i/o heterotrimeric G proteins, has been implicated in numerous diseases, yet remains poorly targeted. Here we report the 3.6 Å structure of the human A 1 R in complex with adenosine and heterotrimeric G i2 protein determined by Volta phase plate cryo-electron microscopy. Compared to inactive A 1 R, there is contraction at the extracellular surface in the orthosteric binding site mediated via movement of transmembrane domains 1 and 2. At the intracellular surface, the G protein engages the A 1 R primarily via amino acids in the C terminus of the Gα i α5-helix, concomitant with a 10.5 Å outward movement of the A 1 R transmembrane domain 6. Comparison with the agonist-bound β 2 adrenergic receptor–G s -protein complex reveals distinct orientations for each G-protein subtype upon engagement with its receptor. This active A 1 R structure provides molecular insights into receptor and G-protein selectivity. The cryo-electron microscopy structure of the human adenosine A 1 receptor in complex with adenosine and heterotrimeric G i2 protein provides molecular insights into receptor and G-protein selectivity.

In late 2022, SARS-CoV-2 Omicron subvariants have become highly diversified, and XBB is spreading rapidly around the world. Our phylogenetic analyses suggested that XBB emerged through the recombination of two cocirculating BA.2 lineages, BJ.1 and BM.1.1.1 (a progeny of BA.2.75), during the summer of 2022. XBB.1 is the variant most profoundly resistant to BA.2/5 breakthrough infection sera to date and is more fusogenic than BA.2.75. The recombination breakpoint is located in the receptor-binding domain of spike, and each region of the recombinant spike confers immune evasion and increases fusogenicity. We further provide the structural basis for the interaction between XBB.1 spike and human ACE2. Finally, the intrinsic pathogenicity of XBB.1 in male hamsters is comparable to or even lower than that of BA.2.75. Our multiscale investigation provides evidence suggesting that XBB is the first observed SARS-CoV-2 variant to increase its fitness through recombination rather than substitutions. XBB is the first recombinant, globally dominant variant of SARS-CoV-2. Here, the authors examine the variant’s origins and virological properties, showing it is the first example of SARS-CoV-2 improving its fitness through recombination.