Membrane-targeting domains are essential for recruiting signaling molecules to membranes, and protein association with lipid membranes often occurs in response to extracellular or intracellular stimuli. However, these interactions are typically transient, allowing for precise temporal regulation of signaling pathways (Campagnola et al., 2015). The dynamic nature of these interactions also affects how proteins locate their target substrates, though the specifics of this process are not well understood. Peripheral membrane proteins play critical roles in key cellular processes such as signaling, cell division, and vesicle trafficking (Munro, 2002; DiNitto et al., 2003; Cho and Stahelin, 2005). These proteins associate with lipid membranes through various mechanisms, including lipid modifications and membrane-targeting domains. Their membrane binding sites can interact directly with specific lipid molecules, outer head groups (Stahelin, 2009), or the internal hydrocarbon backbone (Mulgrew-Nesbitt et al., 2006; Lomize et al., 2007; Murphy et al., 2009). Quantitative studies of protein-membrane affinity are crucial for unraveling the mechanics of these associations, understanding how the biophysical properties of membranes influence interactions, and determining the energetics of protein binding and insertion.

Oleate hydratase (OhyA) is a bacterial flavoenzyme that catalyzes water addition to cis double bonds of unsaturated fatty acids, producing hydroxylated fatty acids (hFA) (Radka et al., 2021b; Radka et al., 2024). OhyA activity facilitates a crucial step in the reduction of linoleic acid in commensal gut bacteria (Yang et al., 2017), and as a biocatalyst in the production of hFA intermediates (Prem et al., 2022). Commensal bacterial OhyA utilizes host unsaturated fatty acids to produce hFAs that work to reduce gut inflammation and bacterial membrane destabilization (Galbraith et al., 1971; Greenway and Dyke, 1979; Raychowdhury et al., 1985; Zheng et al., 2005; Miyamoto et al., 2015; Saika et al., 2019). Recent studies have displayed the role of OhyA as a virulence determinant in Staphylococcus aureus, a common human pathogen, and the primary cause of skin infection (Chalmers and Wylam, 2020). The initial response from the innate immune system, during a S. aureus infection, is for host cells to secrete antimicrobial peptides and fatty acids (Liu et al., 2018). To combat this response, S. aureus expresses OhyA to detoxify antimicrobial fatty acids in the host (Subramanian et al., 2019). A murine skin infection model has observed that OhyA activity represses the immune response, while OhyA disruption compromised S. aureus virulence (Radka et al., 2021a).

We study the S. aureus OhyA as a representative homolog (Robert et al., 2024). OhyA forms a homodimer in solution comprised of three key functional domains (Figure 1A): fatty acid lobe to bind unsaturated fatty acid substrate, FAD-binding lobe to bind coenzyme FAD, and carboxy terminus made of amphipathic helices that bind the membrane bilayer to interact with unsaturated fatty acid substrate (Radka et al., 2021b; Radka et al., 2024). The current model is the carboxy terminus transfers fatty acid from the membrane to the active site through a hydrophobic tunnel. Lipid binding studies demonstrated OhyA is a peripheral membrane-associated protein that directly interacts with the bilayer through surface electrostatic interactions between its carboxy terminus and the phosphate layer of the membrane (Radka et al., 2024). Structural analysis of an OhyA•membrane complex by cryo-electron microscopy showed OhyA assembles into higher order oligomers on the membrane surface, and the oligomers are stabilized by intermolecular interactions between adjacent OhyA dimers (Oldham et al., 2024). Thus, protein-protein and protein-lipid interactions both drive OhyA•membrane complex formation, but decomposition of their contributions to binding has not been investigated.

Figure 1. Purification and thermal stability of GFP-OhyA. (A), AlphaFold-generated dimer model of GFP-OhyA, with colors representing different functional domains. (B), GFP-OhyA appears as a 97 kDa monomer, with a purity of >95% as confirmed by denaturing gel electrophoresis. The theoretical molecular weight of the GFP-OhyA monomer is 96.8 kDa. (C), Gel filtration chromatogram (black) of GFP-OhyA, eluted from a Superose 6 Increase 10/300 GL column, overlaid with the molar mass distribution (green) from multi-angle light scattering (SEC-MALS). According to SEC-MALS, GFP-OhyA primarily exists as a 159.1 kDa dimer. (D), Thermal denaturation analysis of GFP-OhyA was performed to assess structural integrity of the protein. Assays (N = 5) were conducted with 1 mg/mL GFP-OhyA, and the data were fitted to the Boltzmann equation. The mean melting temperature is reported with ± S.D.

Fluorescence correlation spectroscopy (FCS) is a powerful tool for rapid equilibrium analysis of fluorescent molecules and has been used to study the dynamics of protein-membrane binding interactions (Rhoades et al., 2006; Thomas et al., 2015; Kruger et al., 2017; Vesga et al., 2023), membrane toxin conformational alterations, and elucidate membrane-receptor binding (Chen et al., 2009; Harwardt et al., 2018; Ponmalar et al., 2019). FCS is a highly sensitive technique that measures the intensity fluctuation emitted by single molecules passing in and out of a focused light, yielding intensity fluctuations (Elson and Magde, 1974; Elson, 2011; Elson, 2013). The intensity fluctuations correspond to variations in the number of molecules interacting within, and diffusing through, the confocal plane (Elson and Magde, 1974). Evaluation of the decay of the intensity fluctuations enables the determination of diffusion coefficients and binding rate constants. Additionally, spectroscopic techniques detect signals from both aqueous and lipid phases simultaneously, eliminating the need for physical separation of these phases, which could disrupt equilibrium (Santos et al., 2003).

OhyA has shown a nanomolar binding affinity to phosphatidylglycerol, the dominant anionic phospholipid in the S. aureus membrane (Kuhn et al., 2015), in previous surface plasmon resonance (SPR) experiments (Radka et al., 2024). However, these experiments used large unilamellar vesicles (LUVs) composed of both phosphatidylglycerol and phosphatidylcholine due to the L1 sensor chip’s inefficiency in capturing anionic phospholipids. Moreover, SPR only allowed titration of one component (protein), limiting the analysis. In contrast, FCS does not require immobilization of components, allowing for experimental designs with varying concentrations of proteins and/or lipids and providing greater flexibility in the types of molecules that can be studied.

In this study, we employed a green fluorescent protein (GFP)-OhyA chimeric protein in FCS experiments to investigate the dynamics of OhyA binding phosphatidylglycerol unilamellar vesicles, focusing on the effects of membrane curvature and substrate availability on OhyA•membrane interactions. Our results revealed that GFP-OhyA shows a moderate preference for binding to LUVs over small unilamellar vesicles (SUVs), highlighting the role of membrane curvature in driving the interaction. Incorporating the 18:1Δ9 unsaturated fatty acid substrate into vesicles had only a minor impact on OhyA•membrane binding, reinforcing that curvature is the primary factor influencing membrane association. Titration experiments with varying protein and lipid concentrations identified two distinct binding modes: as protein concentration increased, OhyA accumulated on protein-loaded vesicles, whereas increasing the concentration of accessible lipids decreased the protein occupancy on individual vesicles. These findings offer new insights into the dynamic behavior of OhyA on membrane surfaces and suggest that protein distribution on membranes can be highly uneven due to non-stoichiometric interactions.

The gfp-ohyA gene was constructed to contain an amino terminal His6 tag followed by the gfp gene that was linked by a flexible linker (amino acid sequence GGGGSGGSS) to ohyA as previously described (Radka et al., 2024). The initiating methionine residues of GFP and OhyA were mutated to alanine residues to eliminate alternative transcription start sites during overexpression. Thus, the open reading frame encoded fMet-His6 tag-GFP-flexible linker-OhyA. The gfp-ohyA gene was cloned into the pET28a 5′-NcoI restriction site using the Gibson Assembly method to make pGFP-OhyA. Overexpression of gfp-ohyA is driven by the strong bacteriophage T7 promoter in this plasmid.

The thermal stability of GFP-OhyA was determined by a Sypro Orange-based fluorescence assay (Huynh and Partch, 2015). Solutions (30 μL) of GFP-OhyA (1 mg/mL) in 50 mM K2HPO4, 150 mM NaCl, pH 6, and 2.5 × Sypro Orange dye were added to the wells of a ThermoGrid optically clear PCR plate (Denville Scientific). The plates were centrifuged at 1,000 × g for 5 min and then analyzed by the ABI 7300 real-time PCR system as described previously (Radka et al., 2020). The temperature was ramped from 25°C to 95°C at 1°C/min with the fluorescence read six times at each temperature ramp. The resulting data were fitted to a Boltzmann sigmoidal equation to determine the melting point. The experiment was repeated five times, the thermal melting temperature of each replicate was determined independently, and the melting points from each replicate were averaged to determine the reported thermal melting point.

We employed the single-molecule FCS technique to measure how vesicle addition affects protein diffusion, specifically by tracking the protein molecules rather than the vesicles themselves. The observed shift in protein diffusion time is caused by specific binding, as demonstrated by the side-by-side comparison of multiple lipid types (Section 3.2). The key readout from this experiment is the proportion of protein molecules that diffuse freely versus those with inhibited diffusion, which we interpret as the fraction of protein bound. It is important to note that we did not measure the number of protein molecules on each vesicle or the extent of the vesicle surface area covered.

An amino-terminal His-tagged version of GFP-OhyA (Figure 1A) was expressed in Eschericia coli and purified by Ni+ affinity and gel filtration chromatography to obtain a homogenous 97 kDa protein (Figure 1B). GFP-OhyA eluted predominantly as a single species on a Superose 6 Increase 10/300 GL column (Figure 1C). Like OhyA, the apparent molecular weight of GFP-OhyA estimated by its anisotropic light scattering pattern indicated the protein is 80.6% dimer in solution (Figure 1C). The 46.99°C ± 0.29°C melting temperature of GFP-OhyA (Figure 1D) indicates its structural resistance to thermal denaturation is within 1.31°C of OhyA (Radka et al., 2024). These data indicate GFP-OhyA is a stable protein with the correct OhyA quaternary structure.

Fluorescence correlation spectroscopy (FCS) was used to monitor the binding of fluorescently labeled ligands to biomolecules (Figure 2A). Diffusion time (τD), which scales with the hydrodynamic radius of diffusing molecules, was calculated from the autocorrelation of fluorescence time traces. We first compared the diffusion times of GFP-OhyA and Rhodamine PE-labelled large unilamellar vesicles (LUVs) and micelles. Fluorescence bursts in the 60 s time trace correspond to molecules passing through the confocal plane (Figure 2B). The intensity and duration of these bursts reflect fluorophore concentration and residence time in the detection volume.

Figure 2. Microscope set-up and standard diffusion time measurements. (A), Diagram of Nikon Eclipse Ti2 microscope set-up utilizing the confocal laser system. The Nikon Eclipse Ti2 microscope is connected to a PicoHarp300 single-photon counter which transmits the fluorescent signal to the SymPhoTime 64 software for analysis. (B), Diagram of the illumination and detection volumes. Only fluorescent particles that diffuse through the confocal plane are detected by the photon counter for analysis. (C), Fitted ACFs for freely diffusing GFP-OhyA protein in 50 mM KPO4, 150 mM NaCl, pH 6.0. A simple diffusion model (SymPhoTime 64) was utilized to fit the ACF and calculate diffusion parameters for the standards.

Autocorrelation functions (ACF) derived from fluorescence time traces were used to determine diffusion times and molecular concentrations (Equations 1, 2). The best-fit normalized ACFs, G(τ), for GFP-OhyA is shown in Figure 2C, with a diffusion time of 0.032 ± 0.0012 ms. To evaluate lipid-binding preferences, we constructed LUVs of dioleoylphosphatidylglycerol (DOPG, anionic), dioleoylphosphatidylcholine (DOPC, cationic), and micelles of triolein (neutral lipid). Best fit ACFs for LUVs or micelles (Figures 3A–C; Table 1) exhibited longer diffusion times compared to free GFP-OhyA: 2.91 ± 0.31 ms for DOPG LUVs, 2.6 ± 0.27 ms for DOPC LUVs, and 0.65 ± 0.09 ms for triolein micelles. Dynamic light scattering measurements confirmed the lipid particles were monodisperse and of the expected size (Figure 3D). These results confirm that FCS effectively distinguishes free GFP-OhyA from lipid particle-bound protein based on shifts in diffusion time.

Figure 3. Standard diffusion time measurements for differing liposomal compositions. (A–C), Fitted ACFs for freely diffusing DOPG LUVs, DOPC LUVs, and Triolein micelles constructed from 0.188 μM of accessible lipid in 50 mM KPO4, 150 mM NaCl, pH 6.0. A simple diffusion model (SymPhoTime 64) was utilized to fit the ACF and calculate diffusion parameters for the standards. (D), Mass distribution curves for DOPG LUVs, DOPC LUVs, and Triolein micelles constructed from 3 μM of accessible lipid in 50 mM KPO4, 150 mM NaCl, pH 6.0. Measurements were collected via dynamic light scattering (DLS) analysis to confirm the size and homogeneity of each liposomal mixture.

Table 1. Fitted autocorrelation parameters diffusion time (τD), rate (D), reliability (χ2), and the mean hydrodynamic radius for lipid particle standards.

When GFP-OhyA binds its 18:1Δ9 (OA) substrate embedded in lipid membranes, its diffusion time increases–a hallmark of ligand binding to larger biomolecular complexes. Given the larger diameter of lipid particles relative to GFP-OhyA, we assumed that protein-loaded lipid particles diffuse with a similar coefficient as free particles. Mixing GFP-OhyA with lipid particles revealed a notable increase in diffusion time with DOPG LUVs but not DOPC LUVs or triolein micelles (Figure 4). Upon binding DOPG LUVs, GFP-OhyA diffusion time increased to 1.2 ± 0.12 ms and the ACF curve shape changed significantly Equation 3. Specifically, the upper plateau shifted downward, the lower plateau shifted rightward, and the slope connecting the plateaus broadened. These shape changes, combined with the shift to longer diffusion times, indicate that GFP-OhyA binds and interacts preferentially with anionic phospholipids.

Figure 4. DOPG alters the diffusion behavior of GFP-OhyA via binding interactions. Representative normalized autocorrelation functions of 0.22 μM GFP-OhyA mixed with DOPG LUVs, DOPG + 18:1Δ9 LUVs, DOPC, and Triolein micelles constructed from 0.188 μM accessible lipids at 100 nm. Autocorrelation functions were fitted using a 3D-triplet kinetics model (SymPhoTime 64) comprising two components, utilizing a Gaussian 3D distribution. The diffusion time for free GFP-OhyA (τD = 0.032 ms) was held constant. Values for χ2, τD, and D for each liposome composition and size are present in Table 1.

To investigate the effects of substrate availability and membrane curvature on GFP-OhyA binding, we prepared DOPG/OA LUVs and small unilamellar vesicles (SUVs) of DOPG and DOPG/OA (Figures 5A–D). Mixing GFP-OhyA with DOPG/OA LUVs increased its diffusion time to 2.6 ± 0.22 ms. With SUVs, diffusion times were 1.8 ± 0.42 ms for DOPG and 2.2 ± 0.14 ms for DOPG/OA Equation 3. While all DOPG-containing vesicles shifted the lower ACF plateau rightward and broadened the slope connecting the plateaus, only DOPG/OA LUVs caused a downward shift of the upper plateau (Figure 6). These findings indicate that GFP-OhyA interacts with all DOPG-containing vesicles, with larger LUVs exerting a more pronounced effect on the ACF curve than smaller SUVs. Although embedding OA substrates into vesicles had minimal impact on the substrate-free vesicle ACF curve’s shape, it consistently extended GFP-OhyA diffusion times.

Figure 5. Standard diffusion time measurements of OhyA-specific lipid compositions. (A–C), Fitted ACFs for freely diffusing DOPG SUVs, DOPG + 18:1Δ9 SUVs, and DOPG + 18:1Δ9 LUVs constructed from 0.188 μM accessible lipid in 50 mM KPO4, 150 mM NaCl, pH 6.0. A simple diffusion model (SymPhoTime 64) was utilized to fit the ACF and calculate diffusion parameters for the standards. (D), Mass distribution curves for DOPG SUVs, DOPG + 18:1Δ9 SUVs, and DOPG + 18:1Δ9 LUVs constructed from 3 μM accessible lipid in 50 mM KPO4, 150 mM NaCl, pH 6.0. Measurements were collected via dynamic light scattering (DLS) analysis to confirm the size and homogeneity of each liposomal mixture.

Figure 6. Protein•liposome binding alters the diffusion behavior of GFP-OhyA. Representative normalized autocorrelation functions of 0.22 μM GFP-OhyA mixed with DOPG SUVs, DOPG + 18:1Δ9 SUVs, DOPG LUVs, and DOPG + 18:1Δ9 LUVs constructed from 0.188 μM accessible lipids at 30 nm. Autocorrelation functions were fitted using a 3D-triplet kinetics model (SymPhoTime 64) comprising two components, utilizing a Gaussian 3D distribution. The diffusion time for free GFP-OhyA (τD = 0.032 ms) was held constant. Values for χ2, τD, and D for each liposome composition and size are present in Table 1.

To further investigate the concentration-dependent binding of GFP-OhyA to vesicles, we used the SymPhoTime-derived ρ1 and ρ2 parameters from the fitted ACFs, reflecting the contributions of free and bound GFP-OhyA, respectively. The linear relationship between the GFP-OhyA concentrations used in the titration and those detected provides evidence that our measurements accurately correlate with the concentrations applied in each instance (Supplementary Figure S1). The fraction of GFP-OhyA bound was calculated and fitted to a specific binding with Hill slope model (Figure 7) (Equation 4). This binding model enables the extraction of the ligand concentration required to achieve half-maximum binding at equilibrium and fits a Hill slope to measure cooperativity. Results showed that substrate presence and lower membrane curvature influenced binding. At the highest GFP-OhyA concentration, the fraction bound to DOPG vesicles was 0.335 for SUVs (Figure 7A) and 0.442 for LUVs (Figure 7B). When DOPG/OA vesicles were used, these fractions increased to 0.412 for SUVs (Figure 7C) and 0.718 for LUVs (Figure 7D). Positive cooperativity was observed only when GFP-OhyA bound to substrate-free SUVs; in all other cases, the cooperativity was negative. These findings indicate that in both SUVs and LUVs, the presence of embedded substrate enhanced binding compared to substrate-free vesicles, despite reducing the binding affinity by 3- to 4-fold (from 79.9 to 247.4 nM for LUVs, and 117.1–447.0 nM for SUVs). The incomplete binding at high concentrations (fraction bound not reaching 1 asymptotically) suggests a reversible process with potential conformational states preventing complete vesicle saturation.

Figure 7. Fraction bound GFP-OhyA versus protein concentration. Fraction bound calculations were performed utilizing the autocorrelation function parameters ρ1 and ρ2 that describe the contribution of each diffusing species to the correlation function (bound GFP-OhyA vs. unbound GFP-OhyA). A constant accessible lipids concentration (0.188 μM) was used for protein titrations versus (A) DOPG LUVs, (B) DOPG SUVs, (C) DOPG + 18:1Δ9 LUVs, or (D) DOPG + 18:1Δ9 SUVs. Data are fitted to a specific binding with Hill slope model (GraphPad 10.3.0).

The influence of accessible lipid concentration on GFP-OhyA binding was analyzed similarly to the concentration-dependent experiments. A specific binding with Hill slope model was used to fit the protein fraction bound (Figure 8). Interestingly, as lipid concentration increased, GFP-OhyA binding decreased. For DOPG vesicles, the SUVs initially increased in bound fraction before decreasing from 0.618 to 0.170 (Figure 8A), while the LUVs showed a gradual decline in bound fraction from 0.547 to 0.289 (Figure 8B). In DOPG/OA vesicles, the SUVs fraction bound decreased from 0.444 to 0.157 (Figure 8C), and the LUVs bound fraction decreased from 0.573 to 0.217 (Figure 8D). Negative cooperativity was observed across all vesicle types. These results suggest that higher lipid concentrations may impede GFP-OhyA binding.

Figure 8. Fraction bound GFP-OhyA versus accessible lipid concentration (Lipidacc). Fraction bound calculations were performed utilizing the autocorrelation function parameters ρ1 and ρ2 that describe the contribution of each diffusing species to the correlation function (bound GFP-OhyA vs. unbound GFP-OhyA). A constant protein concentration (0.113 μM) was used for lipid titrations using (A) DOPG liposomes LUVs, (B) DOPG SUVs, (C) DOPG + 18:1Δ9 LUVs, or (D) DOPG + 18:1Δ9 SUVs. Data are fitted to a specific binding with Hill slope model (GraphPad 10.3.0).

We normalized our results by calculating the ratio of bound GFP-OhyA to accessible lipid concentration to understand the role of protein distribution in membrane binding. As protein concentration increases with a fixed accessible lipid concentration, the ratio of vesicle-bound protein to accessible lipid concentration rises hyperbolically, demonstrating positive cooperative binding across all DOPG vesicle types (Figure 9A). This curve shape, with a distinct maximum, indicates that the number of vesicles carrying large quantities of protein increases relative to the protein molecules in solution. This suggests a protein:lipid molar ratio-dependent binding mode where protein molecules assemble on individual vesicles.

Figure 9. Ratio of bound GFP-OhyA (GFP-OhyAmem) to accessible lipid (Lipidacc) compared to total GFP-OhyA and accessible lipid titrations. (A), The GFP-OhyAmem:Lipidacc ratio versus GFP-OhyA data were fitted to a specific binding with Hill slope model. A representation of binding is displayed below, depicting interactions between liposome (blue) and GFP-OhyA (green) at a high GFP-OhyA concentration. (B), GFP-OhyA mem:Lipidacc versus Lipidacc data were fitted to a specific binding with Hill slope model (GraphPad 10.3.0). A schematic for each binding mode is shown below each graph.

Conversely, increasing the lipid concentration while maintaining a constant protein concentration results is an exponential decline in the ratio of vesicle-bound protein to accessible lipid concentration, indicating negative cooperative binding across all DOPG vesicle types (Figure 9B). This curve, which exhibits a noticeable minimum, reflects a shift in the equilibrium between bound and unbound protein. As vesicle concentration increases, the protein-to-lipid molar ratio decreases, and the equilibrium shifts to a state with minimal protein bound, as the binding remains highly reversible. The apparent reduction in bound protein is not due to vesicles rendering bound protein undetectable by FCS, but rather to the equilibrium favoring the unbound state. This binding mode involves protein adsorption to the bilayer through electrostatic interactions, potentially disrupting protein assemblies or inhibiting their formation due to limited cooperativity or steric hindrance. Some protein molecules may bind to lipid sites in a way that excludes additional GFP-OhyA subunits from assembling, leaving unincorporated subunits unbound at fixed accessible lipid concentrations. This behavior underscores the dynamic and non-uniform nature of OhyA’s interaction with lipid membranes.

Notably, a membrane bilayer is essential for transitioning OhyA from discrete dimers to oligomeric ring assemblies that encircle vesicles (Radka et al., 2024; Oldham et al., 2024). The near-identical convergence of curves from both titration experiments indicates that OhyA’s association with the membrane is not significantly influenced by membrane curvature or substrate availability. Instead, protein-membrane binding is predominantly driven by protein:lipid molar ratio, reflecting the interplay between protein-protein and protein-lipid interactions.

Phosphorus-31 (31P) NMR spectroscopy was used to investigate the molecular interactions between GFP-OhyA and DOPG. Stacked NMR spectra (Figure 10A) revealed uniform and significant chemical shift changes (0.24 ppm) as the protein concentration increased, transitioning from the unbound state (0.68 ppm) to the bound state (2.14 ppm). These observations indicate fast-exchange binding of GFP-OhyA to DOPG that forms a slightly different size vesicle, characterized by a single sharp peak progressively shifting downfield with increasing protein concentrations.

Figure 10. 31P NMR spectrum of DOPG with GFP-OhyA. 31P NMR spectra were recorded as a function of increasing GFP-OhyA concentration. (A), Stacked plot gives the 31P NMR spectra of free DOPG (0.68 ppm) and protein-bound DOPG (up to 2.14 ppm). Peaks marked with † are due to fast-exchange interaction between GFP-OhyA and DOPG, and peaks marked with ‡ are due to the intermediate-exchange interaction. (B), Chemical shift data versus protein concentration from the fast-exchange binding interaction, fitted to a specific binding with Hill slope model (GraphPad 10.3.0).

At a protein concentration of 7.5 μM, a second smaller, broader peak appeared at 0.24 ppm, intensifying and broadening with increasing protein concentrations. This second peak, which shifts upfield of the unbound DOPG signal, remains at a constant chemical shift position and is consistent with intermediate-exchange binding with another vesicle of the same order.

The presence of both fast- and intermediate-exchange binding suggests two distinct binding events occur between GFP-OhyA and DOPG. At a protein-to-lipid concentration ratio slightly less than 1:50, the fast-exchange binding equilibrates, with no further chemical shift changes observed, while the intermediate-exchange peak intensity and broadening reaches its maximum. Two-dimensional 1H–13C HSQC NMR spectra were collected for DOPG LUVs in the presence and absence of GFP-OhyA. The hydrocarbon peaks of the lipids exhibited significant overlap in both conditions, indicating that the lipid structure and conformation remain unchanged upon protein binding (Supplementary Figure S2).

The dissociation constant (KD) for the fast-exchange binding was determined to be 3.181 ± 1.272 μM (Figure 10B). These results suggest that at low concentrations, GFP-OhyA transiently binds and releases from the membrane bilayer (fast exchange). In contrast, at higher protein concentrations, GFP-OhyA forms longer-lasting interactions on the bilayer as the peak intensity decreased, likely as part of an oligomeric complex.

We propose that OhyA exhibits cooperative switching (Duke et al., 2001), where the molecules dynamically transition between individual molecules and oligomers in response to the concentration of nearby membrane-bound proteins. This transition is driven by conformational changes that promote oligomer formation through