The cellular environment shapes the nuclear pore complex architecture.

Nuclear pore complexes (NPCs) create large conduits for cargo transport between the nucleus and cytoplasm across the nuclear envelope (NE)1-3. These multi-megadalton structures are composed of about thirty different nucleoporins that are distributed in three main substructures (the inner, cytoplasmic and nucleoplasmic rings) around the central transport channel4-6. Here we use cryo-electron tomography on DLD-1 cells that were prepared using cryo-focused-ion-beam milling to generate a structural model for the human NPC in its native environment. We show that-compared with previous human NPC models obtained from purified NEs-the inner ring in our model is substantially wider; the volume of the central channel is increased by 75% and the nucleoplasmic and cytoplasmic rings are reorganized. Moreover, the NPC membrane exhibits asymmetry around the inner-ring complex. Using targeted degradation of Nup96, a scaffold nucleoporin of the cytoplasmic and nucleoplasmic rings, we observe the interdependence of each ring in modulating the central channel and maintaining membrane asymmetry. Our findings highlight the inherent flexibility of the NPC and suggest that the cellular environment has a considerable influence on NPC dimensions and architecture.

Biochemically validated structural model of the 15-subunit intraflagellar transport complex IFT-B.

Cilia are ubiquitous eukaryotic organelles impotant for cellular motility, signaling, and sensory reception. Cilium formation requires intraflagellar transport of structural and signaling components and involves 22 different proteins organized into intraflagellar transport (IFT) complexes IFT-A and IFT-B that are transported by molecular motors. The IFT-B complex constitutes the backbone of polymeric IFT trains carrying cargo between the cilium and the cell body. Currently, high-resolution structures are only available for smaller IFT-B subcomplexes leaving > 50% structurally uncharacterized. Here, we used Alphafold to structurally model the 15-subunit IFT-B complex. The model was validated using cross-linking/mass-spectrometry data on reconstituted IFT-B complexes, X-ray scattering in solution, diffraction from crystals as well as site-directed mutagenesis and protein-binding assays. The IFT-B structure reveals an elongated and highly flexible complex consistent with cryo-electron tomographic reconstructions of IFT trains. The IFT-B complex organizes into IFT-B1 and IFT-B2 parts with binding sites for ciliary cargo and the inactive IFT dynein motor, respectively. Interestingly, our results are consistent with two different binding sites for IFT81/74 on IFT88/70/52/46 suggesting the possibility of different structural architectures for the IFT-B1 complex. Our data present a structural framework to understand IFT-B complex assembly, function, and ciliopathy variants.

RNAdvisor: a comprehensive benchmarking tool for the measure and prediction of RNA structural model quality.

RNA is a complex macromolecule that plays central roles in the cell. While it is well known that its structure is directly related to its functions, understanding and predicting RNA structures is challenging. Assessing the real or predictive quality of a structure is also at stake with the complex 3D possible conformations of RNAs. Metrics have been developed to measure model quality while scoring functions aim at assigning quality to guide the discrimination of structures without a known and solved reference. Throughout the years, many metrics and scoring functions have been developed, and no unique assessment is used nowadays. Each developed assessment method has its specificity and might be complementary to understanding structure quality. Therefore, to evaluate RNA 3D structure predictions, it would be important to calculate different metrics and/or scoring functions. For this purpose, we developed RNAdvisor, a comprehensive automated software that integrates and enhances the accessibility of existing metrics and scoring functions. In this paper, we present our RNAdvisor tool, as well as state-of-the-art existing metrics, scoring functions and a set of benchmarks we conducted for evaluating them. Source code is freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr.

The UBA domain of conjugating enzyme Ubc1/Ube2K facilitates assembly of K48/K63-branched ubiquitin chains.

The assembly of a specific polymeric ubiquitin chain on a target protein is a key event in the regulation of numerous cellular processes. Yet, the mechanisms that govern the selective synthesis of particular polyubiquitin signals remain enigmatic. The homologous ubiquitin-conjugating (E2) enzymes Ubc1 (budding yeast) and Ube2K (mammals) exclusively generate polyubiquitin linked through lysine 48 (K48). Uniquely among E2 enzymes, Ubc1 and Ube2K harbor a ubiquitin-binding UBA domain with unknown function. We found that this UBA domain preferentially interacts with ubiquitin chains linked through lysine 63 (K63). Based on structural modeling, in vitro ubiquitination experiments, and NMR studies, we propose that the UBA domain aligns Ubc1 with K63-linked polyubiquitin and facilitates the selective assembly of K48/K63-branched ubiquitin conjugates. Genetic and proteomics experiments link the activity of the UBA domain, and hence the formation of this unusual ubiquitin chain topology, to the maintenance of cellular proteostasis.

Structural and antigenic variations in the spike protein of emerging SARS-CoV-2 variants.

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus is continuously evolving, and this poses a major threat to antibody therapies and currently authorized Coronavirus Disease 2019 (COVID-19) vaccines. It is therefore of utmost importance to investigate and predict the putative mutations on the spike protein that confer immune evasion. Antibodies are key components of the human immune system's response to SARS-CoV-2, and the spike protein is a prime target of neutralizing antibodies (nAbs) as it plays critical roles in host cell recognition, fusion, and virus entry. The potency of therapeutic antibodies and vaccines partly depends on how readily the virus can escape neutralization. Recent structural and functional studies have mapped the epitope landscape of nAbs on the spike protein, which illustrates the footprints of several nAbs and the site of escape mutations. In this review, we discuss (1) the emerging SARS-CoV-2 variants; (2) the structural basis for antibody-mediated neutralization of SARS-CoV-2 and nAb classification; and (3) identification of the RBD escape mutations for several antibodies that resist antibody binding and neutralization. These escape maps are a valuable tool to predict SARS-CoV-2 fitness, and in conjunction with the structures of the spike-nAb complex, they can be utilized to facilitate the rational design of escape-resistant antibody therapeutics and vaccines.

A structural transition in physical networks.

In many physical networks, including neurons in the brain1,2, three-dimensional integrated circuits3 and underground hyphal networks4, the nodes and links are physical objects that cannot intersect or overlap with each other. To take this into account, non-crossing conditions can be imposed to constrain the geometry of networks, which consequently affects how they form, evolve and function. However, these constraints are not included in the theoretical frameworks that are currently used to characterize real networks5-7. Most tools for laying out networks are variants of the force-directed layout algorithm8,9-which assumes dimensionless nodes and links-and are therefore unable to reveal the geometry of densely packed physical networks. Here we develop a modelling framework that accounts for the physical sizes of nodes and links, allowing us to explore how non-crossing conditions affect the geometry of a network. For small link thicknesses, we observe a weakly interacting regime in which link crossings are avoided via local link rearrangements, without altering the overall geometry of the layout compared to the force-directed layout. Once the link thickness exceeds a threshold, a strongly interacting regime emerges in which multiple geometric quantities, such as the total link length and the link curvature, scale with the link thickness. We show that the crossover between the two regimes is driven by the non-crossing condition, which allows us to derive the transition point analytically and show that networks with large numbers of nodes will ultimately exist in the strongly interacting regime. We also find that networks in the weakly interacting regime display a solid-like response to stress, whereas in the strongly interacting regime they behave in a gel-like fashion. Networks in the weakly interacting regime are amenable to 3D printing and so can be used to visualize network geometry, and the strongly interacting regime provides insights into the scaling of the sizes of densely packed mammalian brains.

Comparison of the predictive power of two models of cortico-cortical connections in primates: the distance rule model and the structural model.

Synaptic tract-tracing studies in macaques have provided a wealth of data about cortico-cortical connections that have been used to identify regularities and propose models and theories to explain cortical connectivity. The two most relevant of these models are the distance rule model (DRM) and the structural model (SM). They relate the strength and laminar pattern of cortico-cortical connections to two different factors: Euclidean distance (according to the DRM) and cortical type distance (according to the SM). If both predictive factors were correlated, the DRM and the SM would be compatible, but quite often, two cortical areas of similar cortical type are far apart from each other. In the present article, we have performed a conceptual analysis of the DRM and the SM to obtain predictions from each of the two models about strength and laminar pattern of cortico-cortical connections. We then tested the predictive power of each model with analyses of several cortico-cortical connectivity databases to check which of them provide the most accurate predictions. We conclude that the DRM and the SM capture the decrease in connection strength with increasing Euclidean and cortical type distances, respectively; but, for laminar pattern, type distance is a better predictor than Euclidean distance.

A structural model of the iRhom-ADAM17 sheddase complex reveals functional insights into its trafficking and activity.

Several membrane-anchored signal mediators such as cytokines (e.g. TNFα) and growth factors are proteolytically shed from the cell surface by the metalloproteinase ADAM17, which, thus, has an essential role in inflammatory and developmental processes. The membrane proteins iRhom1 and iRhom2 are instrumental for the transport of ADAM17 to the cell surface and its regulation. However, the structure-function determinants of the iRhom-ADAM17 complex are poorly understood. We used AI-based modelling to gain insights into the structure-function relationship of this complex. We identified different regions in the iRhom homology domain (IRHD) that are differentially responsible for iRhom functions. We have supported the validity of the predicted structure-function determinants with several in vitro, ex vivo and in vivo approaches and demonstrated the regulatory role of the IRHD for iRhom-ADAM17 complex cohesion and forward trafficking. Overall, we provide mechanistic insights into the iRhom-ADAM17-mediated shedding event, which is at the centre of several important cytokine and growth factor pathways.

The low-resolution structural models of hepatitis C virus RNA subdomain 5BSL3.2 and its distal complex with domain 3'X point to conserved regulatory mechanisms within the Flaviviridae family.

Subdomain 5BSL3.2 of hepatitis C virus RNA lies at the core of a network of distal RNA-RNA contacts that connect the 5' and 3' regions of the viral genome and regulate the translation and replication stages of the viral cycle. Using small-angle X-ray scattering and NMR spectroscopy experiments, we have determined at low resolution the structural models of this subdomain and its distal complex with domain 3'X, located at the 3'-terminus of the viral RNA chain. 5BSL3.2 adopts a characteristic 'L' shape in solution, whereas the 5BSL3.2-3'X distal complex forms a highly unusual 'Y'-shaped kissing junction that blocks the dimer linkage sequence of domain 3'X and promotes translation. The structure of this complex may impede an effective association of the viral polymerase with 5BSL3.2 and 3'X to start negative-strand RNA synthesis, contributing to explain the likely mechanism used by these sequences to regulate viral replication and translation. In addition, sequence and shape features of 5BSL3.2 are present in functional RNA motifs of flaviviruses, suggesting conserved regulatory processes within the Flaviviridae family.

DeepTracer for fast de novo cryo-EM protein structure modeling and special studies on CoV-related complexes.

Information about macromolecular structure of protein complexes and related cellular and molecular mechanisms can assist the search for vaccines and drug development processes. To obtain such structural information, we present DeepTracer, a fully automated deep learning-based method for fast de novo multichain protein complex structure determination from high-resolution cryoelectron microscopy (cryo-EM) maps. We applied DeepTracer on a previously published set of 476 raw experimental cryo-EM maps and compared the results with a current state of the art method. The residue coverage increased by over 30% using DeepTracer, and the rmsd value improved from 1.29 Å to 1.18 Å. Additionally, we applied DeepTracer on a set of 62 coronavirus-related cryo-EM maps, among them 10 with no deposited structure available in EMDataResource. We observed an average residue match of 84% with the deposited structures and an average rmsd of 0.93 Å. Additional tests with related methods further exemplify DeepTracer's competitive accuracy and efficiency of structure modeling. DeepTracer allows for exceptionally fast computations, making it possible to trace around 60,000 residues in 350 chains within only 2 h. The web service is globally accessible at https://deeptracer.uw.edu.

Family management structural model for children with atopic dermatitis.

PURPOSE: To construct a structural model of family management for children with atopic dermatitis. DESIGN AND METHODS: In this cross-sectional study, data were collected using a structured questionnaire. Participants included primary caregivers of children aged 2-12 years who had received a medical diagnosis of atopic dermatitis and had been experiencing the condition for over three months. We used SPSS/WIN 26.0 to analyze the variables and AMOS 23.0 for structural equation modeling. RESULTS: Family functioning resilience, social support, and family coping had significant direct effects on family management. Illness severity, illness duration, and family life difficulty indirectly influenced family management, demonstrating significant total effects. The severity and duration of atopic dermatitis, family life difficulty, family functioning resilience, social support, and family coping explained 78.9% of the model. CONCLUSIONS: The final model was suitable for predicting family management for children with atopic dermatitis. By confirming mediating effects, this study contributes to enhancing family management through nursing interventions. These findings offer valuable insights for developing family-centered nursing strategies to improve family management for children with atopic dermatitis. PRACTICE IMPLICATIONS: Nursing interventions targeting the alleviation of family management challenges and enhancement of family functioning resilience, social support, and family coping are pivotal for improving the well-being of children with atopic dermatitis. Furthermore, tailored intervention development must take into account not only the severity and illness duration of atopic dermatitis in children but also the characteristics of the family. Improving family nursing through such tailored interventions can help enhance children's health and quality of life.

Psychological Dimensions That Explain Integrity in Older Adults: Examination of a Structural Model.

OBJECTIVES: The aim of this research was to confirm whether internal coping strengths, depressive symptomatology and gratitude disposition are significant predictors of integrity in older adults. METHODS: Participated 394 Ecuadorian older adults with ages between 60 and 91 years old. Self-report were applied to assess the different variables under study. Specifically, integrity, coping, resilience, self-efficacy, mood and gratitude were assessed. RESULTS: A confirmatory model was estimated to predict ego-integrity. A personal adjustment factor composed of problem-focused coping strategies, resilience and self-efficacy, and gratitude showed positive and significant relationships with ego-integrity, whereas negative mood negatively predicted integrity. CONCLUSIONS: Integrity is a key variable to strengthen a coherent view of one's life history and highly relevant during ageing. Having personal strengths and dispositions that facilitate adaptation to ageing and maintaining a positive mood predicts the attainment of integrity. CLINICAL IMPLICATIONS: Integrity is an adjustment factor that facilitates adaptation to the stressors of ageing and to major life changes and loss of control indifferent areas of life.

Structural model for ligand binding and channel opening of an insect gustatory receptor.

Insect gustatory receptors play roles in sensing tastants, such as sugars and bitter substances. We previously demonstrated that the BmGr9 silkworm gustatory receptor is a d-fructose-gated ion channel receptor. However, the molecular mechanism of how d-fructose could initiate channel opening were unclear. Herein, we present a structural model for a channel pore and a d-fructose-binding site in BmGr9. Since the membrane topology and oligomeric state of BmGr9 appeared to be similar to those of an insect odorant receptor coreceptor, Orco, we constructed a structural model of BmGr9 based on the cryo-EM Orco structure. Our site-directed mutagenesis data suggested that the transmembrane region 7 forms channel pore and controls channel gating. This model also suggested that a pocket formed by transmembrane helices 2 to 4 and 6 binds d-fructose. Using mutagenesis experiments in combination with docking simulations, we were able to determine the potent binding mode of d-fructose. Finally, based on these data, we propose a conformational change that leads to channel opening upon d-fructose binding. Taken together, these findings detail the molecular mechanism by which an insect gustatory receptor can be activated by its ligand molecule.

Structural models of viral insulin-like peptides and their analogs.

The insulin receptor (IR), the insulin-like growth factor-1 receptor (IGF1R), and the insulin/IGF1 hybrid receptors (hybR) are homologous transmembrane receptors. The peptide ligands, insulin and IGF1, exhibit significant structural homology and can bind to each receptor via site-1 and site-2 residues with distinct affinities. The variants of the Iridoviridae virus family show capability in expressing single-chain insulin/IGF1 like proteins, termed viral insulin-like peptides (VILPs), which can stimulate receptors from the insulin family. The sequences of VILPs lacking the central C-domain (dcVILPs) are known, but their structures in unbound and receptor-bound states have not been resolved to date. We report all-atom structural models of three dcVILPs (dcGIV, dcSGIV, and dcLCDV1) and their complexes with the receptors (µIR, µIGF1R, and µhybR), and probed the peptide/receptor interactions in each system using all-atom molecular dynamics (MD) simulations. Based on the nonbonded interaction energies computed between each residue of peptides (insulin and dcVILPs) and the receptors, we provide details on residues establishing significant interactions. The observed site-1 insulin/µIR interactions are consistent with previous experimental studies, and a residue-level comparison of interactions of peptides (insulin and dcVILPs) with the receptors revealed that, due to sequence differences, dcVILPs also establish some interactions distinct from those between insulin and IR. We also designed insulin analogs and report enhanced interactions between some analogs and the receptors.

Multi-scale ensemble properties of the Escherichia coli RNA degradosome.

In organisms from all domains of life, multi-enzyme assemblies play central roles in defining transcript lifetimes and facilitating RNA-mediated regulation of gene expression. An assembly dedicated to such roles, known as the RNA degradosome, is found amongst bacteria from highly diverse lineages. About a fifth of the assembly mass of the degradosome of Escherichia coli and related species is predicted to be intrinsically disordered - a property that has been sustained for over a billion years of bacterial molecular history and stands in marked contrast to the high degree of sequence variation of that same region. Here, we characterize the conformational dynamics of the degradosome using a hybrid structural biology approach that combines solution scattering with ad hoc ensemble modelling, cryo-electron microscopy, and other biophysical methods. The E. coli degradosome can form punctate bodies in vivo that may facilitate its functional activities, and based on our results, we propose an electrostatic switch model to account for the propensity of the degradosome to undergo programmable puncta formation.

Structural model of human PORCN illuminates disease-associated variants and drug-binding sites.

Wnt signaling is essential for normal development and is a therapeutic target in cancer. The enzyme PORCN, or porcupine, is a membrane-bound O-acyltransferase (MBOAT) that is required for the post-translational modification of all Wnts, adding an essential mono-unsaturated palmitoleic acid to a serine on the tip of Wnt hairpin 2. Inherited mutations in PORCN cause focal dermal hypoplasia, and therapeutic inhibition of PORCN slows the growth of Wnt-dependent cancers. Based on homology to mammalian MBOAT proteins, we developed and validated a structural model of human PORCN. The model accommodates palmitoleoyl-CoA and Wnt hairpin 2 in two tunnels in the conserved catalytic core, shedding light on the catalytic mechanism. The model predicts how previously uncharacterized human variants of uncertain significance can alter PORCN function. Drugs including ETC-159, IWP-L6 and LGK-974 dock in the PORCN catalytic site, providing insights into PORCN pharmacologic inhibition. This structural model enhances our mechanistic understanding of PORCN substrate recognition and catalysis, as well as the inhibition of its enzymatic activity, and can facilitate the development of improved inhibitors and the understanding of disease-relevant PORCN mutants. This article has an associated First Person interview with the joint first authors of the paper.

Marginal structural models for multilevel clustered data.

Marginal structural models (MSMs), which adopt inverse probability treatment weighting in the estimating equations, are powerful tools to estimate the causal effects of time-varying exposures in the presence of time-dependent confounders. Motivated by the Conservation of Hearing Study (CHEARS) Audiology Assessment Arm (AAA) where repeated hearing measurements were clustered by study participants, time, and testing sites, we propose two methods to account for the multilevel correlation structure when fitting the MSMs. The first method directly models the covariance of the repeated outcomes when solving the weighted generalized estimating equations for MSMs, while the second two-stage analysis approach fits cluster-specific MSMs first and then combines the estimated parameters using mixed-effects meta-analysis. Finite sample simulation results suggest that our methods can obtain less biased and more efficient estimates of the parameters by accounting for the multilevel correlation. Moreover, we explore the effects of using fixed- or mixed-effects model to estimate the treatment probability on the parameter estimates of the MSMs in the presence of unmeasured cluster-level confounders. Lastly, we apply our methods to the CHEARS AAA data set, to estimate the causal effects of aspirin use on hearing loss.

PrISM: precision for integrative structural models.

MOTIVATION: A single-precision value is currently reported for an integrative model. However, precision may vary for different regions of an integrative model owing to varying amounts of input information. RESULTS: We develop PrISM (Precision for Integrative Structural Models) to efficiently identify high- and low-precision regions for integrative models. AVAILABILITY AND IMPLEMENTATION: PrISM is written in Python and available under the GNU General Public License v3.0 at https://github.com/isblab/prism; benchmark data used in this paper are available at doi:10.5281/zenodo.6241200. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models.

Longitudinal observational data on patients can be used to investigate causal effects of time-varying treatments on time-to-event outcomes. Several methods have been developed for estimating such effects by controlling for the time-dependent confounding that typically occurs. The most commonly used is marginal structural models (MSM) estimated using inverse probability of treatment weights (IPTW) (MSM-IPTW). An alternative, the sequential trials approach, is increasingly popular, and involves creating a sequence of "trials" from new time origins and comparing treatment initiators and non-initiators. Individuals are censored when they deviate from their treatment assignment at the start of each "trial" (initiator or noninitiator), which is accounted for using inverse probability of censoring weights. The analysis uses data combined across trials. We show that the sequential trials approach can estimate the parameters of a particular MSM. The causal estimand that we focus on is the marginal risk difference between the sustained treatment strategies of "always treat" vs "never treat." We compare how the sequential trials approach and MSM-IPTW estimate this estimand, and discuss their assumptions and how data are used differently. The performance of the two approaches is compared in a simulation study. The sequential trials approach, which tends to involve less extreme weights than MSM-IPTW, results in greater efficiency for estimating the marginal risk difference at most follow-up times, but this can, in certain scenarios, be reversed at later time points and relies on modelling assumptions. We apply the methods to longitudinal observational data from the UK Cystic Fibrosis Registry to estimate the effect of dornase alfa on survival.

Estimation of marginal structural models under irregular visits and unmeasured confounder: calibrated inverse probability weights.

Clinical information collected in electronic health records (EHRs) is becoming an essential source to emulate randomized experiments. Since patients do not interact with the healthcare system at random, the longitudinal information in large observational databases must account for irregular visits. Moreover, we need to also account for subject-specific unmeasured confounders which may act as a common cause for treatment assignment mechanism (e.g. glucose-lowering medications) while also influencing the outcome (e.g. Hemoglobin A1c). We used the calibration of longitudinal weights to improve the finite sample properties and to account for subject-specific unmeasured confounders. A Monte Carlo simulation study is conducted to evaluate the performance of calibrated inverse probability estimators using time-dependent treatment assignment and irregular visits with subject-specific unmeasured confounders. The simulation study showed that the longitudinal weights with calibrated restrictions improved the finite sample bias when compared to the stabilized weights. The application of the calibrated weights is demonstrated using the exposure of glucose lowering medications and the longitudinal outcome of Hemoglobin A1c. Our results support the effectiveness of glucose lowering medications in reducing Hemoglobin A1c among type II diabetes patients with elevated glycemic index ([Formula: see text]) using stabilized and calibrated weights.