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bioRxiv2021

A tug of war between filament treadmilling and myosin induced contractility generates actin cortex

In most eukaryotic cells, actin filaments assemble into a shell-like actin cortex under the plasma membrane, controlling cellular morphology, mechanics, and signaling. The actin cortex is highly polymorphic, adopting diverse forms such as the ring-like structures found in podosomes, axonal rings, and immune synapses. The biophysical principles that underlie the formation of actin rings and cortices remain unknown. Using a molecular simulation platform, called MEDYAN, we discovered that varying the filament treadmilling rate and myosin concentration induces a finite size phase transition in actomyosin network structures. We found that actomyosin networks condense into clusters at low treadmilling rates or high myosin concentration but form ring-like or cortex-like structures at high treadmilling rates and low myosin concentration. This mechanism is supported by our corroborating experiments on live T cells, which exhibit ring-like actin networks upon activation by stimulatory antibody. Upon disruption of filament treadmilling or enhancement of myosin activity, the pre-existing actin rings are disrupted into actin clusters or collapse towards the network center respectively. Our analyses suggest that the ring-like actin structure is a preferred state of low mechanical energy, which is, importantly, only reachable at sufficiently high treadmilling rates.

biophysics↗

bioRxiv2021

Dengue and Zika virus capsid proteins bind to membranes and self-assemble into liquid droplets with nucleic acids

Liquid-liquid phase separation is prone to occur when positively charged proteins interact with nucleic acids. Here, we studied biophysical properties of Dengue (DENV) and Zika (ZIKV) virus capsid proteins to understand the process of RNA genome encapsidation. In this route, the capsid proteins efficiently recruit the viral RNA at the ER membrane to yield nascent viral particles. However, little is known either about the molecular mechanisms by which multiple copies of capsid proteins assemble into nucleocapsids or how the nucleocapsid is recruited and wrapped by the ER membrane during particle morphogenesis. Here, we measured relevant interactions concerning the viral process using purified DENV and ZIKV capsids proteins, membranes mimicking the ER lipid composition and nucleic acids at in vitro conditions. We found that both ZIKV and DENV capsid proteins bound to liposomes at liquid-disordered phase regions and docked exogenous membranes and RNA molecules. When the proteins bound nucleic acids, droplet liquid-liquid phase separation was observed. We characterized these liquid condensates by measuring nucleic acid partition constant and the extent of water dipolar relaxation observing a cooperative process for the formation of the new phase that involves a distinct water organization. Our data supports a new model in which capsid-RNA complexes directly bind the ER membrane, seeding the process of RNA recruitment for viral particle assembly. These results contribute to understand the viral nucleocapsid formation as a stable liquid-liquid phase transition, which could be relevant for Dengue and Zika gemmation, opening new avenues for antiviral intervention.

biophysics↗

bioRxiv2021

The role of epistasis in determining the fitness landscape of HIV proteins

The rapid evolution of HIV is constrained by interactions between mutations which affect viral fitness. In this work, we explore the role of epistasis in determining the fitness landscape of HIV for multiple drug target proteins, including Protease, Reverse Transcriptase, and Integrase. Epistatic interactions between residues modulate the mutation patterns involved in drug resistance with unambiguous signatures of epistasis best seen in the comparison of a maximum entropy sequence co-variation (Potts) model predicted and experimental HIV sequence "prevalences" when expressed as higher-order marginals (beyond triplets) of the sequence probability distribution. In contrast, the evidence for epistasis based on experimental measures of fitness such as replicative capacity is weak; the correspondence with Potts model "prevalence"-based predictions is obscured by site conservation and limited precision. Double mutant cycles provide in principle one of the best ways to probe epistatic interactions experimentally without reference to a particular background, and we find they reveal that the most strongly interacting mutations in HIV involve correlated sets of drug-resistance-associated residues, however the analysis is complicated by the small dynamic range of measurements. The use of correlated models for the design of experiments to probe viral fitness can help identify the epistatic interactions involved in mutational escape, and lead to better inhibitor therapies. Author summaryProtein covariation models provide an alternative to experimental measures for estimating the fitness of mutations in proteins from across a variety of organisms. Yet, for viral proteins, it has been shown that models including epistatic couplings between residues, or other machine learning models perform no better or even worse than a simpler independent model devoid of such epistatic couplings in estimating viral fitness measurements such as replicative capacities, providing weak or ambiguous evidence for epistasis. We show that the evidence for long-range epistasis is strong by the analysis of the high-order marginals of the MSA distribution (up to subsequences of length 14), which are accurately captured by a correlated Potts sequence-covariation model but not by an independent model. While double mutant cycles in principle provide well-established biophysical probes for epistatic interactions, we demonstrate that the analysis and comparison between model and experiment is difficult due to the much smaller dynamic range of the measurements, making them more susceptible to noise.

biophysics↗

bioRxiv2021

Switching an active site helix in dihydrofolate reductase reveals limits to sub-domain modularity

To what degree are individual structural elements within proteins modular such that similar structures from unrelated proteins can be interchanged? We study sub-domain modularity by creating 20 chimeras of an enzyme, E. coli dihydrofolate reductase (DHFR), in which a catalytically important, 10-residue -helical sequence is replaced by -helical sequences from a diverse set of proteins. The chimeras stably fold but have a range of diminished thermal stabilities and catalytic activities. Evolutionary coupling analysis indicates that the residues of this -helix are under selection pressure to maintain catalytic activity in DHFR. We performed molecular dynamics simulations using replica exchange with solute-tempering. Chimeras with low catalytic activity exhibit non-helical conformations that block the binding site and disrupt the positioning of the catalytically essential residue D27. Simulation observables and in vitro measurements of thermal stability and substrate binding affinity are strongly correlated. Several E. coli strains with chromosomally integrated chimeric DHFRs can grow, with growth rates that follow predictions from a kinetic flux model that depends on the intracellular abundance and catalytic activity of DHFR. Our findings show that although -helices are not universally substitutable, the molecular and fitness effects of modular segments can be predicted by the biophysical compatibility of the replacement segment. Statement of Significance-helices are ubiquitous components of protein structure that exhibit a degree of independent folding behavior, making them plausible structural modules within proteins. Here, we assess the effects of switching the sequence of an -helix in an essential enzyme for -helical sequences from evolutionarily unrelated proteins. The resultant chimeric proteins can still fold but enzymatic activity, stability, and cellular growth rates are negatively affected. Computational investigations reveal how residues in an -helix have been shaped by selection pressure to maintain catalytic activity and a specific, helical conformation of the protein. More broadly, we illustrate how molecular and fitness effects of switching protein segments depend on the protein and cellular context.

biophysics↗

bioRxiv2021

Myristoylation and its effects on the human Golgi Reassembly and Stacking Protein 55

GRASP55 is a myristoylated protein localized in the medial/trans-Golgi faces and involved in the Golgi structure maintenance and the regulation of unconventional secretion pathways. It is believed that GRASP55 achieves its main functionalities in the Golgi organization by acting as a tethering factor and, when bound to the lipid bilayer, its orientation relative to the membrane surface is restricted to determine its proper trans-oligomerization. Despite the paramount role of myristoylation in GRASP function, the impact of such protein modification on the membrane-anchoring properties and the structural organization of GRASP remains elusive. Here, an optimized protocol for the myristoylation in E. coli of the membrane-anchoring domain of GRASP55 is presented. The biophysical properties of the myristoylated/non-myristoylated GRASP55 GRASP domain were characterized in a membrane-mimicking micellar environment. Although myristoylation did not cause any impact on the proteins secondary structure, according to our circular dichroism data, it had a significant impact on the proteins thermal stability and solubility. Electrophoresis of negatively charged liposomes incubated with the two GRASP55 constructions showed different electrophoretic mobility for the myristoylated anchored protein only, thus demonstrating that myristoylation is essential for the biological membrane anchoring. Molecular dynamics simulations were used to further explore the anchoring process in determining the restricted orientation of GRASPs in the membrane.

biophysics↗

bioRxiv2021

MELD-DNA: A new tool for capturing protein-DNA binding

Herein we present MELD-DNA, a novel computational approach to address the problem of protein-DNA structure prediction. This method addresses well-known issues hampering current computational approaches to bridge the gap between structural and sequence knowledge, such as large conformational changes in DNA and highly charged electrostatic interaction during binding. MELD-DNA is able to: i) sample multiple binding modes, ii) identify the preferred binding mode from the ensembles, and iii) provide qualitative binding preferences between DNA sequences. We expect the results presented herein will have impact in the field of biophysics (through new software development), structural biology (by complementing DNA structural databases) and supramolecular chemistry (by bringing new insights into protein-DNA interactions).

biophysics↗

bioRxiv2021

SARS-CoV-2 Variants are Selecting for Spike Protein Mutations that Increase Protein Stability

The emergence of SARS-CoV-2 in 2019 has caused severe disruption and a huge number of human deaths across the globe. As the pandemic spreads, a natural result is the emergence of variants with a variety of amino acid mutations. Variants of SARS-CoV-2 with mutations in their spike protein may result in an increased infectivity, increased lethality, or immune escape, and whilst many of these properties can be explained through changes to binding affinity or changes to post-translational modification, many mutations have no known biophysical impact on the structure of protein. The Gibbs free energy of a protein represents a measure of protein stability, with an increased stability resulting in a protein that is more thermodynamically stable, and more robust to changes in external environment. Here we show that mutations in the spike proteins of SARS-CoV-2 are selecting for amino acid changes that result in a more stable protein than expected by chance. We calculate all possible mutations in the SARS-CoV-2 spike protein, and show that many variants are more stable than expected when compared to the background, indicating that protein stability is an important consideration for the understanding of SARS-CoV-2 evolution. Variants exhibit a range of stabilities, and we further suggest that some stabilising mutations may be acting as a "counterbalance" to destabilising mutations that have other properties, such as increasing binding site affinity for the human ACE2 receptor. We suggest that protein folding calculations offer a useful tool for early identification of advantageous mutations.

biophysics↗

bioRxiv2021

Predicting and interpreting large scale mutagenesis data using analyses of protein stability and conservation

Understanding and predicting the functional consequences of single amino acid is central in many areas of protein science. Here we collected and analysed experimental measurements of effects of >150,000 variants in 29 proteins. We used biophysical calculations to predict changes in stability for each variant, and assessed them in light of sequence conservation. We find that the sequence analyses give more accurate prediction of variant effects than predictions of stability, and that about half of the variants that show loss of function do so due to stability effects. We construct a machine learning model to predict variant effects from protein structure and sequence alignments, and show how the two sources of information are able to support one another. Together our results show how one can leverage large-scale experimental assessments of variant effects to gain deeper and general insights into the mechanisms that cause loss of function.

biophysics↗

bioRxiv2021

Making high-dimensional molecular distribution functions tractable through Belief Propagation on Factor Graphs

Molecular dynamics (MD) simulations provide a wealth of high-dimensional data at all-atom and femtosecond resolution but deciphering mechanistic information from this data is an ongoing challenge in physical chemistry and biophysics. Theoretically speaking, joint probabilities of the equilibrium distribution contain all thermodynamic information, but they prove increasingly difficult to compute and interpret as the dimensionality increases. Here, inspired by tools in probabilistic graphical modeling, we develop a factor graph trained through belief propagation that helps factorize the joint probability into an approximate tractable form that can be easily visualized and used. We validate the study through the analysis of the conformational dynamics of two small peptides with 5 and 9 residues. Our validations include testing the conditional dependency predictions through an intervention scheme inspired by Judea Pearl. Secondly we directly use the belief propagation based approximate probability distribution as a high-dimensional static bias for enhanced sampling, where we achieve spontaneous back-and-forth motion between metastable states that is up to 350 times faster than unbiased MD. We believe this work opens up useful ways to thinking about and dealing with high-dimensional molecular simulations.

biophysics↗

bioRxiv2021

Gradient-mixing LEGO robots for purifying DNA origami nanostructures of multiple components rate-zonal centrifugation

DNA origami purification is critical in emerging applications of functionalized DNA nanostructures from basic fundamental biophysics, nanorobots to therapeutics. Advances in DNA origami purification have led to the establishment of rate-zonal centrifugation (RZC) as a scalable, high-yield, and contamination-free approach to purifying DNA origami nanostructures. In RZC purification, a linear density gradient is created using viscous agents, such as glycerol and sucrose, to separate molecules based on their mass and shape during high-rpm centrifugation. However, current methods for creating density gradients are typically time-consuming because of their reliance on slow passive diffusion. Here, we built a LEGO gradient mixer to rapidly create a quasi-continuous density gradient with minimal layering of concentrations using simple rotational motion. We found that rotating two layers of different concentrations at an angle can reduce the diffusion time from a few hours to mere minutes. The instrument needed to perform the movement can be constructed from low-cost components, such as Arduino and LEGO Mindstorms pieces, and has comparable efficacy to commercial gradient mixers currently available. Our results demonstrate that the creation of a linear density gradient can be achieved with minimal labor, time, and cost with this machine. With the recent advances in DNA origami production, we anticipate our findings to further improve the viability of scaling up DNA origami purification in grams quantities. Our simple process enables automated large-scale purification of functionalized DNA origami more feasible in resource-constrained settings.

biophysics↗

bioRxiv2021

Viscoelastic Parameterization of Human Skin Cells to Characterize Material Behavior at Multiple Timescales

Countless biophysical studies have sought distinct markers in the cellular mechanical response that could be linked to morphogenesis, homeostasis, and disease. Here, a novel iterative-fitting methodology is used to investigate the viscoelastic behavior at multiple relaxation times of human skin cells under physiologically relevant conditions. Past investigations often involved parameterizing linear elastic relationships and assuming purely Hertzian contact mechanics. However, linear elastic treatment fails to capture and properly account for the rich temporal information available in datasets. We demonstrate the performance superiority of the proposed iterative viscoelastic characterization method over standard open-search approaches. Our viscoelastic measurements revealed that 2D adherent metastatic melanoma cells exhibit reduced elasticity compared to normal counterparts--melanocytes and fibroblasts, whereas are significantly less viscous than only fibroblasts over timescales spanning three orders of magnitude. Interestingly, melanocytes are stiffer than melanoma cells, while being the less viscous cells measured. The measured loss angle indicates clear differential viscoelastic responses across multiple timescales between the measured cells. We propose the use of viscoelastic properties at multiple timescales as a mechanical biomarker of diseases. Altogether, this method provides new insight into the complex viscoelastic behavior of metastatic melanoma cells relevant to better understanding cancer metastasis aggression.

biophysics↗

bioRxiv2021

Unveiling Mutation Effects on the Structural Dynamics of the Main Protease from SARS-CoV-2 with Hybrid Simulation Methods

The main protease of SARS-CoV-2 (called Mpro or 3CLpro) is essential for processing polyproteins encoded by viral RNA. Macromolecules adopt several favored conformations in solution depending on their structure and shape, determining their dynamics and function. Integrated methods combining the lowest-frequency movements obtained by Normal Mode Analysis (NMA), and the faster movements from Molecular Dynamics (MD), and data from biophysical techniques, are necessary to establish the correlation between complex structural dynamics of macromolecules and their function. In this article, we used a hybrid simulation method to sample the conformational space to characterize the structural dynamics and global motions of WT SARS-CoV-2 Mpro and 48 mutants, including several mutations that appear in P.1, B.1.1.7, B.1.351, B.1.525 and B.1.429+B.1.427 variants. Integrated Hybrid methods combining NMA and MD have been useful to study the correlation between the complex structural dynamics of macromolecules and their functioning mechanisms. Here, we applied this hybrid approach to elucidate the effects of mutation in the structural dynamics of SARS-CoV-2 Mpro, considering their flexibility, solvent accessible surface area analyses, global movements, and catalytic dyad distance. Furthermore, some mutants showed significant changes in their structural dynamics and conformation, which could lead to distinct functional properties. HighlightsO_LISingle surface mutations lead to changes in Mpro structural dynamics. C_LIO_LIMutants can be more stable than WT according to the structural dynamics properties. C_LIO_LIMpromutants can present a distinct functionality in relation to the wild-type. C_LIO_LIPotential viral markers for more pathogenic or transmissible SARS-CoV-2 variants. C_LI

biophysics↗

bioRxiv2021

How clustered protocadherin binding specificity is tuned for neuronal self/non-self-recognition

The stochastic expression of fewer than 60 clustered protocadherin (cPcdh) isoforms provides diverse identities to individual vertebrate neurons and a molecular basis for self/non-self- discrimination. cPcdhs form chains mediated by alternating cis and trans interactions between apposed membranes, which has been suggested to signal self-recognition. Such a mechanism requires that cPcdh cis dimers form promiscuously to generate diverse recognition units, and that trans interactions have precise specificity so that isoform mismatches terminate chain growth. However, the extent to which cPcdh interactions fulfill these requirements has not been definitively demonstrated. Here we report biophysical experiments showing that cPcdh cis interactions are promiscuous, but with preferences favoring formation of heterologous cis dimers. Trans-homophilic interactions are remarkably precise, with no evidence for heterophilic interactions between different isoforms. A new C-type cPcdh crystal structure and mutagenesis data help to explain these observations. Overall, the interaction characteristics we report for cPcdhs help explain their function in neuronal self/non-self-discrimination.

biophysics↗

bioRxiv2021

Cellular organization in lab-evolved and extant multicellular species obeys a maximum entropy law

The prevalence of multicellular organisms is due in part to their ability to form complex structures. How cells pack in these structures is a fundamental biophysical issue, underlying their functional properties. However, much remains unknown about how cell packing geometries arise, and how they are affected by random noise during growth - especially absent developmental programs. Here, we quantify the statistics of cellular neighborhoods of two different multicellular eukaryotes: lab-evolved "snowflake" yeast and the green alga Volvox carteri. We find that despite large differences in cellular organization, the free space associated with individual cells in both organisms closely fits a modified gamma distribution, consistent with maximum entropy predictions originally developed for granular materials. This entropic cellular packing ensures a degree of predictability despite noise, facilitating parent-offspring fidelity even in the absence of developmental regulation. Together with simulations of diverse growth morphologies, these results suggest that gamma-distributed cell neighborhood sizes are a general feature of multicellularity, arising from conserved statistics of cellular packing.

biophysics↗

bioRxiv2021

Modeling the mechanics of growing epithelia with a bilayer plate theory

Epithelia, which consists of cell sheets lying on a substrate, are prevalent structures of multi-cellular organisms. The physical basis of epithelial morphogenesis has been intensely investigated in recent years. However, as 2D mechanics focused most attention, we still lack a rigorous description of how the mechanical interactions between the cell layer and its substrate can lead to 3D distortions. This work provides a complete description of epithelial mechanics using the most straightforward model of an epithelium: a thin elastic bilayer. We first provide experimental evidence in Drosophila tissues that localized alterations of the cell-substrate (the extracellular matrix) can lead to profound 3D shape changes in epithelia. We then develop an analytical model modifying the Foppl-von Karman equation with growth for bilayers. We provide a complete description of all contributions from biophysical characteristics of epithelia. We show how any localized inhomogeneity of stiffness or thickness drastically changes the bending process when the two layers grow differently. Comparison with finite-element simulations and experiments performed on Drosophila wing imaginal discs validate this approach for thin epithelia.

biophysics↗

bioRxiv2021

Studying magnetic susceptibility, microstructural compartmentalisation and chemical exchange in a formalin-fixed ex vivo human brain specimen

PurposeEx vivo imaging is a preferable method to study the biophysical mechanism of white matter orientation-dependent signal phase evolution. Yet, how formalin fixation, commonly used for tissue preservation, affects the phase measurement is not fully known. We, therefore, study the impacts of formalin fixation on magnetic susceptibility, microstructural compartmentalisation and chemical exchange measurement on human brain tissue. MethodsA formalin-fixed, post-mortem human brain specimen was scanned with multiple orientations with respect to the main magnetic field direction for robust bulk magnetic susceptibility measurement with conventional quantitative susceptibility imaging models. Homogeneous white matter tissues were subsequently excised from the whole-brain specimen and scanned in multiple rotations on an MRI scanner to measure the anisotropic magnetic susceptibility and microstructure-related contributions in the signal phase. Electron microscopy was used to validate the MRI findings. ResultsThe bulk isotropic magnetic susceptibility of ex vivo whole-brain imaging is comparable to in vivo imaging, with noticeable enhanced non-susceptibility contributions. The excised specimen experiment reveals that anisotropic magnetic susceptibility and compartmentalisation phase effect were considerably reduced in formalin-fixed white matter tissue. ConclusionsDespite formalin-fixed white matter tissue has comparable bulk isotropic magnetic susceptibility to those measured via in vivo imaging, its orientation-dependent components in the signal phase related to the tissue microstructure is substantially weaker, making it less favourable in white matter microstructure studies using phase imaging.

biophysics↗

bioRxiv2021

Lipid tracking at kilohertz sampling rates on live cell membranes through Interferometric Scattering microscopy

The specific details of the lateral diffusion dynamics in cellular plasma membrane are an open topic in modern biophysics. Many studies have documented several different behaviours, including free (Brownian) motion, confined diffusion, transiently confined (hop) diffusion, anomalous diffusion, and combinations thereof. Here we have employed Interferometric Scattering Microscopy (ISCAT) to explore the lateral diffusion dynamics in the plasma membrane of living cells of a biotinylated lipid analogue that had been labelled with streptavidin-coated gold nanoparticles (20 and 40nm in diameter) at a sampling rate of 2kHz. The data was analysed with an unbiased statistics-driven mean squared displacement analysis pipeline that was designed to identify both the most likely diffusion mode for a specific data set, and the best fit parameters of the most likely model. We found that the prevalent diffusion mode of the tracked lipids, independent of the particle size, is compartmentalized diffusion, although the use of the larger tags resulted in tighter confinement and reduced diffusion rates. Through our analysis and comparison with simulated data, we quantify significant physical parameters, such as average compartment size, dynamic localization uncertainty, and the diffusion rates. We hereby further demonstrate the use of a confinement strength metric that makes it possible to compare diffusivity measurements across techniques and experimental conditions. Statement of SignificanceThis work offers new details on the data analysis of lipid diffusion on cellular membranes in vitro, through Interferometric Scattering microscopy. With this technique, we performed single particle tracking (SPT) experiments at 2kHz sampling rate. We analyzed the data through an unbiased statistics-driven protocol. The data shows that the diffusion motion of the tracked lipids follows mainly the "hopping" diffusion behaviour, whereby transient confinement zones hinder the particle dynamics. Matching the experimental data with diffusion simulations, we have been able to verify the physical parameters inferred by the experimental data analysis. Finally, we showcase a framework to compare SPT data with other techniques, to offer a complete overview of plasma membrane dynamics.

biophysics↗

bioRxiv2021

αS oligomers generated from polyunsaturated fatty acid and dopamine metabolite differentially interact with Aβ to enhance neurotoxicity

It is increasingly becoming clear that neurodegenerative diseases are not as discrete as originally thought to be but rather display significant overlap in histopathological and clinical presentations. For example, nearly half of the patients with Alzheimer disease (AD) and synucleinopathies such as Parkinson disease (PD) show symptoms and pathological features of one another. Yet, the molecular events and features that underlie such comorbidities in neurodegenerative diseases remain poorly understood. Here, inspired to uncover the molecular underpinnings of the overlap between AD and PD, we investigated the interactions between amyloid-{beta} (A{beta}) and -synuclein (S), aggregates of which form the major components of amyloid plaques and Lewy bodies, respectively. Specifically, we focused on S oligomers generated from the dopamine metabolite called dihydroxyphenylacetaldehyde (DOPAL), and a polyunsaturated fatty acid docosahexaenoic acid (DHA). Both S oligomers showed structural and conformational differences confirmed by their disparity in size, secondary structure, susceptibility to proteinase K digestion and cytotoxicity. More importantly, the two oligomers differentially modulated A{beta} aggregation. While both oligomers inhibited A{beta} aggregation to varying extents, they induced structurally different A{beta} assemblies. Furthermore, A{beta} seeded with DHA-derived S oligomers showed greater toxicity than DOPAL-derived S oligomers in SH-SY5Y neuroblastoma cells. These results provide insights into the interactions between two amyloid proteins with empirically distinctive biophysical and cellular manifestations, enunciating a basis for potentially ubiquitous cross-amyloid interactions across many neurodegenerative diseases.

biophysics↗