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.

Human purinergic G protein-coupled receptor P2Y1 (P2Y1 R) is activated by adenosine 5-diphosphate (ADP) to induce platelet activation and thereby serves as an important antithrombotic drug target. Crystal structures of P2Y1 R revealed that one ligand (MRS2500) binds to the extracellular vestibule of this GPCR, whereas another (BPTU) occupies the surface between transmembrane (TM) helices TM2 and TM3. We introduced a total of 20 μs all-atom long-timescale molecular dynamic (MD) simulations to inquire why two molecules in completely different locations both serve as antagonists while ADP activates the receptor. Our results indicate that BPTU acts as an antagonist by stabilizing extracellular helix bundles leading to an increase of the lipid order, whereas MRS2500 blocks signaling by occupying the ligand binding site. Both antagonists stabilize an ionic lock within the receptor. However, binding of ADP breaks this ionic lock, forming a continuous water channel that leads to P2Y1 R activation.

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.

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.

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.

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.

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.

Actin mediates insulin secretion in pancreatic β-cells through remodeling. Hampered by limited resolution, previous studies have offered an ambiguous depiction as depolymerization and repolymerization. We report the in situ structure of actin remodeling in INS-1E β-cells during glucose-stimulated insulin secretion at nanoscale resolution. After remodeling, the actin filament network at the cell periphery exhibits three marked differences: 12% of actin filaments reorient quasi-orthogonally to the ventral membrane; the filament network mainly remains as cell-stabilizing bundles but partially reconfigures into a less compact arrangement; actin filaments anchored to the ventral membrane reorganize from a netlike to a blooming architecture. Furthermore, the density of actin filaments and microtubules around insulin secretory granules decreases, while actin filaments and microtubules become more densely packed. The actin filament network after remodeling potentially precedes the transport and release of insulin secretory granules. These findings advance our understanding of actin remodeling and its role in glucose-stimulated insulin secretion.

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.