Biophysical Reports

We demonstrate the use of fluorescence correlation spectroscopy (FCS) to characterize fluorescently-labeled protein production. We use cell-free protein synthesis to express the protein YFP-CopB, a fusion of Chlamydia Outer Protein (Cop) B and Yellow Fluorescent Protein (YFP). CopB is a [~]50 kDa protein believed to have a critical role in chlamydial infection.1 After adding a plasmid encoding YFP-CopB to an E. coli cell-free lysate, protein expression begins. We track the cell-free reaction over several hours using the EI-FLEX, a commercial instrument with FCS capability. As protein is expressed over time, YFP-CopB increases in concentration, and the EI-FLEX detects an increase in fluorescent signal above the background of the cell-free lysate. The FCS data collected gives information about the size, aggregation tendencies, rates of production and fluorescent protein maturation, and concentration of the YFP-CopB produced. The use of FCS concurrent with cell-free synthesis presents a simple method to characterize proteins of interest as they are produced without the need for purification.

Diffusion within the cytoplasm is fundamental to numerous biological processes. Fluorescence recovery after photobleaching (FRAP) is one of the most common method for quantifying molecular diffusivity in living cells using standard laser scanning confocal microscopy (LSCM). However, accurately measuring fast cytoplasmic diffusion (typically >10 m^2/s) is challenging due to rapid recovery kinetics, weak signal-to-noise ratios, post-bleach signal artifacts, and spatial restrictions affecting normalization. While individual challenges have been addressed in specific contexts, a simple and robust framework to quantify cytoplasmic diffusivity remains elusive. Here, we present a FRAP methodology specifically designed to overcome these obstacles. By utilizing the Gaussian function -- the impulse response (ImpRes) of the diffusion equation in an infinite medium -- our approach leverages the full spatiotemporal dataset through a single-equation three-parameter fitting procedure, thus releasing restrictions to small regions of interest and arbitrary initial time-points. The methodology was validated on three datasets of increasing complexity: in silico simulated recovery profiles, in vitro data from FITC-dextran in glycerol solution, and live-cell imaging of free cytoplasmic GFP. Systematic comparison with existing models demonstrates that the ImpRes approach significantly reduces sensitivity to noise and imperfect fluorescence normalization, while remaining robust against short-term biases, such as transient probe photo-activation. Given its robustness under realistic experimental conditions and its ease of implementation, the proposed FRAP methodology provides a reliable tool for quantitative cytoplasmic analysis.

Protein oligomerization equilibria are central to many biological processes and are often highly sensitive to environmental conditions such as ionic strength, pH, and ligand binding. Quantitative characterization of these equilibria remains experimentally challenging because stable protein complexes frequently dissociate only at concentrations that are difficult to access with conventional biophysical methods. Fluorescence correlation spectroscopy (FCS) is uniquely suited to this problem, as it provides direct access to diffusion coefficients of fluorescently labeled proteins at nanomolar concentrations. However, the quantitative interpretation of FCS data from oligomeric systems requires a rigorous mathematical framework and careful experimental practice that have not previously been described in sufficient detail to guide implementation. Here, we provide a comprehensive description of the experimental workflow and analytical framework for determining dissociation equilibrium constants by FCS, covering instrument calibration, sample preparation, data quality control, after-pulse correction, and nonlinear least-squares fitting. We discuss common sources of error and provide practical guidance on critical experimental considerations including surface passivation, buffer preparation, equilibration time, and the role of labeling efficiency. Using the homotrimeric sliding clamp PCNA as a model system, we demonstrate the complete workflow under a range of KCl concentrations and show that moderate ionic strength stabilizes the PCNA trimer while very high salt partially destabilizes the complex. The approach is general and applicable to any reversible protein self-association reaction accessible by fluorescence detection at low protein concentrations.

Temperature is a fundamental parameter governing all molecular processes, including those that define life. Fluorescence microscopy is a powerful tool to observe molecular processes in living systems in real time. Precise control and measurement of temperature during fluorescence microscopy is therefore essential. We present here a robust temperature measurement based on the excited-state lifetime of the widely available and relatively inexpensive fluorescent dye pentamethine cyanine (Cy5). The excited-state lifetime of Cy5 shows a monotonic decline in the measurement range of 0 {degrees}C - 80 {degrees}C. The measured dependency is linear until 39 {degrees}C and monoexponential above. The dependance of excited-state lifetime upon temperature is used to measure temperature up to a precision of 0.5 {degrees}C or less, a temporal resolution down to <1 millisecond and to resolve temperature gradients with spatial resolutions that are only diffraction-limited. The far-red excitation and emission of Cy5 leaves bandwidth to simultaneously measure at least 3 additional spectral channels in standard fluorescent microscopes simultaneously. We demonstrate determination of temperature during 4-color live-cell fluorescence microscopy for a temperature-controlled experiment. We also show its applicability in measuring temperature gradients and laser-induced sample heating such as during STED nanoscopy.

Dynamic conformational changes in intracellular domains fundamentally affect the function and pharmacology of many membrane proteins. For example, sodium influx through the cardiac voltage-gated sodium channel (NaV1.5) is rapidly terminated through conformational changes that result in pore closure, a transition known as inactivation. Inactivation involves NaV1.5 intracellular regions, particularly the DIII-DIV linker containing the IFM particle (Isoleucine-Phenylalanine-Methionine) and its dysfunction is a major cause of cardiac arrythmias. However, the conformational changes involved in inactivation and their modulation by auxiliary proteins and clinically used drugs remain incompletely characterized, partly because live-cell, site-specific labeling of intracellular regions with small fluorescent dyes remains challenging. Here, we combine live-cell protein semi-synthesis with voltage-clamp fluorometry (VCF), to track intracellular conformational dynamics of the cardiac sodium channel NaV1.5 and monitor their voltage dependence and kinetics in real time. We identify intracellular conformational changes involved in both fast and steady-state inactivation of NaV1.5 and show that both lidocaine and auxiliary proteins affect the kinetics of conformational changes of the DIII-DIV linker. Our work establishes the combination of protein semi-synthesis and voltage-clamp fluorometry as a powerful approach to dissect intracellular conformational changes in membrane proteins.

Small-angle X-ray or neutron scattering (SAXS/SANS) analysis of large unilamellar vesicles (LUVs) is often limited by high-dimensional bilayer models and the lack of dedicated, statistically rigorous workflows. Here, we introduce SAS_MoCa, an open-source Python package that integrates a compositional scattering density profile (SDP) description of lipid bilayers with a separated form factor (SFF) treatment of vesicle size and polydispersity, and couples these highly parameterized models to an adaptive thermodynamic simulated annealing algorithm formulated within a constrained Bayesian framework. SAS_MoCa enables users to incorporate quantitative prior information from, e.g., previous SAXS/SANS studies, dynamic light scattering, NMR, or molecular simulations, and returns full posterior parameter distributions, uncertainties (reported as medians and median absolute deviations) and correlations even from single SAXS curves. Validation on POPC, POPE and DMPC SAXS-only data demonstrates that the method yields reproducible structural parameters with uncertainties comparable to joint SAXS/contrast-variation SANS analyses. The modular architecture of SAS_MoCa facilitates extension to additional lipid systems and future joint SAXS/SANS or SANS-only applications.

Direct validation of the nanoscale structural organization of membrane proteins requires localization precision that matches their molecular dimensions. The sodium-potassium pump, or the Na,K-ATPase is an integral membrane protein responsible for maintaining electrochemical gradients and cellular energy homeostasis. Although its crystal structure is characterized, the organization of the Na,K-ATPase within native plasma membranes, particularly whether it forms functional oligomers, remains an open question. Here, we combined 3D MINFLUX nanoscopy with DNA-PAINT with sub-10 nm localization precision to map the clustering topology of the Na,K-ATPase in mammalian cells. By targeting EGFP-tagged Na,K-ATPase 1 and {beta}1 subunits using anti-GFP nanobodies, we obtained high-density 3D localization maps of the protein in the plasma membrane. To evaluate the point patterns, we developed a computational data-driven spatial point assignment approach that segments apical and basal localizations, mitigating clustering artifacts produced by imaging two membranes in close proximity. Furthermore, we used a spatial statistical approach analyzing sequential nearest-neighbour distances to elucidate supramolecular arrangement information. Our data reveal a preferential nearest-neighbour distance of approximately 7 nm, providing direct visual confirmation of Na,K-ATPase dimerization. Additionally, we identified higher-order nanoclusters composed of up to 21 proteins. These findings provide definitive structural evidence of the dimeric configuration of Na,K-ATPase, establishing a foundation for future research on the functional and regulatory implications of Na,K-ATPase clustering.

Weak, reversible interactions underpin biomolecular recognition, and single-molecule fluorescence (smF) imaging techniques can provide unprecedented insight into those biological processes. Unfortunately, such studies often require micromolar concentrations of fluorophore-labeled biomolecules, which is beyond the accessible range of conventional smF microscopies. Here, we describe a surface-functionalization method based on cloud-point polyethylene glycol (PEG) grafting that enables widefield smF microscopy measurements at micromolar concentrations without the use of nanophotonic devices. Using conventional total internal reflection fluorescence (TIRF) microscopy, we detected single-molecule fluorescence resonance energy transfer (smFRET) from surface-tethered, donor-labeled target molecules with up to 8 micromolar concentrations of freely diffusing, acceptor-labeled analyte molecules in the background--two orders of magnitude higher than typical studies in the literature. Weak, DNA-hybridization and protein-RNA binding equilibria were measured across micromolar range titrations. Together with advances in high-background data analysis, the robust method presented here enables kinetic and thermodynamic analyses of weak biomolecular interactions, especially those limited by nonspecific adsorption and high fluorescence backgrounds, using only standard smF instrumentation.

Single-molecule Forster resonance energy transfer (smFRET) reports on biomolecular conformational dynamics by measuring distance changes between donor and acceptor fluorophores. In diffusion-based smFRET, however, the detection of genuine conformational exchange is routinely confounded by photophysical artefacts, notably acceptor photobleaching and blinking, which produce similar burst-level signatures. Many existing methods for resolving conformational dynamics and dye photophysics rely on fitting kinetic models with a fixed number of states, which is typically unknown. Here we present FENNEC (Fluorescence Event Neural Network for Evaluating and Classifying bursts), a dilated convolutional neural network for diffusion-based smFRET data that simultaneously detects conformational dynamics, acceptor photobleaching, and acceptor blinking within individual bursts, directly from raw photon arrival times. FENNEC is trained entirely on simulated data, and requires no experimental data with assigned labels for training. Crucially, the dynamics classification is independent of the number of underlying states, and therefore provides an analysis and filtering method that complements established methods that extract the number of states and their kinetics. FENNEC can identify a high-confidence subset of static and dynamic bursts, while ambiguous bursts can be excluded or set aside for further analysis. Applied to a dynamic DNA hairpin, FENNEC recovers the expected population distributions. Together, these results provide proof of principle that a classifier trained on simulated photon-level data can identify conformational dynamics and photophysical artefacts in experimental smFRET data, and we invite further evaluation on a range of instruments and systems. FENNEC is freely available at https://github.com/jacrossley/FENNEC.

Streptavidin-biotin, avidin-biotin interactions are classical models for protein-ligand binding, yet the energetic changes accompanying biotin binding remain poorly resolved. Using fluorescent dyes as energy sensors, we show that biotin binding produces two distinct regimes occurring in parallel as concentration of biotin increases cooperativity and conformational rearrangements, wherein cooperativity is observed via exchange broadening of fluorescence linewidth and conformational rearrangements exclusively observed in emission energy. Where the first biotin binding creates the highest contribution to the emission energy. Further analysis of tetramer-tetramer only interactions revealed extremely long ranged intermolecular interactions extending to hundreds of nm. The intermolecular interactions become negligible only at concentrations of approximately 10 nM for both streptavidin and avidin. Affinity values estimated for these diluted samples were below 1 nM.

Quantitative fluorescence imaging techniques such as fluorescence lifetime imaging microscopy and hyperspectral imaging infer molecular contrast from photons distributed across spatial pixels and temporal or spectral channels. In the few-photon regime, however, conventional pixel-wise analysis discards the spatial relationships imposed across neighboring pixels by the microscope point-spread function (PSF). Here we show that this spatially distributed information can be recovered without prior knowledge of emitter positions, spatial support or component assignments. We introduce SPOOL (Spatially Pooled Optical Observation Likelihood), a training-free Poisson inverse framework that jointly recovers source-space amplitudes and quantitative contrast by combining the PSF with temporal-decay or spectral-response dictionaries. For an isolated source, the attainable precision gain is governed by a dimensionless optical quantity: the PSF width expressed in detector pixels. The predicted gain therefore scales with optical sampling rather than with the physical origin of the contrast. The model predicts that lifetime-precision gain scales approximately linearly with the number of pixels spanning the PSF full width at half maximum, a scaling reproduced by Monte Carlo simulations. At one detected photon per foreground pixel, the reconstruction reduces lifetime dispersion sixfold in fluorescent-bead experiments and decreases the lifetime root-mean-square error relative to a high-photon reference from 1.19 to 0.45 ns in dual-labeled cells. The same framework transfers unchanged to hyperspectral imaging, recovering spectral contrast from generic emission bands without prior fluorophore spectra.

Mitochondria are metabolic hubs of the cell that provide energy and metabolites to meet the energetic, biosynthetic and signaling demands of the cell. Mitochondrial activities are characterized by the metabolic fluxes through their internal metabolic pathways. One of the most important mitochondrial metabolic pathways is the electron transport chain (ETC), where electron carriers such as NADH donate their electrons to oxygen to power mitochondrial respiration. Mitochondrial activities are dynamically and spatially regulated during organism development to ensure robust development. Recent work has revealed the existence of a subcellular ETC flux gradient within a single mouse oocyte, where mitochondria closer to the cell membrane display a higher ETC flux, but the mechanism underlying the formation of this gradient is unknown. In this work, we study the origin of the ETC flux gradients by modulating them through perturbations of external oxygen concentration and temperature. Interpreting the data with spatial kinetic modeling of mitochondrial respiration, we discover that the subcellular ETC flux gradient cannot be explained by reaction-diffusion of oxygen alone, but is a result of mitochondrial heterogeneity where mitochondria closer to the cell membrane display larger ETC flux capacity and lower oxygen sensitivity. Our work suggests that kinetically distinct subpopulations of mitochondria are spatially sorted according to their metabolic activities to form intracellular metabolic gradients.

Single-stranded DNA-binding (SSB) proteins play a crucial role in DNA replication by binding to single-stranded DNA (ssDNA) in multiple binding modes, depending on conditions such as salt and protein concentrations. The coverage-dependent effects on the kinetics of these binding modes remain incompletely understood. In particular, the bimodal binding kinetics and the further SSB-ssDNA shortening observed when SSB is removed from the media. Here, we develop a kinetic model extending the Tonks-McGhee-von Hippel framework to incorporate ligand crowding and mode transformations, capturing the inhibition of SSB binding and transitions to higher binding modes as coverage increases. This model quantitatively reproduces experimental binding kinetics and coverage-dependent behaviors observed for human mitochondrial SSB (HmtSSB) and E. coli SSB (EcoSSB). Our findings elucidate the impact of ligand crowding on SSB-ssDNA interactions and provide a generalizable framework for studying multimode ligand binding to polymers, with implications for understanding genome maintenance mechanisms.

Chronic infections persist in large part thanks to protection that biofilms afford their bacterial creators. The extracellular polymeric substance of biofilms is a hydrated matrix of DNA, polysaccharides, and structural proteins, amongst other components, through which nutrients, signalling molecules, and antimicrobial agents must diffuse to reach the bacteria within. Quantitative measurement of transport on the nanoscale within in vivo biofilms remains challenging due to optical heterogeneity, autofluorescence, active remodelling of biofilms and the ambiguity in trajectory reconstruction during single-particle tracking (SPT). Here, we present a methodological framework for measuring molecular transport in defined minimal extracellular matrix models using quantum dots as fluorescent nanoscale probes imaged with high-speed SlimVar microscopy. To establish conditions in which high-diffusivity particle trajectories can be reliably reconstructed, upper limits to quantum dot concentrations were estimated from Brownian motion. The 99th-percentile inter-frame jump distance was estimated from the three-dimensional Brownian jump distance distribution and used to define a target average nearest neighbour distance, and therefore a per-particle volume, used for calculating a concentration which minimises the probability of trajectory collision during data acquisition. Quantum dot movement was imaged at sub-millisecond frame rates and diffusion coefficients were calculated in a 20% glycerol control and in DNA nanostar hydrogels modelling minimal extracellular matrix scaffolds assembled at 250 M and 500 M. Median diffusion coefficients decreased from 94.9 m2*s-1 in glycerol to 15.9 m2*s-1 and 8.3 m2*s-1 in the 250 M and 500 M hydrogels, respectively. More broadly, this work establishes a workflow for quantitative SPT in minimal biofilm models. Rather than attempting to reproduce the full biological complexity of native biofilms, this approach provides the basis of a modular experimental framework in which individual extracellular matrix components can be incorporated sequentially and their effects on molecular transport quantified.

DNA-based technologies rely on short, transient hybridization events, but selecting sequences with desired kinetic properties remains largely empirical because hybridization kinetics are difficult to predict from sequence and slow to measure one sequence at a time. Here, we introduce SPARXS-Hyb, an implementation of SPARXS (Single-molecule Parallel Analysis for Rapid eXploration of Sequence space) for multiplexed sequence-resolved screening of DNA hybridization. Using a surface-immobilized docking-strand library and a quencher-labelled imager-strand library, we screened 128 different DNA sequences in a single kinetic measurement, exposing all sequences to identical experimental conditions. This multiplexed approach removes a major confounding factor of serial measurements, allowing sequence-dependent differences to be compared directly. The resulting dataset reveals sequence-dependent transient binding behaviours and enabled us to identify a sequence with which an order-of-magnitude higher sampling rate can be achieved in DNA-PAINT (DNA points accumulation for imaging in nanoscale topography), a super-resolution microscopy technique based on DNA hybridization. By enabling multiplexed screening across a sequence library, SPARXS-Hyb provides a route to kinetics-guided sequence selection for programmable transient interactions in DNA nanotechnology.

Living cells are highly dynamic and densely crowded environments in which organelles such as vesicles undergo continuous motion that is essential for cellular processes. Therefore, accurate tracking of individual organelles is crucial for understanding intercellular dynamics and functions. However, precise tracking of individual organelles in living cells remains challenging due to high organelle densities, frequent particle overlap, and the coexistence of stationary and motile organelles. In particular, stationary organelles can obscure the trajectories of moving organelles, leading to tracking errors and fragmented tracks. To overcome these challenges, we developed Multiple Particle Tracking via Velocity Filtering (MPT-vVF), an unbiased, semi-automated tracking framework that incorporates a mathematically derived velocity-filtering algorithm to selectively identify and track moving organelles with high accuracy in crowded intracellular environments. MPT-vVF integrates denoising, background subtraction, and a velocity-matching detection step that discriminates true particle motion from noise based on spatiotemporal continuity, followed by robust trajectory linking. We demonstrate that MPT-vVF can accurately resolve nanometer-scale displacements of immobilized beads, highlighting its high tracking precision. We also validate the robustness of MPT-vVF by quantifying the transport of brain-derived neurotrophic factor (BDNF)-mRFP-containing vesicles in living hippocampal neurons. Furthermore, MPT-vVF reveals that exposure to 50-nm nanoplastics impairs vesicular transport, reducing both travel length and speed of BDNF-containing vesicles in living neurons. These findings establish MPT-vVF as a powerful method for quantitative analysis of intracellular organelles in crowded living cells and suggest its broad application to biophysics, cell biology, and soft matter research.

Integral membrane proteins remodel the surrounding lipid bilayer, but quantifying the resulting deformations and linking them to protein density in the membrane has remained challenging. Here, we introduce an integrative methodology that combines all-atom molecular dynamics (MD) simulations with multiscale small-angle X-ray scattering (SAXS) analysis to connect membrane strain to the protein/lipid ratio in proteoliposomes. Using outer membrane phospholipase A (OmpLA) reconstituted into lipid bilayers with both increased and decreased hydrophobic thickness, we systematically probe the effects of positive and negative hydrophobic mismatch.MD simulations demonstrate that OmpLA causes anisotropic, oscillatory thickness deformations extending up to eight times the radius of the first lipid shell surrounding the protein, yet the net change in average membrane thickness remains below 1%. Through our multiscale SAXS analysis, we quantitatively extract structural parameters, ranging from proteoliposome size to internal membrane architecture, using constrained Bayesian inference, with priors derived from MD findings. Specifically, we determine the protein/lipid molar ratio and average membrane strain, revealing excellent agreement between experiment and simulation. In thinner bilayers, substantial protein loss limits the analysis, highlighting the role of bilayer stability in sample preparation. Moreover, the predominance of OmpLA monomers in the thicker membranes is consistent with weak, membrane-mediated repulsive interactions between protein inclusions. Collectively, this integrative approach establishes a framework for quantifying protein-lipid interactions across molecular and mesoscale dimensions.

Weak magnetic fields affect many biological processes across the tree of life, though the precise molecular sensors and pathways involved in such magnetoresponses remain mostly uncharacterized. Fluorescence is a useful tool for investigating magnetic field effects in flavoproteins, as their chromophores fluorescence intensity can be shown to depend on the spin states of electronic radical pairs. Here, we describe a four-state ordinary differential equation model to understand what parameter sets result in fluorescence contrast between spin states in photocycles with singlet and triplet radical pairs. We conclude that only certain sets of parameters result in the fluorescence intensity being a good proxy measurement for singlet yield. In particular, we observe that the illumination intensity required to obtain fluorescence contrast depends on the rate of the slow spin-independent radical termination reactions that recover ground-state oxidized fluorophores. Moreover, to observe a magnetic field effect in fluorescence intensity when an external magnetic field modulates the singlet yield, the illumination intensity must be strong enough such that photoexcitation is not the rate-limiting step. This understanding suggests that flavoproteins that do not exhibit magnetic field effects in their fluorescence emission under certain experimental setups may still be sensitive to weak magnetic fields in terms of function, as magnetosensitivity in fluorescence depends strongly on illumination conditions.

Reconstructing time-resolved inter-residue distance distributions during protein functional dynamics in the solution state is known to be a difficult and important problem. This article presents a technique for extracting spin-spin (as a proxy for residue-residue) distance distributions on doubly-spin-labeled proteins from rapid-scan time-resolved Gd-Gd electron paramagnetic resonance (rs-TiGGER) spectra recorded near room temperature in solution at 240 GHz. We use a best-fit technique that convolves a dipolar kernel matrix with an intrinsic, non-dipolar-broadened (single-labeled) spectrum. The kernel incorporates the effect of solution-state tumbling on the dipolar broadening using a correlation function that bridges the static and rapidly tumbling regimes. We apply the technique to AsLOV2, a protein domain with a dark-state crystal structure that is well-known from X-ray crystallography, but a less well-characterized and disordered tertiary structure that manifests after photoactivation at 450 nm. Informed by principal component analysis, we assume that the underlying distance distribution may be approximated by a sum of two Gaussian distributions. The fits returned time-resolved, light-activated populations with mean distances of [Formula] (dark) and [Formula] (lit) in the wild type, and [Formula] (dark) and [Formula] (lit) in an N414Q mutant, with nearly complete unfolding (within fit uncertainty) of the active, light-sensitive fraction. The extracted distance distributions and their accompanying uncertainties are consistent within uncertainty with molecular dynamics simulations of the equilibrated protein structure.

Lipid droplets (LDs) are cellular organelles responsible for lipid storage and metabolism. The mechanism of biogenesis of LDs involves phase separation of neutral lipids from the surrounding phospholipids, which generates oil lenses embedded in lipid bilayers, also known as nascent LDs. As nascent LDs grow, at some point they bud out of the bilayer, forming nearly spherical droplets. Nascent LDs have different propensity to bud, and it has been proposed that their shape provides information on such propensity; however, LD shape is difficult to determine experimentally. Here we studied the shape of lipid droplets using MD simulations at the coarse-grained level, and compared it to the predictions by an established theory. Our general system setup features an oil lens embedded into a flat, periodic bilayer. We found that the shape of simulated nascent LDs resembles a spherical cap (i.e., it has constant curvature over most of the surface), in excellent agreement with the theory, already for very small droplet sizes. The aspect ratio (height/radius) of nascent LDs increases with increasing LD volume, increasing membrane softness, and increasing surface tension between oil and water, also in agreement with theoretical predictions; however, it remains lower than 1 (i.e., the ratio for a sphere) for LDs of up to 40 nm in diameter. Fitting the simulated LD shapes with a theoretical shape equation suggests that a non-zero surface tension is present in both the monolayer and in the bilayer region. The existence of a relatively high surface tension in the bilayer region is confirmed by local stress calculations, and indicates that the periodic system setup does not reproduce the properties of nascent LDs in the endoplasmic reticulum, where the bilayer tension is two orders of magnitude lower. However, the simulations provide a microscopic view into the properties of droplet embedded vesicles.