Structural and functional implications of phase separation of membrane protein LacY in Escherichia coli

Domain: chemistry
Published: 2026-02-25
Editorial outcome: major_revision

Abstract
Liquid-liquid phase separation (LLPS) controls protein activity and dynamically organizes (macro)molecules in living systems without the need for membrane-bound compartments. Biomolecular condensates of water-soluble proteins have extensively been studied, but little is known about LLPS of membrane proteins. In this work we induce in vivo condensation of lactose permease (LacY), a widely-studied model monomeric inner membrane protein in Escherichia coli, and evaluate how it affects LacY function. We fused LacY with engineered, condensate-forming protein PopTag. We observe major changes in the localization and mobility of LacYPop. Molecular dynamics simulations show how the PopTag domain drives the condensate-like association dynamics of LacYPop through hydrophobic sticker interactions. LacYPop preserves native-level transport activity and outperforms the non-condensed LacY under mild hyperosmotic stress (osmotic upshift). In osmotically stressed cells, membrane-bound biomolecular condensates also reduce deformation of the cytoplasmic membrane. Perturbation experiments suggest that membrane curvature drives the accumulation of LacYPop at the poles of E. coli. Co-condensation of LacY and β-galactosidase LacZ slightly reduces their activity and results in remarkable cellular reorganization of the proteins. Our research shows the localization, dynamics, and function of phase-separated membrane proteins in bacteria and highlights the potential of LLPS for engineering complex metabolic networks in vivo.

Introduction
Phase separation of biomolecules is an emerging field of cell biology1,2. Numerous examples of liquid–liquid phase separation (LLPS) have been discovered in eukaryotic cells (e.g., nucleoli, Cajal bodies, stress granules, U-bodies, etc.3). The physico-chemical properties of condensates, such as viscosity, pH, oxygen concentration, and molecular composition, can differ from the surrounding milieu, favoring or disfavoring certain reactions and interactions4. The propensity of a molecule to partition in a condensate relative to the surrounding medium typically also differs. Formation of biomolecular condensates of protein and/or nucleic acids is governed by attractive intermolecular interactions between motifs in intrinsically disordered regions (IDR), the so-called stickers-and-spacers framework, or repeats of folded domains with connecting linkers5. The ability of a macromolecule to interact with multiple partners simultaneously (multivalency) is a requirement for phase separation6.

The fraction of water-soluble proteins with IDRs is 30–40% in mammalian cells, but is thought to be lower in bacteria3. A subfraction of these molecules enables multivalent interactions that drive biomolecular condensation. Little is known of the phase separation of transmembrane proteins driven by intermolecular protein interactions. Some examples are T-cell receptors in mammalian cells, Rv1747 protein in Mycobacterium tuberculosis, and PodJ in Caulobacter vibrioides. The phase separation of T-cell receptors and their auxiliary proteins plays a role in the transduction of signals within the immune system7. The phase separation and 2D clustering of the ATP-binding cassette transporter Rv1747 is promoted by condensation of two Forkhead-associated cytosolic domains linked by an IDR and tuned by phosphorylation8. In PodJ protein part of the cytosolic coiled-coil domain and IDR are responsible for the condensate formation9. We note that IDRs are present in numerous types of membrane proteins and often implicated in their regulation, but they are generally not seen as a possibility for condensation of the proteins and putative multivalency effects have not been explored10–12. Next to protein-driven phase separation, membrane proteins can be organized via scaffolding proteins like flotillins13,14 or partition differentially in liquid-disordered and liquid-ordered lipid domains in the membrane15, but these mechanisms are not relevant for this study.

Biomolecular condensates of water-soluble proteins can physically associate with the lipid bilayer, but they are not inserted into the membrane16. Such condensates are generally composed of proteins, often with intrinsically disordered regions, and/or nucleic acids, enabling multivalent interactions that drive condensate formation. Membrane association can occur through electrostatic interactions, lipid-binding domains, or curvature-sensing motifs, leading to surface wetting, local membrane deformation, and curvature remodeling. This type of membrane-condensate interaction can remodel membrane morphology through capillary forces, induce nanoscale ruffling at the condensate–membrane interface, and couple protein phase separation to lipid phase behavior. Such condensates are attributed important biological roles, including spatial regulation of signaling pathways, organization of cytoskeletal elements, facilitation of endocytic events, and remodeling of organelle surfaces.

To study the mechanisms and implications of multivalent site-specific interactions between soluble regions of membrane proteins in vivo, we use the condensation domain PopTag to drive phase separation of an integral membrane protein. PopTag is the C-terminal part of the phase-separating protein PopZ, which is important for the asymmetrical division of Caulobacter vibrioides17,18. PopTag consists of three sticker sequences separated by IDRs, allowing multivalent interactions and thus condensate formation. We fused this protein tag to the C-terminus of lactose permease LacY of Escherichia coli. LacY natively lacks intrinsically disordered regions (IDRs); it does not undergo phase separation and is homogenously distributed in the inner membrane. E. coli lacks PopZ orthologues; therefore, introduction of PopTag should be orthogonal to native phase-separating systems. Our synthetic system of a polytopic membrane protein fused to a condensation-driving domain mimics features widely present in transmembrane proteins and can serve as a platform to investigate structural and functional implications of LLPS of membrane proteins10,19. Addition of PopTag to LacY results in a predominantly polar localization of the fusion protein. We show the condensate-like behavior of LacYPop fused to a fluorescent protein mEos3.2 (LacYmEos-Pop, Supplementary Table 1) by fluorescence recovery after photobleaching (FRAP), photoactivated localization microscopy (PALM), and single-molecule displacement mapping (SMdM). Using coarse-grained molecular dynamics simulations, we show how the PopTag domains bridge LacY proteins through hydrophobic sticker interactions and form a dynamic network of protein–protein associations leading to condensate formation. In a series of perturbation experiments, we show that nucleoid exclusion is not critical for the polar localization, while local membrane curvature plays a role. Using 14C-lactose transport experiments, we show that LacY is fully active in condensates and even outperforms native LacY. Finally, we designed and characterized heterocondensates of LacYPop with its downstream lactose-metabolizing enzyme, β-galactosidase (LacZ)-PopTag (LacZPop).

Results
PopTag induces biomolecular condensation of LacY in the inner membrane of E. coli
Genes for LacY-mEos3.2 and LacY-mEos3.2-PopTag fusions were inserted into the pBAD vector and transformed into E. coli BW25113, resulting in BW25113 LacYmEos and BW25113 LacYmEos-Pop. Wide-field fluorescence microscopy shows that LacYmEos is equally distributed in the inner membrane, without preferred localization, whereas LacYmEos-Pop is predominantly present at the cell poles but discrete foci are also visible at the lateral membrane (Fig. 1a). To reveal fine details of LacYmEos-Pop clusters we performed PALM microscopy with live cells and cells fixed with formaldehyde–glutaraldehyde (4% w/v formaldehyde, 0.4% w/v glutaraldehyde in 100 mM sucrose dissolved in MQ water) (Fig. 1b). In live cells, single-molecule localizations, recorded for 30 min (~100.000 frames), show patterns akin those of the wide-field fluorescence microscopy images, whereas reconstructions recorded for 6 min (~20,000 frames) reveal multiple clusters on the lateral membrane, similar to what has been seen for Rv1747 in M. tuberculosis8. The fraction of localizations at the cell poles is two times higher for LacYmEos-Pop (0.62±0.06) than for LacYmEos (0.30±0.04) (Fig. 1c). In fixed cells, the localization of LacYmEos-Pop is preserved, but the apparent size of the clusters is smaller, presumably due to reduction of the “motion blur” (Supplementary Video 1). The difference in cluster size observed in PALM reconstructions reflects the physical size of the clusters and is not due to a different localization precision, which is similar for live (25±13 nm) and fixed (24±13 nm) BW25113 LacYmEos-Pop cells. Thus, LacYmEos-Pop predominantly localizes at cell poles and forms mobile foci in the lateral membrane that become immobile upon fixation.

To investigate the mobility of LacYmEos and LacYmEos-Pop, we performed fluorescence recovery after photobleaching (FRAP) and single-molecule displacement mapping (SMdM20–22) experiments. The fluorescence recovery of LacYmEos-Pop had a half-time of 134±47 s and reached ~14% of the initial fluorescence, and the majority of the molecules partition inside punctuated condensates upon recovery (Fig. 1d and Supplementary Fig. 1). Non-condensed LacYmEos recovered with a half-time of 0.61±0.14 s and reached ~72% of the initial fluorescence (Fig. 1e). The moderate recovery of LacYmEos-Pop reflects protein exchange between the biomolecular condensate at the bleached pole and the lateral membrane (small fraction of total LacYmEos-Pop); the fluorescence of the non-bleached pole does not change during the recovery phase. Photobleaching of the lateral membrane did not result in fluorescence recovery, indicating that LacYPop is tightly sequestered within the polar condensates. The recovery at the poles is not due to reactivation of the fluorescent protein, because we saw no recovery of fluorescence when cells were fully photobleached (Fig. 1d). Furthermore, the fluorescence recovery at the poles is not caused by synthesis of new protein, because the recovery was similar in untreated and translation inhibitor (chloramphenicol)-treated cells.

Next, we examined the mobility of individual LacYmEos and LacYmEos-Pop molecules with SMdM23 (Fig. 1f). The apparent diffusion coefficient (D) of LacYmEos is lower at the cell poles compared to the lateral membrane (D=0.17±0.04 versus 0.22±0.06 μm2/s, two-sided t-test p-value=0.0002) (Fig. 1g). This decrease in D suggests a lower protein mobility at the cell poles but can also be a result of two-dimensional projection of protein movement across the membrane. The apparent diffusion coefficient of LacYmEos-Pop is similar between the cell poles and the lateral membrane (D=0.11±0.02 vs. 0.12±0.01 μm2/s, two-sided t-test p-value=0.34) and is 35% lower than that of polar LacYmEos. Since LacYmEos-Pop mobility in the lateral membrane and at the cell pole is reduced to a similar extent, we conclude that LacYmEos-Pop forms not only large condensates at the cell poles, but also smaller condensates in the lateral membrane.

We performed transmission electron microscopy on cryo-fixed BW25113 LacYmEos and BW25113 LacYmEos-Pop cells to verify the membrane localization of LacYmEos-Pop condensates at higher resolution than observed by light microscopy. We observe electron-dense regions on the cytoplasmic face of the inner membrane at the cell pole in 4 out of 6 BW25113 LacYmEos-Pop cells and in 2 out of 10 BW25113 LacYmEos cells (Fig. 1h, Supplementary Fig. 3). The approximate thickness of these regions is 10 nm, which is similar to the predicted length of the mEos3.2-PopTag part of LacYmEos-Pop. Remarkably, the shape and integrity of the inner membrane adjacent to the electron-dense regions at the cell poles are preserved. These data suggest that LacYmEos-Pop condensates anchor to the inner membrane without deforming it. Collectively, our observations suggest that LacYmEos-Pop form two-dimensional biomolecular condensates in the inner membrane of E. coli, with large condensates at the cell poles and smaller ones in the lateral membrane.

Hydrophobic stickers drive LacYPop condensation
To investigate how the fused PopTag influences the organization of LacY in the membrane, we performed coarse-grained molecular dynamics (MD) simulations of nine LacY proteins in an E. coli-like lipid membrane, with and without a PopTag (Fig. 2a). The simulations were executed in triplicate for better statistics on our observations. The proteins were initially positioned in equally spaced grid configurations. During a 50 μs simulation, both proteins show a clear difference in clustering behavior. LacY proteins without a PopTag formed relatively static small clusters, maintaining approximately three separate clusters throughout the simulation. In contrast, LacYPop proteins show substantially more dynamic interactions, ultimately forming one large cluster during the simulations. Quantitative analysis confirms this difference in clustering dynamics (Fig. 2d and Supplementary Fig. 4), with LacYPop having more fluctuations in the number of protein–protein interactions, indicating the dynamic nature of the formed contacts.

To understand the molecular mechanism behind the PopTag-mediated clustering, we analyzed the specific residue contacts involved in PopTag–PopTag interactions. First, we performed simulations of the PopTag alone (Fig. 2b). The contact map analysis reveals that inter-PopTag interactions in condensates are primarily driven by helix 1 (residues 14–31) and helix 2 (residues 40–58), with notable contacts also observed in the H1–H2 linker region (Fig. 2c, right panel). We then determined the contacts formed by PopTag when fused to LacY in the membrane and found again that H1 and H2 are the primary drivers of inter-PopTag interactions (Fig. 2c, left panel). The interaction interface in both cases is dominated by the amphipathic ɑ-helices with high hydrophobic moment. The hydrophobic faces of these putative helices most likely form the “stickers” for condensation, as indicated by the hydrophobicity profile along the residue index. Contacts in the H1–H2 linker region also result from hydrophobic residue interactions, and these are enhanced in the LacYPop system due to the antiparallel binding of the PopTags. Our MD simulations thus show that the hydrophobic character of the PopTag is important for its self-association properties.

The PopTag helices embed at the membrane surface due to their amphipathic nature, creating membrane-mediated PopTag–PopTag interactions (Fig. 2a). This membrane interaction potentially competes with the solution-mediated condensate formation, as hydrophobic residues engaged with the membrane are unavailable for PopTag–PopTag interactions. It is feasible that a dynamic equilibrium exists between membrane-mediated and solution-mediated condensate formation. To explore this, we performed additional simulations of LacYPop where proteins were configured at high concentration with the PopTags extended into the solvent (Supplementary Fig. 6). This indeed reduced membrane association and allowed more extensive PopTag–PopTag interactions and network formation through association of the helical parts. We thus propose a two-step process for the condensate formation of LacYPop: Initially, PopTags interact with the membrane while exploring protein–protein contacts (Fig. 2a). As the local concentration increases, protein–protein interactions dominate over membrane association, leading to PopTags interacting in solution (Supplementary Fig. 6).

Together, our simulations demonstrate that the PopTags mediate interactions between otherwise non-associating LacY proteins through their hydrophobic interfaces. This creates a dynamic network of protein–protein interactions that drives the condensate-like behavior of LacYPop membrane proteins.

Membrane curvature impacts the distribution of LacY condensates
What determines the predominant polar localization of phase-separated LacY-mEos3.2-PopTag? We tested whether the nucleoid would exclude the condensates from the lateral membrane. We imaged BW25113 LacYmEos and BW25113 LacYmEos-Pop cells after treatment with cephalexin for 4 and 7 h (Fig. 3a, b). Cephalexin leads to elongation of E. coli and accumulation of multiple nucleoids in one cell24. After 4 h of cephalexin treatment, we observed cells with two nucleoids per cell, using DAPI as a DNA-staining dye. LacYmEos is homogeneously distributed in the membrane, while LacYmEos-Pop has the same distribution as in untreated cells: the majority of the condensates are at the cell poles, but some smaller foci are found at the lateral membrane. After 7 h of cephalexin treatment, the number of nucleoids per cell increased, but for both LacY variants, the localization patterns remained, with the majority of LacYmEos-Pop at the poles and LacYmEos homogenously distributed. Importantly, we do not observe accumulation of LacYmEos-Pop in the inter-nucleoid regions, suggesting that LacYmEos-Pop localization is not governed by nucleoid exclusion.

To further investigate the role of the nucleoid in LacYmEos-Pop positioning, we visualized LacYmEos-Pop in nucleoid-free E. coli LY177 cells, where arabinose-induced expression of I-SceI endonuclease leads to nucleoid degradation25, which was confirmed by DAPI staining (Fig. 3c). Upon nucleoid degradation, LacYmEos-Pop is mostly in the polar regions with some foci on the lateral membrane, while LacYmEos remains homogenously distributed over the membrane. These experiments rule out that nucleoid exclusion causes polar location of the LacYmEos-Pop membrane condensates.

Next, we evaluated the effect of membrane curvature on the localization of the proteins, using spheroplasts (spherical, osmotically sensitive cells made by removing part of the outer membrane and peptidoglycan layer) prepared from BW25113 LacYmEos and BW25113 LacYmEos-Pop cells (Fig. 3d)26,27. Spheroplasts made from BW25113 LacYmEos served as a control and showed a homogenous LacYmEos distribution. Spheroplasts from BW25113 LacYmEos-Pop show a variety of LacY-mEos3.2-PopTag distributions, ranging from multiple clusters per spheroplast to almost homogeneous protein distribution. We then measured the same cells 30 min after spheroplast formation and found a dissolution of LacYmEos-Pop condensates from the original poles, and the cell shape change is associated with a more homogenous localization pattern.

To capture the redistribution of LacYmEos-Pop upon spheroplast formation over time, we immobilized E. coli cells with agarose pads, supplemented with the “cocktail” for spheroplast formation to trigger the shape change. The time-lapse videos clearly show the gradual redistribution of large phase-separated condensates from distinct foci to homogeneously localized protein over ~40 min (Supplementary Video 2). Thus, the loss of pole localization of phase-separated membrane-bound condensates coincides with the loss of the pole curvature. To reintroduce curvature in spheroplasts with homogeneously distributed LacYmEos-Pop, we induced plasmolysis of the spheroplasts immobilized under agarose pads supplemented with 0.5 M NaCl. The LacYmEos-Pop distribution was monitored by wide-field fluorescence microscopy (Fig. 3e). Remarkably, upon plasmolysis, LacYmEos-Pop condensates are no longer homogeneously distributed in the spheroplasts but predominantly localize at highly curved, concave membrane regions (Supplementary Fig. 7). Spearman correlation coefficient between the normalized curvature and normalized fluorescence intensity along the deformed spheroplasts contour is 0.24, p-value < 0.0001. These observations highlight the importance of membrane curvature in the spatial distribution of LacYmEos-Pop.

Activity of condensed LacY
To evaluate the effect of condensation on LacY transport activity, we used 14C-lactose and determined the uptake of the substrate by LacYmEos and LacYmEos-Pop in E. coli BW25113 ΔlacY (Fig. 4a). The cells producing LacYmEos-Pop import slightly more lactose compared to the cells with LacYmEos, which is seen as higher plateauing levels of the uptake curves (UMAX = 2.96±0.17 versus 2.48±0.10 nmol lactose/mg total cell protein, extra sum-of-squares F-test p-value=0.0145, Fig. 4b). The expression levels of the LacY variants are similar as determined by fluorescence analysis of cell lysates, for which the proteins were separated by SDS-PAGE. We measured 3.5±0.4×103 LacYmEos and 3.4±0.5×103 LacYmEos-Pop proteins per E. coli cell (Supplementary Fig. 8). Quantification of LacYmEos and LacYmEos-Pop by in-gel fluorescence shows that they do not greatly exceed the native-level expression, which is ~2500 LacY molecules per cell in the fully induced state28.

Further, we tested whether LacY condensation can rescue the decrease of activity under stress conditions, i.e. hyperosmotic stress29. We measured 14C-lactose uptake of cells without stress and upon an increase of NaCl concentration of 160 or 320 mM, resulting in a 2- or 3-fold increase in osmolarity (Fig. 4c). Under the higher osmolarity, the maximum levels of lactose uptake UMAX reduce both for BW25113 ΔlacY LacYmEos and BW25113 ΔlacY LacYmEos-Pop cells (Fig. 4d). Remarkably, under all measured conditions, LacYmEos-Pop outperforms LacYmEos. We then visualized the protein distribution in osmotically stressed BW25113 ΔlacY LacYmEos and BW25113 ΔlacY LacYmEos-Pop cells (Fig. 4e). Both strains show some membrane deformations indicative of plasmolysis, and the effects are more pronounced with the 3-fold than with the 2-fold osmolarity increase. However, we find fewer deformed BW25113 ΔlacY LacYmEos-Pop cells than BW25113 ΔlacY LacYmEos; 24±7% versus 53±8% at the 3-fold osmolarity increase (number of analyzed fields of view is 5, two-sided t-test p-value=0.0003). We find almost no deformed BW25113 ΔlacY LacYmEos-Pop cells at a two-fold osmolarity increase, which suggests that membrane-bound biomolecular condensates support the cytoplasmic membrane and reduce its deformation under hyperosmotic stress. A smaller degree of plasmolysis will diminish the volume decrease, and this may explain the higher UMAX of LacYmEos-Pop-expressing cells.

PopTag is driving the co-condensation of LacY and LacZ in vivo
Heterocondensation of cytoplasmic and membrane proteins has to the best of our knowledge not been explored but could potentially speed up reaction networks by direct pass on of substrates from transporter to enzyme. Hence, we co-expressed LacY and LacZ fusions with mEos3.2 and mRuby fluorescent proteins, respectively, with and without C-terminal PopTag (Table 1). We visualized the localization of membrane (LacY) and cytoplasmic (LacZ) protein for all combinations with and without PopTag by confocal laser-scanning fluorescence microscopy (Fig. 5a). LacYmEos distributed homogeneously across the membrane, and LacYmEos-Pop was predominantly present at the cell poles with smaller clusters in the lateral membrane, independent of co-expression of either LacZmRuby or LacZmRuby-Pop. LacZmRuby is distributed mostly homogeneously in the cytoplasm, independent of co-expression of LacYmEos or LacYmEos-Pop; some inclusion bodies are visible at the cell poles, which is expected as β-galactosidase fusions are prone to aggregation30,31, even at low inducer concentration (0.000001% w/v L-rhamnose) and 30 °C in minimal media. Remarkably, LacZmRuby-Pop is localized differently in cells producing LacYmEos and LacYmEos-Pop. In BW25113 LacYmEos-LacZmRuby-Pop cells, LacZmRuby-Pop forms spherical condensates at the cell poles, while in BW25113 LacYmEos-Pop-LacZmRuby-Pop cells we observe two different morphologies (Fig. 5a). In the first, more frequent scenario, LacZmRuby-Pop forms large dome-shaped condensates at the cell poles and smaller condensates in the lateral membrane colocalizing with LacYmEos-Pop. In the second, less frequent, scenario, LacZmRuby-Pop forms spherical condensates at the cell poles without smaller condensates in the lateral membrane. We hypothesize that in scenario I LacZmRuby-Pop fully covers the in-membrane condensate of LacYmEos-Pop, and in scenario II, large cytosolic condensates of LacZmRuby-Pop only partially interact with in-membrane condensates of LacYmEos-Pop. Co-localization of LacY and LacZ in all measured conditions was quantified by Pearson correlation coefficient with Costes thresholding. A high positive correlation was observed for LacYmEos-Pop-LacZmRuby-Pop (r=0.81) and no or negative correlation for other conditions: −0.12 for LacYmEos-LacZmRuby, −0.02 for LacYmEos-LacZmRuby-Pop, and −0.41 for LacYmEos-Pop-LacZmRuby.

FRAP measurements of the polar LacYmEos-Pop condensates, co-expressed with LacZmRuby-Pop, showed similar recovery patterns as observed with LacYmEos-Pop alone (Supplementary Fig. 9). The half-time of recovery was 134±47 s for LacYmEos-Pop alone and 103±19 s for LacYmEos-Pop in heterocondensates (the extra sum-of-squares F-test p-value=0.71), and the extents of recovery were 0.1409±0.009 for LacYmEos-Pop alone and 0.128±0.009 for LacYmEos-Pop in heterocondensates (extra sum-of-squares F-test p-value=0.35). These data indicate that LacYmEos-Pop condensates have similar material properties when expressed alone or with LacZmRuby-Pop.

Transmission electron microscopy of BW25113 LacYmEos-Pop-LacZmRuby-Pop also shows two scenarios of heterocondensate formation (Fig. 5b, Supplementary Fig. 10). Scenario I was observed in 5 out of 15 cell sections and shows the electron-dense region at the cytoplasmic face of the inner membrane, and it is thicker (~50 nm) than that of BW25113 LacYmEos-Pop cells (~10 nm, Fig. 5b, left). Interestingly, in one cell, a large electron-dense region is seen in the cytoplasm, which is partially in contact with a thin (~10 nm) electron-dense region on the inner membrane (Fig. 5b, right); this may represent scenario II of the confocal images (Fig. 5a). These two scenarios correlate with two types of LacZmRuby-Pop condensates, dome-shaped (more frequent) and spherical (less frequent) that are observed by confocal laser-scanning microscopy at the cell poles of BW25113 LacYmEos-Pop-LacZmRuby-Pop cells (Fig. 5a). The two scenarios can be rationalized when LacY and LacZ have different expression levels: Scenario I would occur when the levels are similar, and Scenario II if LacZ is expressed at a higher level than LacY. We conclude that LacYPop and LacZPop form heterocondensates of varying architectures that are anchored to the inner membrane of E. coli.

We then assessed the activity of LacY and LacZ in the heterocondensates. We constructed E. coli strains without mEos3.2 and mRuby to enable fluorescent read-out of LacZ activity with 4-methylumbelliferyl β-D-galactopyranoside as substrate32,33. The strains are named BW25113 ΔlacY LacY-LacZ, BW25113 ΔlacY LacYPop-LacZ, BW25113 ΔlacY LacY-LacZPop and BW25113 ΔlacY LacYPop-LacZPop (for details see Table 1). LacY activity was similar across all these strains as evaluated by 14C-lactose uptake measurements (Supplementary Fig. 11). The fluorescent substrate, 4-methylumbelliferyl β-D-galactopyranoside (β-MUG), is transported by LacY33 and hydrolyzed by LacZ. The transport negative strain, BW25113 ΔlacY, shows a slight decrease in signal over time, which does not reflect β-galactosidase activity. Strains expressing LacY and LacZ, with or without PopTag, hydrolyze β-MUG, and we estimated the β-galactosidase activity from the slope of the linear increase of fluorescent signal over time (Supplementary Fig. 12). The fluorescence data were adjusted for the amounts of expressed β-galactosidase, determined by fluorescence imaging of SDS–PAGE separated E. coli lysates of LacZ-mRuby fusions, to obtain the specific β-galactosidase activity (Supplementary Fig. 13). The activities of LacZ in all strains were normalized to the mean activity in BW25113 ΔlacY LacY-LacZ cells, which was 1.00±0.03 a.u. The activity increases to 1.62±0.05 a.u. in BW25113 ΔlacY LacY-LacZPop cells, but decreases to 0.80±0.02 and 0.82±0.04 a.u. in BW25113 ΔlacY LacYPop-LacZ and BW25113 ΔlacY LacYPop-LacZPop cells, respectively (Fig. 5c). Clearly, the activity of β-galactosidase is highest when the protein is present in homocondensate and somewhat reduced in heterocondensates with LacY.

Methods
Strain and plasmids
E. coli strain BW25113 [F-, Δ(araD-araB)567, ΔlacZ4787(::rrnB-3), λ-, rph-1, Δ(rhaD-rhaB)568, hsdR514] was used for most experiments. For storage and cloning we used E. coli DH5α [F-, Δ(argF-lac)169, φ80dlacZ58(M15), ΔphoA8, glnX44(AS), λ-, deoR481, rfbC1, gyrA96(NalR), recA1, endA1, thiE1, hsdR17]. All strains and plasmids used are found in Table 1. Plasmids were constructed with the USER cloning protocol and transformed into E. coli via the heat-shock method and subsequently checked via Sanger sequencing by Eurofins Genomics. Plasmid DNA was isolated with the NucleoSpin Plasmid kit (MACHEREY-NAGEL). All protein sequences and primers used in this study are shown in Supplementary Tables 1 and 2, respectively.

Culturing conditions
Antibiotic concentrations used as a selective marker were 100 μg/ml ampicillin (dissolved as 1000× stock) in 50% EtOH, 20 μg/ml chloramphenicol (dissolved as 1000× stock), and 10 μg/ml tetracycline (dissolved as 1000× stock). Lysogeny broth (LB) was prepared using a standard recipe and sterilized by autoclaving. Mops-buffered minimal media (MBM) was prepared as described in refs. 21,46. All measurements were performed in the MBM media after overnight preculturing in LB, followed by overnight preculturing in MBM media as described in refs. 21,22. Briefly 3 mL of LB media supplemented with antibiotic(s), if strains harbor plasmids, was inoculated with a single colony of E. coli and grown overnight at 30 °C with shaking at 180 rpm, after which the preculture in LB was diluted 100-fold in MBM supplemented with 0.1% (v/v) glycerol plus antibiotic(s) and incubated overnight at 30 °C with shaking at 180 rpm. The next day, the MBM preculture was diluted into fresh, prewarmed MBM with 0.1% (v/v) glycerol plus antibiotic(s) to a final OD600 of 0.05 and grown for the needed amount of time, typically 4 h until an OD600 of 0.15 was reached. Unless stated otherwise, expression of lacY fusion genes from pBAD was induced by 0.1% L-arabinose for 4 h, expression of lacZ fusion genes from pACYC was induced by 0.000001% L-rhamnose for 4 h.

For all microscopy measurements, cells were grown in 3 mL of MBM media for 4 h. 1 mL of cell culture was spun down and resuspended in 100 μL of remaining media. 2 μL of cell culture was put on cleaned (by sonication in 5M KOH) 1.5H high-precision glass slides (170 μm thickness, Carl Roth GmbH & Co KG) and immobilized by agarose pads as described elsewhere 21,22.

For electron microscopy, lactose transport and β-galactosidase activity assays, cells were grown using the following protocol: 3 mL of LB preculture (with the appropriate antibiotic(s)) was inoculated with a single colony of the appropriate E. coli strain and grown overnight at 30 °C with shaking at 180 rpm. The next day, the LB preculture was diluted 100× into 20 mL of MBM minimal media supplemented with 0.1% glycerol plus antibiotic(s) and grown overnight at 30 °C with shaking at 180 rpm. The next day, the MBM preculture was diluted into 100 mL of MBM media with 0.1% glycerol plus antibiotic(s) to reach a final OD600 of 0.05. Induction of genes coding for LacY and LacZ variants was done by adding 500 μL of 20% arabinose (f.c. 0.1%) and 100 μL of 0.001% rhamnose (f.c. 0.000001%), respectively, and the cells were incubated for 4 h to reach an OD600 of ~0.15.

To block the division of E. coli 20 μg/ml of cephalexin dissolved in MQ water was added to the cells at the moment of dilution to OD600 of 0.05 and treatment was continued for 4 or 7 h.

For nucleoid degradation, E. coli LY177 [ΔrecA-Tc ydeO::I-Sce1CS, ilvA::I-Sce1CS] was used25. Expression of the I-SceI gene was induced with 0.2% L-Arabinose for 2 h from pSN1, and lacY fusions were expressed from the pACYC vector and 0.5% L-Rhamnose as an inducer. In the liquid media and agarose plates for E. coli LY177, carrying pACYC, 0.2% glucose was used to reduce the leaky expression of the gene coding for I-SceI endonuclease (J. Losa, personal communication, 2024). To obtain the E. coli LY177 with two plasmids, the cells were first transformed with the pACYC vector, carrying genes of the lacY variants, and plated on agar with chloramphenicol plus tetracycline. Next, a new batch of competent cells was made from these cells, grown in the presence of chloramphenicol plus tetracycline. They were subsequently transformed with pSN1, carrying endonuclease the I-SceI gene, and plated on agar with chloramphenicol, tetracycline, ampicillin supplemented with 0.2% glucose. The cells were used within a week to prevent nucleoid degradation due to leaky expression of I-SceI.

The protocol for spheroplast preparation was adapted from refs. 26,27. Cells were grown in the presence of 20 μg/ml cephalexin for 4 h, while the gene of interest was expressed. Then 1 ml of cell culture was concentrated two times by centrifugation at 8200×g in a Spectrafuge™ 16M centrifuge for 1 min. To 500 μL of cell culture, 500 μL of 2M glucose solution, 5 μL of 200 μg/mL lysozyme, plus 5 μL of 0.5 M EDTA (pH 8.0) were added, and the cells were incubated at room temperature for 15 min. 2.5 μL of 1 M MgCl2 was added to quench the spheroplasting, and cells were concentrated to 100 μL.

Wide-field fluorescence microscopy
A Zeiss Axio Observer microscope with a ×100 oil immersion objective (1.4 NA) was used for imaging of E. coli cells. Fluorescence of mEos3.2 green state was excited by a 470 nm LED, and the emission was collected in the 500–550 nm wavelength range. For nucleoid staining we used DAPI at a final concentration of 15 μM (incubated for 15 min), and the fluorescence was excited by 365 nm LED and the emitted light was collected in the 420–470 nm range. Phase contrast for intact bacterial cells or bright field images for spheroplasts were also collected.

Fluorescence recovery after photobleaching (FRAP)
For non-condensated LacYmEos, FRAP measurements were performed on Zeiss LSM710 ConfoCor3 equipped with a Plan-Apochromat ×100 oil-immersion objective (NA=1.40). The 488-nm laser was used for fluorescence recording at 15% laser intensity. Individual cells were measured in fields of view of 8.94×8.94 μm2 (512×512 pixels), roughly half of a cell was used as a bleached region of interest (ROI). Measurements were performed at maximal scanning speed, with pixel dwell time of 0.39 μs. Prior to bleaching, 5 fluorescence images were taken every 0.243 s. During bleaching, the ROI was scanned once with 488-nm illumination at 100% laser intensity. After bleaching, 15 fluorescence images were taken every 0.243 s. In total, n=21 BW25113 LacYmEos cells were measured. Because the intensity of the readout laser also bleaches LacYmEos and the mobility of the protein is relatively high, we also measured fluorescent signal over time from the non-bleached half of a cell and used it to correct the recovery for bleaching during readout.

For condensed LacYmEos-Pop, FRAP measurements were performed on a Leica Stellaris 8 microscope equipped with ×63 Plan-Apochromat water-immersion objective (NA 1.20). For fluorescence recording, a 488-nm laser was used at 10% intensity, depending on the brightness of the cells. Several cells were measured simultaneously in the same field of view of 64.87×64.87 μm (512×512 pixels), with one bleached region of interest (ROI) per cell. ROIs were defined individually for each cell based on its geometry: polar regions (n=20) enclose the polar condensate, lateral membrane regions (n=25) cover the membrane area between the two polar condensates, and whole-cell ROIs (n=23) include the entire cell (Supplementary Fig. 2). Measurements were performed at maximal scanning speed, with pixel dwell time of 1.76 μs. Prior to bleaching, 5 fluorescence images were taken every 1 s. During bleaching, the ROIs were scanned 30 times with 488-nm illumination at 100% laser intensity. After bleaching, fluorescence images were taken every 1 min for a total of 20 min. Contrast-based autofocus with a 20 μm range was applied before every measurement to prevent z-drift over a long timescale. Additionally, to test whether fluorescence recovery at the cell poles was due to newly synthesized protein, as BW25113 LacYmEos-Pop cells were treated with 20 μg/mL chloramphenicol for one hour (after 4 h of protein expression), prior to the FRAP measurements (n=17 cells).

Recovery data for both LacYmEos and LacYmEos-Pop were normalized by setting the average fluorescence intensity before the photobleaching step to 1 and the intensity immediately after the photobleaching step to 0. The recovery curve was fitted with the exponential plateau equation.

Single-molecule displacement mapping (SMdM)
SMdM measurements were performed as described previously21,22 with some modifications. Briefly, a 405 nm laser pulse (OBIS 405 LX, 50 mW max. power) was used to photoconvert mEos3.2 from a green fluorescent state (507 nm ex./516 nm em.) to red (572 nm ex./580 nm em.), and two readout beams of 561 nm laser (OBIS LS 561-150) were used with time separation (Δt) of 10 ms. Time separation between the excitation 561 nm pulses was increased from 1.5 to 10 ms as mobility of slow diffusing proteins is better captured at higher Δt values23. The emitted signal was collected by an EM-CCD camera (C9100-13, Hamamatsu), using an ET605/70M bypass filter (Chroma).

ThunderSTORM plugin of ImageJ (https://zitmen.github.io/thunderstorm/) was used for the peak detection to obtain single-protein localizations along with the localization uncertainty values. We used localization uncertainty values for the correction of the measured diffusion coefficient. Knowing the localization uncertainty, we can estimate the apparent diffusion coefficient, which is related to this uncertainty (Dloc.unc), using the following equation:

σ2start + σ2end = 2nDloc.unc Δt (1)

where n is the number of dimensions, σstart and σend are uncertainties in the localization of starting and ending positions of the protein displacements over the Δt time period. Assuming that the localization uncertainties for the start and end positions are equal and that n=2, Eq. 1 simplifies to:

Dloc.unc = σ2start + σ2end / 2n Δt (2)

To obtain the diffusion coefficient of moving proteins, the probability density distribution of measured displacements as a function of time separation (Δt) was fitted with an adjusted probability density function (PDF) of a 2-dimensional random-walk diffusion model with background correction and normalized for the maximum search radius47:

p(r, Δt) = 1/L * e^(-r2 / 4DL Δt + b) * r / 2DL Δt * e^(-r2 / 4DL Δt + b) (3)

where DL is the lateral diffusion coefficient, r is the peak-to-peak displacement, Δt is the time separation between 561 nm readout laser pulses (10 ms in this case), b is a background correction coefficient, and rmax is 200 nm. Because proteins with lower mobility are more affected by localization uncertainty, we subtract the Dloc.unc from the DL to correct for this effect.

To reconstruct diffusion maps, we binned each cell into square selections with a side of 100 nm and fitted the displacements starting within a bin with Eq. 3. This was done for bins with at least 100 displacements.

Photoactivated localization microscopy (PALM)
For super-resolution microscopy, the same home-built setup was used as for SMdM measurements. To convert and excite mEos3.2 fluorescent protein, we utilized the same pulse pattern as for SMdM measurements. Briefly, a low-intensity 1 ms 405 nm photoactivation pulse was followed by a 0.5 ms 561 nm readout pulse within the same camera frame and again in the subsequent frame; the frame time of the camera was 17.86 ms for 25×25 μm2. This sequence was repeated for 100,000 frames. Peak detection was done using ThunderSTORM plugin (https://zitmen.github.io/thunderstorm/) for ImageJ software, using appropriate camera parameters. Super-resolution images were reconstructed using a custom Python script48. The localized molecules, resulting in the form of sub-pixel x, y coordinates along with their lateral localization uncertainties, were subsequently plotted in reconstruction images as 2D Gaussians centered at (x, y) with σ equal to the corresponding localization uncertainty.

Confocal microscopy
The Leica Stellaris 8 microscope with white light laser was used to localize different variants of LacY-mEos3.2 and LacZ-mRuby in E. coli cells. For excitation of mEos3.2, 489 nm laser light was used and the emitted light in the 500–581 nm range was collected; 560 nm laser was used to excite mRuby, and the emitted light in the range 581–700 nm was collected.

Electron microscopy
Cells were grown as described in the “Culturing conditions” section. E. coli cells were concentrated by centrifugation at 4000×g to the minimal volume possible (paste-like suspension) and transferred to a 3 mm copper gold-plated type B (flat-surfaced) carrier (Leica). Cells immobilized by high-pressure freezing (EM ICE, Leica) were freeze-substituted in 1% (w/v) OsO4 plus 0.5% uranyl acetate in acetone with 5% water, using the quick freeze substitution method49. Samples were embedded in Epon resin, and ultra-thin sections of ~100 nm were collected on formvar-coated and carbon-evaporated copper grids and inspected using a TALOS L120C (Thermo Scientific) transmission electron microscope (TEM). For the ultrastructural analysis, we have visually selected cells where the inner and outer membranes are clearly visible and a periplasm thickened at the cell pole; we excluded cells that were sectioned at high angles relative to the long axis of the cell. The images were analyzed by two researchers independently (D.L. and R.de.B.), and cells labeled as condensate positive by both researchers were marked as “positive” in the final analysis. The thickness of the pre-membrane electron-dense layer was analyzed by line profiles of the electron density perpendicular to the membrane, using ImageJ software.

Molecular dynamics simulations
Protein structures (LacY, LacYPop, and PopTag) were modeled using AlphaFold350. All simulations were performed using Gromacs 2024.3 with the Martini 3 force field51–53. Coarse-grained models were chosen over all-atom representations to enable longer timescale simulations while maintaining sufficient chemical detail for protein–protein interactions and membrane dynamics. The Martini 3 protein model requires explicit assignment of secondary structure elements during model building. Since the opening of the periplasmic cavity is the rate-limiting transport step in LacY, we applied structural restraints derived from the outward-facing structure (PDB: 6VBG55) to the AlphaFold3 models, which overwise predicts the inward-facing state most abundant in the PDB56.

For the PopTag, we assigned these structural elements based on the Jpred tool and all-atom simulations from a previous study, since AlphaFold predictions struggle to represent the ensemble characteristics of intrinsically disordered regions17,56–58. The three helix motifs of the PopTag were modeled as H1 (residues 14–31), H2 (residues 40–58), and H3 (residues 62–76), with remaining residues modeled as coils. For the intrinsically disordered region at the N-terminus of PopTag (residues 1–13), Martini parameters were tuned using the -idr-tune flag in Martinize2, to better reproduce the expanded conformations of disordered regions59. Placing the proteins into the simulation box was done using Bentopy, while the membranes were constructed using the insane tool60,61. Lipid compositions were chosen to represent the E. coli inner membrane (75% POPE, 20% POPG, 5% cardiolipin)62. During system preparation, each simulation box was solvated, neutralized, and NaCl was added to reach a concentration of 150 mM.

The slab condensate model was constructed with 100 molecules contained within the central 15 nm of a 15×15×50 nm simulation box, resulting in a concentration of 15 mM of protein. For the membrane simulations, 9 copies of LacY or LacYPop were placed in a regular grid configuration into a membrane of 35 nm×35 nm, maintaining equal distances between adjacent membrane proteins.

The initial configurations underwent energy minimization using Gromacs’ steepest descent algorithm, followed by equilibration and production simulations. Equilibration was conducted for 50 ns using a 10 fs timestep, while production runs used a 20 fs timestep for a simulation time of 50 μs. The simulation convergence was verified by looking at the number of protein clusters over time, which converged during the simulation (see Supplementary Fig. 4). Temperature and pressure were regulated during equilibration and production simulations using the v-rescale thermostat and c-rescale barostat, respectively. All simulations employed semi-isotropic pressure coupling. For membrane simulations, pressure was maintained at 1 bar (τp = 12 ps, β = 3e-4 bar-1). For slab condensate simulations, pressure coupling in the xy-plane was disabled to maintain a fixed interface area, while a default pressure coupling along the z-axis was applied to allow the condensate to equilibrate to its natural density. All simulations were conducted at 300 K (τt = 1 ps) with separate coupling groups for solvent, lipid, and protein when applicable.

Other nonbonded simulation parameters followed Martini3 recommendations for Gromacs, with specific settings for large membranes (verlet-buffer-tolerance=-1, rlist=1.35 nm)53,63,64. All simulations were performed in triplicate for better statistics on the results. Analysis of the simulation data was performed using the MDAnalysis 2.9.0 Python library65,66. The protein–protein contacts were identified using a distance-based criterion, with residues considered in contact when their backbone beads are within 15 Å of each other. Backbone bead contacts were used for computational efficiency and validated against whole residue–residue contacts, confirming consistent results (Supplementary Fig. 5a). The number of protein clusters was defined as the number of distinct protein groups where proteins within each group shared at least one residue–residue contact with another protein in that group. Within these clusters, the number of protein–protein contacts was quantified as the count of unique protein pairs in contact. Contact lifetime analysis revealed contacts persist for a microsecond timescale, demonstrating stable protein associations beyond thermal fluctuations (Supplementary Fig. 5b). For clarity, the time evolution traces of the protein cluster metrics were calculated over the whole production simulation and smoothed with a median filter with a window size of 25 ns. To create the contact maps, we calculated the specific residue–residue contacts across all simulation frames and determined their frequency. Snapshots of the molecular dynamics trajectories were rendered using visual molecular dynamics (VMD 1.9.4a57) software67 and figures were made using Matplotlib 3.9.268.

Transport assays
Cells were grown as described in the “Culturing conditions” section. Part of the cells were taken for imaging, and the rest were used for transport assays. After harvesting by centrifugation (10 min at 4000×g), the cells were resuspended to an OD600 of ~25 in MBM media plus 10 mM glucose, which was also used as assay buffer. [D-glucose-1-14C] lactose (56 mCi/mmol) was purchased from American Radiolabeled Chemicals. For the assay, concentrated cells were diluted into MBM media plus 10 mM glucose to an OD600 of 1 and prewarmed at 30 °C. At time zero of the assay 14C-lactose was added to a final concentration of 10 μM. The assay volume was 150 μL, and at given time intervals (10, 40, 70, 100, and 150 s), samples of 25 μL were taken, and the transport reaction was quenched with 2 mL ice-cold 0.1 M LiCl, and the mixture was filtered immediately over prewetted nitrocellulose filters with a pore diameter of 0.45 μm (Protean, Cytiva). Subsequently, the filters were washed with 2 mL ice-cold 0.1 M LiCl and then dissolved in 2 mL Ultimagold™ scintillation fluid (PerkinElmer). Radioactivity, reflecting the uptake of 14C-lactose, was determined with a Perkin Elmer Tri-carb 2800TR scintillation counter.

For uptake of lactose under osmotic stress conditions, 12 μL of the MBM media in the assay and the quench buffer was replaced by 2 or 4 M NaCl to reach a final additional concentration of 160 and 320 mM, respectively.

β-Galactosidase activity assay
Cells were grown as described in the “Culturing conditions” section. Cells are harvested by centrifugation at 4000×g at 4 °C for 10 min and concentrated to a final OD600 of 5 in phosphate-buffered saline (PBS, 0.40 g NaCl, 0.01 g KCl, 0.07 g Na2HPO4, 0.01 g KH2PO4 in 50 mL MQ water, pH 7.4) on ice. 190 μL of cells were added to a black μClear Flat Bottom 96-well plate for fluorescence measurements (Greiner). 10 μL of 1 mM 4-methylumbelliferyl β-D-galactopyranoside (β-MUG) was added to each well, to reach a final concentration of 50 μM. Upon cleavage by β-galactosidase, the emission maximum of β-MUG shifts from 375 to 445 nm; the increase at 445 nm was used to determine the β-galactosidase activity. The Spark Multimode plate reader (TECAN) was used to monitor the progress of the reaction at 445 nm at 30 °C with β-MUG excitation at 320 nm wavelength. Emission spectra were measured in the range of 345–550 nm, immediately after β-MUG addition and after the time-series measurement (Supplementary Fig. 14). Fluorescence at 445 nm wavelength was measured every minute for 19 min.

Statistics and reproducibility
Data in the text are presented as mean ± SD unless otherwise specified. Normality of data distribution was tested by the Shapiro–Wilk test. Two-sided Student's t-test was used at the significance level of 5% to compare mean values of two datasets. Linear or exponential plateau functions were fitted to the datasets using relevant regression models, and parameters of these models, such as slope or plateau level, were used to determine parameters, for e.g. 14C-lactose uptake. The fitting parameters of lactose uptake experiments and FRAP recovery curves, including recovery half-time and extent, were compared using the extra sum-of-squares F-test at the significance level of 5%. GraphPad Prism 10 software was used to perform all statistical tests and to make plots.

Fluorescent microscopy measurements presented in Figs. 1a and 5a were done twice with similar results. Fluorescent microscopy measurements presented in Figs. 3a–e and 4e were done once with similar results on different fields of view. Electron microscopy measurements presented in Figs. 1h and 5b were done once.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study investigates the in vivo phase separation behavior of a membrane protein, LacY, in Escherichia coli by artificially inducing condensate formation through fusion with the PopTag domain. The authors further examine how membrane curvature, osmotic stress, and co-expression with LacZ influence the localization and functionality of these condensates. I have several concerns and comments:\n(1) There remains unclear to what extent this artificially induced phase separation can be extrapolated to naturally occurring phenomena.\n(2) The fluorescence recovery of LacYmEos-Pop is reported to be only ~20%, which is notably low for a bona fide phase-separated system. Such limited recovery raises concerns about whether the observed structures truly represent dynamic condensates. Moreover, the possibility that the observed recovery arises, at least in part, from the reactivation of fluorescent proteins from a dark state rather than from molecular exchange cannot be excluded. The authors should address this point and consider additional controls or experiments to distinguish between these possibilities.\n(3) In line 100, the authors states that the apparent size of the clusters is smaller, presumably due to reduction of the “motion blur”. However, I am skeptical that motion blur alone could account for such a substantial change in cluster size. Motion blur typically affects localization precision and overall resolution in PALM imaging. In this context, it is unclear whether the observed spot size truly reflects the physical size of the clusters or merely the resolution limit of the imaging system. Clarification and quantification is needed.\n(4) Moreover, the actual cluster dimensions are difficult to assess, as the scale bar is missing from Figure 1b. Please include it. The authors should also consider potential fixation-induced alterations in the appearance of phase-separated structures (Irgen-Gioro, Yoshida et al. eLife 2022;11:e79903).\n(5) Additional methodological details are needed: what was the exposure time used for PALM imaging? Furthermore, a color bar indicating localization counts should be added to Figure 1b to facilitate comparison between live and fixed cells.\n(6) The co-condensation of LacY and LacZ is intriguing; however, the functional assay reveals a decrease in β-galactosidase activity in heterocondensates compared to LacZPop alone. The underlying cause of this reduction remains unclear. The authors should clarify whether it results from molecular crowding within the condensate, altered conformational dynamics, or imbalances in protein expression levels. Additionally, quantitative co-localization analysis between LacYPop and LacZPop would strengthen the microscopy-based conclusions and provide a clearer picture of heterocondensate architecture.\n(7) Key quantitative parameters—such as the intracellular density of LacY‑Pop, absolute copy numbers, and the stoichiometry between PopTag and LacY—are not reported, limiting the ability to assess the physiological relevance of the observed effects.\n(8) The Supplementary Videos are missing.\n(9) The title is too broad and should specify the particular membrane protein being studied.\n(10) LacY lacks intrinsic disordered regions (IDRs) and does not phase-separate in its native state. PopTag is an orthogonally introduced multivalent module that does not interfere with endogenous phase-separation systems. This important background information is not sufficiently explained in the Introduction and should be clearly stated to justify the system design.\n(11) There are inconsistencies between Figure 1e and Figure 1f. The figure legend appears to reverse the image order: “Diffusion maps of E. coli BW25113 LacYmEos (top) and BW25113 LacYmEos-Pop (bottom)” may be incorrectly labeled.\n(12) In Supplementary Table 2, some primer sequences include the base “U”. If this represents modified nucleotides (e.g., RNA bases), please clarify; otherwise, verify and correct the sequences.\n(13) The manuscript contains several formatting issues that should be addressed. For instance, extraneous lines are present in Figure 2; line 254 refers to “3d” but should cite “Figure 3d”; the font size in Figure 4 is inconsistent with other figures; and the Supplementary information includes two entries labeled “Figure S8” on pages 11 and 14, respectively.", "reviewer_2": "The authors probe the impact of condensation at cellular membrane on the activity of the protein LacY in E. Coli. LacY is a transmembrane protein. To achieve this, they fuse a pop tag (the c-terminal domain of popZ) to LacY and probe the condensation of the protein. using a combination of experimental and computational methods. They also probe the effect of co-condensation (ie of a membrane, LacY, and cytosolic protein, LacZ) on protein activity. For this they make fusions of LacY and LacZ, with and without pop tags and express them together in various combinations. The main highlights of the paper are:\nI. The LacY-pop fusion forms condensates with different propensities at the poles vs lateral regions of the membrane\nII. They provide evidence suggesting that this is due to differences in membrane curvature in these two regions\nIII. They also show that the LacY-pop fusion leads to slightly higher substrate transport, compared to the LacY without the pop tag\nIV. The LacY-pop fusion also leads to better lactose uptake under osmotic stress conditions\nV. They observe co-condensation of LacY and LacZ, but this co-condensation does not lead to increased activity; it even seems to suppress the activity.\nIn general, the manuscript is well written, and it is an interesting study. However, some of the conclusions are not fully supported by the data presented and certain methodological details are missing, as outlined below:\nMajor remarks:\n1. The authors measure the protein diffusion at the poles and in the lateral regions for LacY-pop and find that they are similar. They should also perform FRAP separately for the poles and the lateral condensates. This will add further confidence that the material properties are similar.\n2. For the MD simulation the initial set up is not ideal for an unbiased comparison. They start with the proteins evenly spaced, using the same spacing for the LacY and LacY-pop. However, the Lac-Y pop will on average have a larger radius of gyration, which means that the probability that the proteins can find each other and interact will naturally be greater. The authors should use a non-biased initial set up where the spacing scales with the average protein size. Additionally, another robust test would be start from a case where the proteins are already clustered and measure whether the clustering at equilibrium are significantly different.\n3. The criteria used for determining if a cluster is formed in the MD simulation seems quite weak. They put two proteins in cluster if at least they share one residue-residue contact. What is the average strength of such a contact in comparison to the average thermal energy (KT)? Ideally, such interactions should be multiples of KT to constitute a viable contact.\n4. Related to that are the number of protein-protein interactions normalized by protein size? For instance, the mutant with the pop tag will naturally have more contacts because it has more residues. Please discuss in greater detail whether the reported protein-protein contacts are normalized or not.\n5. Based on figure 2 and the SI, the authors claim that pop-tag interactions are primarily driven by helix 1. The data presented only partially supports this. Based on fig 2c, a large fraction of the contacts are localized in the region between H1 and H2. They do not comment on this. They also report that the interactions are primarily hydrophobic in nature. However, the intermediate regions, seem to be charged regions. Are they? Or at least regions with low hydrophobicity. If so, it seems that charged or other interactions are a key.\n6. In Fig 2b, it is unclear why the slab geometry was used to determine the contacts for the pop-tag condensate in figure 2c. Ideally, this measurement is done on an isotropic system to avoid the effects of having interfaces.\n7. In the experiments, it would be useful to characterize the material properties of the heterogeneous condensates, as done for the single-protein ones. This may help shed light on why the activity is affected in the manner reported.\n8. The structures from alphafold3 used for setting up the simulations were these relaxed via all-atom simulations before using martinize2? Are the secondary structures static in the martini simulations or are they allowed to fluctuate?\n9. What is the rationale for the pressure set up used in the slab simulations? What are these so-called default pressure parameters?\n10. The authors state that the Martini-parameters were tuned using the idr-tune flag. Please discuss in greater detail what this means. Tuned in what sense?\nMinor remarks:\n1. The caption of Figure 2 needs further details so readers can immediately tell what they are looking at. For instance, mentioning that proteins are embedded in a membrane etc.\n2. Describe what are spheroplasts at first usage.\n3. Labels are needed throughout Figure 1. It is difficult to tell what exactly one should be looking for in Fig 1G for instance.", "reviewer_3": "Key results\nIn this study the authors examine phase separation of membrane proteins in live E. coli cells by expressing fusions composed of membrane protein LacY and a PopTag scaffold domain to drive phase separation. They examine the effect of this tag on the localization, dynamics, and activity of LacY, and explore the activity of downstream enzyme LacZ with or without the same tag. The manuscript is clearly written and the topic is of interest, particularly on how engineered co-condensation at a membrane might affect enzyme activity in a cascade. However, the authors’ mechanistic arguments should be strengthened with additional evidence, and additional quantification/clarification is needed. The following are some points for revision.\nMajor points\n1. The introduction should include a review of the existing literature on membrane-localized biomolecular condensates (surface wetting, biological significance, curvature remodeling, etc.). I would disagree with the characterization that membrane protein phase separation is uncharted territory given the examples mentioned in the introduction. I also wonder whether a dynamic protein monolayer counts as a condensate if it does not recruit components from the bulk, as the classification can become unclear at the nanoscale (though this broad question does not need to be addressed in this manuscript).\n2. In Figure 1G, arrows or other indicators would be helpful as the thin layer is hard to see. What methods were used to classify whether the layer was present in each image and measure its thickness?\n3. The MD simulations do not include mEos3.2, but this domain contributes significant size and surface charge to the whole protein construct, and these features can affect phase separation/protein-protein interactions. Can the authors discuss how the MD results might change if they included this domain?\n4. Residue-membrane interactions competing with scaffold protein condensate formation is an interesting idea. Since the alternate simulation setup in Fig S4 resulted in quite different interaction networks from those in Fig 2, can the authors comment on which interactions might correspond better to the observations in vivo?\n5. The experiments in Fig. 3 are interesting, especially the spheroplast-plasmolysis rescue experiment. However, these figures are not enough evidence to make a claim that membrane curvature drives condensate formation. Quantifying fluorescence intensity against local membrane curvature in the datasets from Fig. 3D/E could support the claim that only the high-curvature regions promote condensate formation (and provide an opportunity for larger sample size and a statistical test). Also, during osmotic upshift there can be many changes to the intracellular chemical environment (ionic strength, pH, crowding, etc.) which are known to govern biomolecular condensate phase separation. Are there ways to confirm that the observed condensate formation is not due to one of these factors?\n6. In Figure 4E deformed cells seem hard to detect in the PopTag fluorescence images, given that there is mostly only signal near the poles. Can the authors show images where the morphology is easier to see?\nMinor points\n1. Several scale bar sizes are missing throughout the figure captions.\n2. In Figure 1C, which dataset was quantified? It seems like datasets from different imaging methods (Fig. 1A-B) might give very different localization results.\n3. The authors say that condensation is primarily driven by Helix 1, but the contact map in Fig. 2C seems to indicate that the linker between H1 and H2 plays an equally significant role. Could the authors comment?\n4. The authors mention that “PopTags mediate interactions between otherwise non-associating LacY proteins”, but simulated LacY proteins did cluster (Fig S3).\n5. In Figure S6 can the authors quantify protein level by gel densitometry, to support the argument that the different construct activities in Fig 4 are not due to different expression levels?\n6. Significance signs are missing in Figure S8.\n7. In Figure S10 the monomer band does not appear to be ~145 kDa, based on the ladder.\n8. A typo: “Having characterized the structure and interactions of LacZPop membrane condensates experimentally and computationally” I think the authors meant to write LacYPop."}, "author_response": "Response to REVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThis study investigates the in vivo phase separation behavior of a membrane protein, LacY, in Escherichia\ncoli by artificially inducing condensate formation through fusion with the PopTag domain. The authors\nfurther examine how membrane curvature, osmotic stress, and co-expression with LacZ influence the\nlocalization and functionality of these condensates.\nI have several concerns and comments:\n(1) There remains unclear to what extent this artificially induced phase separation can be extrapolated to\nnaturally occurring phenomena.\nThe PopTag-LacY fusion is artificial, but it will serve as a valuable tool for elucidating the liquid-liquid phase\nseparation (LLPS) behavior of native transmembrane proteins. We empahsize that many membrane\nproteins have intrinsically disordered cytoplasmic regions (IDRs), forming accessory domains of poorly\nunderstood functions (10.1016/j.bbamem.2015.08.002, 10.1371/journal.pone.0158594,\n10.1007/s00018-017-2562-5, 10.1021/cb200231c). These IDRs can potentially drive LLPS in native\nmembrane proteins. We use PopTag as a well characterized condensation-inducing tag to mimic this\nprocess and investigate its structural and functional implications for the well-studied lactose permease\nLacY. We added text in the introduction (lines 60 – 63 and 83 - 90) to make the rationale and benefit of\nthe system clearer.\n(2) The fluorescence recovery of LacYmEos-Pop is reported to be only ~20%, which is notably low for a\nbona fide phase-separated system. Such limited recovery raises concerns about whether the observed\nstructures truly represent dynamic condensates. Moreover, the possibility that the observed recovery\narises, at least in part, from the reactivation of fluorescent proteins from a dark state rather than from\nmolecular exchange cannot be excluded. The authors should address this point and consider additional\ncontrols or experiments to distinguish between these possibilities.\nThis is an important point. We extended the FRAP dataset and carefully reanalyzed the data (see lines 561\n- 569). Additional data and a different normalization procedure resulted in ~10% recovery fraction for\nLacY-mEos-Pop, which is similar to what has been reported for the original cytoplasmic PopTag\n[10.1038/s41467-024-47330-4] and other self-condensing proteins, e.g. aggresomes\n[10.1126/sciadv.abh2929]. In 10.1038/s41467-024-47330-4, the authors attribute the partial recovery to\n“gel-like” state of condensates. In our case, complete recovery is not possible because a substantial\nfraction (>30%) of total fluorescence is irreversibly lost during bleaching of a pole. Further, the equilibrium\nshortly after bleaching is partial because protein exchange between poles and lateral membrane is much\nfaster than protein transfer from one pole to another. We now also measured FRAP at the lateral\nmembrane and observed no recovery, also pointing towards the trapping of the LacYPop to the polar\ncondensates. Lateral membrane bleaching has been added to Figure 1d (lines 135 - 141). Importantly, the\ndiffusion of LacYPop molecules within the pole condensates is only 4% lower (within the error margin) than\nin lateral condensates, and thus the system is dynamic with a very high partitioning of the condensate-\nforming molecules.\nWe performed a series of additional experiments to address the concerns of the reviewer: (i) Bleaching\nexperiments on entire cells resulted in no recovery of fluorescence, indicating that the observed recovery\nat polar condensates is not due to reactivation of the fluorescent protein. (ii) Fluorescence recovery at\nthe poles of chloramphenicol-treated cells was similar to that of untreated cells, demonstrating that\nrecovery is not caused by synthesis of new protein. Data is presented on Figure 1d and details of these\ncontrol experiments are provided in lines 137 – 141 and 574 – 579.\n(3) In line 100, the authors states that the apparent size of the clusters is smaller, presumably due to\nreduction of the “motion blur”. However, I am skeptical that motion blur alone could account for such a\nsubstantial change in cluster size. Motion blur typically affects localization precision and overall resolution\nin PALM imaging. In this context, it is unclear whether the observed spot size truly reflects the physical\nsize of the clusters or merely the resolution limit of the imaging system. Clarification and quantification is\nneeded.\nBy “motion blur” we mean that in our PALM reconstructions the localization of proteins was averaged\nover either 6 min (~20 000 frames) or 30 min (~100 000 frames). In live cells, where protein condensates\ncan move in the membrane, this leads to a “motion blur”, which is less pronounced at shorter\nreconstruction times. In fixed cells, where protein condensates do not move, the “motion blur” is less but\nindividual molecules still diffuse, which affects the localization precision. We find that localization\nprecision calculated for living and fixed E. coli BW25113 LacYmEos3-Pop are similar: 25±13 nm for living and\n24±13 nm for fixed cells. To clarify concerns of the reviewer, we changed the text (lines 121 - 124).\n(4) Moreover, the actual cluster dimensions are difficult to assess, as the scale bar is missing from Figure\n1b. Please include it. The authors should also consider potential fixation-induced alterations in the\nappearance of phase-separated structures (Irgen-Gioro, Yoshida et al. eLife 2022;11:e79903).\nScale bars have been added to figure 1b. Although we cannot fully exclude fixation-induced artifacts,\nour fixed and living cells look very similar in a widefield microscope (see images below), unlike images in the\npaper given by the reviewer.\n(5) Additional methodological details are needed: what was the exposure time used for PALM imaging?\nFurthermore, a color bar indicating localization counts should be added to Figure 1b to facilitate\ncomparison between live and fixed cells.\nWe introduced color bars in Figure 1b. For PALM image reconstructions we used 2D Gaussian rendering,\nwhich takes into account localization uncertainties of individual molecules. Therefore, intensity in the\nreconstructed images are roughly proportional to localization counts, but measured in a.u. instead of\ncounts/pixel. We provide these and other technical details on PALM imaging in the Methods section (lines\n617 – 625).\n(6) The co-condensation of LacY and LacZ is intriguing; however, the functional assay reveals a decrease\nin β-galactosidase activity in heterocondensates compared to LacZPop alone. The underlying cause of this\nreduction remains unclear. The authors should clarify whether it results from molecular crowding within\nthe condensate, altered conformational dynamics, or imbalances in protein expression levels.\nAdditionally, quantitative co-localization analysis between LacYPop and LacZPop would strengthen the\nmicroscopy-based conclusions and provide a clearer picture of heterocondensate architecture.\nWe added a discussion on the reduced LacZ β-galactosidase activity in heterocondensates in lines 455 -\n463. We have no evidence that the reduced activity is due to a lower expression level or different\ncrowding. We propose that the activity is related to the shape and size of the condensates, thus the\nmembrane-assisted 2D or 2.5D condensates are less active presumably due to a different stabilization of\nLacZ oligomers than in the 3D homocondensates.\nTo quantify the co-localization between LacYPop and LacZPop, we calculated Pearson correlation coefficients\nwith Costes thresholding for all conditions, presented on the Fig. 5a, see new test in lines 315 - 319.\n(7) Key quantitative parameters—such as the intracellular density of LacY-Pop, absolute copy numbers,\nand the stoichiometry between PopTag and LacY—are not reported, limiting the ability to assess the\nphysiological relevance of the observed effects.\nThank you for pointing this out. We now performed in-gel fluorescence analysis of of LacYmEos and LacYmEos-\nPop fusion proteins using cell lysates (see new Fig. S6), and we determined absolute protein concentrations\nusing calibration with known amounts of a purified mEos3.2 fused to thioredoxin-1 (TrxAmEos). Absolute\ncopy numbers of LacYmEos and LacYmEos-Pop are 3.5 ± 0.4 x 103 and 3.4 ± 0.5 x 103 proteins/cell respectively,\nwhich are similar to the copy number of native LacY (~2.5 x 103 proteins per cell, doi:\n10.1371/journal.pcbi.1002010). Thus, expressed LacY protein amounts to less than 1% of total membrane\nprotein. These new results are presented in lines 267 - 272 and Supplemental figure S6.\nIf the question of the reviewer regarding stoichiometry relates to the ratio between the PopTags to LacY\nmolecules within the cells, the ratio is 1:1, as they are expressed as a fusion protein. If, instead, the\nquestion refers to the number of interactions between different LacYPop molecules, this is estimated to\nrange between one and three, since the α-helices of PopTag are expected to act as the interaction motifs\n(“stickers”) driving condensate formation. However, the precise number of such interactions cannot be\ndetermined.\n(8) The Supplementary Videos are missing.\nSupplementary videos have been added.\n(9) The title is too broad and should specify the particular membrane protein being studied.\nWe changed the title to “Structural and functional implications of in vivo phase separation of membrane\nprotein LacY in Escherichia coli”\n(10) LacY lacks intrinsic disordered regions (IDRs) and does not phase-separate in its native state. PopTag\nis an orthogonally introduced multivalent module that does not interfere with endogenous phase-\nseparation systems. This important background information is not sufficiently explained in the\nIntroduction and should be clearly stated to justify the system design.\nWe modified the introduction to emphasize these points (lines 83 - 90).\n(11) There are inconsistencies between Figure 1e and Figure 1f. The figure legend appears to reverse the\nimage order: “Diffusion maps of E. coli BW25113 LacYmEos (top) and BW25113 LacYmEos-Pop (bottom)”\nmay be incorrectly labeled.\nThanks for noticing, the legend has been changed and is now in line with the Figures 1e and 1f; figures 1f\nand 1g in the revised manuscript.\n(12) In Supplementary Table 2, some primer sequences include the base “U”. If this represents modified\nnucleotides (e.g., RNA bases), please clarify; otherwise, verify and correct the sequences.\nUracil nucleotides are included in all the primers used for USER cloning. Uracils are needed to enable\nrecognition and cleavage by the USER (Uracil-Specific Excision Reagent) Enzyme to create sticky ends. This\npoint is now explained in the legend of Supplementary Table 2.\n(13) The manuscript contains several formatting issues that should be addressed. For instance, extraneous\nlines are present in Figure 2; line 254 refers to “3d” but should cite “Figure 3d”; the font size in Figure 4 is\ninconsistent with other figures; and the Supplementary information includes two entries labeled “Figure\nS8” on pages 11 and 14, respectively.\nWe apologize for these mistakes, and they have now been corrected.\nReviewer #2 (Remarks to the Author):\nThe authors probe the impact of condensation at cellular membrane on the activity of the protein LacY in\nE. Coli. LacY is a transmembrane protein. To achieve this, they fuse a pop tag (the c-terminal domain of\npopZ) to LacY and probe the condensation of the protein. using a combination of experimental and\ncomputational methods. They also probe the effect of co-condensation (ie of a membrane, LacY, and\ncytosolic protein, LacZ) on protein activity. For this they make fusions of LacY and LacZ, with and without\npop tags and express them together in various combinations. The main highlights of the paper are:\nI. The LacY-pop fusion forms condensates with different propensities at the poles vs lateral regions of the\nmembrane\nII. They provide evidence suggesting that this is due to differences in membrane curvature in these two\nregions\nIII. They also show that the LacY-pop fusion leads to slightly higher substrate transport, compared to the\nLacY without the pop tag\nIV. The LacY-pop fusion also leads to better lactose uptake under osmotic stress conditions\nV. They observe co-condensation of LacY and LacZ, but this co-condensation does not lead to increased\nactivity; it even seems to suppress the activity.\nIn general, the manuscript is well written, and it is an interesting study. However, some of the conclusions\nare not fully supported by the data presented and certain methodological details are missing, as outlined\nbelow:\nMajor remarks:\n1. The authors measure the protein diffusion at the poles and in the lateral regions for LacY-pop and find\nthat they are similar. They should also perform FRAP separately for the poles and the lateral condensates.\nThis will add further confidence that the material properties are similar.\nWe find that the diffusion inside the condensates is the same at the poles and in the lateral membrane,\nwhich is based on the single-molecule measurements (Figure 1f,g). Bleaching of individual condensates at\nthe lateral membrane is not possible, due to their small size. However, FRAP of the lateral membrane\nshowed no recovery (new Figure 1d and text in lines 135 - 141), which might be due to the very strong\npartitioning of LacYPop in the polar condensates.\n2. For the MD simulation the initial set up is not ideal for an unbiased comparison. They start with the\nproteins evenly spaced, using the same spacing for the LacY and LacY-pop. However, the Lac-Y pop will on\naverage have a larger radius of gyration, which means that the probability that the proteins can find each\nother and interact will naturally be greater. The authors should use a non-biased initial set up where the\nspacing scales with the average protein size. Additionally, another robust test would be start from a case\nwhere the proteins are already clustered and measure whether the clustering at equilibrium are\nsignificantly different.\nWe thank the reviewer for this comment on a potential bias in our initial simulation setup. We have\nextended our simulations to 50 μs, which is sufficiently long to observe multiple binding and unbinding\nevents of the membrane proteins, ensuring the initial configuration does not bias our analysis of cluster\nstability and size. The new data presented in Figure 2\n3. The criteria used for determining if a cluster is formed in the MD simulation seems quite weak. They\nput two proteins in cluster if at least they share one residue-residue contact. What is the average strength\nof such a contact in comparison to the average thermal energy (KT)? Ideally, such interactions should be\nmultiples of KT to constitute a viable contact.\nWe thank the reviewer for raising this important point. Our clustering criterion was designed to capture\nlarge-scale differences in clustering behavior between LacY and LacYPop in an interpretable manner. This\napproach revealed the clustering differences, which were validated with extended 50 μs simulations\nduring revision.\nThe energetic basis for these clustering differences is significant. Our contact map analysis (Figure 2c)\nshows that PopTag-PopTag interactions occur primarily through the amphipathic α-helices, mainly the\nhydrophobic interfaces of helix 1 and 2. Each helix contains approximately 15 residues. Literature values\nfor hydrophobic interactions range from 0.5-2 kT per residue\n(https://doi.org/10.1016/j.jmb.2011.02.053), meaning a single helical interface has a significant favorable\nenergy. Additionally, the proteins in larger clusters engage through multiple PopTag-PopTag contacts\nsimultaneously.\n4. Related to that are the number of protein-protein interactions normalized by protein size? For instance,\nthe mutant with the pop tag will naturally have more contacts because it has more residues. Please discuss\nin greater detail whether the reported protein-protein contacts are normalized or not.\nWe thank the reviewer for pointing out this clarification. The reported protein-protein contacts are not\nnormalized by protein size as they are counted per membrane protein, not per residue. Our primary focus\nwas on the qualitative clustering behavior, whether proteins form contacts or not, rather than quantifying\nabsolute residue contact numbers. We have clarified this point in the revised text (legend to the Figure\n2d).\n5. Based on figure 2 and the SI, the authors claim that pop-tag interactions are primarily driven by helix 1.\nThe data presented only partially supports this. Based on fig 2c, a large fraction of the contacts are\nlocalized in the region between H1 and H2. They do not comment on this. They also report that the\ninteractions are primarily hydrophobic in nature. However, the intermediate regions, seem to be charged\nregions. Are they? Or at least regions with low hydrophobicity. If so, it seems that charged or other\ninteractions are a key.\nWe thank the reviewer for this observation. The extended simulations performed during revision now\nreveal much more pronounced helix 2 involvement in LacYPop, with clear helix 1-helix 2 and helix 2-helix 2\ninteractions. The H1-H2 linker also shows contacts due to its hydrophobic residues, alongside charged\nresidues (ASP36, LYS35, ARG38). Additionally, when helices 1 and 2 from different PopTag domains\ninteract antiparallel, their connecting linker regions are brought into proximity, increasing the contacts.\nWe conclude that hydrophobic interactions are the primary driving force for PopTag condensation. We\nhave clarified this in the revised text to acknowledge the contributions of the H1-H2 linker region to the\ncontact map. See changes in lines 184 – 185 and 190 – 192.\n6. In Fig 2b, it is unclear why the slab geometry was used to determine the contacts for the pop-tag\ncondensate in figure 2c. Ideally, this measurement is done on an isotropic system to avoid the effects of\nhaving interfaces.\nWe used a slab geometry as a standard protocol for building condensates without knowing the\nequilibrium density beforehand, while maximizing the internal condensate region and minimizing\ninterface effects. To validate this approach, we calculated the condensate density from the slab\nsimulations and constructed an isotropic condensate simulation at this density. This simulation was run\nfor 20 μs and used to calculate a reference contact map. Comparison of the contact maps shows that\ninteraction patterns qualitatively agree between both geometries, confirming that our slab-derived\ncontact analysis accurately represents the inter-PopTag interactions.\n7. In the experiments, it would be useful to characterize the material properties of the heterogeneous\ncondensates, as done for the single-protein ones. This may help shed light on why the activity is affected\nin the manner reported.\nWe now performed FRAP measurements in cells with LacYPop with co-expression of LacZPop. We observed\nthe same recovery pattern as for cell expressing LacYPop alone, that is, similar half-times of recovery (122\n± 36 seconds for LacYmEos-Pop alone vs. 103 ± 19 seconds for LacYmEos-Pop in heterocondensate, extra sum-of-\nsquares F test p-value = 0.7620) and extents of recovery (0.132 ± 0.007 for LacYmEos-Pop alone vs. 0.128 ±\n0.009 for LacYmEos-Pop in heterocondensate, extra sum-of-squares F test p-value = 0.6958) (lines 320 - 327\nand Supplementary Figure S7). Information about the statistical test is added to the “Statistical analysis”\nsection lines 746-748 Thus, on the basis of FRAP experiments, we find no difference in the material\nproperties of LacYPop in homo- and heterocondensate.\n8. The structures from alphafold3 used for setting up the simulations were these relaxed via all-atom\nsimulations before using martinize2? Are the secondary structures static in the martini simulations or are\nthey allowed to fluctuate?\nThe AlphaFold3 structures were used directly without prior all-atom relaxation. We validated these\nstructures by comparing them to available crystal structures of LacY, which showed good agreement with\nthe experimental models.\nIn our Martini simulations, secondary structures are constrained using an elastic network model,\npreventing changes in secondary structure elements. However, the proteins are still allowed to fluctuate\nand move within these constraints, maintaining realistic dynamics at the coarse-grained level. While\nallowing changes in secondary structure could provide additional model accuracy, we deemed this beyond\nthe scope of the current study since our primary focus was on protein-protein interactions mediated by\nPopTag domains. See changes in lines 652 – 659.\n9. What is the rationale for the pressure set up used in the slab simulations? What are these so-called\ndefault pressure parameters?\nThe slab simulation pressure setup makes the system incompressible in the xy-directions, keeping the\ninterface area fixed, while allowing the z-axis (box height) to equilibrate and establish the correct\ncondensate density. This prevents artificial expansion or contraction of the slab interface while enabling\nthe condensate to find its natural thickness. The \"default pressure parameters\" refer to standard Martini3\npressure coupling settings (1 bar, τ_p = 12 ps, β = 3×10⁻⁴ bar⁻¹) applied in the xy-plane, while pressure\ncoupling in the z-direction is disabled to preserve the slab geometry. This information is now added in\nlines 686 - 689.\n10. The authors state that the Martini-parameters were tuned using the idr-tune flag. Please discuss in\ngreater detail what this means. Tuned in what sense?\nThe idr-tune flag is an option in Martinize2 that optimizes Martini force field parameters specifically for\nintrinsically disordered regions (IDRs). Standard Martini parameters tend to over compact IDRs compared\nto experimental observations. The idr-tune flag addresses this by adjusting the bonded interactions in the\nMartini3 protein model to better reproduce the expanded conformations of disordered regions. This\nparameter optimization has been validated to improve the correspondence with all-atom simulations and\nexperimental data (https://doi.org/10.1038/s41467-025-58199-2). This information is now added in lines\n666-667.\nMinor remarks:\n1. The caption of Figure 2 needs further details so readers can immediately tell what they are looking at.\nFor instance, mentioning that proteins are embedded in a membrane etc.\nWe thank the reviewer for indicating that this was not clear, we improved the figure legend to clarify.\n2. Describe what are spheroplasts at first usage.\nDescription of spheroplasts has been added in lines 235-236\n3. Labels are needed throughout Figure 1. It is difficult to tell what exactly one should be looking for in Fig\n1G for instance.\nWe added arrows pointing to the edges of the membrane-anchored LacYmEos-Pop condensates in right panel\nof Figure 1h, and we refer to them in the legend.\nReviewer #3 (Remarks to the Author):\nKey results\nIn this study the authors examine phase separation of membrane proteins in live E. coli cells by expressing\nfusions composed of membrane protein LacY and a PopTag scaffold domain to drive phase separation.\nThey examine the effect of this tag on the localization, dynamics, and activity of LacY, and explore the\nactivity of downstream enzyme LacZ with or without the same tag. The manuscript is clearly written and\nthe topic is of interest, particularly on how engineered co-condensation at a membrane might affect\nenzyme activity in a cascade. However, the authors’ mechanistic arguments should be strengthened with\nadditional evidence, and additional quantification/clarification is needed. The following are some points\nfor revision.\nMajor points\n1. The introduction should include a review of the existing literature on membrane-localized biomolecular\ncondensates (surface wetting, biological significance, curvature remodeling, etc.). I would disagree with\nthe characterization that membrane protein phase separation is uncharted territory given the examples\nmentioned in the introduction. I also wonder whether a dynamic protein monolayer counts as a\ncondensate if it does not recruit components from the bulk, as the classification can become unclear at\nthe nanoscale (though this broad question does not need to be addressed in this manuscript).\nWe thank the reviewer for this comment. We expanded the introduction with membrane-localized\ncondensates (lines 67 - 77). “Uncharted territory” is perhaps too strong, but the amount of mechanistic\ninsight in membrane-localized condensates is minimal. Text has been changed in lines 52 - 53. We now\nrefer to the membrane condensates as 2D condensates, but we notice that (based on our analysis) they\ndisplay all the features of classical 3D condensates except that components are recruited from the\nmembrane and not from the cytoplasm.\n2. In Figure 1G, arrows or other indicators would be helpful as the thin layer is hard to see. What methods\nwere used to classify whether the layer was present in each image and measure its thickness?\nWe added arrows to Figure 1h (previously Figure 1g) indicating the edges of the electron-dense regions\non the cytoplasmic face of the inner membrane.\nTo classify the EM images, we inspected them visually and, first, selected those with intact membrane\nintegrity and periplasmic space. Then, the layer was identified as a distinct electron-dense region on the\ncytoplasmic face of the inner membrane. The analysis was blinded to the experiment group and\nindependently performed by two researchers, only cells labeled as condensate positive by both\nresearchers were marked as “positive” in the final analysis.\nTo estimate the thickness of the layer, we analyzed line profiles of the electron density perpendicular to\nthe membrane using ImageJ. All this information is now added in the Methods section (lines 642 – 647).\n3. The MD simulations do not include mEos3.2, but this domain contributes significant size and surface\ncharge to the whole protein construct, and these features can affect phase separation/protein-protein\ninteractions. Can the authors discuss how the MD results might change if they included this domain?\nWe thank the reviewer for this comment. mEos3.2 serves purely as a fluorescent reporter and is not\nexpected to influence the PopTag-mediated clustering mechanism we are investigating. mEos3.2 was\ndesigned as a true monomeric protein (10.1038/nmeth.2021). Omitting mEos3.2 from our simulations\nreduced the computational complexity.\n4. Residue-membrane interactions competing with scaffold protein condensate formation is an\ninteresting idea. Since the alternate simulation setup in Fig S4 resulted in quite different interaction\nnetworks from those in Fig 2, can the authors comment on which interactions might correspond better to\nthe observations in vivo?\nWe suggest a two-step process for the condensate formation of LacYPop: Initially, PopTags interact with\nthe membrane while exploring protein-protein contacts (Fig 2). As the local concentration increases,\nprotein-protein interactions dominate over membrane association, leading to PopTags interacting in\nsolution (Fig S4). Sampling these different states in separate simulations is necessary due to MD timescale\nlimitations. The full process of LacY-bound PopTags detaching from the membrane and forming\ncondensates in solution is beyond current simulation timescales. In cells, we expect PopTags initially\nsample both membrane and protein interactions, with condensate formation occurring when protein-\nprotein interactions outcompete membrane association. These points are better articulated in the revised\nmanuscript (lines 203 - 206).\n5. The experiments in Fig. 3 are interesting, especially the spheroplast-plasmolysis rescue experiment.\nHowever, these figures are not enough evidence to make a claim that membrane curvature drives\ncondensate formation. Quantifying fluorescence intensity against local membrane curvature in the\ndatasets from Fig. 3d,e could support the claim that only the high-curvature regions promote condensate\nformation (and provide an opportunity for larger sample size and a statistical test). Also, during osmotic\nupshift there can be many changes to the intracellular chemical environment (ionic strength, pH,\ncrowding, etc.) which are known to govern biomolecular condensate phase separation. Are there ways to\nconfirm that the observed condensate formation is not due to one of these factors?\nWe thank the reviewer for these suggestions. We analyzed the correlation between local membrane\ncurvature and fluorescence intensity in LacYmEos-Pop spheroplasts, using the Kappa plugin for ImageJ\n[https://github.com/fiji/Kappa]. This analysis confirmed modest but significant correlation (ρ=0.24,\np<0.0001; Spearman correlation). We added the plots for normalized fluorescence intensity and\nnormalized curvature as a Supplementary Figure S5, and we present the data in lines 256 - 258.\nWe agree that effects of ionic strength, pH, crowding and other parameters are affected by osmotic\nupshift, and we cannot rule out that they play a role in the formation of 2D membrane condensates.\nWe also do not see ways to dissect these parameters in in vivo studies. Importantly, we do find the formation\nof the condensates at the cell poles in non-stressed cells, that is, at “normal” ionic strength, pH and\ncrowding levels. We feel that local curvature effects are the most plausible explanation. We now discuss\nthese points in more detail (lines 428 - 433).\n6. In Figure 4E deformed cells seem hard to detect in the PopTag fluorescence images, given that there is\nmostly only signal near the poles. Can the authors show images where the morphology is easier to see?\nWe did not observe cell deformations with a 2-fold increase in osmolarity, using the LacYmEos-Pop expressing\nstrain. We changed the text in lines 283 - 288 to clarify this. For the 4-fold increase in osmolarity, the\nzoom-in of the deformations is shown in Figure 4e.\nMinor points\n1. Several scale bar sizes are missing throughout the figure captions.\nScale bar sizes have been added to all the figure legends.\n2. In Figure 1C, which dataset was quantified? It seems like datasets from different imaging methods (Fig.\n1A-B) might give very different localization results.\nThe percentages in Figure 1c were calculated from single-protein localization microscopy. This\ninformation has been added to the legend of Figure 1.\n3. The authors say that condensation is primarily driven by Helix 1, but the contact map in Fig. 2C seems\nto indicate that the linker between H1 and H2 plays an equally significant role. Could the authors\ncomment?\nWe thank the reviewer for this question. Our contact map analysis indeed shows significant H1-H2 linker\ninteractions.\nComparing our two systems shows that linker interactions appear more prominent in the membrane-\nbound LacYPop simulation due to constrained geometry that brings linker regions into close proximity,\nwhile the solution-mediated condensate simulation shows fewer linker contacts with mainly hydrophobic\nresidues of the linker interacting. This demonstrates that the high linker contacts arise from geometric\nconstraints (antiparallel interactions of PopTags) rather than residue-specific interactions.\nWe have clarified this in the revised text to acknowledge the contributions of the H1-H2 linker region to\nthe contact map.\n4. The authors mention that “PopTags mediate interactions between otherwise non-associating LacY\nproteins”, but simulated LacY proteins did cluster (Fig S3).\nWe thank the reviewer for this comment. During extended simulations performed for revision, we\nobserved more LacY clustering than expected, which lead us to revisit our simulation setup. We identified\nthat the inward-facing conformation predicted by AlphaFold3 (the most abundant state in the PDB)\ninduced local membrane curvature that promoted non-specific protein association. Since opening of the\nperiplasmic cavity is the rate-limiting step in LacY transport, the outward-facing conformation represents\nthe more prevalent protein conformation (https://doi.org/10.1073/pnas.1112157108). We therefore\nrepeated all simulations with structural restraints derived from the outward-facing structure (PDB: 6VBG)\napplied to the AlphaFold3 models (see revised methods).\nWith the corrected outward-facing conformation, LacY proteins exhibit substantially reduced clustering.\nWhile some transient, dynamic clustering is observed, this is expected at the high protein densities used\nin our simulations and does not lead to formation of large stable aggregates. In contrast, LacYPop forms\nstill form large clusters in our updated model, demonstrating that PopTag-mediated interactions drive the\nobserved membrane reorganization. All results in the manuscript have been updated using these\nimproved simulations.\n5. In Figure S6 can the authors quantify protein level by gel densitometry, to support the argument that\nthe different construct activities in Fig 4 are not due to different expression levels?\nWe redid the experiments with in-gel fluorescence of LacYmEos and LacYmEos-Pop fusion proteins and using\ncalibration curves to quantify the levels of LacY. See also our response to comment 7 of reviewer 1. The\nnew data are presented in lines 267 - 272 and new Supplemental figure S6.\n6. Significance signs are missing in Figure S8.\nWe added the significance signs to the supplementary Figure S9 (previously Figure S8).\n7. In Figure S10 the monomer band does not appear to be ~145 kDa, based on the ladder.\nmRuby fluorescent protein remains folded in SDS-PAA gels, as we observe its fluorescence signal. Hence,\nthe electrophoretic mobility of LacZ-mRuby fusions is higher than predicted from their molecular weight.\nWe clarify this point in the legend of Figure S11 (previously Figure S10).\n8. A typo: “Having characterized the structure and interactions of LacZPop membrane condensates\nexperimentally and computationally” I think the authors meant to write LacYPop.\nWe thank the reviewer for catching this typo. We have corrected \"LacZPop\" to \"LacYPop\"."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I believe that most of my earlier questions have been addressed and I appreciate the authors’ clarifications. I will leave it to the editor to evaluate the significance of this work. Moreover, I still have several concerns:\nIn the FRAP experiments, different bleaching conditions were used (different microscopes, objectives, bleaching schemes (1× vs 30× at 100%), different readout intensities and time resolutions). Could the authors clarify why these parameters differed?\nAdditionally, the comparison between fluorescence recovery at the polar and lateral membranes is an important aspect of the authors’ interpretation. However, the Methods section does not describe whether the same ROI size, bleaching area, and laser power were used for both regions. This is important for the repeatbility of this work.", "reviewer_2": "Thanks to the authors for thoroughly addressing most of the points raised in the initial review. However, the contact analysis for determining the protein clusters remains weak. The authors should assess the dependence of the cluster/contact analysis on their criterion. One shared residue is a very soft criterion for determining clusters. Also, this should be compared to KT in the simulation. Additionally the residues have different sizes, so a global cutoff of 15A for determining contacts is rather crude. Instead this cutoff should be residue-size dependent.", "reviewer_3": "The authors have improved the manuscript with additional quantification, improved simulations, and expanded text and methodological details. The manuscript is suitable for publication.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "REVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nI believe that most of my earlier questions have been addressed and I appreciate\nthe authors’ clarifications. I will leave it to the editor to evaluate the significance of\nthis work. Moreover, I still have several concerns:\nIn the FRAP experiments, different bleaching conditions were used (different\nmicroscopes, objectives, bleaching schemes (1× vs 30× at 100%), different\nreadout intensities and time resolutions). Could the authors clarify why these\nparameters differed?\nAdditionally, the comparison between fluorescence recovery at the polar and\nlateral membranes is an important aspect of the authors’ interpretation. However,\nthe Methods section does not describe whether the same ROI size, bleaching area,\nand laser power were used for both regions. This is important for the repeatbility of\nthis work.\nWe used two different microscopes because the Zeiss LSM 710 microscope was\nbroken after the measurements with LacYmEos were completed. After that, we\nmeasured FRAP with LacYmEos-Pop on a newly purchased Leica Stellaris 8. The\nreadout intensities were adjusted to achieve similar image quality with the two\ndifferent setups.\nOur experiments show that condensated LacYmEos-Pop and non-condensated\nLacYmEos have very different localization and mechanical properties and thus\nrequire different parameters for optimal FRAP recordings. Therefore, we adjusted\nthe number of bleaching steps and the time-steps in the recovery measurements\ndepending on the sample. For LacYmEos, the fluorescence recovery occurred within\nseconds, and therefore, shorter bleaching was necessary to avoid the loss of signal\nin the entire cell, and shorter time-steps were used to follow the recovery. In\ncontrast, for LacYmEos-Pop, the content exchange between polar condensates was\nslow; near-complete bleaching of one cell pole required 30 bleaching steps but did\nnot lead to much loss of fluorescence at the opposite cell pole, and the recovery\nhappened only on the timescale of minutes (see Supplementary Figure S1).\nBecause of these dramatic differences in protein behaviour, recovery could not be\nreasonably measured at the same settings for LacYmEos and LacYmEos-Pop.\nWe had used either 3% or 10% of readout laser power for the bleaching of polar\nLacYmEos-Pop in several measurements. All measurements with LacYmEos-Pop were\nperformed at 10% laser power, therefore we decided to remove the data measured\nat 3% and, instead, perform additional experiments with 10% laser intensity.\nBecause of the new measurements, our estimates of the extent of recovery and\nhalf-time of recovery changed slightly, from 13 % and 122 sec in the previous\nversion of the manuscript, to 14 % and 134 sec in the revised version. To prove\nthat changes in readout laser intensity do not significantly affect the measured\nrecovery curves, we compared the data for polar LacYmEos-Pop measured with 3%\nand 10% readout laser power (see Fig. R1). Indeed, the fitting parameters for the\n1\nrecovery curves measured at two different laser powers are not statistically\ndifferent (extra sum-of-squares F test, p-value = 0.4 for extents of recovery and\n0.99 for half-times of recovery).\nFigure R1. Fluorescence recovery profiles of LacYmEos-Pop at the poles of E. coli\ncells, measured at 3% (n=23) and 10% (n=20) of readout laser intensity. Data are\npresented as mean ± SEM, and recovery curves were fitted using an exponential\nplateau equation.\nWe extended the description of the FRAP measurements in the Methods\nsection (lines 559 – 592; pages 23 and 24). New data are presented in SFig.2\nand SFig.9.\nReviewer #2 (Remarks to the Author):\nThanks to the authors for thoroughly addressing most of the points raised in the\ninitial review. However, the contact analysis for determining the protein clusters\nremains weak. The authors should assess the dependence of the cluster/contact\nanalysis on their criterion. One shared residue is a very soft criterion for\ndetermining clusters. Also, this should be compared to KT in the simulation.\nAdditionally the residues have different sizes, so a global cutoff of 15A for\ndetermining contacts is rather crude. Instead this cutoff should be residue-size\ndependent.\nWe thank the reviewer for this constructive feedback on improving the robustness\nof our contact analysis. To address the concern about using a global cutoff, we\nvalidated our approach by comparing contact detection using all beads per residue\n(cutoff 1.5 nm) versus only backbone beads (cutoff 1.0 nm). While the different\n2\ncutoffs detect slightly different absolute numbers of contacts, the clustering\nbehaviour and trends remain consistent between both methods (SFig. S5, panel\na). We reduced the cutoff when including all residue beads to account for the\noverall size change, yet both approaches yield the same conclusions. This\nindicates that for the analysis of the large-scale clustering, the specific detailed\nshape of individual residues is not critical.\nTo assess whether contacts represent meaningful interactions beyond thermal\nfluctuations, we analyzed contact lifetimes as a direct measure of interaction\nstability (SFig. S5, panel b). The contact lifetime distributions show a multi-\nexponential decay rather than simple exponential kinetics. LacY and LacYPop show\nbroadly similar lifetime distributions, though LacYPop exhibits slightly more long-\nlived contacts while LacY has more medium-duration contacts. The microsecond\ntimescales of these contacts demonstrate they persist well beyond thermal\nfluctuation timescales and represent meaningful protein associations rather than\ntransient collisions.\nWe also tested the sensitivity of our clustering analysis by systematically varying\nthe minimum number of residue-residue contacts required to define protein-protein\ninteractions (2, 3, 4, and 5 residues). Analysis shows that only 7% of the formed\nprotein-protein contacts involve fewer than 5 residues, indicating that most protein\npairs in clusters interact through multiple residues. Applying stricter contact\nthresholds slightly varies individual cluster sizes but preserves all the key trends in\nour analysis, confirming that our conclusions are robust to the choice of contact\ncriterion.\nThese validation analyses confirm that our contact criterion reliably captures the\nfundamental differences in clustering behaviour between LacY and LacYPop. Our\nfocus is on comparing the large-scale clustering patterns observed for these\nmembrane proteins, where we see that LacY forms smaller clusters while LacYPop\nforms larger ones.\n3"}, {"round": 3, "reviewer_comments": {"reviewer_1": "All my concerns have been solved. I haven't further question.", "reviewer_2": "The authors have satisfactorily addressed all concerns raised in the review. I am pleased to recommend this work for publication.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter