European Biophysics Journal

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.

The primary question in this work is whether a turbidity trace contains more than one resolved kinetic event before any rate or aggregate-mass information is supplied. A blind, three-gate hierarchical Gompertz procedure applied to human interleukin-1 receptor antagonist (IL-1ra) at 53 {degrees}C in phosphate retained one phase at 1 and 2 mg/mL, but selected two phases at 5 and 10 mg/mL. Thus the first resolved multiphase behavior appears at the low protein concentration of 5 mg/mL. Source optical-rate markers were withheld until after selection. Those observed optical rates were then compared with blind-fit phase rates, and the same comparison was expressed on a conditional mass-equivalent scale using the published 5.3-fold Type-I/Type-II turbidity response ratio. A separate 50 {degrees}C citrate series (4, 6, 8, 10, and 14 mg/mL) also selected two phases in every retained trace; its printed Vmax values were used only as post-selection comparators. Finally, two challenging 40 {degrees}C high-concentration traces (180 and 200 mg/mL) were digitized from the instrument-output plot and each retained two phases. All three conditions are analyzed independently. Blind recognition is therefore the central result; the mass-equivalent conversion is a secondary, post-selection interpretation of optical phase rates. The absolute {micro}M/min scale remains conditional on an endpoint aggregate fraction measured elsewhere.

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.

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.

Quantitative analysis of action potential (AP) waveforms is central to studies of neuronal excitability, ion channel function, disease mechanisms, and pharmacological modulation. However, AP analysis is still often performed using partially manual workflows, laboratory-specific spreadsheets, or proprietary software environments that can limit reproducibility, transparency, and throughput. Here we present APpar, a freely available, open-source software tool for extracting AP parameters, developed for use with the OriginLab software package Origin/OriginPro. APpar detects APs from membrane voltage recordings using a user-defined derivative criterion and calculates a comprehensive set of excitability parameters, including resting membrane potential, AP threshold, dV/dt at threshold, overshoot, undershoot, AP amplitude, AP half-amplitude, rise time, decay time, AP duration, AP half-width, AP width at 0 mV, AP area above voltage threshold, dV/dtMAX, dV/dtMIN, interspike interval for the respective AP. Because AP threshold is a particularly sensitive and method-dependent measurement, APpar includes a TRUE-threshold validation algorithm. After the initial forward dV/dt threshold crossing is identified, the software finds AP overshoot, searches backward to the closest preceding local dV/dt maximum, then searches backward to the user-defined dV/dt crossing and recalculates AP parameters from this validated threshold point. We validated APpar using APs from dorsal root ganglion neurons current-clamp recordings, including copied identical APs, current-evoked repetitive firing, and long-duration spontaneous firing. The software produced stable measurements from identical copied APs and extracted dynamic changes in AP parameters across repetitive and spontaneous firing sequences. APpar provides a transparent, customizable, and Origin-compatible framework for reproducible AP analysis in neuronal electrophysiology. Significance statementAction potential waveform analysis is essential for interpreting neuronal excitability, but many AP measurements remain vulnerable to user-dependent threshold placement, manual cursor selection, and inconsistent parameter definitions. APpar, a freely available, open-source software tool, addresses this problem by automating AP detection and parameter extraction within the OriginLab environment widely available to electrophysiology laboratories. The software formalizes definitions of AP threshold, amplitude, duration, half-width, afterhyperpolarization, derivative-based parameters, and firing metrics, and introduces a TRUE-threshold validation algorithm that recalculates AP parameters from a derivative-validated threshold point. This workflow reduces operator-dependent variability while preserving user control over physiologically meaningful detection criteria. HighlightsAutomated action potential waveform analysis within OriginLab Origin environments AP threshold validation improves reproducibility of derivative-based threshold detection Extracts action potential kinetics, amplitudes, widths, and dV/dt measurements Open-source workflow supports reproducible neuronal electrophysiology data analysis Validated using repetitive and spontaneous firing in DRG neurons

The ability of small molecules to cross the blood-brain barrier (BBB) remains a major bottleneck in neurotherapeutic development. While experimental assays and machine learning approaches provide approximate permeability estimates, they lack atomistic insight into the underlying transport mechanisms. Here, we employ all-atom molecular dynamics simulations of a compositionally realistic BBB lipid bilayer to characterize the passive permeation of two bioactive propolis-derived compounds, Caffeic Acid Phenethyl Ester (CAPE) and Artepillin-C (ARC). Using steered molecular dynamics and umbrella sampling, we computed free energy profiles, diffusion coefficients, and permeability metrics across the membrane. CAPE encounters a modest barrier at the lipid headgroup region but minimal resistance within the hydrophobic core, resulting in a low free energy barrier ([~]2-3 kcal/mol) and favorable permeability (logP_eff {approx} 0.28). In contrast, ARC exhibits a substantial energetic barrier within the membrane core, leading to high resistivity and strongly unfavorable permeability (logP_eff {approx} -10.91). The heterogeneous lipid model reproduces experimentally consistent membrane properties and reveals how lipid composition modulates transport energetics. These findings provide mechanistic insight into BBB permeability and demonstrate the utility of atomistic simulations for guiding the design of neuroactive therapeutics.

Integrins are adhesion proteins that diffuse along the cell membrane, bind to ligands, and cluster with each other in the early stage of cell adhesion. Integrin clustering and its specific spatial distribution play important roles in subsequent biological processes; however, the mechanisms that give rise to their characteristic spatial distribution remain poorly understood. To address this issue, we developed a cell adhesion model that incorporates cell membrane deformation and integrin dynamics. A hybrid continuous/discrete model was applied to represent membrane deformation, whereas Brownian dynamics combined with a transition state model was used to describe integrin dynamics and binding kinetics. Comparison of numerical simulations of cell adhesion to a substrate with experimental observations at the early stage of adhesion successfully reproduced the characteristic spatial distribution of integrin clusters, in which high-density clusters formed at the periphery of the region adhering to the substrate. These results suggest that the cellular-scale distribution of integrin clusters can be reproduced using only minimal elements, such as adhesion-driven membrane deformation and integrin-ligand binding. In addition, we found that the strength of integrin-ligand binding regulates the degree of clustering by changing the size of the part of the membrane that is deformed, thereby mechanically supporting the mechanical involvement of the actin cytoskeleton in integrin clustering. Furthermore, the formation and spatial distribution of integrin clusters were shown to be determined not only by the static mechanical equilibrium of membrane deformation and physical adsorption, but also by membrane spreading/deformation and the dynamic behavior of integrins. This suggests that the size and spatial distribution of integrin clusters may be controllable by modulating the speed of membrane spreading.

Glycosaminoglycans (GAGs) are linear, negatively charged, polysaccharides that mediate a wide variety of biologically critical interactions with proteins, underpinning growth factor signalling, extracellular matrix assembly and numerous disease processes. However, GAG-protein interactions remain under characterised, in part because of the lack of high-throughput tools to systematically profile binding across the GAG interactome. In this paper we present a novel Surface Plasmon Resonance-based array methodology utilising 16 commonly sourced GAG preparations (including chondroitin sulphate (CS), dermatan sulphate (DS), heparan sulphate, heparin, hyaluronan and keratan sulphate) allowing the specificity and affinity of GAG-binding proteins to be determined. As proof of principle, we have validated the array using four established GAG-binding proteins (antithrombin III, CD44, heavy chain 1 from inter--inhibitor and Slit2), generating data consistent with the known binding specificities and quantifying affinities for many of the interactions. The array also reveals previously unreported GAG interactions, including Slit2 binding to CS and DS, and CD44 binding to chondroitin sulphate E.

The cytoskeleton is a dynamic biopolymer network whose shear rheological properties are crucial for cellular physiology and pathology. However, its mechanical behavior spans multiple spatiotemporal scales, and the coupling of dynamic remodeling and viscoelastic dissipation mechanisms poses a challenge for traditional models to comprehensively capture complex cellular responses. This study aims to establish a multiscale cytoskeletal network model that integrates the bio-chemo-mechanical properties of local linked proteins, the viscoelasticity of actin filaments, and their deformation states. Developing a boundary-modified finite element method with an incremental iterative algorithm, we demonstrated the dynamic remodeling of network and the resultant rheological properties of cytoskeleton by extending the predictive time scale to one thousand seconds. The results not only reproduced the short- and intermediate-term power-law creep behavior and long-term strain plateau response of the cytoskeletal network observed in shear rheological experiments, but also indicate that the synergy among the chemo-mechanical coupling of cross-linked proteins and the bending-to-tension transition of actin filaments govern both the network remodeling and its power-law response evolutionary, whereas the steady-state properties of actin filaments determine the long-term network behavior. Simulations of cancerous and drug effects show that cancer-induced softening and reduced filament viscosity lead to accelerated cytoskeletal responses and decreased apparent shear modulus, respectively; and drug-enhanced filament prestress, along with promoting association or inhibiting dissociation of cross-linked proteins, can effectively increase the steady-state shear modulus. These findings advance the understanding of the spatiotemporal evolution and pathological mechanisms of cellular mechanical responses and provide insights for regulating polymer network performance.

Reaction diffusion (RD) systems play a fundamental role in numerous biochemical and biophysical processes. Here, we present a novel analytical framework for solving RD equations by applying the Wentzel Kramers Brillouin Jeffreys (WKBJ) formalism to Ca nanodomains generated by individual membrane channels, a widely used paradigm for intracellular Ca signaling. Previous models have primarily focused on stationary Ca nanodomains while neglecting diffusion and saturation of intracellular Ca buffers and sensors. In contrast, we derive analytical solutions without these simplifying assumptions. Our analysis demonstrates that sustained Ca influx generates continuously expanding distributions of free Ca, whereas Ca bound buffers and sensors propagate as traveling waves. These predictions are supported experimentally by measurements of one-dimensional fluorescence profiles produced by single-channel activity and two-dimensional profiles generated by whole cell Ca currents. The analytical framework developed here readily extends Michaelis Menten type kinetics to reaction diffusion systems and may therefore be broadly applicable to biochemical and biophysical processes in which diffusion cannot be neglected.

The glycocalyx of red blood cells (RBCs), a negatively charged surface layer rich in sialic acid residues, plays a crucial role in modulating RBC aggregation. In pathological conditions such as diabetes and sepsis, glycocalyx degradation is often observed along with abnormal RBC aggregation. However, the mechanistic relationship between these phenomena remains poorly defined. In this study, we investigate the effects of enzymatic glycocalyx degradation on RBC aggregation under physiologically relevant flow conditions. Using neuraminidase from Clostridium perfringens (C. welchii) at varying concentrations, we selectively removed sialic acid residues from the RBC glycocalyx, simulating different levels of desialylation observed in health and disease. Confocal microscopy confirmed the dose-dependent depletion of membrane sialic acid, while microfluidic experiments revealed a significant increase in both the size and stability of the RBC aggregates after enzymatic treatment. Our findings suggest that glycocalyx integrity is a crucial biophysical determinant of RBC aggregation, likely influencing both electrostatic repulsion and hydrodynamic forces. This study provides new insights into how the enzymatic modification of the glycocalyx contributes to pathological hemorheology and may inform future strategies for the diagnosis or treatment of vascular diseases.

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.

Nile Red is widely used for the detection of microplastics because its fluorescence emission is sensitive to local polarity and can distinguish hydrophobic plastics from hydrophilic ones. The fluorescence of the molecular rotor, 9-(dicyanovinyl)-julolidine (DCVJ) is less sensitive to polarity but more to viscosity. DCVJ is less widely used for microplastic analysis, although it has been used to detect polystyrene nanobeads. Here, we compared these dyes with standard samples from the Hawaii Pacific University Polymer Kit 1.0 and confirmed that Nile Red, in general, was better for the detection and identification of microplastics. Fluorescence emission was analyzed using photography, as well as spectroscopy. The color and peak emission wavelength of some stained environmental microplastics were affected by additives. Raman spectroscopy was used to confirm the chemical identity of such samples. Although DCVJ emits green fluorescence on binding to some microplastics, a peak at 620 nm has been reported with polystyrene nanobeads, attributed to dimer/excimer formation. We confirmed this property and directly observed diffraction-limited spots using fluorescence microscopy, attributed to single or just a few nanobeads. Nile Red also stains polystyrene nanobeads and gave stronger signals than with DCVJ, but Nile Red was prone to false positives due to dye aggregation in aqueous solutions.

This study aimed to (1) develop an elasticity framework for the sprint force-velocity (F-V) relationship and (2) examine how maximal force (F_{0}), maximal velocity (v_{0}), and sprint distance modulate the four derived elasticity metrics, and (3) explore these elasticity metrics' interrelation. After modelling the F-V relationship differential equation, four elasticity metrics were defined as force elasticity (F_{e}), the elasticity of sprint time to F_{0}; velocity elasticity (v_{e}), the elasticity of sprint time to v_{0}; the force-velocity elasticity norm {(\mathrm{F}-\mathrm{V}}_{\mathrm{EN}}=\sqrt{F_{e}^{2}+v_{e}^{2}}), capturing the combined sprint time sensitivity to proportional changes in F_{0} and v_{0}; and the force-velocity elasticity ratio {(\mathrm{F}-\mathrm{V}}_{\mathrm{ER}}=F_{e}{\div v}_{e}), indicating which variable dominates the sprint time response. Model simulations showed that F_{e} decreased with rising F_{0} and increased with rising v_{0}, while v_{e} showed the opposite pattern. With increasing sprint distance, F_{e} decreased and v_{e} increased. Given its negligible effect on sprint time, ignoring air resistance yields a conservation law (2F_{e}+v_{e}\equiv 1), indicating that a gain in one elasticity metric necessarily diminishes the other in a fixed proportion. This framework also identifies a valley distance (d_{valley}) at {\mathrm{F}-\mathrm{V}}_{\mathrm{ER}}=2, where {\mathrm{F}-\mathrm{V}}_{\mathrm{EN}} is minimized (\sqrt{0.2}) and sprint time is least responsive to changes in F-V relationship variables. Empirical data confirmed that the two theoretical laws still hold approximately when air resistance is considered. By linking changes in F_{0} and v_{0} to sprint time across different distances, the elasticity framework provides a quantitative basis for estimating the theoretical sprint time response to documented changes in F-V relationship variables.

The growing number of approved nucleic acid therapeutics illustrates the potential to treat diseases by targeting their genetic blueprints in vivo. The 10-23 DNAzyme is capable of cleaving a wide range of target RNA with high selectivity. However, its poor performance in vivo restricts its therapeutic application as gene silencing agent. Studies on ribozymes have shown that the crowded environment in cells and associated effects can impact ribozyme folding and thermostability, resulting in a change in activity. This opens up the question whether DNAzymes are also affected by molecular crowding. Here, we investigate the functional and structural influence of molecular crowding conditions on the 10-23 DNAzyme. The stability and activity of a PrP-specific 10-23 DNAzyme were examined in presence of PEG, dextran, and osmolytes. Our results indicate that osmolytes decrease DNAzyme activity in a concentration-dependent manner, while certain PEG and dextran concentrations promote activity. To rationalize our observations, we studied the cosolutes effect on physicochemical solution properties and the structure of the DNAzyme:RNA complex using FCS and SAXS. The data reveal that enhanced activity is observed under conditions where a combination of physiochemical properties matches an optimum that seems to be dependent on the metal ion cofactor. Structural influence under such conditions is indicated less. We propose that a certain degree of molecular crowding is required to favor a state, which allows for higher catalytic turnover. In addition, we show that the requirement for magnesium and manganese as a cofactor remains unchanged under the conditions applied. Our work contributes to a better understanding of how the cellular environment affects DNAzyme structure and function.

Cetaceans reduce the net cost of sustained swimming through intermittent locomotion, alternating active fluking with unpowered gliding. The energy balance of this strategy is central to understanding survival rates, population sustainability, and the effects of anthropogenic and environmental pressures. While active-phase energetics have been characterized extensively, the glide phase remains largely unexplored. Here we derive the optimal glide duration (Topt) and the maximum glide duration beyond which energy savings vanish (Tzero) for three odontocetes spanning a 20-fold range in body mass, using high-fidelity CAD models and wall-modeled large eddy simulations. We show analytically that speed retention at Topt and mass-specific peak energy savings are both fully determined by the active-to-passive drag ratio, propulsive efficiency, and swimming speed, independently of body morphometry and drag coefficient, and are therefore invariant across species at any given speed. These passive-phase optima extend the known size-independent active-phase invariants to the glide phase, towards a scale-independent energetic framework for burst-and-glide locomotion in small cetaceans.

PURPOSEBenzalkonium chloride (BAK), a common preservative in eye drops, has a major side effect of dry eye. The mechanisms are typically attributed to BAK cytotoxicity. However, due to its surfactant properties, BAK can disrupt the tear film lipid layer (TFLL), leading to dry eye. This study examined the stability and compressibility of the TFLL and the impact of the presence of BAK. METHODSMeibomian gland secretion (meibum, source of the TFLL) was collected from sacrificed cows eyelids. Lipids were extracted by dissolving meibum in chloroform to a final concentration of 1 mg/mL, with one solution additionally containing 0.1 mg/mL BAK. Each solution was overlaid on a water subphase in a Langmuir Trough-Blodgett trough. The changes of surface pressure ({pi}) with area (A) for the lipid film upon compression were monitored, and the corresponding compression modulus (Cs-1) at each data point was determined. RESULTSThe {pi}-A isotherms for meibum lipid monolayers exhibited near-reversible behavior with a smooth profile with a maximum {pi} of approximately 32 mN/m. The Cs-1-{pi} isotherms of the meibum lipid monolayer show that the films are gel-like with a constant compressive modulus of 24-32 mN/m within the surface pressure range of 8-30 mN/m. In contrast, adding BAK dramatically decreased the maximum surface pressure to only 10 mN/m and the compressive modulus to only 2-10 mN/m. CONCLUSIONSThis study demonstrated that BAK disrupts the meibum lipid layer by forming a monolayer with decreased stability and reduced compressive resistance, a mechanism that may underlie its dry-eye side effect yet has largely been neglected.

Keratin proteins are fundamental structural components of hair fibers, contributing to their mechanical resilience, elasticity, and fracture resistance. However, systematic molecular-scale characterization of keratin unfolding mechanics across protein types remains limited, restricting the connection between protein-level deformation mechanisms and hierarchical hair fiber mechanics. Here, we establish a comparative molecular-dynamics-based framework for characterizing the unfolding behavior and nanomechanical response of a curated dataset of 51 keratin proteins. We conduct implicit atomistic molecular dynamics (MD) simulations, including equilibration and steered molecular dynamics (SMD) under four accelerated pulling velocities, to quantify unfolding forces, energy absorption, and structure-property relationships. These accelerated pulling conditions are interpreted as computational probes of relative molecular-scale trends, rather than direct reproductions of experimental hair-fiber strain-rate regimes. Across these accelerated SMD conditions, the simulations show rate-sensitive increases in unfolding force and energy absorption, consistent with constrained molecular relaxation during faster molecular pulling. Stronger correlations between nanomechanical properties and molecular descriptors emerge at higher pulling rates, and the nanomechanical responses of different keratin types (Type I and II) are also compared. The findings provide molecular-level insights into protein unfolding mechanisms that may contribute to the mechanical behavior of hierarchical keratin structures. This study establishes a quantitative framework for comparative keratin unfolding mechanics, providing molecular-level descriptors for future multiscale modeling of hair fiber behavior. These results support applications in biomaterial design, hair fiber durability analysis, and bioinspired material engineering. Future work will integrate these nanomechanical descriptors with fiber-level mechanics and machine learning-based keratin design.

This paper presents and critically reviews the results of original and some literature based experimental studies conducted by the authors last years on the structural organization of DNA in dormant (starvation stress), anabiotic dormant (4 HR treatment) E. coli cells, as well as the K12 {Delta}dps strain, which lacks the Dps protein (Dps null E. coli). The experimental data includes small-angle synchrotron radiation diffraction (SAXS) and transmission electron microscopy (TEM) data. Synchrotron radiation diffraction experiments on K12{Delta}dps cells allowed us to conclude that peaks at 44.3, 22.1, and 14.8 angstrom resolutions are associated exclusively with ordered DNA organization. Peaks at 44.3, 22.1, and 14.8 angstrom resolutions are also observed for samples of dormant (starvation stress) cells and anabiotically dormant cells. Therefore, this ordered DNA organization also applies to samples of dormant and anabiotically dormant cells. A model is proposed that considers the ordered DNA organization in the cell as a cholesteric liquid crystal. The powder diffraction pattern calculated based on this model is compared with experimental small angle X ray scattering (SAXS) data obtained on Dps-null cell samples. The model completely reproduces the key features of the experimental diffraction pattern from Dps-null cell samples. Accordingly, the cholesteric liquid crystal model corresponds to DNA packaging in dormant and anabiotically dormant cells. Cholesteric liquid crystal ordering should be further considered in all models of cellular DNA packaging. To address the question of which structural organization of DNA predominates in the cell: the cholesteric liquid crystal or nanocrystalline or whether they coexist and fully manifest themselves under different external conditions, it is necessary to utilize the latest methodological advances in structural analysis.

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.