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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.

The availability of AlphaFold2 has led to great excitement in the scientific community─particularly among drug hunters─due to the ability of the algorithm to predict protein structures with high accuracy. However, beyond globally accurate protein structure prediction, it remains to be determined whether ligand binding sites are predicted with sufficient accuracy in these structures to be useful in supporting computationally driven drug discovery programs. We explored this question by performing free-energy perturbation (FEP) calculations on a set of well-studied protein-ligand complexes, where AlphaFold2 predictions were performed by removing all templates with30% identity to the target protein from the training set. We observed that in most cases, the ΔΔ

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.

Summary 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.

The blood-brain barrier (BBB) strictly regulates the exchange of ions and molecules between the blood and the central nervous system. Tight junctions (TJs) are multimeric structures that control the transport through the paracellular spaces between the adjacent brain endothelial cells of the BBB. Claudin-5 (Cldn5) proteins are essential for TJ formation and assemble into multiprotein complexes via

Conformational transitions of globular proteins into amyloid fibrils are complex multistage processes exceedingly challenging to simulate using molecular dynamics (MD). Slow monomer diffusion rates and rugged free energy landscapes disfavor swift self-assembly of orderly amyloid architectures within timescales accessible to all-atom MD. Here, we conduct a multiscale MD study of the amyloidogenic self-assembly of insulin: a small protein with complex topology defined by two polypeptide chains interlinked by three disulfide bonds. In order to avoid kinetic traps, unconventional pre-planarized insulin conformations are used as amyloid building blocks. These starting conformers generated through uniaxial compression of the native monomer in various spatial directions represent 6 distinct (out of 16 conceivable) 2D topological classes varying in N- / C-terminal segments of insulin’s A- and B-chains being placed inside, or outside of the central loop constituted by the middle sections of both chains and Cys7A-Cys7B / Cys19B-Cys20A disulfide bonds. Simulations of the fibrillar self-assembly are initiated through a biased in-register alignment of 2, 3 or 4 layers of flat conformers belonging to a single topological class. The various starting topologies are conserved throughout the self-assembly process resulting in polymorphic amyloid fibrils varying in structural features such as helical twist, presence of cavities, as well as the overall stability. Some of the protofilament structures obtained in this work are highly compatible with the earlier biophysical studies on insulin amyloid and high resolution studies on insulin-derived amyloidogenic peptide models postulating presence of steric zippers. Our approach provides in silico means to study amyloidogenic tendencies and viable amyloid architectures of larger disulfide-constrained proteins with complex topologies.

The rationale for prioritizing sustainability in our world, where climate change and unplanned population growth have increased noticeably and resources are exhausted, has affected almost every sector. The logistics sector, which is one of these sectors and undoubtedly directly affects the living resources, constitutes the subject of our study. The study deals with the logistics activities carried out by the companies on the basis of sustainability and the aim of the article is to provide a framework that will contribute to the sustainability initiative of the companies carrying out logistics activities. It provides gains to the literature by investigating the important factors that greatly affect the performance of logistics activities. Accordingly, logistics activities are detailed under the titles of environmental impact, waste management, transportation and warehouse management, resource usage, social impact, internal factors, external factors and logistics performance factors. These titles are the main dimensions that emerged as a result of literature research and expert opinions. The most important sub-criteria that affect the sustainable logistics performance, which are handled within the scope of the main dimensions, have been determined as 44 with Pareto analysis. These determined parameters are analyzed with the formation of a comprehensive structural model and their importance levels are interpreted, and the findings are interpreted. The results of this study, in which sustainable logistics practices are at the forefront, form the basis for improving the sustainability performance of the logistics sector.

Funder: Phenix Industrial Consortium, Experimental structure determination can be accelerated with artificial intelligence (AI)-based structure-prediction methods such as AlphaFold. Here, an automatic procedure requiring only sequence information and crystallographic data is presented that uses AlphaFold predictions to produce an electron-density map and a structural model. Iterating through cycles of structure prediction is a key element of this procedure: a predicted model rebuilt in one cycle is used as a template for prediction in the next cycle. This procedure was applied to X-ray data for 215 structures released by the Protein Data Bank in a recent six-month period. In 87% of cases our procedure yielded a model with at least 50% of Cα atoms matching those in the deposited models within 2 Å. Predictions from the iterative template-guided prediction procedure were more accurate than those obtained without templates. It is concluded that AlphaFold predictions obtained based on sequence information alone are usually accurate enough to solve the crystallographic phase problem with molecular replacement, and a general strategy for macromolecular structure determination that includes AI-based prediction both as a starting point and as a method of model optimization is suggested.

The metabolic enzyme, enolase, plays a crucial role in the cytoplasm where it maintains cellular energy production within the process of glycolysis. The main role of enolase in glycolysis is to convert 2-phosphoglycerate to phosphoenolpyruvate; however, enolase can fulfill roles that deviate from this function. In pathogenic bacteria and fungi, enolase is also located on the cell surface where it functions as a virulence factor. Surface-expressed enolase is a receptor for human plasma proteins, including plasminogen, and this interaction facilitates nutrient acquisition and tissue invasion. A novel approach to developing antifungal drugs is to inhibit the formation of this complex. To better understand the structure of enolase and the interactions that may govern complex formation, we have solved the first X-ray crystal structure of enolase from Aspergillus fumigatus (2.0 Å) and have shown that it preferentially adopts a dimeric quaternary structure using native mass spectrometry. Two additional X-ray crystal structures of A. fumigatus enolase bound to the endogenous substrate 2-phosphoglycerate and product phosphoenolpyruvate were determined and kinetic characterization was carried out to better understand the details of its canonical function. From these data, we have produced a model of the A. fumigatus enolase and human plasminogen complex to provide structural insights into the mechanisms of virulence and aid future development of small molecules or peptidomimetics for antifungal drug design.

Purpose To evaluate the potential of a Cox marginal structural model (MSM) to estimate the time-varying causal inference of a known clinical trial association where the effectiveness of inhaled corticosteroid- (ICS-) versus non-ICS-containing treatments has been compared in patients with chronic obstructive pulmonary disease (COPD). Methods This retrospective study from 2006 to 2016 used linked data from Clinical Practice Research Datalink-GOLD, Hospital Episode Statistics and Office for National Statistics mortality. A Cox MSM, incorporating a new-user design, was deemed capable of replicating a clinical trial-like pathway. Repeated outcomes for exacerbation events and stabilised weights were used to include time-varying and fixed covariate exposures. Results Of 45,958 patients, 55% were male; 52% had moderate COPD. ICS-treated patients had a higher incidence of comorbid asthma than non-ICS-treated patients. Adjusted hazard risk ratios for any exacerbation event: ICS/long-acting β2-agonist (LABA) versus long-acting muscarinic antagonist (LAMA), 1.07 (95% confidence interval 1.04-1.10); ICS/LABA versus LABA/LAMA, 1.05 (1.00-1.10); ICS/LABA/LAMA versus LAMA, 1.04 (1.01-1.06); ICS/LABA/LAMA versus LABA/LAMA 1.02 (0.97-1.07). Conclusions The Cox MSM was not able to fully demonstrate results consistent with the previously established benefits of ICS-containing treatments seen in clinical trials. Future studies should continue to investigate causal inference methods and their capability to estimate the long-term outcomes of treatment in COPD.

Integrative modeling of macromolecular structures usually results in an ensemble of models that satisfy the input information. The model precision, or variability among these models is estimated globally, i.e., a single precision value is reported for the model. However, it would be useful to identify regions of high and low precision. For instance, low-precision regions can suggest where the next experiments could be performed and high-precision regions can be used for further analysis, e.g., suggesting mutations. We develop PrISM (Precision for Integrative Structural Models), using autoencoders, a type of unsupervised deep neural network, to efficiently and accurately annotate precision for integrative models. The method is benchmarked and tested on five examples of binary protein complexes and five examples of large protein assemblies. The annotated precision is shown to be consistent with localization density maps, while providing more fine-grained information. Finally, the generated networks are also interpreted by gradient-based attention analysis. Significance Statement Validation of integrative models and data is an open research challenge. This is timely due to the new worldwide Protein Data Bank archive for integrative structures (http://pdb-dev.wwpdb.org). Currently, a single precision value is reported for an integrative model. However, precision may vary for different regions of an integrative model owing to varying amounts of information available for different regions. We develop a method using unsupervised deep learning to efficiently and accurately annotate precision for regions of an integrative model. This will ultimately improve the quality and utility of deposited structures.

Social epidemiology aims to identify social structural risk factors, thus informing targets and timing of interventions. Ascertaining which interventions will be most effective and when they should be implemented is challenging because social conditions vary across the life course and are subject to time-varying confounding. Marginal structural models (MSMs) may be useful but can present unique challenges when studying social epidemiologic exposures over the life course. We describe selected MSMs corresponding to common theoretical life-course models and identify key issues for consideration related to time-varying confounding and late study enrollment. Using simulated data mimicking a cohort study evaluating the effects of depression in early, mid-, and late life on late-life stroke risk, we examined whether and when specific study characteristics and analytical strategies may induce bias. In the context of time-varying confounding, inverse-probability–weighted estimation of correctly specified MSMs accurately estimated the target causal effects, while conventional regression models showed significant bias. When no measure of early-life depression was available, neither MSMs nor conventional models were unbiased, due to confounding by early-life depression. To inform interventions, researchers need to identify timing of effects and consider whether missing data regarding exposures earlier in life may lead to biased estimates.

International audience; Phosphoglycerate mutases (PGAMs) catalyse the reversible isomerisation of 3-phosphoglycerate and 2-phosphoglycerate, a step of glycolysis. PGAMs can be sub-divided into 2,3-biphosphoglycerate dependent (dPGAM) and independent (iPGAM) enzymes. In plants, phosphoglycerate isomerisation is carried out by cytosolic iPGAM. Despite its crucial role in catabolism, little is known about post-translational modifications of plant iPGAM. In Arabidopsis thaliana, phosphoproteomics analyses have previously identified a iPGAM phosphopeptide where serine 82 is phosphorylated. Here, we show that this phosphopeptide is less abundant in dark-adapted compared to illuminated Arabidopsis leaves. In silico comparison of iPGAM protein sequences and 3D-structural modelling of AtiPGAM2 based on non-plant iPGAM enzymes suggest a role for phosphorylated serine in the catalytic reaction mechanism. This is confirmed by the activity (or the lack thereof) of mutated recombinant Arabidopsis iPGAM2 forms, affected in different steps of the reaction mechanism. We thus propose that the occurrence of the S82-phosphopeptide reflects iPGAM2 steady-state catalysis. Based on this assumption, the metabolic consequences of a higher iPGAM activity in illuminated versus darkened leaves are discussed.

Deep learning-based graph generation approaches have remarkable capacities for graph data modeling, allowing them to solve a wide range of real-world problems. Making these methods able to consider different conditions during the generation procedure even increases their effectiveness by empowering them to generate new graph samples that meet the desired criteria. This paper presents a conditional deep graph generation method called SCGG that considers a particular type of structural conditions. Specifically, our proposed SCGG model takes an initial subgraph and autoregressively generates new nodes and their corresponding edges on top of the given conditioning substructure. The architecture of SCGG consists of a graph representation learning network and an autoregressive generative model, which is trained end-to-end. More precisely, the graph representation learning network is designed to compute continuous representations for each node in a graph, which are not only affected by the features of adjacent nodes, but also by the ones of farther nodes. This network is primarily responsible for providing the generation procedure with the structural condition, while the autoregressive generative model mainly maintains the generation history. Using this model, we can address graph completion, a rampant and inherently difficult problem of recovering missing nodes and their associated edges of partially observed graphs. The computational complexity of the SCGG method is shown to be linear in the number of graph nodes. Experimental results on both synthetic and real-world datasets demonstrate the superiority of our method compared with state-of-the-art baselines.

BACKGROUND: Obesity is a risk factor for falls in older adults, but the effects of body fat distribution and its interaction with other factors are not well established. OBJECTIVES: To verify the occurrence of falls among older adults with and without abdominal obesity and the effects of sociodemographic, health, and behavioral variables on this outcome. DESIGN AND SETTING: A cross-sectional study in an urban area of Alcobaça, Brazil. METHODS: Men and women older than 60 years with (270) and without (184) abdominal obesity were included. Sociodemographic, health, and behavioral data were collected using validated questionnaires in Brazil. Descriptive and path analyses were performed (P < 0.05). RESULTS: The occurrence of falls was high in participants with abdominal obesity (33.0%). In both groups, a higher number of morbidities (β = 0.25, P < 0.001; β = 0.26, P = 0.002) was directly associated with a higher occurrence of falls. Among participants without abdominal obesity, a lower number of medications (β = -0.16; P = 0.04), a higher number of depressive symptoms (β = 0.15; P = 0.04), worse performance on the agility and dynamic balance tests (β = 0.37; P < 0.001), and lower functional disability for basic activities of daily living (β = -0.21; P = 0.006) were directly associated with the occurrence of falls. CONCLUSION: Adults older than 60 years with abdominal obesity have a higher prevalence of falls. Different factors were associated with the occurrence of falls in both groups.

Molten globule (MG) is the name given to a compact, non-native conformation of proteins that has stimulated the imagination and work in the protein folding field for more than 40 years. The MG has been proposed to play a central role in the folding reaction and in important cell functions, and to be related to the onset of misfolding diseases. Due to its inherent intractability to high-resolution studies, atomistic structural models have not yet been obtained. We present here an integrative atomistic model of the MG formed at acidic pH by the apoflavodoxin from the human pathogen Helicobacter pylori. This MG has been previously shown to exhibit the archetypical expansion, spectroscopic and thermodynamic features of a molten conformation. To obtain the model, we have analyzed the stability of wild-type and 55 apoflavodoxin mutants to derive experimental equilibrium Φ values that have been used in biased molecular dynamics simulations to convert the native conformation into an MG ensemble. The ensemble has been refined to reproduce the experimental hydrodynamic radius and circular dichroism (CD) spectrum. The refined ensemble, deposited in PDB-Dev, successfully explains the characteristicsup1/supH-nuclear magnetic resonance (NMR) and near-UV CD spectral features of the MG as well as its solvent-accessible surface area (SASA) change upon unfolding. This integrative model of an MG will help to understand the energetics and roles of these elusive conformations in protein folding and misfolding. Interestingly, the apoflavodoxin MG is structurally unrelated to previously described partly unfolded conformations of this protein, exemplifying that equilibrium MGs need not to reflect the properties of kinetic intermediates.

Kinesin superfamily proteins are microtubule-based molecular motors driven by the energy of ATP hydrolysis. Among them, the kinesin-4 family is a unique motor that inhibits microtubule dynamics. Although mutations of kinesin-4 cause several diseases, its molecular mechanism is unclear because of the difficulty of visualizing the high-resolution structure of kinesin-4 working at the microtubule plus-end. Here, we report that KLP-12, aFrom meter-long structures that allow nerve cells to stretch across a body to miniscule ‘hairs’ required for lung cells to clear mucus, many life processes rely on cells sporting projections which have the right size for their role. Networks of hollow filaments known as microtubules shape these structures and ensure that they have the appropriate dimensions. Controlling the length of microtubules is therefore essential for organisms, yet how this process takes place is still not fully elucidated. Previous research has shown that microtubules continue to grow when their end is straight but stop when it is curved. A family of molecular motors known as kinesin-4 participate in this process, but the exact mechanisms at play remain unclear. To investigate, Tuguchi, Nakano, Imasaki et al. focused on the KLP-12 protein, a kinesin-4 equivalent which helps to controls the length of microtubules in the tiny worm

ApoB-100 is a member of a large lipid transfer protein superfamily and is one of the main apolipoproteins found on low-density lipoprotein (LDL) and very low-density lipoprotein (VLDL) particles. Despite its clinical significance for the development of cardiovascular disease, there is limited information on apoB-100 structure. We have developed a novel method based on the “divide and conquer” algorithm, using PSIPRED software, by dividing apoB-100 into five subunits and 11 domains. Models of each domain were prepared using I-TASSER, DEMO, RoseTTAFold, Phyre2, and MODELLER. Subsequently, we used disuccinimidyl sulfoxide (DSSO), a new mass spectrometry cleavable cross-linker, and the known position of disulfide bonds to experimentally validate each model. We obtained 65 unique DSSO cross-links, of which 87.5% were within a 26 Å threshold in the final model. We also evaluated the positions of cysteine residues involved in the eight known disulfide bonds in apoB-100, and each pair was measured within the expected 5.6 Å constraint. Finally, multiple domains were combined by applying constraints based on detected long-range DSSO cross-links to generate five subunits, which were subsequently merged to achieve an uninterrupted architecture for apoB-100 around a lipoprotein particle. Moreover, the dynamics of apoB-100 during particle size transitions was examined by comparing VLDL and LDL computational models and using experimental cross-linking data. In addition, the proposed model of receptor ligand binding of apoB-100 provides new insights into some of its functions.

Objective To examine the association of long-term weight change with RA risk in a large prospective cohort study. Methods The Nurses’ Health Study II started in 1989 (baseline); after exclusions, we studied 108 505 women 25–42 years old without RA. Incident RA was reported by participants and confirmed by medical record review. Body weight was reported biennially through 2015. We investigated two time-varying exposures: weight changes from baseline and from age 18; change was divided into five categories. We used a marginal structural model approach to account for time-varying weight change and covariates. Results Over 2 583 266 person-years, with a median follow-up time of 25.3 years, 541 women developed RA. Compared with women with stable weight from baseline, weight change was significantly associated with increased RA risk [weight gain 2–2 kg: RR = 2.05 (95% CI 1.28, 3.28)]. Weight gain of 10 kg or more from age 18 compared with stable weight was also associated with increased RA risk [10–< 20 kg: RR = 2.12 (95% CI 1.37, 3.27), ≥20 kg: RR = 2.31 (95% CI 1.50, 3.56)]. Consistent findings were observed for seropositive and seronegative RA. Conclusion Long-term weight gain was strongly associated with increased RA risk in women, with weight gain of ≥20 kg associated with more than a three-fold increased RA risk. Maintenance of healthy weight may be a strategy to prevent or delay RA.

Methods for estimating optimal treatment strategies typically assume unlimited access to resources. However, when a health system has resource constraints, such as limited funds, access to medication, or monitoring capabilities, medical decisions must account for competition between individuals in resource usage. The problem of incorporating resource constraints into optimal treatment strategies has been solved for point exposures (1), that is, treatment strategies entailing a decision at just one time point. However, attempts to directly generalize the point exposure solution to dynamic time-varying treatment strategies run into complications. We sidestep these complications by targeting the optimal strategy within a clinically defined subclass. Our approach is to employ dynamic marginal structural models to estimate (counterfactual) resource usage under the class of candidate treatment strategies and solve a constrained optimization problem to choose the optimal strategy for which expected resource usage is within acceptable limits. We apply this method to determine the optimal dynamic monitoring strategy for people living with HIV when resource limits on monitoring exist using observational data from the HIV-CAUSAL Collaboration.

An increasing number of protein structures are determined by cryo-electron microscopy (cryo-EM) and stored in the Electron Microscopy Data Bank (EMDB). To interpret determined cryo-EM maps, several methods have been developed that model the tertiary structure of biomolecules, particularly proteins. Here we show how to use two such methods, VESPER and MAINMAST, which were developed in our group. VESPER is a method mainly for two purposes: fitting protein structure models into an EM map and aligning two EM maps locally or globally to capture their similarity. VESPER represents each EM map as a set of vectors pointing toward denser points. By considering matching the directions of vectors, in general, VESPER aligns maps better than conventional methods that only consider local densities of maps. MAINMAST is a de novo protein modeling tool designed for EM maps with resolution of 3-5 Å or better. MAINMAST builds a protein main chain directly from a density map by tracing dense points in an EM map and connecting them using a tree-graph structure. This article describes how to use these two tools using three illustrative modeling examples. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Protein structure model fitting using VESPER Alternate Protocol: Atomic model fitting using VESPER web server Basic Protocol 2: Protein de novo modeling using MAINMAST.

The formins constitute a large class of multi-domain polymerases that catalyze the localization and growth of unbranched actin filaments in cells from yeast to mammals. The conserved FH2 domains form dimers that bind actin at the barbed end of growing filaments and remain attached as new subunits are added. Profilin–actin is recruited and delivered to the barbed end by formin FH1 domains via the binding of profilin to interspersed tracts of poly-l-proline. We present a structural model showing that profilin–actin can bind the FH2 dimer at the barbed end stabilizing a state where profilin prevents its associated actin subunit from directly joining the barbed end. It is only with the dissociation of profilin from the polymerase that an actin subunit rotates and docks into its helical position, consistent with observations that under physiological conditions optimal elongation rates depend on the dissociation rate of profilin, independently of cellular concentrations of actin subunits.

A recent study by Xu et al. (Nature,2021, 594, 535−540) provided strongevidence that cryptochrome 4 (Cry4) is a key protein to endow migratory birds with the magneticcompass sense. The investigation compared the magneticfield response of Cry4 from migratoryand nonmigratory bird species and suggested that a difference in magnetic sensitivity could exist.Thisfinding prompted an in-depth investigation into Cry4 protein differences on the structuraland dynamic levels. In the present study, the pigeon Cry4 (ClCry4) crystal structure was used toreconstruct the missing avian Cry4 protein structures via homology modeling for carefullyselected bird species. The reconstructed Cry4 structure from European robin, Eurasian blackcap,zebrafinch, chicken, and pigeon were subsequently simulated dynamically and analyzed. Thestudied avian Cry4 structures showflexibility in analogous regions pointing to similar activationmechanisms and/or signaling interaction partners. It can be concluded that the experimentallyrecorded difference in the magneticfield sensitivity of Cry4 from different birds is unlikely to bedue to solely intrinsic dynamics of the proteins but requires additional factors that have not yet been identified.

PurposeThis study was done to assess the dimensions of professional burnout and turnover intention among nurses working in hospitals during the coronavirus disease 2019 (COVID-19) pandemic in Iran based on a structural model.MethodsThis cross-sectional study was performed among 170 nurses working in two referral hospitals of COVID-19 in Tehran Province, Iran, from September to December 2020. Data were collected using the sociodemographic form, Maslach Burnout Inventory (MBI), and Turnover Intention Questionnaire. Data were analyzed with SPSS and Amos software version 22 using independent t-test, ANOVA, and structural equation model.ResultsThe mean scores for burnout in emotional fatigue, depersonalization, and personal accomplishment dimensions were 25.38 ± 7.55, 9.47 ± 4.40, and 34.94 ± 7.80, respectively, moreover for the turnover intention, the score was 6.51 ± 3.17. The reduced personal accomplishment was identified as a positive predictor of turnover intention (p = 0.01). Work position and interest in attending the organization were significantly correlated with the turnover intention (p < 0.05).ConclusionsThere is an immediate need to prepare nurses to cope better with the COVID-19 outbreak. Work-related stressors during the COVID-19 pandemic have led to an increase in nurses' burnout and turnover intention. Identifying and managing the factors related to professional burnout will make it possible to prevent the nurses' turnover intention in such critical situations.

Funding: We like to acknowledge the SOPR-CIHR funding for NDR, and AHRQ Inspire PHC award for the application of this study. This statistical research was supported by Natural Sciences and Engineering Research Council (NSERC) PhD scholarship (CGS: 534600). As different scientific disciplines begin to converge on machine learning for causal inference, we demonstrate the application of machine learning algorithms in the context of longitudinal causal estimation using electronic health records. Our aim is to formulate a marginal structural model for estimating diabetes care provisions in which we envisioned hypothetical (i.e. counterfactual) dynamic treatment regimes using a combination of drug therapies to manage diabetes: metformin, sulfonylurea and SGLT-2i. The binary outcome of diabetes care provisions was defined using a composite measure of chronic disease prevention and screening elements [27] including (i) primary care visit, (ii) blood pressure, (iii) weight, (iv) hemoglobin A1c, (v) lipid, (vi) ACR, (vii) eGFR and (viii) statin medication. We used several statistical learning algorithms to describe causal relationships between the prescription of three common classes of diabetes medications and quality of diabetes care using the electronic health records contained in National Diabetes Repository. In particular, we generated an ensemble of statistical learning algorithms using the SuperLearner framework based on the following base learners: (i) least absolute shrinkage and selection operator, (ii) ridge regression, (iii) elastic net, (iv) random forest, (v) gradient boosting machines, and (vi) neural network. Each statistical learning algorithm was fitted using the pseudo-population generated from the marginalization of the time-dependent confounding process. Covariate balance was assessed using the longitudinal (i.e. cumulative-time product) stabilized weights with calibrated restrictions. Our results indicated that the treatment drop-in cohorts (with respect to metformin, sulfonylurea and SGLT-2i) may have improved diabetes care provisions in relation to treatment naive (i.e. no treatment) cohort. As a clinical utility, we hope that this article will facilitate discussions around the prevention of adverse chronic outcomes associated with type II diabetes through the improvement of diabetes care provisions in primary care. Postprint

We explore the impact of time-varying subsequent therapy on the statistical power and treatment effects in survival analysis. The marginal structural model (MSM) with stabilized inverse probability treatment weights (sIPTW) was used to account for the effects due to the subsequent therapy. Simulations were performed to compare the MSM-sIPTW method with the conventional method without accounting for the time-varying covariate such as subsequent therapy that is dependent on the initial response of the treatment effect. The results of the simulations indicated that the statistical power, thereby the Type I error, of the trials to detect the frontline treatment effect could be inflated if no appropriate adjustment was made for the impact due to the add-on effects of the subsequent therapy. Correspondingly, the hazard ratio between the treatment groups may be overestimated by the conventional analysis methods. In contrast, MSM-sIPTW can maintain the Type I error rate and gave unbiased estimates of the hazard ratio for the treatment. Two real examples were used to discuss the potential clinical implications. The study demonstrated the importance of accounting for time-varying subsequent therapy for obtaining unbiased interpretation of data.

OBJECTIVE This study analysed the association between caesarean section and early childhood caries (ECC), estimating the effects using regression and causal inference models. METHODS This was a historical cohort study of 697 mother-child dyads, conducted in Sao Luis, Brazil. The caesarean section was the exposure, and the severity of ECC (dmft) was the outcome. Covariates household income, maternal schooling, maternal hypertension, maternal obesity and birth weight were adjusted for in the models. The effects were estimated by Poisson regression (Means Ratio-MR) and causal inference using a marginal structural model (MSM) (MR and Average Treatment Effect-ATE coefficients), weighted by the inverse probability (IPW) of exposure. RESULTS Caesarean section was protective against caries in the bivariate (MR 0.81; CI 0.70-0.94; P = 0.005) and multivariate (MR 0.78; CI 0.67-0.91; P = 0.002) models. In MSM analyses, the caesarean section had no effect on ECC (ATE = -0.35; P = 0.107), controlling for IPW of exposure. CONCLUSION The apparent association between caesarean section and ECC severity seems spurious, as it did not persist after employing a superior approach to estimating causality.

Leucine-zipper transcription regulator 1 (LZTR1) is a highly mutated tumor suppressor gene, involved in the pathogenesis of several cancer types and developmental disorders. In proteasomal degradation, it acts as an adaptor protein responsible for the recognition and recruitment of substrates to be ubiquitinated in Cullin3-RING ligase E3 (CRL3) machinery. LZTR1 belongs to the BTB-Kelch family, a multi-domain protein where the Kelch propeller plays as the substrate recognition region and for which no experimental structure has been solved. Recently, large effort mutational analyses pointed to the role of disease-associated LZTR1 mutations in the RAS/MAPK signaling pathway and RIT1, a small Ras-related GTPase protein, has been identified by mass spectroscopy to interact with LZTR1. Hence, a better understanding of native structure, molecular mechanism, and substrate specificity would help clarifying the role of LZTR1 in pathological diseases, thus promoting advancement in the development of novel therapeutic strategies. Here, we address the interaction model between adaptor LZTR1 and substrate RIT1 by applying an integrated computational approach, including molecular modeling and docking techniques. We observe that the interaction model LZTR1-RIT1 is stabilized by an electrostatic bond network established between the two protein surfaces, which is reminiscent of homologous ubiquitin ligases complexes. Then, running MD simulations, we characterize differential conformational dynamics of the multi-domain LZTR1, offering interesting implications on the mechanistic role of specific point mutations. We identify G248R and R283Q as damaging mutations involved in the recognition process of the substrate RIT1 and R412C as a possible allosteric mutation from the Kelch to the C-term BTB-domain. Our findings provide important structural insights on targeting CRL3s for drug discovery.

Summary Young adults have a high societal relevance but are still an under-represented target group in health promotion. Health literacy is widely acknowledged as one of the strongest predictors and key determinant of health, so its influence on work ability is of great interest. The purpose of the study was to examine the associations between health-related skills and work ability within the structural model of health literacy of Lenartz, Soellner and colleagues, which explains health behaviour and health through the indirect and direct influence of six ‘advanced skills’ (‘self-perception’, ‘proactive approach to health’, ‘dealing with health information’, ‘self-control’, ‘self-regulation’ and ‘communication and cooperation’). The cross-sectional study was based on baseline data of a health literacy promotion intervention (495 vocational school students, 59.0% female, age span 18–25 years). Structural equation modelling with partial least squares was used to examine the associations between the six constructs of the model and the Work Ability Index (WAI). Mean WAI score was 39.7 ± 4.5 (51.1% categorized ‘moderate’/‘poor’). Five out of six constructs of the model showed a statistically significant indirect or direct effect, respectively, on work ability. The model explained 24.8% of the WAI score variance. Our findings show associations between the health literacy model and the work ability among young employees. In view of demographic change, it is crucial to develop and analyse target group-specific health literacy interventions. The model offers new facets in the modelling of health literacy.

Uncoupling protein 1 (UCP1) is found in the inner mitochondrial membrane of brown adipocytes. In the presence of long-chain fatty acids (LCFAs), UCP1 increases the proton conductance, which, in turn, increases fatty acid oxidation and energy release as heat. Atomic models of UCP1 and UCP2 have been generated based on the NMR backbone structure of UCP2 in dodecylphosphocholine (DPC), a detergent known to inactivate UCP1. Based on NMR titration experiments on UCP1 with LCFA, it has been proposed that K56 and K269 are crucial for LCFA binding and UCP1 activation. Given the numerous controversies on the use of DPC for structure-function analyses of membrane proteins, we revisited those UCP1 mutants in a more physiological context by expressing them in the mitochondria of Saccharomyces cerevisiae. Mitochondrial respiration, assayed on permeabilized spheroplasts, enables the determination of UCP1 activation and inhibition. The K56S, K269S, and K56S/K269S mutants did not display any default in activation, which shows that the NMR titration experiments in DPC detergent are not relevant to UCP1 function.

There is long-standing interest in accurately modeling the structural features of peptides bound and presented by class I MHC proteins. This interest has grown with the advent of rapid genome sequencing and the prospect of personalized, peptide-based cancer vaccines, as well as the development of molecular and cellular therapeutics based on T cell receptor recognition of peptide-MHC. However, while the speed and accessibility of peptide-MHC modeling has improved substantially over the years, improvements in accuracy have been modest. Accuracy is crucial in peptide-MHC modeling, as T cell receptors are highly sensitive to peptide conformation and capturing fine details is therefore necessary for useful models. Studying nonameric peptides presented by the common class I MHC protein HLA-A*02:01, here we addressed a key question common to modern modeling efforts: from a set of models (or decoys) generated through conformational sampling, which is best? We found that the common strategy of decoy selection by lowest energy can lead to substantial errors in predicted structures. We therefore adopted a data-driven approach and trained functions capable of predicting near native decoys with exceptionally high accuracy. Although our implementation is limited to nonamer/HLA-A*02:01 complexes, our results serve as an important proof of concept from which improvements can be made and, given the significance of HLA-A*02:01 and its preference for nonameric peptides, should have immediate utility in select immunotherapeutic and other efforts for which structural information would be advantageous.