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Modulators of the G protein-coupled A2A adenosine receptor (A2AAR) have been considered promising agents to treat Parkinson's disease, inflammation, cancer, and central nervous system disorders. Herein, we demonstrate that a thiophene modification at the C8 position in the common adenine scaffold converted an A2AAR agonist into an antagonist. We synthesized and characterized a novel A2AAR antagonist, 2 (LJ-4517), with Ki = 18.3 nM. X-ray crystallographic structures of 2 in complex with two thermostabilized A2AAR constructs were solved at 2.05 and 2.80 Å resolutions. In contrast to A2AAR agonists, which simultaneously interact with both Ser2777.42 and His2787.43, 2 only transiently contacts His2787.43, which can be direct or water-mediated. The n-hexynyl group of 2 extends into an A2AAR exosite. Structural analysis revealed that the introduced thiophene modification restricted receptor conformational rearrangements required for subsequent activation. This approach can expand the repertoire of adenosine receptor antagonists that can be designed based on available agonist scaffolds.

Investigating the 3D structures and rearrangements of organelles within a single cell is critical for better characterizing cellular function. Imaging approaches such as soft X-ray tomography have been widely applied to reveal a complex subcellular organization involving multiple inter-organelle interactions. However, 3D segmentation of organelle instances has been challenging despite its importance in organelle characterization. Here we propose an intensity-based post-processing tool to identify and separate organelle instances. Our tool separates sphere-like (insulin vesicle) and columnar-shaped organelle instances (mitochondrion) based on the intensity of raw tomograms, semantic segmentation masks, and organelle morphology. We validate our tool using synthetic tomograms of organelles and experimental tomograms of pancreatic β-cells to separate insulin vesicle and mitochondria instances. As compared to the commonly used connected regions labeling, watershed, and watershed + Gaussian filter methods, our tool results in improved accuracy in identifying organelles in the synthetic tomograms and an improved description of organelle structures in β-cell tomograms. In addition, under different experimental treatment conditions, significant changes in volumes and intensities of both insulin vesicle and mitochondrion are observed in our instance results, revealing their potential roles in maintaining normal β-cell function. Our tool is expected to be applicable for improving the instance segmentation of other images obtained from different cell types using multiple imaging modalities.

Comprehensive modeling of a whole cell requires an integration of vast amounts of information on various aspects of the cell and its parts. To divide and conquer this task, we introduce Bayesian metamodeling, a general approach to modeling complex systems by integrating a collection of heterogeneous input models. Each input model can in principle be based on any type of data and can describe a different aspect of the modeled system using any mathematical representation, scale, and level of granularity. These input models are 1) converted to a standardized statistical representation relying on probabilistic graphical models, 2) coupled by modeling their mutual relations with the physical world, and 3) finally harmonized with respect to each other. To illustrate Bayesian metamodeling, we provide a proof-of-principle metamodel of glucose-stimulated insulin secretion by human pancreatic β-cells. The input models include a coarse-grained spatiotemporal simulation of insulin vesicle trafficking, docking, and exocytosis; a molecular network model of glucose-stimulated insulin secretion signaling; a network model of insulin metabolism; a structural model of glucagon-like peptide-1 receptor activation; a linear model of a pancreatic cell population; and ordinary differential equations for systemic postprandial insulin response. Metamodeling benefits from decentralized computing, while often producing a more accurate, precise, and complete model that contextualizes input models as well as resolves conflicting information. We anticipate Bayesian metamodeling will facilitate collaborative science by providing a framework for sharing expertise, resources, data, and models, as exemplified by the Pancreatic β-Cell Consortium.

Cryo-electron tomography provides the opportunity for unsupervised discovery of endogenous complexes in situ. This process usually requires particle picking, clustering and alignment of subtomograms to produce an average structure of the complex. When applied to heterogeneous samples, template-free clustering and alignment of subtomograms can potentially lead to the discovery of structures for unknown endogenous complexes. However, such methods require scoring functions to measure and accurately rank the quality of aligned subtomogram clusters, which can be compromised by contaminations from misclassified complexes and alignment errors. Here, we provide the first study to assess the effectiveness of more than 15 scoring functions for evaluating the quality of subtomogram clusters, which differ in the amount of structural misalignments and contaminations due to misclassified complexes. We assessed both experimental and simulated subtomograms as ground truth data sets. Our analysis showed that the robustness of scoring functions varies largely. Most scores were sensitive to the signal-to-noise ratio of subtomograms and often required Gaussian filtering as preprocessing for improved performance. Two scoring functions, Spectral SNR-based Fourier Shell Correlation and Pearson Correlation in the Fourier domain with missing wedge correction, showed a robust ranking of subtomogram clusters without any preprocessing and irrespective of SNR levels of subtomograms. Of these two scoring functions, Spectral SNR-based Fourier Shell Correlation was fastest to compute and is a better choice for handling large numbers of subtomograms. Our results provide a guidance for choosing an accurate scoring function for template-free approaches to detect complexes from heterogeneous samples.

Type 2 diabetes is characterized by β and α cell dysfunction. We used phasor-FLIM (Fluorescence Lifetime Imaging Microscopy) to monitor oxidative phosphorylation and glycolysis in living islet cells before and after glucose stimulation. In healthy cells, glucose enhanced oxidative phosphorylation in β cells and suppressed oxidative phosphorylation in α cells. In Type 2 diabetes, glucose increased glycolysis in β cells, and only partially suppressed oxidative phosphorylation in α cells. FLIM uncovers key perturbations in glucose induced metabolism in living islet cells and provides a sensitive tool for drug discovery in diabetes.

Characterizing relationships between cell structures and functions requires mesoscale mapping of intact cells showing subcellular rearrangements following stimulation; however, current approaches are limited in this regard. Here, we report a unique application of soft x-ray tomography to generate three-dimensional reconstructions of whole pancreatic β cells at different time points following glucose-stimulated insulin secretion. Reconstructions following stimulation showed distinct insulin vesicle distribution patterns reflective of altered vesicle pool sizes as they travel through the secretory pathway. Our results show that glucose stimulation caused rapid changes in biochemical composition and/or density of insulin packing, increased mitochondrial volume, and closer proximity of insulin vesicles to mitochondria. Costimulation with exendin-4 (a glucagon-like peptide-1 receptor agonist) prolonged these effects and increased insulin packaging efficiency and vesicle maturation. This study provides unique perspectives on the coordinated structural reorganization and interactions of organelles that dictate cell responses.

The construction of a predictive model of an entire eukaryotic cell that describes its dynamic structure from atomic to cellular scales is a grand challenge at the intersection of biology, chemistry, physics, and computer science. Having such a model will open new dimensions in biological research and accelerate healthcare advancements. Developing the necessary experimental and modeling methods presents abundant opportunities for a community effort to realize this goal. Here, we present a vision for creation of a spatiotemporal multi-scale model of the pancreatic β-cell, a relevant target for understanding and modulating the pathogenesis of diabetes.

CCR5 is the primary chemokine receptor utilized by HIV to infect leukocytes, whereas CCR5 ligands inhibit infection by blocking CCR5 engagement with HIV gp120. To guide the design of improved therapeutics, we solved the structure of CCR5 in complex with chemokine antagonist [5P7]CCL5. Several structural features appeared to contribute to the anti-HIV potency of [5P7]CCL5, including the distinct chemokine orientation relative to the receptor, the near-complete occupancy of the receptor binding pocket, the dense network of intermolecular hydrogen bonds, and the similarity of binding determinants with the FDA-approved HIV inhibitor Maraviroc. Molecular modeling indicated that HIV gp120 mimicked the chemokine interaction with CCR5, providing an explanation for the ability of CCR5 to recognize diverse ligands and gp120 variants. Our findings reveal that structural plasticity facilitates receptor-chemokine specificity and enables exploitation by HIV, and provide insight into the design of small molecule and protein inhibitors for HIV and other CCR5-mediated diseases.

CC chemokine receptor 2 (CCR2) is one of 19 members of the chemokine receptor subfamily of human class A G-protein-coupled receptors. CCR2 is expressed on monocytes, immature dendritic cells, and T-cell subpopulations, and mediates their migration towards endogenous CC chemokine ligands such as CCL2 (ref. 1). CCR2 and its ligands are implicated in numerous inflammatory and neurodegenerative diseases including atherosclerosis, multiple sclerosis, asthma, neuropathic pain, and diabetic nephropathy, as well as cancer. These disease associations have motivated numerous preclinical studies and clinical trials (see http://www.clinicaltrials.gov) in search of therapies that target the CCR2-chemokine axis. To aid drug discovery efforts, here we solve a structure of CCR2 in a ternary complex with an orthosteric (BMS-681 (ref. 6)) and allosteric (CCR2-RA-[R]) antagonist. BMS-681 inhibits chemokine binding by occupying the orthosteric pocket of the receptor in a previously unseen binding mode. CCR2-RA-[R] binds in a novel, highly druggable pocket that is the most intracellular allosteric site observed in class A G-protein-coupled receptors so far; this site spatially overlaps the G-protein-binding site in homologous receptors. CCR2-RA-[R] inhibits CCR2 non-competitively by blocking activation-associated conformational changes and formation of the G-protein-binding interface. The conformational signature of the conserved microswitch residues observed in double-antagonist-bound CCR2 resembles the most inactive G-protein-coupled receptor structures solved so far. Like other protein-protein interactions, receptor-chemokine complexes are considered challenging therapeutic targets for small molecules, and the present structure suggests diverse pocket epitopes that can be exploited to overcome obstacles in drug design.

Gap junction channels mediate intercellular signalling that is crucial in tissue development, homeostasis and pathologic states such as cardiac arrhythmias, cancer and trauma. To explore the mechanism by which Ca(2+) blocks intercellular communication during tissue injury, we determined the X-ray crystal structures of the human Cx26 gap junction channel with and without bound Ca(2+). The two structures were nearly identical, ruling out both a large-scale structural change and a local steric constriction of the pore. Ca(2+) coordination sites reside at the interfaces between adjacent subunits, near the entrance to the extracellular gap, where local, side chain conformational rearrangements enable Ca(2+)chelation. Computational analysis revealed that Ca(2+)-binding generates a positive electrostatic barrier that substantially inhibits permeation of cations such as K(+) into the pore. Our results provide structural evidence for a unique mechanism of channel regulation: ionic conduction block via an electrostatic barrier rather than steric occlusion of the channel pore.

G-protein-coupled receptors (GPCRs) signal primarily through G proteins or arrestins. Arrestin binding to GPCRs blocks G protein interaction and redirects signalling to numerous G-protein-independent pathways. Here we report the crystal structure of a constitutively active form of human rhodopsin bound to a pre-activated form of the mouse visual arrestin, determined by serial femtosecond X-ray laser crystallography. Together with extensive biochemical and mutagenesis data, the structure reveals an overall architecture of the rhodopsin-arrestin assembly in which rhodopsin uses distinct structural elements, including transmembrane helix 7 and helix 8, to recruit arrestin. Correspondingly, arrestin adopts the pre-activated conformation, with a ∼20° rotation between the amino and carboxy domains, which opens up a cleft in arrestin to accommodate a short helix formed by the second intracellular loop of rhodopsin. This structure provides a basis for understanding GPCR-mediated arrestin-biased signalling and demonstrates the power of X-ray lasers for advancing the frontiers of structural biology.

Chemokines and their receptors control cell migration during development, immune system responses, and in numerous diseases, including inflammation and cancer. The structural basis of receptor:chemokine recognition has been a long-standing unanswered question due to the challenges of structure determination for membrane protein complexes. Here, we report the crystal structure of the chemokine receptor CXCR4 in complex with the viral chemokine antagonist vMIP-II at 3.1 angstrom resolution. The structure revealed a 1:1 stoichiometry and a more extensive binding interface than anticipated from the paradigmatic two-site model. The structure helped rationalize a large body of mutagenesis data and together with modeling provided insights into CXCR4 interactions with its endogenous ligand CXCL12, its ability to recognize diverse ligands, and the specificity of CC and CXC receptors for their respective chemokines.

Despite tremendous successes of GPCR crystallography, the receptors with available structures represent only a small fraction of human GPCRs. An important role of the modeling community is to maximize structural insights for the remaining receptors and complexes. The community-wide GPCR Dock assessment was established to stimulate and monitor the progress in molecular modeling and ligand docking for GPCRs. The four targets in the present third assessment round presented new and diverse challenges for modelers, including prediction of allosteric ligand interaction and activation states in 5-hydroxytryptamine receptors 1B and 2B, and modeling by extremely distant homology for smoothened receptor. Forty-four modeling groups participated in the assessment. State-of-the-art modeling approaches achieved close-to-experimental accuracy for small rigid orthosteric ligands and models built by close homology, and they correctly predicted protein fold for distant homology targets. Predictions of long loops and GPCR activation states remain unsolved problems.

Molecular determinants regulating the activation of class B G-protein-coupled receptors (GPCRs) by native peptide agonists are largely unknown. We have investigated here the interaction between the corticotropin releasing factor receptor type 1 (CRF1R) and its native 40-mer peptide ligand Urocortin-I directly in mammalian cells. By incorporating unnatural amino acid photochemical and new click-chemical probes into the intact receptor expressed in the native membrane of live cells, 44 intermolecular spatial constraints have been derived for the ligand-receptor interaction. The data were analyzed in the context of the recently resolved crystal structure of CRF1R transmembrane domain and existing extracellular domain structures, yielding a complete conformational model for the peptide-receptor complex. Structural features of the receptor-ligand complex yield molecular insights on the mechanism of receptor activation and the basis for discrimination between agonist and antagonist function.

Small molecules targeting allosteric pockets of G protein-coupled receptors (GPCRs) have a great therapeutic potential for the treatment of neurologic and other chronic disorders. Here we performed virtual screening for orthosteric and putative allosteric ligands of the human dopamine D3 receptor (D3R) using two optimized crystal-structure-based models: the receptor with an empty binding pocket (D3R(APO)), and the receptor complex with dopamine (D3R(Dopa)). Subsequent biochemical and functional characterization revealed 14 novel ligands with a binding affinity of better than 10 μM in the D3R(APO) candidate list (56% hit rate), and 8 novel ligands in the D3R(Dopa) list (32% hit rate). Most ligands in the D3R(APO) model span both orthosteric and extended pockets and behave as antagonists at D3R, with compound 7 showing the highest potency of dopamine inhibition (IC₅₀ = 7 nM). In contrast, compounds identified by the D3R(Dopa) model are predicted to occupy an allosteric site at the extracellular extension of the pocket, and they all lack the anchoring amino group. Compounds targeting the allosteric site display a variety of functional activity profiles, where behavior of at least two compounds (23 and 26) is consistent with noncompetitive allosteric modulation of dopamine signaling in the extracellular signal-regulated kinase 1 and 2 phosphorylation and β-arrestin recruitment assays. The high affinity and ligand efficiency of the chemically diverse hits identified in this study suggest utility of structure-based screening targeting allosteric sites of GPCRs.

With the recent successes in determining membrane protein structures, we explore the tractability of determining representatives for the entire human membrane proteome. This proteome contains 2,925 unique integral α-helical transmembrane domain sequences that cluster into 1,201 families sharing more than 25% sequence identity. Structures of 100 optimally selected targets would increase the fraction of modelable human α-helical transmembrane domains from 26% to 58%, thus providing structure/function information not otherwise available.