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Results for “Biophysics”

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bioRxiv2018

Precision measurement of cis-regulatory energetics in living cells

Gene expression in all organisms is controlled by cooperative interactions between DNA-bound transcription factors (TFs), but quantitatively measuring TF-DNA and TF-TF interactions remains difficult. Here we introduce a strategy for precisely measuring the Gibbs free energy of such interactions in living cells. This strategy centers on the measurement and modeling of \"allelic manifolds\", a multidimensional generalization of the classical genetics concept of allelic series. Allelic manifolds are measured using reporter assays performed on strategically designed cis-regulatory sequences. Quantitative biophysical models are then fit to the resulting data. We used this strategy to study regulation by two Escherichia coli TFs, CRP and {sigma}70 RNA polymerase. Doing so, we consistently obtained energetic measurements precise to ~ 0.1 kcal/mol. We also obtained multiple results that deviate from the prior literature. Our strategy is compatible with massively parallel reporter assays in both prokaryotes and eukaryotes, and should therefore be highly scalable and broadly applicable.

biophysics↗

bioRxiv2018

Higher order assembling of the mycobacterial essential polar growth factor DivIVA/Wag31

How proteins localize at the pole remains an enigma. DivIVA/Wag31, which is an essential pole organizing protein in mycobacteria, can assemble at the negatively curved side of the membrane at the growing pole to form a higher order structural scaffold for maintaining cellular morphology and localizing various target proteins for cell-wall biogenesis. A single-site phosphorylation in Wag31 is linked to the regulation of peptidoglycan biosynthesis for optimal mycobacterial growth. The structural organization of polar scaffold formed by coiled-coil rich Wag31, which is a target for anti-tubercular agent amino-pyrimidine sulfonamide, remains to be determined. Here, we report biophysical characterizations of a phospho-mimetic (T73E) and a phospho-ablative (T73A) form of mycobacterial Wag31 using circular dichroism, small angle solution X-ray scattering and atomic force microscopy. While data obtained from both variants of Wag31 in solution states suggested formation of alpha-helical, large, elongated particles, their structural organizations were different. Atomic force microscopic images of Wag31 indicate polymer formation, with occasional curving, sharp bending and branching. Observed structural features in this first view of the polymeric forms of Wag31 suggest a basis for higher order network scaffold formation for polar protein localization.

biophysics↗

bioRxiv2018

Achieving Reversible Ligand-Protein Unbinding with Deep Learning and Molecular Dynamics through RAVE

In this work we demonstrate how to leverage our recent iterative deep learning-all atom molecular dynamics (MD) technique \"Reweighted autoencoded variational Bayes for enhanced sampling (RAVE)\" (Ribeiro, Bravo, Wang, Tiwary, J. Chem. Phys. 149, 072301 (2018)) for sampling protein-ligand unbinding mechanisms and calculating absolute binding affinities when plagued with difficult to sample rare events. RAVE iterates between rounds of MD and deep learning, and unlike other enhanced sampling methods, it stands out in simultaneously learning both a low-dimensional physically interpretable reaction coordinate (RC) and associated free energy. Here, we introduce a simple but powerful extension to RAVE which allows learning a position-dependent RC expressed as a superposition of piecewise linear RCs valid in different metastable states. With this approach, we retain the original physical interpretability of a RAVE-derived RC while making it applicable to a wider range of complex systems. We demonstrate how in its multi-dimensional form introduced here, RAVE can efficiently simulate the unbinding of the tightly bound benzene-lysozyme (L99A variant) complex, in all atom-precision and with minimal use of human intuition except for the choice of a larger dictionary of order parameters. These simulations had a 100 % success rate, and took between 3-50 nanoseconds for a process that takes on an average close to few hundred milliseconds, thereby reflecting a seven order of magnitude acceleration relative to straightforward MD. Furthermore, without any time-dependent biasing, the trajectories display clear back-and- forth movement between various metastable intermediates, demonstrating the reliability of the RC and its probability distribution learnt in RAVE. Our binding free energy is in good agreement with other reported simulation results. We thus believe that RAVE, especially in its multi-dimensional variant introduced here, will be a useful tool for simulating the dissociation process of practical biophysical systems with rare events in an automated manner with minimal use of human intuition.

biophysics↗

bioRxiv2018

Zinc Boosts EGCG’s hIAPP Amyloid Inhibition Both in Solution and Membrane

Amyloid aggregation of human islet amyloid polypeptide (hIAPP) is linked to insulin-producing islet cell death in type II diabetes. Previous studies have shown the amyloid inhibiting effects of zinc (Zn) and insulin that are co-present with hIAPP in islet cells, and the lipid membrane has been shown to significantly influence the aggregation kinetics. Increasing number of studies report the importance of developing small molecule inhibitors to suppress the hIAPPs toxicity. Particularly, the ability of epigallocatechin-gallate (EGCG) to inhibit amyloid aggregation of a variety of amyloid peptide/proteins including hIAPP initiated numerous studies including the development of compounds to potentially treat amyloid diseases. In this study, by using a combination of thioflavin-T fluorescence and transmission electron microscopy experiments, we demonstrate a significant enhancement in EGCGs efficiency, when mixed with Zn, to significantly suppress hIAPP amyloid aggregation both in presence and absence of lipid membrane. Circular dichroism experiments indicate the formation and stabilization of a helical structure of hIAPP in presence of EGCG:Zn complex. Our results also reveal the ability of EGCG or EGCG:Zn to suppress hIAPPs cellular toxicity and that the ability of EGCG to chelate with Zn suppresses zincs cellular toxicity. We suggest that the reported results would be useful to develop strategies to trap hIAPP intermediates for further biophysical and structural studies, and also to devise approaches to abolish amyloid aggregation and cellular toxicity.

biophysics↗

bioRxiv2018

Probing inhomogeneous diffusion in the microenvironments of phase-separated polymers under confinement

Biopolymer condensates formed by liquid-liquid phase separation of proteins and nucleic acids have been recently discovered to be prevalent in biology. These dynamic condensates behave like biochemical reaction vessels but little is known about their structural organization and properties. Their biophysical properties and catalytic functions are likely related to condensate size, and thus it is critical that we study them on scales found in vivo. However, previous in vitro studies of condensate assembly and physical properties have involved condensates up to 1000 times larger than those found in vivo. Here, we report the application of confinement microscopy to visualize condensates and control their sizes by creating appropriate confinement length scales relevant to the cell environment. We observe anomalous diffusion of probe particles embedded within confined condensates, as well as heterogeneous dynamics in condensates formed from PEG/dextran and in ribonucleoprotein complexes of RNA and the RNA-binding protein Dhh1. We propose that the non-Gaussian dynamics we observe may indicate a hopping diffusion mechanism inside condensates. We also observe that for dextran-rich condensates, but not for ribonucleo condensates, probe particle diffusion depends on condensate size.

biophysics↗

bioRxiv2018

OSCA/TMEM63 are an Evolutionarily Conserved Family of Mechanically Activated Ion Channels

IntroductionMechanotransduction, the conversion of mechanical cues into biochemical signals, is crucial for many biological processes in plants and animals1,2. In mammals, some mechanosensory processes such as touch sensation and vascular development are mediated by the PIEZO family of mechanically activated (MA) ion channels3-5. In plants, the impact of gravity or soil properties on root development, wind on stem growth, and turgor pressure on plant-cell size and shape are proposed to involve activation of MA ion channels6,7. Homologues of the bacterial MA channel MscS (MSLs) exist in plants, and MSL8 is shown to be involved in pollen hydration8; however, the identity of the MA channels required for most mechanotransduction processes in plants have remained elusive9. Here, we identify various members of the 15 OSCA proteins from Arabidopsis thaliana (previously reported as hyperosmolarity sensors10,11) as MA ion channels. Purification and reconstitution of OSCA1.2 in liposomes induced stretch-activated currents, suggesting that OSCAs are inherently mechanosensitive, pore-forming ion channels. This conclusion is confirmed by a high-resolution electron microscopy structure of OSCA1.2 described in a companion paper12. Beyond plants, we present evidence that fruit fly, mouse, and human TMEM63 family of proteins, homologues of OSCAs, induce MA currents when expressed in naive cells. Our results suggest that OSCA/TMEM63 proteins are the largest family of MA ion channels identified, and are conserved across eukaryotes. We anticipate that further characterization of OSCA isoforms which have diverse biophysical properties, will help gain substantial insight on the molecular mechanism of MA ion channel gating and permeation. OSCA1.1 mutant plants have impaired leaf and root growth under stress, potentially linking this ion channel to a mechanosensory role11. We expect future studies to uncover novel roles of OSCA/TMEM63 channels in mechanosensory processes across plants and animals.

biophysics↗

bioRxiv2018

Information content of downwelling skylight for non-imaging visual systems

Light-sensitive proteins (opsins) are expressed in non-imaging tissues like the brain, dermis and reproductive organs of most animals. Such tissues have been shown to sense the intensity and spectrum of light over time. Functional links to circadian and reproductive rhythms have been speculated but remain uncertain. Here we use information theory to quantify the natural scene for non-imaging opsins, i.e., spectral patterns in downwelling skylight. Our approach synthesizes measurements of natural downwelling spectra, atmospheric distortions, and weather, with the biophysical constraints of opsins and biochemical clocks, while minimizing assumptions about how organisms process such information. We find that tissues expressing multiple opsins could use twilight to extract significant information about lunar phase and time of day in many climates. In contrast, information in light intensity is far less robust to atmospheric perturbations. Thus our work quantifies circalunar and circadian regularities in the spectrum of downwelling radiance salient to non-imaging opsins.

biophysics↗

bioRxiv2018

Collective intercellular communication through ultra-fast hydrodynamic trigger waves

The biophysical relationships between sensors and actuators [1-5] have been fundamental to the development of complex life forms; Abundant flows are generated and persist in aquatic environments by swimming organisms [6-13], while responding promptly to external stimuli is key to survival [14-19]. Here, akin to a chain reaction [20-22], we present the discovery of hydrodynamic trigger waves in cellular communities of the protist Spirostomum ambiguum, propagating hundreds of times faster than the swimming speed. Coiling its cytoskeleton, Spirostomum can contract its long body by 50% within milliseconds [23], with accelerations reaching 14g-forces. Surprisingly, a single cellular contraction (transmitter) is shown to generate long-ranged vortex flows at intermediate Reynolds numbers, which can trigger neighbouring cells, in turn. To measure the sensitivity to hydrodynamic signals (receiver), we further present a high-throughput suction-flow device to probe mechanosensitive ion channel gating [24] by back-calculating the microscopic forces on the cell membrane. These ultra-fast hydrodynamic trigger waves are analysed and modelled quantitatively in a universal framework of antenna and percolation theory [25, 26]. A phase transition is revealed, requiring a critical colony density to sustain collective communication. Our results suggest that this signalling could help organise cohabiting communities over large distances, influencing long-term behaviour through gene expression, comparable to quorum sensing [16]. More immediately, as contractions release toxins [27], synchronised discharges could also facilitate the repulsion of large predators, or conversely immobilise large prey. We postulate that beyond protists numerous other freshwater and marine organisms could coordinate with variations of hydrodynamic trigger waves.

biophysics↗

bioRxiv2018

High-Performance Image-Based Measurements of Biological Forces and Interactions in a Dual Optical Trap

Optical traps enable nanoscale manipulation of individual biomolecules while measuring molecular forces and lengths. This ability relies on the sensitive detection of optically trapped particles, typically accomplished using laser-based interferometric methods. Recently, precise and fast image-based particle tracking techniques have garnered increased interest as a potential alternative to laser-based detection, however successful integration of image-based methods into optical trapping instruments for biophysical applications and force measurements has remained elusive. Here we develop a camera-based detection platform that enables exceptionally accurate and precise measurements of biological forces and interactions in a dual optical trap. In demonstration, we stretch and unzip DNA molecules while measuring the relative distances of trapped particles from their trapping centers with sub-nanometer accuracy and precision, a performance level previously only achieved using photodiodes. We then use the DNA unzipping technique to localize bound proteins with extraordinary sub-base-pair precision, revealing how thermal DNA fluctuations allow an unzipping fork to sense and respond to a bound protein prior to a direct encounter. This work significantly advances the capabilities of image tracking in optical traps, providing a state-of-the-art detection method that is accessible, highly flexible, and broadly compatible with diverse experimental substrates and other nanometric techniques.

biophysics↗

bioRxiv2018

The Dynamic Conformational Landscapes of the Protein Methyltransferase SETD8

Elucidating conformational heterogeneity of proteins is essential for understanding protein functions and developing exogenous ligands for chemical perturbation. While structural biology methods can provide atomic details of static protein structures, these approaches cannot in general resolve less populated, functionally relevant conformations and uncover conformational kinetics. Here we demonstrate a new paradigm for illuminating dynamic conformational landscapes of target proteins. SETD8 (Pr-SET7/SET8/KMT5A) is a biologically relevant protein lysine methyltransferase for in vivo monomethylation of histone H4 lysine 20 and nonhistone targets. Utilizing covalent chemical inhibitors and depleting native ligands to trap hidden high-energy conformational states, we obtained diverse novel X-ray structures of SETD8. These structures were used to seed massively distributed molecular simulations that generated six milliseconds of trajectory data of SETD8 in the presence or absence of its cofactor. We used an automated machine learning approach to reveal slow conformational motions and thus distinct conformational states of SETD8, and validated the resulting dynamic conformational landscapes with multiple biophysical methods. The resulting models provide unprecedented mechanistic insight into how protein dynamics plays a role in SAM binding and thus catalysis, and how this function can be modulated by diverse cancer-associated mutants. These findings set up the foundation for revealing enzymatic mechanisms and developing inhibitors in the context of conformational landscapes of target proteins.

biophysics↗

bioRxiv2018

The Influence of Molecular Reach and Diffusivity on the Efficacy of Membrane-Confined Reactions

Signalling by surface receptors often relies on tethered reactions whereby an enzyme bound to the cytoplasmic tail f a receptor catalyses reactions on substrates within reach. The overall length and stiffness of the receptor tail, the enzyme, and the substrate determine a biophysical parameter termed the molecular reach of the reaction. This parameter determines the probability that the receptor-tethered-enzyme will contact the substrate, in the volume proximal to the membrane, when separated by different distances within the membrane plane. Here, we develop particle-based stochastic reaction-diffusion models to study the interplay between molecular reach and diffusion. We find that increasing the molecular reach can increase reaction efficacy when diffusion is slow, but when diffusion is fast, increasing molecular reach reduces reaction efficacy. This switching is lost if reactions are forced to take place within the 2D plasma membrane instead of the 3D volume proximal to it, or if molecules diffuse in 3D. We show results in the context of immune receptors (PD-1 dephosphorylating CD28), a standard opposing kinase-phosphatase reaction, and a minimal two-particle model. The work highlights the 3D nature of many 2D membrane-confined interactions, illustrating a role for molecular reach in controlling biochemical reactions.

biophysics↗

bioRxiv2018

Lipid unsaturation properties govern the sensitivity of membranes to photo-induced oxidative stress

Unsaturated lipid oxidation is a fundamental process involved in different aspects of cellular bioenergetics; dysregulation of lipid oxidation is often associated with cell aging and death. In order to study how lipid oxidation affects membrane biophysics, we used a chlorin photosensitizer to oxidize vesicles of various lipid compositions and degree of unsaturation in a controlled manner. We observed different shape transitions that can be interpreted as an increase in the area of the targeted membrane followed by a decrease. These area modifications induced by the chemical modification of the membrane upon oxidation, were followed in situ by Raman Tweezers Microspectroscopy (RTM). We found that the membrane area increase corresponds to the lipids peroxidation and is initiated by the delocalization of the targeted double bonds in the tails of the lipids. The subsequent decrease of membrane area can be explained by the formation of cleaved secondary products. As a result of these area changes, we observe vesicle permeabilization after a time lag that is characterized in relation with the level of unsaturation. The evolution of photosensitized vesicle radius was measured and yields an estimation of the mechanical changes of the membrane over oxidation time. The membrane is both weakened and permeabilized by the oxidation. Interestingly, the effect of unsaturation level on the dynamics of vesicles undergoing photooxidation is not trivial and thus carefully discussed. Our findings shed light on the fundamental dynamic mechanisms underlying the oxidation of lipid membranes, and highlight the role of unsaturations on their physical and chemical properties O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/451591v2_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@c17ecdorg.highwire.dtl.DTLVardef@16bc938org.highwire.dtl.DTLVardef@167a85eorg.highwire.dtl.DTLVardef@5ee754_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2018

Kinematic and Thermodynamic Limits on the Enhanced Diffusion of Enzymes

A number of enzymes reportedly exhibit enhanced diffusion in the presence of their substrates, with a Michaelis-Menten-like concentration dependence. Although no definite explanation of this phenomenon has emerged, a physical picture of enzyme self-propulsion using energy from the catalyzed reaction has been widely considered. Here, we present a kinematic and thermodynamic analysis of enzyme self-propulsion that is independent of any specific propulsion mechanism. Using this theory, along with biophysical data compiled for all enzymes so far shown to undergo enhanced diffusion, we show that the propulsion speed required to generate experimental levels of enhanced diffusion exceeds the speeds of well-known active biomolecules, such as myosin, by several orders of magnitude. Furthermore, the minimum power dissipation required to account for enzyme self-propulsion markedly exceeds the chemical power available from enzyme-catalyzed reactions. Alternative explanations for the observation of enhanced enzyme diffusion merit strong consideration.

biophysics↗

bioRxiv2026

Dynamical Regimes in Rejuvenation

Biological aging is accompanied by systematic changes in epigenetic modifications and chromatin organization. The reversal of the effects of aging, rejuvenation, is experimentally achieved by the transient induction of factors that modify these marks in cells and organisms. Here, we show that key features of rejuvenation experiments emerge from the biophysical interplay between dynamic epigenetic marks and the three-dimensional conformation of chromatin. Using a minimal field theory and molecular dynamics simulations, we show that the system responds in three distinct temporal regimes. The intermediary regime fulfills necessary conditions for successful rejuvenation. In this regime, the system spends time near a separatrix, allowing for high epigenetic plasticity, while memory retained in the chromatin conformation enables restoration of the original epigenetic correlations. Analysis of sequencing data further supports the predicted coupling between chromatin compaction and epigenetic correlations. Our results provide a physical explanation for how rejuvenation may remodel age-associated epigenetic states without irreversibly erasing cellular identity. We identify a general mechanism by which memory stored in a slow structural variable permits reversible remodeling of a faster internal state.

biophysics↗

bioRxiv2026

Melanin Suppresses Aβ Aggregation and Toxicity

The aggregation of amyloid-{beta} (A{beta}) peptides into insoluble deposits is a characteristic hallmark of Alzheimer's disease (AD) and related neurodegenerative disorders. While AD is the most common cause of dementia, there are currently no disease-modifying treatments which are both affordable and adverse-free. In this study, we report that melanin, a pigment which is commonly found in nature and is abundant in parts of the human brain, suppresses the aggregation of the 42-amino acid A{beta} variant (A{beta}42). Using biophysical and biochemical techniques, we show that melanin delays A{beta}42 aggregation while also reducing the amount of A{beta}42 that converts into aggregates. Using thioflavin T assays paired with chemical kinetics, we characterised the melanin-induced inhibition of A{beta}42 aggregation in vitro. Using MALDI-MS, we elucidate the molecular basis of this effect by showing that melanin prevents A{beta}42 dimerisation. We then demonstrate that melanin also reverts the aggregation process by dissolving pre-formed A{beta}42 fibrils. Finally, we show that melanin reduces A{beta}42 aggregation and rescues A{beta}42 toxicity in an SH-SY5Y neuroblastoma cell model. Our study shows that melanin disrupts the aggregation and cytotoxicity of A{beta}42, and suggests that compounds derived from human metabolites may offer promising avenues to combat amyloid formation.

biophysics↗

bioRxiv2014

Untangling cross-frequency coupling in neuroscience

Cross-frequency coupling (CFC) has been proposed to coordinate neural dynamics across spatial and temporal scales. Despite its potential relevance for understanding healthy and pathological brain function, the standard CFC analysis and physiological interpretation come with fundamental problems. For example, apparent CFC can appear because of spectral correlations due to common non-stationarities that may arise in the total absence of interactions between neural frequency components. To provide a road map towards an improved mechanistic understanding of CFC, we organize the available and potential novel statistical/modeling approaches according to their biophysical interpretability. While we do not provide solutions for all the problems described, we provide a list of practical recommendations to avoid common errors and to enhance the interpretability of CFC analysis.\n\nHighlightsFundamental caveats and confounds in the methodology of assessing CFC are discussed.\n\nSignificant CFC can be observed without any underlying physiological coupling.\n\nNon-stationarity of a time-series leads to spectral correlations interpreted as CFC.\n\nWe offer practical recommendations, which can relieve some of the current confounds.\n\nFurther theoretical and experimental work is needed to ground the CFC analysis.

Neuroscience↗

bioRxiv2014

Near-optimal decoding of transient stimuli from coupled neuronal subpopulations

Coupling between sensory neurons impacts their tuning properties and correlations in their responses. How such coupling affects sensory representations and ultimately behavior remains unclear. We investigated the role of neuronal coupling during visual processing using a realistic biophysical model of the vertical system (VS) cell network in the blow fly. These neurons are thought to encode the horizontal rotation axis during rapid free flight manoeuvres. Experimental findings suggest neurons of the vertical system are strongly electrically coupled, and that several downstream neurons driving motor responses to ego-rotation receive inputs primarily from a small subset of VS cells. These downstream neurons must decode information about the axis of rotation from a partial readout of the VS population response. To investigate the role of coupling, we simulated the VS response to a variety of rotating visual scenes and computed optimal Bayesian estimates from the relevant subset of VS cells. Our analysis shows that coupling leads to near-optimal estimates from a subpopulation readout. In contrast, coupling between VS cells has no impact on the quality of encoding in the response of the full population. We conclude that coupling at one level of the fly visual system allows for near-optimal decoding from partial information at the subsequent, pre-motor level. Thus, electrical coupling may provide a general mechanism to achieve near-optimal information transfer from neuronal subpopulations across organisms and modalities.

Neuroscience↗

bioRxiv2014

Gene regulatory network inference from perturbed time-series expression data via ordered dynamical expansion of non-steady state actors

The reconstruction of gene regulatory networks from gene expression data has been the subject of intense research activity over the past many decades. A variety of models and methods have been developed to address different aspects of this important problem. However, these techniques are often difficult to scale, are narrowly focused on particular biological and experimental platforms, and require experimental data that are typically unavailable and difficult to ascertain. The more recent availability of higher-throughput sequencing platforms, combined with more precise modes of genetic perturbation, presents an opportunity to formulate more robust and comprehensive approaches to gene network inference. Here, we propose a step-wise framework for identifying gene-gene regulatory interactions that expand from a known point of genetic or chemical perturbation using time series gene expression data. This novel approach sequentially identifies non-steady state genes post-perturbation and incorporates them into a growing series of low-complexity optimization problems. The governing ordinary differential equations of this model are rooted in the biophysics of stochastic molecular events that underlie gene regulation, delineating roles for both protein and RNA-mediated gene regulation. We show the successful application of our core algorithms for network inference using simulated and real datasets.

Bioinformatics↗