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bioRxiv2020

The Mechanism of Vesicle Solubilization by the Detergent Sodium Dodecyl Sulfate

Membrane solubilization by sodium dodecyl sulfate (SDS) is indispensable for many established biotech-nological applications, including viral inactivation and protein extraction. Although the ensemble thermo-dynamics have been thoroughly explored, the underlying molecular dynamics have remained inaccessible, owing to major limitations of traditional measurement tools. Here, we integrate multiple advanced biophysical approaches to gain multi-angle insight into the time-dependence and fundamental kinetic steps associated with the solubilization of single sub-micron sized vesicles in response to SDS. We find that the accumulation of SDS molecules on in-tact vesicles triggers biphasic solubilization kinetics comprising an initial vesicle expansion event followed by rapid lipid loss and micellization. Our findings support a general mechanism of detergent-induced membrane solubilization and we expect the framework of correlative biophysical technologies presented here will form a general platform for elucidating the complex kinetics of membrane perturbation induced by a wide variety of surfactants and disrupting agents.

biophysics↗

bioRxiv2020

A predictive coarse-grained model for position-specific effects of post-translational modifications on disordered protein phase separation

Biomolecules undergo liquid-liquid phase separation (LLPS) resulting in the formation of multicomponent protein-RNA membraneless organelles in cells. However, the physiological and pathological role of post translational modifications (PTMs) on the biophysics of phase behavior is only beginning to be probed. To study the effect of PTMs on LLPS in silico, we extend our transferable coarse-grained model of intrinsically disordered proteins to include phosphorylated and acetylated amino acids. Using the parameters for modified amino acids available for fixed charge atomistic forcefields, we parameterize the size and atomistic hydropathy of the coarse-grained modified amino acid beads, and hence the interactions between the modified and natural amino acids. We then elucidate how the number and position of phosphorylated and acetylated residues alter the proteins single chain compactness and its propensity to phase separate. We show that both the number and the position of phosphorylated threonines/serines or acetylated lysines can serve as a molecular on/off switch for phase separation in the well-studied disordered regions of FUS and DDX3X, respectively. We also compare modified residues to their commonly used PTM mimics for their impact on chain properties. Importantly, we show that the model can predict and capture experimentally measured differences in the phase behavior for position-specific modifications, showing that the position of modifications can dictate phase separation. In sum, this model will be useful for studying LLPS of post-translationally modified intrinsically disordered proteins and predicting how modifications control phase behavior with position-specific resolution. Statement of SignificancePost-translational modifications are important regulators of liquid-liquid phase separation (LLPS) which drives the formation of biomolecular condensates. Theoretical methods can be used to characterize the biophysical properties of intrinsically disordered proteins (IDPs). Our recent framework for molecular simulations using a C-centered coarse-grained model can predict the effect of various perturbations such as mutations (Dignon et al. PloS Comput. Biol, 2018) and temperature (Dignon et al, ACS Cent. Sci., 2019) on LLPS. Here, we expand this framework to incorporate modified residues like phosphothreonine, phosphoserine and acetylysine. This model will prove useful for simulating the phase separation of post-translationally modified IDPs and predicting how position-specific modifications can control phase behavior across the large family of proteins known to be phosphorylated and acetylated.

biophysics↗

bioRxiv2020

Quantitative mapping of membrane nanoenvironments through single-molecule imaging of solvatochromic probes

Environmentally-sensitive fluorophores report on their local biochemical or biophysical environments through changes in their emission. We combine the solvatochromic probe di-4-ANEPPDHQ with multi-channel SMLM and quantitative analysis of the resulting marked point patterns to map biophysical environments in the mammalian cell membrane. We show that plasma membrane properties can be mapped with nanoscale resolution, and that partitioning between ordered and disordered regions is observed on length scales below 300 nm.

biophysics↗

bioRxiv2020

Mind reading of the proteins: Deep-learning to forecast molecular dynamics

Molecular dynamics (MD) simulations have emerged to become the back-bone of todays computational biophysics. Simulation tools such as, NAMD, AMBER and GROMACS have accumulated more than 100,000 users. Despite this remarkable success, now also bolstered by compatibility with graphics processor units (GPUs) and exascale computers, even the most scalable simulations cannot access biologically relevant timescales - the number of numerical integration steps necessary for solving differential equations in a million-to-billion-dimensional space is computationally in-tractable. Recent advancements in Deep Learning has made it such that patterns can be found in high dimensional data. In addition, Deep Learning have also been used for simulating physical dynamics. Here, we utilize LSTMs in order to predict future molecular dynamics from current and previous timesteps, and examine how this physics-guided learning can benefit researchers in computational biophysics. In particular, we test fully connected Feed-forward Neural Networks, Recurrent Neural Networks with LSTM / GRU memory cells with TensorFlow and PyTorch frame-works trained on data from NAMD simulations to predict conformational transitions on two different biological systems. We find that non-equilibrium MD is easier to train and performance improves under the assumption that each atom is independent of all other atoms in the system. Our study represents a case study for high-dimensional data that switches stochastically between fast and slow regimes. Applications of resolving these sets will allow real-world applications in the interpretation of data from Atomic Force Microscopy experiments.

biophysics↗

bioRxiv2020

Kinetic analysis reveals the rates and mechanisms of protein aggregation in a multicellular organism

The accumulation of insoluble protein aggregates containing amyloid fibrils has been observed in many different human protein misfolding diseases1,2, and their pathological features have been recapitulated in diverse model systems3. In vitro kinetic studies have provided a quantitative understanding of how the fundamental molecular level processes of nucleation and growth lead to amyloid formation4. However, it is not yet clear to what extent these basic biophysical processes translate to amyloid formation in vivo, given the complexity of the cellular and organismal environment. Here we show that the aggregation of a fluorescently tagged polyglutamine (polyQ) protein into {micro}m-sized inclusions in the muscle tissue of living C. elegans can be quantitatively described by a molecular model where stochastic nucleation occurs independently in each cell, followed by rapid aggregate growth. Global fitting of the image-based aggregation kinetics reveals a nucleation rate corresponding to 0.01 h-1 per cell at 1 mM intracellular protein concentration, and shows that the intrinsic stochasticity of nucleation accounts for a significant fraction of the observed animal-to-animal variation. Our results are consistent with observations for the aggregation of polyQ proteins in vitro5 and in cell culture6, and highlight how nucleation events control the overall progression of aggregation in the organism through the spatial confinement into individual cells. The key finding that the biophysical principles associated with protein aggregation in small volumes remain the governing factors, even in the complex environment of a living organism, will be critical for the interpretation of in vivo data from a wide range of protein aggregation diseases.

biophysics↗

bioRxiv2020

Theoretical basis for stabilizing messenger RNA through secondary structure design

RNA hydrolysis presents problems in manufacturing, long-term storage, world-wide delivery, and in vivo stability of messenger RNA (mRNA)-based vaccines and therapeutics. A largely unexplored strategy to reduce mRNA hydrolysis is to redesign RNAs to form double-stranded regions, which are protected from in-line cleavage and enzymatic degradation, while coding for the same proteins. The amount of stabilization that this strategy can deliver and the most effective algorithmic approach to achieve stabilization remain poorly understood. Here, we present simple calculations for estimating RNA stability against hydrolysis, and a model that links the average unpaired probability of an mRNA, or AUP, to its overall hydrolysis rate. To characterize the stabilization achievable through structure design, we compare AUP optimization by conventional mRNA design methods to results from more computationally sophisticated algorithms and crowdsourcing through the OpenVaccine challenge on the Eterna platform. These computational tests were carried out on both model mRNAs and COVID-19 mRNA vaccine candidates. We find that rational design on Eterna and the more sophisticated algorithms lead to constructs with low AUP, which we term superfolder mRNAs. These designs exhibit wide diversity of sequence and structure features that may be desirable for translation, biophysical size, and immunogenicity, and their folding is robust to temperature, choice of flanking untranslated regions, and changes in target protein sequence, as illustrated by rapid redesign of superfolder mRNAs for B.1.351, P.1, and B.1.1.7 variants of the prefusion-stabilized SARS-CoV-2 spike protein. Increases in in vitro mRNA half-life by at least two-fold appear immediately achievable. Significance statementMessenger RNA (mRNA) medicines that encode and promote translation of a target protein have shown promising use as vaccines in the current SARS-CoV-2 pandemic as well as infectious diseases due to their speed of design and manufacturing. However, these molecules are intrinsically prone to hydrolysis, leading to poor stability in aqueous buffer and major challenges in distribution. Here, we present a principled biophysical model for predicting RNA degradation, and demonstrate that the stability of any mRNA can be increased at least two-fold over conventional design techniques. Furthermore, the predicted stabilization is robust to post-design modifications. This conceptual framework and accompanying algorithm can be immediately deployed to guide re-design of mRNA vaccines and therapeutics to increase in vitro stability.

biophysics↗

bioRxiv2020

Extreme stiffness of neuronal synapses and implications for synaptic adhesion and plasticity

Synapses play a critical role in neural circuits, and they are potential sites for learning and memory. Maintenance of synaptic adhesion is critical for neural circuit function, however, biophysical mechanisms that help maintain synaptic adhesion are not clear. Studies with various cell types demonstrated the important role of stiffness in cellular adhesions. Although synaptic stiffness could also play a role in synaptic adhesion, stiffnesses of synapses are difficult to characterize due to their small size and challenges in verifying synapse identity and function. To address these challenges, we have developed an experimental platform that combines atomic force microscopy, fluorescence microscopy, and transmission electron microscopy. Here, using this platform, we report that functional, mature, excitatory synapses had an average elastic modulus of approximately 200 kPa, two orders of magnitude larger than that of the brain tissue, suggesting stiffness might have a role in synapse function. Similar to various functional and anatomical features of neural circuits, synaptic stiffness had a lognormal-like distribution, hinting a possible regulation of stiffness by processes involved in neural circuit function. In further support of this possibility, we observed that synaptic stiffness was correlated with spine size, a quantity known to correlate with synaptic strength. Using established stages of the long-term potentiation timeline and theoretical models of adhesion cluster dynamics, we developed a biophysical model of the synapse that not only explains extreme stiffness of synapses, their statistical distribution, and correlation with spine size, but also offers an explanation to how early biomolecular and structural changes during functional potentiation could lead to strengthening of synaptic adhesion. According to this model, synaptic stiffness serves as an indispensable physical messenger, feeding information back to synaptic adhesion molecules to facilitate maintenance of synaptic adhesion.

biophysics↗

bioRxiv2020

Evolution of structural dynamics in bilobed proteins

Novel biophysical tools allow the structural dynamics of proteins, and the regulation of such dynamics by binding partners, to be explored in unprecedented detail. Although this has provided critical insights into protein function, the means by which structural dynamics direct protein evolution remains poorly understood. Here, we investigated how proteins with a bilobed structure, composed of two related domains from the type-II periplasmic binding protein domain family, have undergone divergent evolution leading to modification of their structural dynamics and function. We performed a structural analysis of ~600 bilobed proteins with a common primordial structural core, which we complemented with biophysical studies to explore the structural dynamics of selected examples by single-molecule Forster resonance energy transfer and Hydrogen-Deuterium exchange mass spectrometry. We show that evolutionary modifications of the structural core, largely at its termini, enables distinct structural dynamics, allowing the diversification of these proteins into transcription factors, enzymes, and extra-cytoplasmic transport-related proteins. Structural embellishments of the core created new interdomain interactions that stabilized structural states, reshaping the active site geometry, and ultimately, altered substrate specificity. Our findings reveal an as yet unrecognized mechanism for the emergence of functional promiscuity during long periods of protein evolution and are applicable to a large number of domain architectures.

biophysics↗

bioRxiv2020

Computationally-efficient spatiotemporal correlation analysis super-resolves anomalous diffusion

Anomalous diffusion dynamics in confined nanoenvironments govern the macroscale properties and interactions of many biophysical and material systems. Currently, it is difficult to quantitatively link the nanoscale structure of porous media to anomalous diffusion within them. Fluorescence correlation spectroscopy super-resolution optical fluctuation imaging (fcsSOFI) has been shown to extract nanoscale structure and Brownian diffusion dynamics within gels, liquid crystals, and polymers, but has limitations which hinder its wider application to more diverse, biophysically-relevant datasets. Here, we parallelize the least-squares curve fitting step on a GPU improving computation times by up to a factor of 40, implement anomalous diffusion and two-component Brownian diffusion models, and make fcsSOFI more accessible by packaging it in a user-friendly GUI. We apply fcsSOFI to simulations of the protein fibrinogen diffusing in polyacrylamide of varying matrix densities and super-resolve locations where slower, anomalous diffusion occurs within smaller, confined pores. The improvements to fcsSOFI in speed, scope, and usability will allow for the wider adoption of super-resolution correlation analysis to diverse research topics.

biophysics↗

bioRxiv2021

Single Cell Biological Microlasers Powered by Deep Learning

Cellular lasers are cutting-edge technologies for biomedical applications. Due to the enhanced interactions between light and cells in microcavities, cellular properties and subtle changes of cells can be significantly reflected by the laser emission characteristics. In particular, transverse laser modes from single-cell lasers which utilize Fabry-Perot cavities are highly correlated to the spatial biophysical properties of cells. However, the high chaotic and complex variation of laser modes limits their practical applications for cell detections. Deep learning technique has demonstrated its powerful capability in solving complex imaging problems, which is expected to be applied for cell detections based on laser mode imaging. In this study, deep learning technique was applied to analyze laser modes generated from single-cell lasers, in which a correlation between laser modes and physical properties of cells was built. As a proof-of-concept, we demonstrated the predictions of cell sizes using deep learning based on laser mode imaging. In the first part, bioinspired cell models were fabricated to systematically study how cell sizes affect the characteristics of laser modes. By training a convolutional neuron network (CNN) model with laser mode images, predictions of cell model diameters with a sub-wavelength accuracy were achieved. In the second part, deep learning was employed to study laser modes generated from biological cells. By training a CNN model with laser mode images acquired from astrocyte cells, predictions of cell sizes with a sub-wavelength accuracy were also achieved. The results show the great potential of laser mode imaging integrated with deep learning for cell analysis and biophysical studies.

biophysics↗

bioRxiv2021

An active finite viscoelastic model for gastric smooth muscle contraction

A coupled electromechanical model to describe the transduction process of cellular electrical activity into mechanical deformation has been presented. The model consolidates a biophysical smooth muscle cell model, a biophysical actin-myosin interaction model, a sliding filament model and a viscoelastic constitutive model to construct an active finite viscoelastic model. The key input to this model is an electrical pulse which then estimates the resulting stress and deformation in the cell. The proposed model was used to recreate experimental observations performed on canine and porcine gastric tissue strips. In all cases, the simulation results were well matched with the experimental data (R2 > 0.9).

biophysics↗

bioRxiv2021

Structural and thermodynamic analyses of human TMED1 (p24γ1) Golgi dynamics

The transmembrane emp24 domain-containing proteins (TMED), also called p24 proteins, are members of a family of sorting receptors present in all representatives of the domain Eukarya and abundantly present in all subcompartments of the early secretory pathway, namely the endoplasmic reticulum (ER), the Golgi, and the intermediate compartment. Although essential during the bidirectional transport between the ER and the Golgi, there is still a lack of information regarding the TMEDs structure, oligomerization propensity, and biophysics of their interactions with the transport cargo. Here, we describe the first high-resolution structure of the Golgi dynamics (GOLD) domain of a TMED1 representative and its biophysical characterization in solution. The crystal structure showed a dimer formation that is also present in solution in a salt-dependent manner, suggesting that the GOLD domain can form homodimers even in the absence of the TMED1 coiled-coil region. A molecular dynamics description of the dimer stabilization, with a phylogenetic analysis of the residues important for the oligomerization and a model for the orientation towards the lipid membrane are also presented.

biophysics↗

bioRxiv2021

Diffusion barriers and adaptive carbon uptake strategies enhance the modeled performance of the algal CO2-concentrating mechanism

Many photosynthetic organisms enhance the performance of their CO2-fixing enzyme Rubisco by operating a CO2-concentrating mechanism (CCM). Most CCMs in eukaryotic algae supply concentrated CO2 to Rubisco in an organelle called the pyrenoid. Ongoing efforts seek to engineer an algal CCM into crops that lack a CCM to increase yields. To advance our basic understanding of the algal CCM, we develop a chloroplast-scale reaction-diffusion model to analyze the efficacy and the energy efficiency of the CCM in the green alga Chlamydomonas reinhardtii. We show that achieving an effective and energetically efficient CCM requires a physical barrier such as thylakoid stacks or a starch sheath to reduce CO2 leakage out of the pyrenoid matrix. Our model provides insights into the relative performance of two distinct inorganic carbon uptake strategies: at air-level CO2, a CCM can operate effectively by taking up passively diffusing external CO2 and catalyzing its conversion to HCO3-, which is then trapped in the chloroplast; however, at lower external CO2 levels, effective CO2 concentration requires active import of HCO3-. We also find that proper localization of carbonic anhydrases can reduce futile carbon cycling between CO2 and HCO3-, thus enhancing CCM performance. We propose a four-step engineering path that increases predicted CO2 saturation of Rubisco up to seven-fold at a theoretical cost of only 1.5 ATP per CO2 fixed. Our system-level analysis establishes biophysical principles underlying the CCM that are broadly applicable to other algae and provides a framework to guide efforts to engineer an algal CCM into land plants. Significance StatementEukaryotic algae mediate approximately one-third of CO2 fixation in the global carbon cycle. Many algae enhance their CO2-fixing ability by operating a CO2-concentrating mechanism (CCM). Our model of the algal CCM lays a solid biophysical groundwork for understanding its operation. The models consistency with experimental observations supports existing hypotheses about the operating principles of the algal CCM and the functions of its component proteins. We provide a quantitative estimate of the CCMs energy efficiency and compare the performance of two distinct CO2 assimilation strategies under varied conditions. The model offers a quantitative framework to guide the engineering of an algal CCM into land plants and supports the feasibility of this endeavor.

biophysics↗

bioRxiv2021

Ion dynamics at the energy-deprived tripartite synapse

The anatomical and functional organization of neurons and astrocytes at tripartite synapses is essential for reliable neurotransmission, which critically depends on ATP. In low energy conditions, synaptic transmission fails, accompanied by a breakdown of ion gradients, changes in membrane potentials and cell swelling. The resulting cellular damage and cell death are causal to the often devastating consequences of an ischemic stroke. The severity of ischemic damage depends on the age and the brain region in which a stroke occurs, but the reasons for this differential vulnerability are far from understood. In the present study, we address this question by developing a comprehensive biophysical model of a glutamatergic synapse to identify key determinants of synaptic failure during energy deprivation. Our model is based on fundamental biophysical principles, includes dynamics of the most relevant ions, i.e., Na+, K+, Ca2+, Cl- and glutamate, and is calibrated with experimental data. It confirms the critical role of the Na+/K+-ATPase in maintaining ion gradients, membrane potentials and cell volumes. Our simulations demonstrate that the system exhibits two stable states, one physiological and one pathological. During energy deprivation, the physiological state may disappear, forcing a transit to the pathological state, which can be reverted when blocking voltage-gated Na+ and K+ channels. Our model predicts that the transition to the pathological state is favoured if the extracellular space fraction is small. A reduction in the extracellular space volume fraction, as, e.g. observed with ageing, will thus promote the brains susceptibility to ischemic damage. Our work provides new insights into the brains ability to recover from energy deprivation, with translational relevance for diagnosis and treatment of ischemic strokes. Author summaryThe brain consumes energy to keep ion concentrations at normal working conditions. In the case of energy deprivation (ED), e.g., during a stroke, synaptic communication fails first. Inspired by our recent experimental work on ED, we formulated a novel computational model to explore initial events during ED. Our model reproduces time courses for several ions from different experimental data. In some cases, the system returns to baseline upon restoring energy supply. In others, we observe that neurons and astrocytes cannot recover accompanied by cell swelling. There is a threshold depending on the depth and duration of ATP depletion differentiating these cases. Also, smaller extracellular spaces hamper recovery more. This result may explain clinical observations of increased vulnerability to stroke as the size of the extracellular space shrinks with ageing.

biophysics↗

bioRxiv2021

First-principles model of optimal translation factors stoichiometry

Enzymatic pathways have evolved uniquely preferred protein expression stoichiometry in living cells, but our ability to predict the optimal abundances from basic properties remains underdeveloped. Here we report a biophysical, first-principles model of growth optimization for core mRNA translation, a multi-enzyme system that involves proteins with a broadly conserved stoichiometry spanning two orders of magnitude. We show that a parsimonious flux model constrained by proteome allocation is sufficient to predict the conserved ratios of translation factors through maximization of ribosome usage The analytical solutions, without free parameters, provide an interpretable framework for the observed hierarchy of expression levels based on simple biophysical properties, such as diffusion constants and protein sizes. Our results provide an intuitive and quantitative understanding for the construction of a central process of life, as well as a path toward rational design of pathway-specific enzyme expression stoichiometry.

biophysics↗

bioRxiv2021

Discovery and characterization of Hv1-type proton channels in reef-building corals.

Voltage-dependent proton-permeable channels are membrane proteins mediating a number of important physiological functions. Here we report the presence of a gene encoding for Hv1 voltage-dependent, proton-permeable channels in two species of reef-building corals. We performed a characterization of their biophysical properties and found that these channels are fast-activating and modulated by the pH gradient in a distinct manner. The biophysical properties of these novel channels make them interesting model systems. We have also developed an allosteric gating model that provides mechanistic insight into the modulation of voltage-dependence by protons. This work also represents the first functional characterization of any ion channel in scleractinian corals. We discuss the implications of the presence of these channels in the membranes of coral cells in the calcification and pH regulation processes and possible consequences of ocean acidification related to the function of these channels.

biophysics↗

bioRxiv2021

Adhesion dynamics regulate cell intercalation behaviour in an active tissue

Cell intercalation is a key cell behaviour of morphogenesis and wound healing, where local cell neighbour exchanges can cause dramatic tissue deformations such as body axis extension. Here, we develop a mechanical model to understand active cell intercalation behaviours in the context of an epithelial tissue. Extending existing descriptions, such as vertex models, the junctional actomyosin cortex of every cell is modelled as a continuum morphoelastic rod, explicitly representing cortices facing each other at bicellular junctions. Cells are described directly in terms of the key subcellular constituents that drive dynamics, with localised stresses from the contractile actomyosin cortex and adhesion molecules coupling apposed cortices. This multi-scale apposed-cortex formulation reveals key behaviours that drive tissue dynamics, such as cell-cell shearing and flow of junctional material past cell vertices. We show that cell neighbour exchanges can be driven by purely junctional mechanisms. Active contractility and viscous turnover in a single bicellular junction are sufficient to shrink and remove a junction. Next, the 4-way vertex is resolved and a new, orthogonal junction extends passively. The adhesion timescale defines a frictional viscosity that is an important regulator of these dynamics, modulating tension transmission in the tissue as well as the speeds of junction shrinkage and growth. The model additionally predicts that rosettes, which form when a vertex becomes common to many cells, are likely to occur in active tissues with high adhesive friction. SIGNIFICANCECell intercalation, or neighbour exchange, is a crucial behaviour that can drive tissue deformations, dissipate stress and facilitate wound healing. Substantial experimental work has identified the key molecular players facilitating intercalation, but there remains a lack of consensus and understanding of their physical roles. Existing biophysical models that represent cell-cell contacts with single edges cannot study the continuous dynamics of intercalation, involving shear between coupled cell cortices. Deriving a continuum description of the cell cortex, explicitly coupling neighbouring cortices with adhesions, we define the biophysical conditions required for successful neighbour exchanges. Furthermore, we show how the turnover of adhesion molecules specifies a viscous friction that regulates active tissue dynamics.

biophysics↗

bioRxiv2021

Transient nuclear deformation primes epigenetic state and promotes cell reprogramming

Cell reprogramming has wide applications in tissue regeneration, disease modeling and personalized medicine, but low reprogramming efficiency remains a challenge. In addition to biochemical cues, biophysical factors can modulate the epigenetic state and a variety of cell functions. However, how biophysical factors help overcome the epigenetic barrier for cell reprogramming are not well understood. Here we utilized microfluidic channels to induce a transient deformation of the cell nucleus, which caused the disassembly of the nuclear lamina and a downregulation of DNA methylation and histone (H3K9) for 12-24 hours. These global decreases of heterochromatin marks at the early stage of cell reprogramming strikingly enhanced the conversion of fibroblasts into neurons and induced pluripotent stem cells. Consistently, inhibition of DNA methylation and H3K9 methylation partially mimicked the effects of mechanical squeezing on iN reprogramming efficiency. Knocking down lamin A had similar effects to squeezing on enhancing the reprogramming efficiency. Based on these findings, we developed a scalable microfluidic system that enabled a continuous cell processing to effectively prime the epigenetic state for cell reprogramming, demonstrating the potential of mechano-biotechnology for cell engineering.

biophysics↗