Biophysical journal最新文献

The epidermal growth factor receptor (EGFR) governs pivotal signaling pathways in cell proliferation and survival, with mutations implicated in numerous cancers. The organization of EGFR on the plasma membrane (PM) is influenced by the lipids and the cortical actin (CA) cytoskeleton. Despite the presence of a putative actin-binding domain (ABD) spanning 13 residues, a direct interaction between EGFR and CA has not been definitively established. While disrupting the cytoskeleton can impact EGFR behavior, suggesting a connection, the influence of the static actin cytoskeleton has been found to be indirect. Here, we investigate the potential interaction between EGFR and CA, as well as the extent to which CA regulates EGFR's distribution on the PM using SRRF'n'TIRF, a spatiotemporal super-resolution microscopy technique that provides sub-100 nm resolution and ms-scale dynamics from the same data set. To label CA, we constructed PMT-mEGFP-F-tractin, which combines an inner leaflet targeting domain PMT, fluorescent probe mEGFP, and the actin-binding protein F-tractin. In addition to EGFR-mEGFP, we included two control constructs: 1) an ABD deletion mutant, EGFRΔ^ABD-mEGFP serving as a negative control and 2) EGFR-mApple-F-tractin, where F-tractin is fused to the C-terminus of EGFR-mApple, serving as the positive control. We find that EGFR-mEGFP and EGFRΔ^ABD-mEGFP show similar membrane dynamics, implying that EGFR-mEGFP dynamics and organization are independent of CA. EGFR dynamics show CA dependence when F-tractin is anchored to the cytoplasmic tail. Together, our results demonstrate that EGFR does not directly interact with the CA in its resting and activated state.

We present the first-ever, fully discrete, stochastic model of triggered cardiac Ca²⁺ dynamics. Using anatomically accurate subcellular cardiac myocyte geometries, we simulate the molecular players involved in Ca²⁺ handling using high-resolution stochastic and explicit-particle methods at the level of an individual cardiac dyadic junction. Integrating data from multiple experimental sources, the model not only replicates the findings of traditional in silico studies and complements in vitro experimental data but also reveals new insights into the molecular mechanisms driving cardiac dysfunction under stress and disease conditions. We improve upon older, nondiscrete models using the same realistic geometry by incorporating molecular mechanisms for spontaneous, as well as triggered calcium-induced calcium release (CICR). Action potentials are used to activate L-type calcium channels (LTCC), triggering CICR through ryanodine receptors (RyRs) on the surface of the sarcoplasmic reticulum. These improvements allow for the specific focus on the couplon: the structure-function relationship between LTCC and RyR. We investigate the electrophysical effects of normal and diseased action potentials on CICR and interrogate the effects of dyadic junction deformation through detubulation and orphaning of RyR. Our work demonstrates the importance of the electrophysical integrity of the calcium release unit on CICR fidelity, giving insights into the molecular basis of heart disease. Finally, we provide a unique, detailed, molecular view of the CICR process using advanced rendering techniques. This easy-to-use model comes complete with tutorials and the necessary software for use and analysis to maximize usability and reproducibility. Our work focuses on quantifying, qualifying, and visualizing the behavior of the molecular species that underlie the function and dysfunction of subcellular cardiomyocyte systems.

Many animal cells that crawl on extracellular substrates exhibit durotaxis, i.e., directed migration toward stiffer substrate regions. This has implications in several biological processes including tissue development and tumor progression. Here, we introduce a phenomenological model for single-cell durotaxis that incorporates both elastic deformation-mediated cell-substrate interactions and the stochasticity of cell migration. Our model is motivated by a key observation in an early demonstration of durotaxis: a single, contractile cell at a sharp interface between a softer and a stiffer region of an elastic substrate reorients and migrates toward the stiffer region. We model migrating cells as self-propelling, persistently motile agents that exert contractile traction forces on their elastic substrate. The resulting substrate deformations induce elastic interactions with mechanical boundaries, captured by an elastic potential. The dynamics is determined by two crucial parameters: the strength of the cellular traction-induced boundary elastic interaction (A), and the persistence of cell motility (Pe). Elastic forces and torques resulting from the potential orient cells perpendicular (parallel) to the boundary and accumulate (deplete) them at the clamped (free) boundary. Thus, a clamped boundary induces an attractive potential that drives durotaxis, while a free boundary induces a repulsive potential that prevents antidurotaxis. By quantifying the steady-state position and orientation probability densities, we show how the extent of accumulation (depletion) depends on the strength of the elastic potential and motility. We compare and contrast crawling cells with biological microswimmers and other synthetic active particles, where accumulation at confining boundaries is well known. We define metrics quantifying boundary accumulation and durotaxis, and present a phase diagram that identifies three possible regimes: durotaxis, and adurotaxis with and without motility-induced accumulation at the boundary. Overall, our model predicts how durotaxis depends on cell contractility and motility, successfully explains some previous observations, and provides testable predictions to guide future experiments.

Hemolysis, including subclinical hemolysis, is a potentially severe complications of mechanical heart valves (MHVs), which leads to shortened red blood cell (RBC) lifespan and hemolytic anemia. Serious hemolysis is usually associated with structural deterioration and regurgitation. However, the shear stress in MHVs' narrow leakage slits is much lower than the shear stress threshold causing hemolysis and the mechanisms in this context remain largely unclear. This study investigated the hemolysis mechanism of RBCs in cell-size slits under high shear rates by establishing in vitro microfluidic devices and a coarse-grained molecular dynamics (CGMD) model, considering both fluid and structural effects simultaneously. Microfluidic experiments and computational simulation revealed six distinct dynamic states of RBC traversal through MHVs' microscale slits under various shear rates and slit sizes. It elucidated that RBC dynamic states were influenced by not only by fluid forces but significantly by the compressive force of slit walls. The variation of the potential energy of the cell membrane indicated its stretching, deformation, and rupture during traversal, corresponding to the six dynamic states. The maximum forces exerted on membrane by water particles and slit walls directly determined membrane rupture, serving as a critical determinant. This analysis helps in understanding the contribution of the slit walls to membrane rupture and identifying the threshold force that leads to membrane rupture. The hemolysis mechanism of traversing microscale slits is revealed to effectively explain the occurrences of hemolysis and subclinical hemolysis.

Molecular dynamics (MD) simulation of biological processes has always been a challenging task due to the long timescales of the processes involved and the large amount of output data to handle. Markov state models (MSMs) have been introduced as a powerful tool in this area of research, as they provide a mechanistically comprehensible synthesis of the large amount of MD data and, at the same time, can be used to rapidly estimate experimental properties of biological processes. Herein, we propose a method for building MSMs of ion channel permeation from MD trajectories, which directly evaluates the current flowing through the channel from the model's transition matrix (T), which is crucial for comparing simulations and experimental data. This is achieved by including in the model a flux matrix that summarizes information on the charge moving across the channel between each pair of states of the MSM and can be used in conjunction with T to predict the ion current. A procedure to drastically reduce the number of states in the MSM while preserving the estimated ion current is also proposed. Applying the method to the KcsA channel returned an MSM with five states with significant equilibrium occupancy, capable of accurately reproducing the single-channel ion current from microsecond MD trajectories.

Experimental studies of collective dynamics in lipid bilayers have been challenging due to the energy resolution required to observe these low-energy phonon-like modes. However, inelastic x-ray scattering (IXS) measurements-a technique for probing vibrations in soft and biological materials-are now possible with sub-meV resolution, permitting direct observation of low-energy, phonon-like modes in lipid membranes. Here, IXS measurements with sub-meV energy resolution reveal a low-energy optic-like phonon mode at roughly 3 meV in the liquid-ordered (L_o) and liquid-disordered phases of a ternary lipid mixture. This mode is only observed experimentally at momentum transfers greater than 5 nm^-1 in the L_o system. A similar gapped mode is also observed in all-atom molecular dynamics (MD) simulations of the same mixture, indicating that the simulations accurately represent the fast, collective dynamics in the L_o phase. Its optical nature and the Q range of the gap together suggest that the observed mode is due to the coupled motion of cholesterol-lipid pairs, separated by several hydrocarbon chains within the membrane plane. Analysis of the simulations provides molecular insight into the origin of the mode in transient, nanoscale substructures of hexagonally packed hydrocarbon chains. This nanoscale hexagonal packing was previously reported based on MD simulations and, later, by NMR measurements. Here, however, the integration of IXS and MD simulations identifies a new signature of the L_o substructure in the collective lipid dynamics, thanks to the recent confluence of IXS sensitivity and MD simulation capabilities.

We demonstrate the use of multiscale polymer modeling to quantitatively predict DNA linear dichroism (LD) in shear flow. LD is the difference in absorption of light polarized along two perpendicular axes and has long been applied to study biopolymer structure and drug-biopolymer interactions. As LD is orientation dependent, the sample must be aligned in order to measure a signal. Shear flow via a Couette cell can generate the required orientation; however, it is challenging to separate the LD due to changes in polymer conformation from specific interactions, e.g., drug-biopolymer. In this study, we have applied a combination of Brownian dynamics and equilibrium Monte Carlo simulations to accurately predict polymer alignment, and hence flow LD, at modest computational cost. As the optical and conformational contributions to the LD can be explicitly separated, our findings allow for enhanced quantitative interpretation of LD spectra through the use of an in silico model to capture conformational changes. Our model requires no fitting and only five input parameters: the DNA contour length, persistence length, optical factor, solvent quality, and relaxation time, all of which have been well characterized in prior literature. The method is sufficiently general to apply to a wide range of biopolymers beyond DNA, and our findings could help guide the search for new pharmaceutical drug targets via flow LD.

TAR DNA binding protein 43 (TDP-43) is a nuclear RNA/DNA-binding protein with pivotal roles in RNA-related processes such as splicing, transcription, transport, and stability. The high binding affinity and specificity of TDP-43 toward its cognate RNA sequences (GU-rich) is mediated by highly conserved residues in its tandem RNA recognition motif (RRM) domains (aa: 104-263). Importantly, the loss of RNA binding to the tandem RRMs caused by physiological stressors and chemical modifications promotes cytoplasmic mislocalization and pathological aggregation of TDP-43. Despite the substantial implications of RNA binding in TDP-43 function and pathology, its precise effects on the intradomain stability, and conformational dynamics of the tandem RRMs is not properly understood. Here, we employed all-atom molecular dynamics (MD) simulations to assess the effect of RNA binding on the conformational landscape and intradomain stability of TDP-43 tandem RRMs. RNA limits the overall conformational space of the tandem RRMs and promotes intradomain stability through a combination of specific base stacking interactions and transient electrostatic interactions. In contrast, tandem RRMs exhibit a high intrinsic conformational plasticity in the absence of RNA, which, surprisingly, is accompanied by a tendency of RRM1 to adopt partially unfolded conformations. Overall, our simulations reveal how RNA binding dynamically tunes the structural and conformational landscape of TDP-43 tandem RRMs, contributing to physiological function and mitigating pathological aggregation.

Intrinsically disordered proteins (IDPs) often contain proline residues that undergo cis/trans isomerization. While molecular dynamics (MD) simulations have the potential to fully characterize the proline cis and trans subensembles, they are limited by the slow timescales of isomerization and force field inaccuracies. NMR spectroscopy can report on ensemble-averaged observables for both the cis-proline and trans-proline states, but a full atomistic characterization of these conformers is challenging. Given the importance of proline cis/trans isomerization for influencing the conformational sampling of disordered proteins, we employed a combination of all-atom MD simulations with enhanced sampling (metadynamics), NMR, and small-angle x-ray scattering (SAXS) to characterize the two subensembles of the ORF6 C-terminal region (ORF6_CTR) from SARS-CoV-2 corresponding to the proline-57 (P57) cis and trans states. We performed MD simulations in three distinct force fields: AMBER03ws, AMBER99SB-disp, and CHARMM36m, which are all optimized for disordered proteins. Each simulation was run for an accumulated time of 180-220 μs until convergence was reached, as assessed by blocking analysis. A good agreement between the cis-P57 populations predicted from metadynamic simulations in AMBER03ws was observed with populations obtained from experimental NMR data. Moreover, we observed good agreement between the radius of gyration predicted from the metadynamic simulations in AMBER03ws and that measured using SAXS. Our findings suggest that both the cis-P57 and trans-P57 conformations of ORF6_CTR are extremely dynamic and that interdisciplinary approaches combining both multiscale computations and experiments offer avenues to explore highly dynamic states that cannot be reliably characterized by either approach in isolation.