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Results for “Biophysics”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2016

Probing cytoskeletal modulation of passive and active intracellular dynamics using nanobody-functionalized quantum dots

The cytoplasm is a highly complex and heterogeneous medium that is structured by the cytoskeleton. Cytoskeletal organization and dynamics are known to modulate cytoplasmic transport processes, but how local transport dynamics depends on the highly heterogeneous intracellular organization of F-actin and microtubules is poorly understood. Here we use a novel delivery and functionalization strategy to utilize quantum dots (QDs) as probes for transport dynamics in different sub-cellular environments. Rapid imaging of non-functionalized QDs revealed two populations with a 100-fold difference in diffusion constant. Depolymerization of actin increased the fast diffusing fraction, suggesting that slow QDs are trapped inside the actin network. When nanobody-functionalized QDs were targeted to different kinesin motor proteins and moved over microtubules, they did not experience strong actin-induced transverse displacements, as suggested previously. Only kinesin-1 bound QDs displayed subtle directional fluctuations, because the specific subset of stable microtubules used by this motor underwent prominent undulations. Using actin-targeting agents we found that the actin network suppresses most microtubule shape remodeling, rather than promoting them. These results demonstrate how the spatial and structural heterogeneity of the cytoskeleton imposes large variations in non-equilibrium intracellular dynamics.

biophysics↗

bioRxiv2016

Charge influences substrate recognition and self-assembly of hydrophobic FG sequences

The nuclear pore complex controls the passage of molecules via hydrophobic phenylalanine-glycine (FG) domains on nucleoporins. Such FG-domains consist of repeating units of FxFG, FG, or GLFG sequences, which can be interspersed with highly charged amino acid sequences. Despite the high density of charge exhibited in certain FG-domains, if and how charge influences FG-domain self-assembly and selective binding of nuclear transport receptors is largely unexplored. Studying how individual charged amino acids contribute to nuclear pore selectivity is challenging with modern in vivo and in vitro techniques due to the complexity of nucleoporin sequences. Here, we present a rationally designed approach to deconstruct essential components of nucleoporins down to 14 amino acid sequences. With these nucleoporin-based peptides, we systematically dissect how charge type and placement of charge influences self-assembly and selective binding of FG-containing gels. Specifically, we find that charge type determines which hydrophobic substrates FG sequences recognize while spatial localization of charge tunes hydrophobic self-assembly and receptor selectivity of FG sequences.

biophysics↗

bioRxiv2016

Voltage-dependent K+ Channels Improve the Energy Efficiency of Signalling in Blowfly Photoreceptors

Voltage-dependent conductances in many spiking neurons are tuned to reduce action potential energy consumption, so improving the energy efficiency of spike coding. However, the contribution of voltage-dependent conductances to the energy efficiency of analogue coding, by graded potentials in dendrites and non-spiking neurons, remains unclear. We investigate the contribution of voltage-dependent conductances to the energy efficiency of analogue coding by modelling blowfly R1-6 photoreceptor membrane. Two voltage-dependent delayed rectifier K+ conductances (DRs) shape the membrane's voltage response and contribute to light adaptation. They make two types of energy saving. By reducing membrane resistance upon depolarisation they convert the cheap, low bandwidth membrane needed in dim light to the expensive high bandwidth membrane needed in bright light. This investment of energy in bandwidth according to functional requirements can halve daily energy consumption. Second, DRs produce negative feedback that reduces membrane impedance and increases bandwidth. This negative feedback allows an active membrane with DRs to consume at least 30% less energy than a passive membrane with the same capacitance and bandwidth. Voltage gated conductances in other non-spiking neurons, and in dendrites, might be organized to make similar savings.

biophysics↗

bioRxiv2016

Monolayer-crystal streptavidin support films provide an internal standard of cryo-EM image quality

Analysis of images of biotinylated Escherichia coli 70S ribosome particles, bound to streptavidin affinity grids, demonstrates that the image-quality of particles can be predicted by the image-quality of the monolayer crystalline support film. The quality of the Thon rings is also a good predictor of the image-quality of particles, but only when images of the streptavidin crystals extend to relatively high resolution. When the estimated resolution of streptavidin was 5 [A] or worse, for example, the ribosomal density map obtained from 22,697 particles went to only 9.5 [A], while the resolution of the map reached 4.0 [A] for the same number of particles, when the estimated resolution of streptavidin crystal was 4 [A] or better. It thus is easy to tell which images in a data set ought to be retained for further work, based on the highest resolution seen for Bragg peaks in the computed Fourier transforms of the streptavidin component. The refined density map obtained from 57,826 particles obtained in this way extended to 3.6 [A], a marked improvement over the value of 3.9 [A] obtained previously from a subset of 52,433 particles obtained from the same initial data set of 101,213 particles after 3-D classification. These results are consistent with the hypothesis that interaction with the air-water interface can damage particles when the sample becomes too thin. Streptavidin monolayer crystals appear to provide a good indication of when that is the case.

biophysics↗

bioRxiv2016

Revealing compartmentalised membrane diffusion in living cells with interferometric scattering microscopy

Single-particle tracking is a powerful tool for studying single molecule behaviour involving plasma membrane-associated events in cells. Here, we show that interferometric scattering microscopy (iSCAT) combined with gold nanoparticle labeling can be used to follow the motion of membrane proteins in the plasma membrane of live cultured mammalian cell lines and hippocampal neurons. The unique combination of microsecond temporal resolution and nanometer spatial precision reveals signatures of a compartmentalised plasma membrane in neurons.

biophysics↗

bioRxiv2016

OpenMM 7: Rapid Development of High Performance Algorithms for Molecular Dynamics

OpenMM is a molecular dynamics simulation toolkit with a unique focus on extensibility. It allows users to easily add new features, including forces with novel functional forms, new integration algorithms, and new simulation protocols. Those features automatically work on all supported hardware types (including both CPUs and GPUs) and perform well on all of them. In many cases they require minimal coding, just a mathematical description of the desired function. They also require no modification to OpenMM itself and can be distributed independently of OpenMM. This makes it an ideal tool for researchers developing new simulation methods, and also allows those new methods to be immediately available to the larger community.

biophysics↗

bioRxiv2018

Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells

We present a rapid and label-free method for hematologic screening for diseases and syndromes, utilizing quantitative phase imaging (QPI) and machine learning. We aim to establish an efficient blood examination framework that does not suffer from the drawbacks of conventional blood assays, which are incapable of profiling single cells or using labeling procedures. Our method involves the synergistic employment of QPI and machine learning. The high-dimensional refractive index information arising from the QPI-based profiling of single red blood cells is processed to screen for diseases and syndromes using machine learning, which can utilize high-dimensional data beyond the human level. Accurate screening for iron-deficiency anemia, reticulocytosis, hereditary spherocytosis, and diabetes mellitus is demonstrated (>99% accuracy) using the proposed method. Furthermore, we highlight the synergy between QPI and machine learning in the proposed method by analyzing the performance of the method.

biophysics↗

bioRxiv2016

Markov modeling reveals novel intracellular modulation of the human TREK-2 selectivity filter

Two-pore domain potassium (K2P) channel ion conductance is regulated by diverse stimuli that directly or indirectly gate the channel selectivity filter (SF). Recent crystal structures for the TREK-2 member of the K2P family reveal distinct \"up\" and \"down\" states assumed during activation via mechanical stretch. We performed 195 ps of all-atom, unbiased molecular dynamics simulations of the TREK-2 channel to probe how membrane stretch regulates the SF gate. Markov modeling reveals a novel \"pinched\" SF configuration that stretch activation rapidly destabilizes. Free-energy barrier heights calculated for critical steps in the conduction pathway indicate that this pinched state impairs ion conduction. Our simulations predict that this low-conductance state is accessed exclusively in the compressed, \"down\" conformation in which the intracellular helix arrangement allosterically pinches the SF. By explicitly relating structure to function, we contribute a critical piece of understanding to the evolving K2P puzzle.

biophysics↗

bioRxiv2016

Decoding Single Molecule Time Traces with Dynamic Disorder

Single molecule time trajectories of biomolecules provide glimpses into complex folding landscapes that are difficult to visualize using conventional ensemble measurements. Recent experiments and theoretical analyses have highlighted dynamic disorder in certain classes of biomolecules, whose dynamic pattern of conformational transitions is affected by slower transition dynamics of internal state hidden in a low dimensional projection. A systematic means to analyze such data is, however, currently not well developed. Here we report a new algorithm - Variational Bayes-double chain Markov model (VB-DCMM) - to analyze single molecule time trajectories that display dynamic disorder. The proposed analysis employing VB-DCMM allows us to detect the presence of dynamic disorder, if any, in each trajectory, identify the number of internal states, and estimate transition rates between the internal states as well as the rates of conformational transition within each internal state. Applying VB-DCMM algorithm to single molecule FRET data of H-DNA in 100 mM-Na+ solution, followed by data clustering, we show that at least 6 kinetic paths linking 4 distinct internal states are required to correctly interpret the duplex-triplex transitions of H-DNA.\n\nAuthor SummaryWe have developed a new algorithm to better decode single molecule data with dynamic disorder. Our new algorithm, which represents a substantial improvement over other methodologies, can detect the presence of dynamic disorder in each trajectory and quantify the kinetic characteristics of underlying energy landscape. As a model system, we applied our algorithm to the single molecule FRET time traces of H-DNA. While duplex-triplex transitions of H-DNA are conventionally interpreted in terms of two-state kinetics, slowly varying dynamic patterns corresponding to hidden internal states can also be identified from the individual time traces. Our algorithm reveals that at least 4 distinct internal states are required to correctly interpret the data.

biophysics↗

bioRxiv2016

Impact of implementation choices on quantitative predictions of cell-based computational models

Cell-based models provide a powerful computational tool for studying the mechanisms underlying the growth and dynamics of biological tissues in health and disease. An increasing amount of quantitative data with cellular resolution has paved the way for the quantitative parameterisation and validation of such models. However, the numerical implementation of cell-based models remains challenging, and little work has been done to understand to what extent implementation choices may influence model predictions. Here, we consider the numerical implementation of a popular class of cell-based models called vertex models, which are often used to study epithelial tissues. In two-dimensional vertex models, a tissue is approximated as a tessellation of polygons and the vertices of these polygons move due to mechanical forces originating from the cells. Such models have been used extensively to study the mechanical regulation of tissue topology in the literature. Here, we analyse how the model predictions may be affected by numerical parameters, such as the size of the time step, and non-physical model parameters, such as length thresholds for cell rearrangement. We find that vertex positions and summary statistics are sensitive to several of these implementation parameters. For example, the predicted tissue size decreases with decreasing cell cycle durations, and cell rearrangement may be suppressed by large time steps. These findings are counter-intuitive and illustrate that model predictions need to be thoroughly analysed and implementation details carefully considered when applying cell-based computational models in a quantitative setting.

biophysics↗

bioRxiv2016

Intensity-based axial localization approaches for multifocal plane microscopy

Multifocal plane microscopy (MUM) can be used to visualize biological samples in three dimensions over large axial depths and provides for the high axial localization accuracy that is needed in applications such as the three-dimensional tracking of single particles and superresolution microscopy. This report analyzes the performance of intensity-based axial localization approaches as applied to MUM data using Fisher information calculations. In addition, a new non-parametric intensity-based axial location estimation method, Multi-Intensity Lookup Algorithm (MILA), is introduced that, unlike typical intensity-based methods that make use of a single intensity value per data image, utilizes multiple intensity values per data image in determining the axial location of a point source. MILA is shown to be robust against potential bias induced by differences in the sub-pixel location of the imaged point source. The method's effectiveness on experimental data is also evaluated.

biophysics↗

bioRxiv2016

The impact of a simple α helical transmembrane peptide on lipid membrane dynamics

Transmembrane domains integrate proteins into cellular membranes and support their function. The capacity of these prevalently a-helical structures in mammals to influence membrane properties is poorly understood. Combining experiments with molecular dynamics simulations, we provide evidence that helical transmembrane peptides with their rough surface reduce lateral mobility of membrane constituents. The molecular mechanism involves trapping of lipid acyl chains on the rough surface and segregation of cholesterol from the vicinity of peptides. The observations are supported by our toy model indicating strong effect of rough objects on membrane dynamics. Herein described effect has implications for the organization and function of biological membranes, especially the plasma membrane with high cholesterol content.

biophysics↗

bioRxiv2016

Stochastic Model for Direction Changes of Swimming Bacteria

In this work we introduce a stochastic model to describe directional changes in the movement of swimming bacteria. We use the probability density function (PDF) of turn angles, measured on tumbling E. coli wild-type, to build a Langevin equation for the deflection of the bacterial body swimming in isotropic media. We solved analytically this equation by means of the Green function method and show that three parameters are sufficient to describe the movement: a characteristic time, the steady-state solution and a control parameter. We conclude that the tumble motion, which is manifested as abrupt turns, is primarily caused by the rotational boost generated by the flagellar motor and complementarily by the rotational diffusion introduced by noise. We show that, in the tumble motion, the deflection is a non-stationary stochastic processes during times where the tumble occurs. By tuning the control parameter our model is able to explain small turns of the bacteria around their centers of mass along the run. We show that the deflection during the run is an Ornstein-Uhlenbeck process, which for typical run times is stationary. We conclude that, along the run, the rotational boosts do not exist or are neglectable and that only the rotational diffusion remains. Thus we have a single model to explain the turns of the bacterium during the run or tumble movements, through a control parameter that can be tuned through a critical value that can explain the transition between the two turn behaviours. This model is also able to explain very satisfactory all available statistical experimental data, such as PDFs and average values of turning angles and times, of both run and tumble motions.

biophysics↗

bioRxiv2016

CTCF and Cohesin Regulate Chromatin Loop Stability with Distinct Dynamics

Abstract/summaryFolding of mammalian genomes into spatial domains is critical for gene regulation. CTCF and cohesin control domain location by folding domains into loop structures, which are thought to be highly stable. Combining genomic, biochemical and single-molecule imaging approaches, we show that although CTCF and cohesin can physically interact, CTCF binds chromatin much more dynamically than cohesin (~1 min vs. ~22 min residence time). Moreover, after unbinding, CTCF quickly rebinds another cognate site unlike cohesin (~1 min vs. ~33 min). Thus, CTCF and cohesin form a rapidly exchanging \"dynamic complex\" rather than a typical stable complex. Since CTCF and cohesin are required for loop domain formation, our results suggest that chromatin loops constantly break and reform throughout the cell cycle.

biophysics↗

bioRxiv2016

Diffusion or advection? Mass transfer and diffusive boundary layer landscapes of the brown alga Fucus vesiculosus

The role of hyaline hairs on the thallus of brown-algae in the genus Fucus is long debated and several functions have been proposed. We used a novel motorized setup for 2D-and 3D-mapping with O2-microsensors to investigate the spatial heterogeneity of the diffusive boundary layer (DBL) and O2 flux around single and multiple tufts of hyaline hairs on the thallus of Fucus vesiculosus. Flow was a major determinant of DBL thickness, where higher flow decreased DBL thickness and increased O2 flux between algal thallus and the surrounding seawater. However, the topography of the DBL varied and did not directly follow the contour of the underlying thallus. Areas around single tufts of hyaline hairs exhibited both increased and decreased DBL thickness as compared to areas over smooth thallus surfaces. Over thallus areas with several hyaline hair tufts, the overall effect was a local increase in the DBL thickness. We also found indications for advective O2 transport driven by pressure gradients or vortex-shedding downstream from dense tufts of hyaline hairs alleviating local mass-transfer resistance imposed by thickened DBL. Mass-transfer dynamics around hyaline hair tufts are thus more complex than hitherto assumed and may have important implications for algal physiology and plant-microbe interactions.

biophysics↗

bioRxiv2016

A magnetic protein compass ?

Qin et al. (A magnetic protein compass, Nature Materials 15, 217-226, 2016) claim that \"MagR is the first known protein that carries an intrinsic magnetic moment at ambient temperature\". We show here that the claim must, unfortunately, be fundamentally wrong.

biophysics↗

bioRxiv2016

Resolving Biology Beyond the Diffraction Limit with Single-Molecule Localization Microscopy

Optical imaging provides a window into the microscopic world, but the level of observable detail is ultimately limited by the wavelength of light being employed. By harnessing the physics of photoswitchable dyes and fluorescent proteins, single-molecule localization microscopy (SMLM) provides a window into the nano-world of biology. This mini-review article provides a short overview of SMLM and discusses some of its prospects for the future.

biophysics↗

bioRxiv2016

Quantifying the Heat Dissipation from Molecular Motor’s Transport Properties in Nonequilibrium Steady States

Theoretical analysis, which maps single molecule time trajectories of a molecular motor onto unicyclic Markov processes, allows us to evaluate the heat dissipated from the motor and to elucidate its dependence on the mean velocity and diffusivity. Unlike passive Brownian particles in equilibrium, the velocity and diffusion constant of molecular motors are closely inter-related to each other. In particular, our study makes it clear that the increase of diffusivity with the heat production is a natural outcome of active particles, which is reminiscent of the recent experimental premise that the diffusion of an exothermic enzyme is enhanced by the heat released from its own catalytic turnover. Compared with freely diffusing exothermic enzymes, kinesin-1 whose dynamics is confined on one-dimensional tracks is highly efficient in transforming conformational fluctuations into a locally directed motion, thus displaying a significantly higher enhancement in diffusivity with its turnover rate. Putting molecular motors and freely diffusing enzymes on an equal footing, our study offers thermodynamic basis to understand the heat enhanced self-diffusion of exothermic enzymes.

biophysics↗