Driving forces of the complex formation between highly charged disordered proteins.

Highly disordered complexes between oppositely charged intrinsically disordered proteins present a new paradigm of biomolecular interactions. Here, we investigate the driving forces of such interactions for the example of the highly positively charged linker histone H1 and its highly negatively charged chaperone, prothymosin α (ProTα). Temperature-dependent single-molecule Förster resonance energy transfer (FRET) experiments and isothermal titration calorimetry reveal ProTα-H1 binding to be enthalpically unfavorable, and salt-dependent affinity measurements suggest counterion release entropy to be an important thermodynamic driving force. Using single-molecule FRET, we also identify ternary complexes between ProTα and H1 in addition to the heterodimer at equilibrium and show how they contribute to the thermodynamics observed in ensemble experiments. Finally, we explain the observed thermodynamics quantitatively with a mean-field polyelectrolyte theory that treats counterion release explicitly. ProTα-H1 complex formation resembles the interactions between synthetic polyelectrolytes, and the underlying principles are likely to be of broad relevance for interactions between charged biomolecules in general.

Extreme dynamics in a biomolecular condensate.

Proteins and nucleic acids can phase-separate in the cell to form concentrated biomolecular condensates1-4. The functions of condensates span many length scales: they modulate interactions and chemical reactions at the molecular scale5, organize biochemical processes at the mesoscale6 and compartmentalize cells4. Understanding the underlying mechanisms of these processes will require detailed knowledge of the rich dynamics across these scales7. The mesoscopic dynamics of biomolecular condensates have been extensively characterized8, but their behaviour at the molecular scale has remained more elusive. Here, as an example of biomolecular phase separation, we study complex coacervates of two highly and oppositely charged disordered human proteins9. Their dense phase is 1,000 times more concentrated than the dilute phase, and the resulting percolated interaction network10 leads to a bulk viscosity 300 times greater than that of water. However, single-molecule spectroscopy optimized for measurements within individual droplets reveals that at the molecular scale, the disordered proteins remain exceedingly dynamic, with their chain configurations interconverting on submicrosecond timescales. Massive all-atom molecular dynamics simulations reproduce the experimental observations and explain this apparent discrepancy: the underlying interactions between individual charged side chains are short-lived and exchange on a pico- to nanosecond timescale. Our results indicate that, despite the high macroscopic viscosity of phase-separated systems, local biomolecular rearrangements required for efficient reactions at the molecular scale can remain rapid.

Huxley muscle model surrogates for high-speed multi-scale simulations of cardiac contraction.

The computational requirements of the Huxley-type muscle models are substantially higher than those of Hill-type models, making large-scale simulations impractical or even impossible to use. We constructed a data-driven surrogate model that operates similarly to the original Huxley muscle model but consumes less computational time and memory to enable efficient usage in multiscale simulations of the cardiac cycle. The data was collected from numerical simulations to train deep neural networks so that the neural networks' behavior resembles that of the Huxley model. Since the Huxley muscle model is history-dependent, time series analysis is required to take the previous states of the muscle model into account. Recurrent and temporal convolutional neural networks are typically used for time series analysis. These networks were trained to produce stress and instantaneous stiffness. Once the networks have been trained, we compared the similarity of the produced stresses and achieved speed-up to the original Huxley model, which indicates the potential of the surrogate model to replace the model efficiently. We presented the creation procedure of the surrogate model and integration of the surrogate model into the finite element solver. Based on similarities between the surrogate model and the original model in several types of numerical experiments, and also achieved speed-up of an order of magnitude, it can be concluded that the surrogate model has the potential to replace the original model within multiscale simulations. Finally, we used our surrogate model to simulate a full cardiac cycle in order to demonstrate the application of the surrogate model in larger-scale problems.

Integration of Surrogate Huxley Muscle Model into Finite Element Solver for Simulation of the Cardiac Cycle.

Clinicians can use biomechanical simulations of cardiac functioning to evaluate various real and fictional events. Our present understanding of the molecular processes behind muscle contraction has inspired Huxley-like muscle models. Huxley-type muscle models, unlike Hill-type muscle models, are capable of modeling non-uniform and unstable contractions. Huxley's computational requirements, on the other hand, are substantially higher than those of Hill-type models, making large-scale simulations impractical to use. We created a data-driven surrogate model that acts similarly to the original Huxley muscle model but requires substantially less processing power in order to make the Huxley muscle models easier to use in computer simulations. We gathered data from multiple numerical simulations and trained a deep neural network based on gated-recurrent units. Once we accomplished satisfying precision, we integrated the surrogate model into our finite element solver and simulated a full cardiac cycle. Clinical Relevance- This enables clinicians to track the effects of changes in muscles at the microscale to the cardiac contraction (macroscale).

In Silico Clinical Trials for Cardiovascular Disease.

The SILICOFCM project mainly aims to develop a computational platform for in silico clinical trials of familial cardiomyopathies (FCMs). The unique characteristic of the platform is the integration of patient-specific biological, genetic, and clinical imaging data. The platform allows the testing and optimization of medical treatment to maximize positive therapeutic outcomes. Thus, adverse effects and drug interactions can be avoided, sudden cardiac death can be prevented, and the time between the commencement of drug treatment and the desired result can be shortened. This article presents a parametric model of the left ventricle automatically generated from patient-specific ultrasound images by applying an electromechanical model of the heart. Drug effects were prescribed through specific boundary conditions for inlet and outlet flow, ECG measurements, and calcium function for heart muscle properties. Genetic data from patients were incorporated through the material property of the ventricle wall. Apical view analysis involves segmenting the left ventricle using a previously trained U-net framework and calculating the bordering rectangle based on the length of the left ventricle in the diastolic and systolic cycle. M-mode view analysis includes bordering of the characteristic areas of the left ventricle in the M-mode view. After extracting the dimensions of the left ventricle, a finite elements mesh was generated based on mesh options, and a finite element analysis simulation was run with user-provided inlet and outlet velocities. Users can directly visualize on the platform various simulation results such as pressure-volume, pressure-strain, and myocardial work-time diagrams, as well as animations of different fields such as displacements, pressures, velocity, and shear stresses.

Single-molecule Detection of Ultrafast Biomolecular Dynamics with Nanophotonics.

Single-molecule Förster resonance energy transfer (FRET) is a versatile technique for probing the structure and dynamics of biomolecules even in heterogeneous ensembles. However, because of the limited fluorescence brightness per molecule and the relatively long fluorescence lifetimes, probing ultrafast structural dynamics in the nanosecond time scale has thus far been very challenging. Here, we demonstrate that nanophotonic fluorescence enhancement in zero-mode waveguides enables measurements of previously inaccessible low-nanosecond dynamics by dramatically improving time resolution and reduces data acquisition times by more than an order of magnitude. As a prototypical example, we use this approach to probe the dynamics of a short intrinsically disordered peptide that were previously inaccessible with single-molecule FRET measurements. We show that we are now able to detect the low-nanosecond correlations in this peptide, and we obtain a detailed interpretation of the underlying distance distributions and dynamics in conjunction with all-atom molecular dynamics simulations, which agree remarkably well with the experiments. We expect this combined approach to be widely applicable to the investigation of very rapid biomolecular dynamics.

Structure and dynamics of the quaternary hunchback mRNA translation repression complex.

A key regulatory process during Drosophila development is the localized suppression of the hunchback mRNA translation at the posterior, which gives rise to a hunchback gradient governing the formation of the anterior-posterior body axis. This suppression is achieved by a concerted action of Brain Tumour (Brat), Pumilio (Pum) and Nanos. Each protein is necessary for proper Drosophila development. The RNA contacts have been elucidated for the proteins individually in several atomic-resolution structures. However, the interplay of all three proteins during RNA suppression remains a long-standing open question. Here, we characterize the quaternary complex of the RNA-binding domains of Brat, Pum and Nanos with hunchback mRNA by combining NMR spectroscopy, SANS/SAXS, XL/MS with MD simulations and ITC assays. The quaternary hunchback mRNA suppression complex comprising the RNA binding domains is flexible with unoccupied nucleotides functioning as a flexible linker between the Brat and Pum-Nanos moieties of the complex. Moreover, the presence of the Pum-HD/Nanos-ZnF complex has no effect on the equilibrium RNA binding affinity of the Brat RNA binding domain. This is in accordance with previous studies, which showed that Brat can suppress mRNA independently and is distributed uniformly throughout the embryo.

Small-Angle X-ray Scattering Curves of Detergent Micelles: Effects of Asymmetry, Shape Fluctuations, Disorder, and Atomic Details.

Small-angle X-ray scattering (SAXS) is a widely used experimental technique, providing structural and dynamic insight into soft-matter complexes and biomolecules under near-native conditions. However, interpreting the one-dimensional scattering profiles in terms of three-dimensional structures and ensembles remains challenging, partly because it is poorly understood how structural information is encoded along the measured scattering angle. We combined all-atom SAXS-restrained ensemble simulations, simplified continuum models, and SAXS experiments of a n-dodecyl-ß-d-maltoside (DDM) micelle to decipher the effects of model asymmetry, shape fluctuations, atomic disorder, and atomic details on SAXS curves. Upon interpreting the small-angle regime, we find remarkable agreement between (i) a two-component triaxial ellipsoid model fitted against the data and (ii) a SAXS-refined all-atom ensemble. However, continuum models fail at wider angles, even if they account for shape fluctuations, disorder, and asymmetry of the micelle. We conclude that modeling atomic details is mandatory for explaining SAXS curves at wider angles.

Optimal threshold of the prostate health index in predicting aggressive prostate cancer using predefined cost-benefit ratios and prevalence.

PURPOSES: The aim of the study was to determine optimal threshold of the Prostate Health Index (Phi) for predicting aggressive prostate cancer (PCa), taking into account misclassification costs, prevalence, and plausible risk factors. METHODS: This prospective cohort study analyzed patients undergoing prostate biopsy and Phi testing. The primary endpoint was aggressive PCa, defined as biopsy Gleason score ≥ 7. The data about age, total prostate-specific antigen (PSA), percentage of free PSA (%fPSA), and digital rectal examination (DRE) were extracted from the patient files. We divided the patients to the low- and high-risk group. The clinical usefulness of the Phi was assessed by the decision curve analysis. The predictive performance was assessed using the area under the receiver operating characteristic curve (AUC), per-class metrics, and the potential reduction in unnecessary biopsies. The uncertain interval of Phi values was also determined. RESULTS: There were 200 men included in the study, 35 (17.5%) of them having aggressive PCa. Important predictors of aggressive PCa were %fPSA, DRE, Phi, and belonging to the high-risk group. With optimal threshold of 30.7, about 32% unnecessary biopsies would be avoided. The optimal threshold of Phi was lower in the high-risk group than in the low-risk group. The AUC for detection of aggressive PCa was 0.791. Per-class metrics showed that the Phi has insufficient diagnostic accuracy. The lower and upper limits of the uncertain interval were 41.8 and 51.4, respectively. CONCLUSION: Different thresholds of the Phi could be optimal, depending on prevalence, patient characteristics, and misclassification costs. Further studies with a larger patient sample are necessary to confirm our conclusions.

Quantifying the influence of the ion cloud on SAXS profiles of charged proteins.

Small-angle X-ray scattering (SAXS) is a popular experimental technique used to obtain structural information on biomolecules in solution. SAXS is sensitive to the overall electron density contrast between the biomolecule and the buffer, including contrast contributions from the hydration layer and the ion cloud. This property may be used advantageously to probe the properties of the ion cloud around charged biomolecules. However, in turn, contributions from the hydration layer and ion cloud may complicate the interpretation of the data, because these contributions must be modelled during structure validation and refinement. In this work, we quantified the influence of the ion cloud on SAXS curves of two charged proteins, bovine serum albumin (BSA) and glucose isomerase (GI), solvated in five different alkali chloride buffers of 100 mM or 500 mM concentrations. We compared three computational methods of varying physical detail, for deriving the ion cloud effect on the radius of gyration Rg of the proteins, namely (i) atomistic molecular dynamics simulations in conjunction with explicit-solvent SAXS calculations, (ii) non-linear Poisson-Boltzmann calculations, and (iii) a simple spherical model in conjunction with linearized Poisson-Boltzmann theory. The calculations for BSA are validated against experimental data. We find favorable agreement among the three computational methods and the experiment, suggesting that the influence of the ion cloud on Rg, as detected by SAXS, may be predicted with nearly analytic calculations. Our analysis further suggests that the ion cloud effect on Rg is dominated by the long-range distribution of the ions around the proteins, as described by Debye-Hückel theory, whereas the local salt structure near the protein surface plays a minor role.

Merging In-Solution X-ray and Neutron Scattering Data Allows Fine Structural Analysis of Membrane-Protein Detergent Complexes.

In-solution small-angle X-ray and neutron scattering (SAXS/SANS) have become popular methods to characterize the structure of membrane proteins, solubilized by either detergents or nanodiscs. SANS studies of protein-detergent complexes usually require deuterium-labeled proteins or detergents, which in turn often lead to problems in their expression or purification. Here, we report an approach whose novelty is the combined analysis of SAXS and SANS data from an unlabeled membrane protein complex in solution in two complementary ways. First, an explicit atomic analysis, including both protein and detergent molecules, using the program WAXSiS, which has been adapted to predict SANS data. Second, the use of MONSA which allows one to discriminate between detergent head- and tail-groups in an ab initio approach. Our approach is readily applicable to any detergent-solubilized protein and provides more detailed structural information on protein-detergent complexes from unlabeled samples than SAXS or SANS alone.

Temperature-Dependent Atomic Models of Detergent Micelles Refined against Small-Angle X-Ray Scattering Data.

Surfactants have found a wide range of industrial and scientific applications. In particular, detergent micelles are used as lipid membrane mimics to solubilize membrane proteins for functional and structural characterization. However, an atomic-level understanding of surfactants remains limited because many experiments provide only low-resolution structural information on surfactant aggregates. In this work, small-angle X-ray scattering is combined with molecular dynamics simulations to derive fully atomic models of two maltoside micelles at temperatures between 10 °C and 70 °C. The micelles take the shape of general tri-axial ellipsoids and decrease in size and aggregation number with increasing temperature. Density profiles of hydrophobic groups and water along the three principal axes reveal that the minor micelle axis closely mimics lipid membranes. The results suggest that coupling atomic simulations with low-resolution data allows the structural characterization of surfactant aggregates.

Virtual histology study of atherosclerotic plaque composition in patients with stable angina and acute phase of acute coronary syndromes without ST segment elevation.

INTRODUCTION: Rupture of vulnerable atherosclerotic plaques is the cause of most acute coronary syndromes (ACS). Postmortem studies which compared stable coronary lesions and atherosclerotic plaques in patients who have died because of ACS indicated high lipid-core content as one of the major determinants of plaque vulnerability. OBJECTIVE: Our primary goal was to assess the potential relations of plaque composition determined by IVUS-VH (Intravascular Ultrasound -Virtual Histology) in patients with stable angina and subjects in acute phase of ACS without ST segment elevation. METHODS: The study comprised of 40 patients who underwent preintervention IVUS examination.Tissue maps were reconstructed from radio frequency data using IVUS-VH software. RESULTS: We analyzed 53 lesions in 40 patients. Stable angina was diagnosed in 24 patients (29 lesions), while acute phase of ACS without ST elevation was diagnosed in 16 patients (24 lesions). In the patients in acute phase of ACS without ST segment elevation IVUS-VH examination showed a significantly larger area of the necrotic core at the site of minimal lumen area and a larger mean of the necrotic core volume in the entire lesion comparing to stable angina subjects (1.84+/-0.90 mm2 vs. 0.96+/-0.69 mm2; p<0.001 and 20.94+/-15.79 mm3 vs. 11.54+/-14.15 mm3; p<0.05 respectively). CONCLUSION: IVUS-VH detected that the necrotic core was significantly larger in atherosclerotic lesions in patients in acute phase of ACS without ST elevation comparing to the stable angina subjects and that it could be considered as a marker of plaque vulnerability.

Radiology Services Costs and Utilization Patterns Estimates in Southeastern Europe-A Retrospective Analysis from Serbia.

OBJECTIVE: Assessment of costs matrix and patterns of prescribing of radiology diagnostic, radiation therapy, nuclear medicine, and interventional radiology services. Another aim of the study was insight into drivers of inappropriate resource allocation. METHODS: An in-depth, retrospective bottom-up trend analysis of services consumption patterns and expenses was conducted from the perspective of third-party payer, for 205,576 inpatients of a large tertiary care university hospital in Serbia (1,293 beds) from 2007 to 2010. RESULTS: A total of 20,117 patients in 2007, 17,436 in 2008, 19,996 in 2009, and 17,579 in 2010 were radiologically examined, who consumed services valued at €2,713,573.99 in 2007, €4,529,387.36 in 2008, €5,388,585.15 in -2009, and €5,556,341.35 in 2010. CONCLUSIONS: The macroeconomic crisis worldwide and consecutive health policy measures caused a drop in health care services diversity offered in some areas in the period 2008 to 2009. In spite of this, in total it increased during the time span observed. The total cost of services increased because of a rise in overall consumption and population morbidity. An average radiologically examined patient got one frontal chest graph, each 7th patient got an abdomen ultrasound examination, each 19th patient got a computed tomography endocranium check, and each 25th patient got a head nuclear magnetic resonance. Findings confirm irrational prescribing of diagnostic procedures and necessities of cutting costs. The consumption patterns noticed should provide an important momentum for policymakers to intervene and ensure higher adherence to guidelines by clinicians.

Hemodynamic flow modeling through an abdominal aorta aneurysm using data mining tools.

Geometrical changes of blood vessels, called aneurysm, occur often in humans with possible catastrophic outcome. Then, the blood flow is enormously affected, as well as the blood hemodynamic interaction forces acting on the arterial wall. These forces are the cause of the wall rupture. A mechanical quantity characteristic for the blood-wall interaction is the wall shear stress, which also has direct physiological effects on the endothelial cell behavior. Therefore, it is very important to have an insight into the blood flow and shear stress distribution when an aneurysm is developed in order to help correlating the mechanical conditions with the pathogenesis of pathological changes on the blood vessels. This insight can further help in improving the prevention of cardiovascular diseases evolution. Computational fluid dynamics (CFD) has been used in general as a tool to generate results for the mechanical conditions within blood vessels with and without aneurysms. However, aneurysms are very patient specific and reliable results from CFD analyses can be obtained by a cumbersome and time-consuming process of the computational model generation followed by huge computations. In order to make the CFD analyses efficient and suitable for future everyday clinical practice, we have here employed data mining (DM) techniques. The focus was to combine the CFD and DM methods for the estimation of the wall shear stresses in an abdominal aorta aneurysm (AAA) underprescribed geometrical changes. Additionally, computing on the grid infrastructure was performed to improve efficiency, since thousands of CFD runs were needed for creating machine learning data. We used several DM techniques and found that our DM models provide good prediction of the shear stress at the AAA in comparison with full CFD model results on real patient data.

A 3-D model of ligand transport in a deforming extracellular space.

Cells communicate through shed or secreted ligands that traffic through the interstitium. Force-induced changes in interstitial geometry can initiate mechanotransduction responses through changes in local ligand concentrations. To gain insight into the temporal and spatial evolution of such mechanotransduction responses, we developed a 3-D computational model that couples geometric changes observed in the lateral intercellular space (LIS) of mechanically loaded airway epithelial cells to the diffusion-convection equations that govern ligand transport. By solving the 3-D fluid field under changing boundary geometries, and then coupling the fluid velocities to the ligand transport equations, we calculated the temporal changes in the 3-D ligand concentration field. Our results illustrate the steady-state heterogeneities in ligand distribution that arise from local variations in interstitial geometry, and demonstrate that highly localized changes in ligand concentration can be induced by mechanical loading, depending on both local deformations and ligand convection effects. The occurrence of inhomogeneities at steady state and in response to mechanical loading suggest that local variations in ligand concentration may have important effects on cell-to-cell variations in basal signaling state and localized mechanotransduction responses.