Biophysical journal最新文献

Influenza A virus particles assemble at the plasma membrane of infected cells. During assembly all components of the virus come together in a coordinated manner to deform the membrane into a protrusion eventually forming a new, membrane-enveloped virus. Here we integrate recent molecular insights of this process, particularly concerning the structure of the matrix protein 1 (M1), within a theoretical framework describing the mechanics of virus assembly. Our model describes M1 polymerization and membrane protrusion formation, explaining why it is efficient for M1 to form long strands assembling into helices in filamentous virions. Eventually, we find how the architecture of M1 helices is controlled by physical properties of viral proteins and the host cell membrane. Finally, by considering the growth force and speed of viral filaments, we propose that the helical geometry of M1 strands might have evolved to optimize for fast and efficient virus assembly and growth.

Weak protein interactions are associated with a broad array of biological functions and are often implicated in molecular dysfunction accompanying human disease. In addition, these interactions are a critical determinant in the effective manufacturing, stability, and administration of biotherapeutic proteins. Despite their prominence, much remains unknown about how molecular attributes influence the hydrodynamic and thermodynamic contributions to the overall interaction mechanism. To systematically probe these contributions, we have evaluated self-interaction in a diverse set of proteins that demonstrate a broad range of behaviors from attractive to repulsive. Analysis of the composite trending in the data provides a convenient interconversion among interaction parameters measured from the concentration dependence of the molecular weight, diffusion coefficient, and sedimentation coefficient, as well as insight into the relationship between thermodynamic and hydrodynamic interactions. We find relatively good agreement between our data and a model for interacting hard spheres in the range of weak self-association. In addition, we propose an empirically derived, general scaling relationship applicable across a broad range of self-association and repulsive behaviors.

DEAD-box helicases use ATP to unwind short double-stranded RNA (dsRNA). The helicase core consists of two discrete domains, termed RecA_N and RecA_C. The nucleotide binding site is harbored in RecA_N, while both RecA_N and RecA_C are involved in RNA recognition and ATP hydrolysis. In the absence of nucleotides or RNA, RecA_N and RecA_C do not interact ("open" form of the enzyme). In the presence of both RNA and ATP the two domains come together ("closed" form), building the composite RNA binding site and stimulating ATP hydrolysis. Because of the different roles and thermodynamic properties of the ADP-bound and ATP-bound states in the catalytic cycle, the conformations of DEAD-box helicases in complex with ATP and ADP are assumed to be different. However, the available crystal structures do not recapitulate these supposed differences and show identical conformations of DEAD-box helicases independent of the identity of the bound nucleotide. Here, we use NMR to demonstrate that the conformations of the ATP- and ADP-bound forms of the DEAD-box helicase Vasa are indeed different, contrary to the results from x-ray crystallography. These differences do not relate to the populations of the open and closed forms, but are intrinsic to the RecA_N domain. NMR chemical shift analysis reveals the regions of RecA_N where the average conformations of Vasa-ADP and Vasa-ATP are most different and indicates that these differences may contribute to modulating the affinity of the two nucleotide-bound complexes for RNA substrates.

Gastrulation is a critical process during embryonic development that transforms a single-layered blastula into a multilayered embryo with distinct germ layers, which eventually give rise to all the tissues and organs of the organism. Studies across species have uncovered the mechanisms underlying the building blocks of gastrulation movements, such as localized in-plane and out-of-plane epithelial deformations. The next challenge is to understand dynamics on the scale of the embryo: this requires quantifying strain tensors, which rigorously describe the differences between the deformed configurations taken on by local clusters of cells at time instants of observation and their reference configuration at an initial time. We present a systematic strategy for computing such tensors from the local dynamics of cell clusters, which are chosen across the embryo from several regions whose morphogenetic fate is central to viable gastrulation. As an application of our approach, we demonstrate a strategy of identifying distinct Drosophila morphological domains using strain tensors.

Lipid compositional asymmetry across the leaflets of the plasma membrane is an ubiquitous feature in eukaryotic cells. How this asymmetry is maintained is thought to be primarily controlled by active transport of lipids between leaflets. This strategy is facilitated by the fact that long-tail phospholipids and sphingolipids diffuse through the lipid bilayer slowly-taking many hours or days. However, a lipid like cholesterol-which is the most abundant lipid in the plasma membrane of animal cells-has been harder to pinpoint in terms of its favored side. In this work we show that, when a saturated lipid is added to a mix of the unsaturated lipid palmitoyl-oleoyl-phosphatidylcholine (POPC) and cholesterol, both cholesterol and the long-tail phospholipids organize asymmetrically across the membrane's leaflets naturally. In these extruded unilamellar vesicles, most cholesterol as well as the saturated lipid-dipalmitoylphosphatidylcholine or sphingomyelin-segregated to the inner leaflet while POPC preferentially localized in the outer leaflet. This asymmetric arrangement generated a slight phospholipid number imbalance favoring the outer leaflet and thus opposite to where cholesterol and the saturated lipids preferentially partitioned. These results were obtained using magic-angle spinning nuclear magnetic resonance (MAS NMR) in combination with small-angle neutron scattering (SANS) using isotope labeling to differentiate lipid species. We suggest that sidedness in membranes can be driven by thermodynamic processes. In addition, our MAS NMR results show that the lower bound for cholesterol's flip-flop half-time at 45°C is 10 ms, which is at least two orders of magnitude slower than current MD simulations predict. This result stands in stark contrast to previous work that suggested that cholesterol's flip-flop half-time at 37°C has an upper bound of 10 ms.

Gene transcription by an RNA polymerase (RNAP) enzyme requires that double-stranded DNA be locally and transiently opened, which results in an increase of DNA supercoiling downstream of the RNAP and a decrease of supercoiling upstream of it. When the DNA is initially torsionally relaxed and the RNAP experiences sufficiently large rotational drag, these variations lead to positively supercoiled plectonemes ahead of the RNAPs and negatively supercoiled ones behind it, a feature known as "twin supercoiled domain" (TSD). This work aims at deciphering into some more detail the torsional dynamics of circular DNA molecules being transcribed by RNAP enzymes. To this end, we performed Brownian dynamics simulations with a specially designed coarse-grained model. Depending on the superhelical density of the DNA molecule and the ratio of RNAP's twist injection rate and rotational relaxation speed, simulations reveal a rich panel of behaviors, which sometimes differ markedly from the crude TSD picture. In particular, for sufficiently slow rotational relaxation speed, positively supercoiled plectonemes never form ahead of an RNAP that transcribes a DNA molecule with physiological negative supercoiling. Rather, negatively supercoiled plectonemes form almost periodically at the upstream side of the RNAP and grow up to a certain length before detaching from the RNAP and destabilizing rapidly. The extent to which topological barriers hinder the dynamics of TSDs is also discussed.

Single-molecule force spectroscopy (SMFS) techniques play a pivotal role in unraveling the mechanics and conformational transitions of biological macromolecules under external forces. Among these techniques, multiplexed magnetic tweezers (MT) are particularly well suited to probe very small forces, ≤1 pN, critical for studying noncovalent interactions and regulatory conformational changes at the single-molecule level. However, to apply and measure such small forces, a reliable and accurate force-calibration procedure is crucial. Here, we introduce a new approach to calibrate MT based on thermal motion using the Hadamard variance (HV). To test our method, we perform bead-tether Brownian dynamics simulations that mimic our experimental system and compare the performance of the HV method against two established techniques: power spectral density (PSD) and Allan variance (AV) analyses. Our analysis includes an assessment of each method's ability to mitigate common sources of additive noise, such as white and pink noise, as well as drift, which often complicate experimental data analysis. We find that the HV method exhibits overall similar or higher precision and accuracy, yielding lower force estimation errors across a wide range of signal-to-noise ratios (SNRs) and drift speeds compared with the PSD and AV methods. Notably, the HV method remains robust against drift, maintaining consistent uncertainty levels across the entire studied SNR and drift speed spectrum. We also explore the HV method using experimental MT data, where we find overall smaller force estimation errors compared with PSD and AV approaches. Overall, the HV method offers a robust method for achieving sub-pN resolution and precision in multiplexed MT measurements. Its potential extends to other SMFS techniques, presenting exciting opportunities for advancing our understanding of mechanosensitivity and force generation in biological systems. To make our methods widely accessible to the research community, we provide a well-documented Python implementation of the HV method as an extension to the Tweezepy package.

Multiple-particle tracking (MPT) is a microscopy technique capable of simultaneously tracking hundreds to thousands of nanoparticles in a biological sample and has been used extensively to characterize biological microenvironments, including the brain extracellular space (ECS). Machine learning techniques have been applied to MPT data sets to predict the diffusion mode of nanoparticle trajectories as well as more complex biological variables, such as biological age. In this study, we develop a machine learning pipeline to predict and investigate changes to the brain ECS due to injury using supervised classification and feature importance calculations. We first validate the pipeline on three related but distinct MPT data sets from the living brain ECS-age differences, region differences, and enzymatic degradation of ECS structure. We predict three ages with 86% accuracy, three regions with 90% accuracy, and healthy versus enzyme-treated tissue with 69% accuracy. Since injury across groups is normally compared with traditional statistical approaches, we first used linear mixed effects models to compare features between healthy control conditions and injury induced by two different oxygen glucose deprivation exposure times. We then used machine learning to predict injury state using MPT features. We show that the pipeline predicts between the healthy control, 0.5 h OGD treatment, and 1.5 h OGD treatment with 59% accuracy in the cortex and 66% in the striatum, and identifies nonlinear relationships between trajectory features that were not evident from traditional linear models. Our work demonstrates that machine learning applied to MPT data is effective across multiple experimental conditions and can find unique biologically relevant features of nanoparticle diffusion.

Lipid membranes play a crucial role in regulating the body's water balance by adjusting their properties in response to hydration. The intercellular lipid matrix of the stratum corneum (SC), the outermost skin layer, serves as the body's primary defense against environmental factors. Osmolytes, including urocanic acid (UCA) and glycerol, are key components of the natural moisturizing factor that help the SC resist osmotic stress from dry environments. This study examines the effects of UCA and glycerol (each at 5 mol %) on isolated human SC lipids. For this, different techniques were employed, offering complementary information of the system's multiscale characteristics, including humidity-scanning quartz crystal microbalance with dissipation monitoring, infrared spectroscopy, x-ray diffraction, electrical impedance spectroscopy, and studies of water loss and permeability. Our results show that UCA increases water sorption and makes lipid films more liquid-like at high relative humidity, without significantly altering the lipid lamellar structure, chain order, or orthorhombic chain packing. Lipid films containing UCA exhibited higher water loss and significantly higher model drug permeability compared to lipid films without UCA. Further, incorporation of UCA resulted in kinetically faster changes in electrical properties upon contact with aqueous solution compared with control lipids. These observations suggest that UCA reduces lipid cohesion in regions other than the acyl chain-rich leaflets, which may impact SC desquamation. In contrast, glycerol did not influence the hydration or permeability of the SC lipid matrix. However, it increased the proportion of orthorhombic domains at high humidities and slowed the kinetics of the hydration process, as evidenced by slower changes in the dielectric properties of the lipid film. These findings suggest that glycerol enhances lipid cohesion rather than increasing water uptake, which is typically the expected function of humectants. Consequently, UCA and glycerol appear to have distinct roles in maintaining epidermal homeostasis.