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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 1,423 records · Page 79Linked to original sources

bioRxiv2020

Characterization of gene repression by designed transcription activator-like effector dimer proteins

Gene regulation by control of transcription initiation is a fundamental property of living cells. Much of our understanding of gene repression originated from studies of the E. coli lac operon switch, where DNA looping plays an essential role. To validate and generalize principles from lac for practical applications, we previously described artificial DNA looping driven by designed Transcription Activator-Like Effector Dimer (TALED) proteins. Because TALE monomers bind the idealized symmetrical lac operator sequence in two orientations, our prior studies detected repression due to multiple DNA loops. We now quantitatively characterize gene repression in living E. coli by a collection of individual TALED loops with systematic loop length variation. Fitting of a thermodynamic model allows unequivocal demonstration of looping and comparison of the engineered TALED repression system with the natural lac repressor system. Statement of SignificanceWe are designing and testing in living bacteria artificial DNA looping proteins engineered based on principles learned from studies of the E. coli lac repressor. The engineered proteins are based on artificial dimers of Transcription Activator-Like Effector (TALE) proteins that have programmable DNA binding specificities. The current work is the first to create unique DNA repression loops using this approach. Systematic study of repression as a function of loop size, with data fitting to a thermodynamic model, now allows this system to be compared in detail with lac repressor loops, and relevant biophysical parameters to be estimated. This approach has implications for the artificial regulation of gene expression.

biophysics↗

bioRxiv2020

Theoretical analysis predicts an optimal therapeutic strategy in distinct parkinsonian landscapes of the striatum

Parkinsons disease (PD) results from a loss of dopaminergic neurons. The age of disease onset, its progression and symptoms vary significantly between patients, pointing to a complex relationship between neuron loss and PD etiology. Yet, our understanding of the clinical variability remains incomplete. Here, we use biophysical modelling to characterize the dopaminergic landscape in the healthy and denervated striatum. Based on currently proposed mechanisms causing PD, we model three distinct denervation patterns, and show notable differences in the dopaminergic network as denervation progresses. We find local and global differences in the activity of two types of striatal neurons depending on the denervation pattern. Finally, we identify an optimal cellular strategy for maintaining normal dopamine signaling when neurons degenerate stochastically within our model. Our results derive a conceptual framework in which the clinical variability of PD is rooted in distinct denervation patterns and forms testable predictions for future PD research.

biophysics↗

bioRxiv2020

Electric field mediated fibronectin hydroxyapatite interaction: A molecular insight

In experimental research driven biomaterials science, the influence of different material properties (elastic stiffness, surface energy, etc.), and to a relatively lesser extent, the biophysical stimulation (electric/magnetic) on the cell-material interaction has been extensively investigated. Considering the central importance of the protein adsorption on cell-material interaction, the role of physiochemical factors on the protein adsorption is also probed. Despite its significance, the quantitative analysis of many such aspects remains largely unexplored in biomaterials science. In recent studies, the critical role of electric field stimulation towards modulation of cell functionality on implantable biomaterials has been experimentally demonstrated. Given this background, we investigated the influence of external electric field stimulation (upto 1.00 V/nm) on fibronectin (FN) adsorption on hydroxyapatite, HA (100) surface at 300K using all-atom MD simulation method. Fibronectin adsorption was found to be governed by the attractive electrostatic interaction, which changed with the electric field strength. Non-monotonous changes in structural integrity of fibronectin were recorded with the change in field strength and direction. This can be attributed to the spatial rearrangement of local charges and global structural changes of the protein. The dipole moment vectors of fibronectin, water and HA quantitatively exhibited similar pattern of orienting themselves parallel to the field direction, with field strength dependent increase in their magnitudes. No significant change has been recorded for radial distribution function of water surrounding fibronectin. Field dependent variation in the salt bridge nets and number of hydrogen bonds between fibronectin and hydroxyapatite were also examined. One of the important results in the context of the cell-material interaction is that the RGD sequence of FN was exposed to solvent side, when the field was applied along a direction outward perpendicular to HA (001) surface. Summarizing, the present study provides quantitative insights into the influence of electric field stimulation on biomolecular interactions involved in fibronectin adsorption on hydroxyapatite surface.

biophysics↗

bioRxiv2020

Three-Dimensional Label-Free Imaging and Quantification of Migrating Cells during Wound Healing

The wound healing assay provides essential information about collective cell migration and cell-to-cell interactions. It is a simple, effective, and widely used tool for observing the effect of numerous chemical treatments on wound healing speed. To perform and analyze a wound healing assay, various imaging techniques have been utilized. However, image acquisition and analysis are often limited in two-dimensional space or require the use of exogenous labeling agents. Here, we present a method for imaging large-scale wound healing assays in a label-free and volumetric manner using optical diffraction tomography (ODT). We performed quantitative high-resolution three-dimensional (3D) analysis of cell migration over a long period without difficulties such as photobleaching or phototoxicity. ODT enables the reconstruction of the refractive index (RI) tomogram of unlabeled cells, which provides both structural and biochemical information about the individual cell at subcellular resolution. Stitching multiple RI tomograms enables long-term (24 h) and large field-of-view imaging (> 800 x 400 m2) with a lateral resolution of 110 nm. We demonstrated the thickness changes of leading cells and studied the effects of cytochalasin D. The 3D RI tomogram also revealed increased RI values in leading cells compared to lagging cells, suggesting the formation of a highly concentrated subcellular structure. STATEMENT OF SIGNIFICANCEThe wound healing assay is a simple but effective tool for studying collective cell migration (CCM) that is widely used in biophysical studies and high-throughput screening. However, conventional imaging and analysis methods only address two-dimensional properties in a wound healing assay, such as gap closure rate. This is unfortunate because biological cells are complex 3D structures, and their dynamics provide significant information about cell physiology. Here, we presented three-dimensional (3D) label-free imaging for wound healing assays and investigated the 3D dynamics of CCM. High-resolution subcellular structures as well as their collective dynamics were imaged and analyzed quantitatively. Our label-free quantitative 3D analysis method provides a unique opportunity to study the behavior of migrating cells during the wound healing process.

biophysics↗

bioRxiv2020

Temperature Dependence of the Krokinobacter rhodopsin 2 Kinetics

Here we applied target analysis to a temperature dependent flash photolysis dataset of the light-driven sodium ion pump Krokinobacter rhodopsin 2 (KR2) at sodium pumping conditions. With an increase in temperature from 10 - 40 {degrees}C, the overall photocycle duration was accelerated by a factor of six, while single transitions like the L to M transition increased by a factor of 40. Using kinetic modeling with the Eyring constraint as well as spectral corrections on the datasets the spectral position as well as the equilibria of the different photointermediates could be resolved. The results provide further insight into KR2s photocycle and energetics. STATEMENT OF SIGNIFICANCEKR2 is the most prominent member of the new class of non-proton cation pumps, as it represents an interesting new optogenetic tool. Despite widespread biophysical investigations, the molecular mechanisms of light-induced sodium pumping in KR2 are still not sufficiently understood. Therefore, an expanded set of thermodynamic parameters is essential for a complete picture. Our study of the KR2 photocycle shows that different steps in the photocycle are affected differently by temperature changes. Rigorous data analysis provides strong evidence that the transient states observed in time-resolved experiments represent rather equilibria between the different photocycle intermediates than pure intermediates. Gaining access to the dynamics and energetics of KR2 helps to answer long standing open questions concerning the molecular mechanism of cation pumping.

biophysics↗

bioRxiv2020

Unfolding of the chromatin fiber driven by overexpression of bridging factors

Nuclear molecules control the functional properties of the chromatin fiber by shaping its morphological properties. The biophysical mechanisms controlling how bridging molecules compactify the chromatin are a matter of debate. On the one side, bridging molecules could cross-link faraway sites and fold the fiber through the formation of loops. Interacting bridging molecules could also mediate long-range attractions by first tagging different locations of the fiber and then undergoing microphase separation. Using a coarse-grained model and Monte Carlo simulations, we study the conditions leading to compact configurations both for interacting and non-interacting bridging molecules. In the second case, we report on an unfolding transition at high densities of the bridging molecules. We clarify how this transition, which disappears for interacting bridging molecules, is universal and controlled by entropic terms. In general, chains are more compact in the case of interacting bridging molecules since, in this case, interactions are not valence-limited. However, this result is conditional on the ability of our simulation methodology to relax the system towards its ground state. In particular, we clarify how, unless using reaction dynamics that change the length of a loop in a single step, the system is prone to remain trapped in metastable, compact configurations featuring long loops.

biophysics↗

bioRxiv2020

High-Pressure NMR Reveals Volume and Compressibility Differences Between Two Stably Folded Protein Conformations

Proteins often interconvert between different conformations in ways critical to their function. While manipulating such equilibria for biophysical study is often challenging, the application of pressure is a potential route to achieve such control by favoring the population of lower volume states. Here, we use this feature to study the interconversion of ARNT PAS-B Y456T, which undergoes a dramatic beta-strand slip as it switches between two stably-folded conformations. Coupling high pressure and biomolecular NMR, we obtained the first quantitative data testing two key hypotheses of this process: the slipped conformation is both smaller and less compressible than the wildtype equivalent, and the interconversion proceeds through a chiefly-unfolded intermediate state. Our work exemplifies how these approaches, which can be generally applied to protein conformational switches, can provide unique information that is not easily accessible through other techniques.

biophysics↗

bioRxiv2020

Biomechanical Characterization of SARS-CoV-2 Spike RBD and Human ACE2 Protein-Protein Interaction

The current COVID-19 pandemic has led to a devastating impact across the world. SARS-CoV-2 (the virus causing COVID-19) is known to use receptor-binding domain (RBD) at viral surface spike (S) protein to interact with the angiotensin-converting enzyme 2 (ACE2) receptor expressed on many human cell types. The RBD-ACE2 interaction is a crucial step to mediate the host cell entry of SARS-CoV-2. Recent studies indicate that the ACE2 interaction with the SARS-CoV-2 S protein has higher affinity than its binding with the structurally identical S protein of SARS-CoV-1, the virus causing the 2002-2004 SARS outbreak. However, the biophysical mechanism behind such binding affinity difference is unclear. This study utilizes a combined single-molecule force spectroscopy and steered molecular dynamics (SMD) simulation approach to quantify the specific interactions between CoV-2 or CoV-1 RBD and ACE2. Depending on the loading rates, the unbinding forces between CoV-2 RBD and ACE2 range from 70 to 110 pN, and are 30-50% higher than those of CoV-1 RBD and ACE2 under similar loading rates. SMD results indicate that CoV-2 RBD interacts with the N-linked glycan on Asn90 of ACE2. This interaction is mostly absent in the CoV-1 RBD-ACE2 complex. During the SMD simulations, the extra RBD-N-glycan interaction contributes to a greater force and prolonged interaction lifetime. The observation is confirmed by our experimental force spectroscopy study. After the removal of N-linked glycans on ACE2, its mechanical binding strength with CoV-2 RBD decreases to a similar level of the CoV-1 RBD-ACE2 interaction. Together, the study uncovers the mechanism behind the difference in ACE2 binding between SARS-CoV-2 and SARS-CoV-1, and could aid in the development of new strategies to block SARS-CoV-2 entry. STATEMENT OF SIGNIFICANCEThis study utilizes a combined single-molecule force spectroscopy and steered molecular dynamics simulation approach to quantify the specific interactions between SARS-CoV-2 or SARS-CoV-1 receptor-binding domain and human ACE2. The study reveals the mechanism behind the difference in ACE2 binding between SARS-CoV-2 and SARS-CoV-1, and could aid in the development of new strategies to block SARS-CoV-2 entry.

biophysics↗

bioRxiv2020

Investigating the effects of molecular crowding on the kinetics of protein aggregation

The thermodynamics and kinetics of protein folding and protein aggregation in vivo are of great importance in numerous scientific areas including fundamental biophysics research, nanotechnology, and medicine. However, these processes remain poorly understood in both in vivo and in vitro systems. Here we extend an established model for protein aggregation that is based on the kinetic equations for the moments of the polymer size distribution by introducing macromolecular crowding particles into the model using scaled-particle and transition-state theories. The model predicts that the presence of crowders can either speed up, cause no change to, or slow down the progress of the aggregation compared to crowder-free solutions, in striking agreement with experimental results from nine different amyloid-forming proteins that utilized dextran as the crowder. These different dynamic effects of macromolecular crowding can be understood in terms of the change of excluded volume associated with each reaction step.

biophysics↗

bioRxiv2020

Membrane composition and lipid to protein ratio modulate amyloid kinetics of yeast prion protein

Understanding of prion aggregation in membrane environment may help to ameliorate neurodegenerative complications caused by the amyloid forms of prions. Here, we investigated the membrane binding induced aggregation of yeast prion protein Sup35. Using the combination of fluorescence correlation spectroscopy (FCS) at single molecule resolution and other biophysical studies, we establish that lipid composition and lipid/protein ratio are key modulators of the aggregation kinetics of Sup35. In the presence of zwitterionic membrane, Sup35 exhibited a novel biphasic aggregation kinetics at lipid/protein ratio ranging between 20:1 and 70:1 (termed here as the Optimum Lipid Concentration, OLC). In ratios below (Low Lipid Concentration, LLC) and above (ELC, Excess Lipid Concentration) that range, the aggregation was found to be monophasic. In contrast, in the presence of negatively charged membrane, we did not observe any bi-phasic aggregation kinetics in the entire range of protein to lipid ratios. The toxicity of the aggregates formed within OLC range was found to be greater. Our results provide a mechanistic description of the role that membrane-concentration/composition-modulated-aggregation may play in neurodegenerative diseases.

biophysics↗

bioRxiv2020

Swarming bacteria undergo localized dynamic phase transition to form stress-induced biofilms

Self-organized multi-cellular behaviors enable cells to adapt and tolerate stressors to a greater degree than isolated cells. However, whether and how cellular communities alter their collective behaviors adaptively upon exposure to stress is largely unclear. Here we address this question using Bacillus subtilis, a model system for bacterial multicellularity. We discover that, upon exposure to a spatial gradient of kanamycin, swarming bacteria activate matrix genes and transit to biofilms. The initial stage of this transition is underpinned by a stress-induced multi-layer formation, emerging from a biophysical mechanism reminiscent to motility-induced phase separation (MIPS). The physical nature of the process suggests that stressors which suppress the expansion of swarms would induce biofilm formation. Indeed, a simple physical barrier also induces a swarm-to-biofilm transition. Based on the gained insight, we propose a promising strategy of antibiotic treatment to effectively inhibit the transition from swarms to biofilms by targeting the localized phase transition.

biophysics↗

bioRxiv2020

A benchmarking model for validation and standardization of traction force microscopy analysis tools

Traction Force Microscopy (TFM) has become a well-established technique to assay the biophysical force produced by cells cultured on soft substrates of controlled stiffness. However, experimental conditions as well as computational implementations can have a large impact on the analysis results accuracy and reproducibility. While this can be alleviated using appropriate controls and a rigorous analytical approach, the comparison of results across studies remains difficult and there is a need for validation and benchmarking tools. To validate the accuracy of and compare various computational TFM analysis algorithms, we developed a virtual in silico model of a cell contracting on a soft substrate of controlled stiffness. The model utilizes user-defined parameters for the cell dimensions as well as for the strength and spatial distribution of a contraction dipole to calculate the deformation that would result on a soft substrate due to the cell contraction. The deformation is computed using the forward analytical stress-strain tensor calculation in the Fourier space. The resulting displacement field is used to apply, using image processing, a deformation on a real or simulated image of fluorescent microspheres embedded into a soft hydrogel, which is normally obtain experimentally by TFM imaging. The deformation field and resulting image then serve as input in the PIV and TFM analysis. The model also enables to create movies of a dynamic cell contraction, such as that of a cardiomyocyte, to validate the time accuracy of the TFM analysis after application of image processing algorithm, such as denoising. Our tool therefore addresses the need for validation and standardization of TFM analytical algorithms and its experimental implementations.

biophysics↗

bioRxiv2020

Low-cost, bottom-up fabrication of large-scale single-molecule nanoarrays by DNA origami placement

Large-scale nanoarrays of single biomolecules enable high-throughput assays while unmasking the underlying heterogeneity within ensemble populations. Until recently, creating such grids which combine the unique advantages of microarrays and single-molecule experiments (SMEs) has been particularly challenging due to the mismatch between the size of these molecules and the resolution of top-down fabrication techniques. DNA Origami Placement (DOP) combines two powerful techniques to address this issue: (i) DNA origami, which provides a [~] 100-nm self-assembled template for single-molecule organization with 5 nm resolution, and (ii) top-down lithography, which patterns these DNA nanostructures, transforming them into functional nanodevices via large-scale integration with arbitrary substrates. Presently, this technique relies on state-of-the-art infrastructure and highly-trained personnel, making it prohibitively expensive for researchers. Here, we introduce a bench-top technique to create meso-to-macro-scale DNA origami nanoarrays using self-assembled colloidal nanoparticles, thereby circumventing the need for top-down fabrication. We report a maximum yield of 74%, two-fold higher than the statistical limit of 37% imposed on non-specific molecular loading alternatives. Furthermore, we provide a proof-of-principle for the ability of this nanoarray platform to transform traditionally low-throughput, stochastic, single-molecule assays into high-throughput, deterministic ones, without compromising data quality. Our approach has the potential to democratize single-molecule nanoarrays and demonstrates their utility as a tool for biophysical assays and diagnostics.

biophysics↗

bioRxiv2020

Sticking around: Optimal cell adhesion patterning for energy minimization and substrate mechanosensing

Cell mechanotransduction, in which cells sense and respond to the physical properties of their micro-environments, is proving fundamental to understanding cellular behaviours across biology. Tissue stiffness (Youngs modulus) is typically regarded as the key control parameter and bioengineered gels with defined mechanical properties have become an essential part of the toolkit for interrogating mechanotransduction. We here, however, show using a mechanical cell model that the effective substrate stiffness experienced by a cell depends not just on the engineered mechanical properties of the substrate but critically also on the particular arrangement of adhesions between cell and substrate. In particular, we find that cells with different adhesion patterns can experience two different gel stiffnesses as equivalent and will generate the same mean cell deformations. For small adhesive patches, which mimic experimentally observed focal adhesions, we demonstrate that the observed dynamics of adhesion growth and elongation can be explained by energy considerations. Significantly we show different focal adhesions dynamics for soft and stiff substrates with focal adhesion growth not preferred on soft substrates consistent with reported dynamics. Equally, fewer and larger adhesions are predicted to be preferred over more and smaller, an effect enhanced by random spot placing with the simulations predicting qualitatively realistic cell shapes in this case. The model is based on a continuum elasticity description of the cell and substrate system, with an active stress component capturing cellular contractility. This work demonstrates the necessity of considering the whole cell-substrate system, including the patterning of adhesion, when investigating cell stiffness sensing, with implications for mechanotransductive control in biophysics and tissue engineering. Author summaryCells are now known to sense the mechanical properties of their tissue micro-environments and use this as a signal to control a range of behaviours. Experimentally, such cell mechanotransduction is mostly investigated using carefully engineered gel substrates with defined stiffness. Here we show, using a model integrating active cellular contractility with continuum mechanics, that the way in which a cell senses its environment depends critically not just on the stiffness of the gel but also on the spatial patterning of adhesion sites. In this way, two gels of substantially different stiffnesses can be experienced by the cell as similar, if the adhesions are located differently. Exploiting this insight, we demonstrate that it is energetically favourable for small adhesions to grow and elongate on stiff substrates but that this is not the case on soft substrates. This is consistent with experimental observations that nascent adhesions only mature to stable focal adhesion (FA) sites on stiff substrates where they also grow and elongate. These focal adhesions (FAs) have been the focus of work on mechanotransduction. However, our paper demonstrates that there is a fundamental need to consider the combined cell and micro-environment system moving beyond a focus on individual FAs.

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bioRxiv2020

Lipoprotein particles interact with membranes and transfer their cargo without receptors

Lipid transfer from lipoprotein particles to cells is essential for lipid homeostasis. High density lipoprotein (HDL) particles are mainly captured by cell-membrane-associated scavenger receptor class B type 1 (SR-B1) from the blood stream while low and very low density lipoprotein (LDL, VLDL) particles are mostly taken up by receptor-mediated endocytosis. However, the role of the target lipid membrane itself in the transfer process has been largely neglected so far. Here, we study how lipoprotein particles (HDL, LDL and VLDL) interact with synthetic lipid bilayers and cell-derived membranes and transfer their cargo subsequently. Employing cryo-electron microscopy, spectral imaging and fluorescence (cross) correlation spectroscopy allowed us to observe integration of all major types of lipoprotein particles into the membrane and delivery of their cargo in a receptor-independent manner. Importantly, biophysical properties of the target cell membranes change upon cargo delivery. The concept of receptor-independent interaction of lipoprotein particles with membranes helps to better understand lipoprotein particle biology and can be exploited for novel treatments of dyslipidemia diseases.

biophysics↗

bioRxiv2020

Capturing non-local through-bond effects when fragmenting molecules for quantum chemical torsion scans

Accurate molecular mechanics force fields for small molecules are essential for predicting protein-ligand binding affinities in drug discovery and understanding the biophysics of biomolecular systems. Torsion potentials derived from quantum chemical (QC) calculations are critical for determining the conformational distributions of small molecules, but are computationally expensive and scale poorly with molecular size. To reduce computational cost and avoid the complications of distal through-space intramolecular interactions, molecules are generally fragmented into smaller entities to carry out QC torsion scans. However, torsion potentials, particularly for conjugated bonds, can be strongly affected by through-bond chemistry distal to the torsion it-self. Poor fragmentation schemes have the potential to significantly disrupt electronic properties in the region around the torsion by removing important, distal chemistries, leading to poor representation of the parent molecules chemical environment and the resulting torsion energy profile. Here we show that a rapidly computable quantity, the fractional Wiberg bond order (WBO), is a sensitive reporter on whether the chemical environment around a torsion has been disrupted. We show that the WBO can be used as a surrogate to assess the robustness of fragmentation schemes and identify conjugated bond sets. We use this concept to construct a validation set by exhaustively fragmenting a set of druglike organic molecules and examine their corresponding WBO distributions derived from accessible conformations that can be used to evaluate fragmentation schemes. To illustrate the utility of the WBO in assessing fragmentation schemes that preserve the chemical environment, we propose a new fragmentation scheme that uses rapidly-computable AM1 WBOs, which are available essentially for free as part of standard AM1-BCC partial charge assignment. This approach can simultaneously maximize the chemical equivalency of the fragment and the substructure in the larger molecule while minimizing fragment size to accelerate QC torsion potential computation for small molecules and reducing undesired through-space steric interactions.

biophysics↗

bioRxiv2020

Phase separation of tunable biomolecular condensates predicted by an interacting particle model

Phase separation is emerging as key principle in the spatiotemporal organization of living cells. Given its relevance in the regulation of numerous biological functions, including gene transcription and chromatin architecture, modeling biomolecular condensation is gaining interest. Yet, most models developed so far rely on specific descriptions and/or experimentally inaccessible properties. Here we propose a theoretical model, where phase separation is explained by means of interaction probabilities between particles. With minimum model requirements, particle condensates emerge above a critical interaction probability. We tested the model predictions with single molecule experiments of tunable transcription factor condensates in the nucleus of living cells. Phase separation, condensate sizes, diffusion behavior, and mobility parameters, quantified by data analysis and machine learning, are fully recapitulated by our model. Our combined theoretical and experimental approach provides a general framework to investigate the biophysical parameters controlling phase separation in living cells and in other soft matter-based interacting systems.

biophysics↗

bioRxiv2020

Mechanical principles governing the shapes of dendritic spines

Dendritic spines are small, bulbous protrusions along the dendrites of neurons and are sites of excitatory postsynaptic activity. The morphology of spines has been implicated in their function in synaptic plasticity and their shapes have been well-characterized, but the potential mechanics underlying their shape development and maintenance have not yet been fully understood. In this work, we explore the mechanical principles that could underlie specific shapes using a minimal biophysical model of membrane-actin interactions. Using this model, we first identify the possible force regimes that give rise to the classic spine shapes - stubby, filopodia, thin, and mushroom-shaped spines. We also use this model to investigate how the spine neck might be stabilized using periodic rings of actin or associated proteins. Finally, we use this model to predict that the cooperation between force generation and ring structures can regulate the energy landscape of spine shapes across a wide range of tensions. Thus, our study provides insights into how mechanical aspects of actin-mediated force generation and tension can play critical roles in spine shape maintenance.

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