The lipid droplet (LD) is a fat-storing organelle, surrounded by numerous coat proteins and a phospholipid (PL) monolayer (Thiam et al., 2013; Walther et al., 2017). LDs store excess metabolic energy as highly reduced carbon triacylglycerol (TG) and can mobilize fatty acids for energy generation or membrane biosynthesis (Ducharme and Bickel, 2008; Walther and Farese, 2012). Due to their key role in metabolism, failure of LD biogenesis leads to metabolic diseases, such as lipodystrophy. Additionally, overwhelming the capacity of cells to form LDs is thought to be crucial for the development of diseases linked to obesity, such as fatty liver disease (Greenberg et al., 2011).

Current models of LD biogenesis posit that lipid droplet assembly complexes (LDACs) in the endoplasmic reticulum (ER) bilayer determine LD formation sites and facilitate LD growth (Arlt et al., 2022; Chung et al., 2019; Prasanna et al., 2021). LDACs, consisting of seipin and lipid droplet assembly factor 1 (LDAF1) in humans, or seipin/Fld1, Ldb16, and Ldo in yeast, efficiently catalyze the initial stages of LD formation (Chung et al., 2019; Klug et al., 2021; Teixeira et al., 2018; Wang et al., 2014). The absence of seipin, effectively removing LDAF1 as well (Chung et al., 2019), changes LD number and morphology dramatically, resulting in aggregated, small LDs and/or few supersized LDs (Fei et al., 2008; Salo et al., 2016; Szymanski et al., 2007; Wang et al., 2016). Therefore, investigating how seipin works is key to understanding LD biogenesis.

Human seipin is an undecamer, forming a ring structure in the ER. Each subunit contains a lumenal domain, flanked by two transmembrane (TM) segments and short cytoplasmic tails (Arlt et al., 2022; Chung et al., 2019; Klug et al., 2021; Lundin et al., 2006; Sui et al., 2018; Yan et al., 2018). The lumenal domain has a conserved hydrophobic helix (HH) thought to insert into the lumenal leaflet of the ER membrane. It was suggested that the HH of human seipin, and in particular S165 and S166, is a key tethering site for TG (Prasanna et al., 2021; Zoni et al., 2021b) and might provide a binding site of LDAF1 (Chung et al., 2019). Yeast seipin lacks the HH, which may explain why it is not sufficient for function in LD formation (Arlt et al., 2022; Klug et al., 2021; Wang et al., 2014). A recent study on yeast seipin suggests its binding partner, Ldb16, provides several serine and threonine residues in the PL tail region, thereby working as a replacement of the conserved HH of seipin (Klug et al., 2021).

In this study, we capitalized on new information on seipin TM segments to investigate their roles in TG nucleation and LD maturation using all-atom (AA) and coarse-grained (CG) molecular dynamics (MD) simulations. We discover a cage-like geometry of seipin TM segments facilitates a conversion of a planar oil lens into a unique ER-LD neck structure. Using cell experiments and CG simulations, we provide evidence that conserved, positively charged residues at the end of seipin’s TM segments are critical for LD growth.

Seipin TM segments are thought to be critical for seipin functions (Chung et al., 2019). The resolved structures, however, do not include TM segments likely because of their flexibility (Chung et al., 2019; Yan et al., 2018). Therefore, we modeled seipin structure including the TM segments with the residues ranging from Arg23 to Arg265 (Figure 1) based on a yeast seipin structural model (Arlt et al., 2022; Klug et al., 2021) and our cryoelectron microscopy data of purified human seipin that partially resolved the TM segments (Figure 1—figure supplement 1; see Methods). The locations and orientations of TM segments in the cryoelectron data (Figure 1—figure supplement 1) were used in modeling the TM segments. However, due to the low resolution, we could not identify the residues in the TM segments. Therefore, the TM segments of the seipin model present here are subject to further validation. Next, we compared the residues of the TM segments of the resolved yeast seipin model (Arlt et al., 2022; Klug et al., 2021) and our human seipin model (Figure 1c). While the aromatic hydrophobic residues of the TM segments face the other subunits (outward) in the human seipin model, the nonaromatic hydrophobic residues of the TM segments are in those positions in the yeast seipin model.

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How each part of seipin functions in LD biogenesis is not clearly known. To analyze the interactions between protein residues and lipids, we performed the AA simulation of human seipin in a 3-palmitoyl-2-oleoyl-D-glycero-1-phosphatidylcholine (POPC) bilayer containing 6% triolein for 3 µs. To study protein–lipid interactions, we reduced the resolution of the AA simulation by molecularly grouping each lipid or protein residue as illustrated in Figure 2a, b. We then calculated the normalized coordination number by molecule or the coordination number per molecule ($\left|\left|s\right|\right|$). This quantity, thought of as the concentration-independent coordination number, indicates how much each protein residue prefers interaction with PL or TG (see Methods and Figure 2c). The HH exhibited a narrow spike, indicating preferential aggregation with TG (Figure 2d). S166 had the largest value with TG in the analysis, consistent with other computational studies using CG simulations with the Shinoda–DeVane–Klein (SDK) or MARTINI force fields (Prasanna et al., 2021; Zoni et al., 2021b). Although the modified parameters of TG have a reduced charge distribution to reproduce the interfacial tension against water (Kim and Voth, 2021), the TG glycerol moiety can form hydrophilic interactions with protein residues in our AA simulation (e.g., S166). In contrast, the N- and C-terminal TM segments have weaker but broader interactions with TG. Because N- and C-terminal TM segments, and HH, have helical structures, the attraction map had a weakly defined periodicity (Figure 2d). For instance, V163, S166, and F170 faced the membrane center, increasing the accessibility of TG. In contrast, F164 and L168 faced the lumenal interface, which prevented interactions with TG.

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We further compared protein residues’ attractions to TG glycerol groups or TG tail groups by normalizing the coordination number by the number of grouped atoms. While the coordination number per molecule ($\left|\left|s\right|\right|$) indicates a propensity of each protein residue for each lipid type, PL or TG (Figure 2c, d), the coordination number per grouped atom ($\left|\left|s_A\right|\right|$) provides a propensity for each group type, in this case, a TG glycerol group or TG tail group (Figure 2—figure supplement 1). As expected, S166 had a strong interaction with TG glycerol groups as they form a hydrophilic interaction (Figure 2—figure supplement 1). The alignment of the insertion depths of S166 and TG glycerol moiety likely amplified the interaction. W257, which can form a hydrophilic interaction with TG glycerol groups, had a high value as well (Figure 2—figure supplement 1). However, we note that those results were normalized by the number of CG atoms. If we compare the coordination number of TG glycerol groups and that of TG tail groups, TG tail groups will mostly have a higher value because there are 12 hydrophobic tail groups and 1 glycerol group for each TG molecule at the reduced resolution (Figure 2a). Therefore, while hydrophilic interactions at the hydrophobic phase are significant, the largest driving force of TG nucleation inside the seipin ring is provided by hydrophobic interactions of TG with seipin HH and N- and C-terminal TM segments.

To understand how seipin influences the dynamics of lipids, we computed the position-dependent diffusion coefficient relative to the center of the mass of the lumenal domain (Figure 2—figure supplement 2). While all lipids had comparable diffusion coefficients in the protein-free region (7.5–10.0 nm away from the seipin center), diffusion became slower near the TM segments and HH due to interactions with the protein. The decreased rate of diffusion coefficient is correlated with the contact area of protein. For instance, lumenal PLs up to 7 nm from the seipin center had the lowest diffusion coefficients because of the HH and switch region in the lumenal leaflet. In contrast, the cytosolic leaflet only contained the N- and C-terminal TM segments at the seipin boundary, leading to higher diffusion coefficients. The diffusion coefficients for TG were between those of cytosolic and lumenal PLs because TG molecules close to the lumenal leaflet can interact with the HH. In addition, strong attractions of TG with protein residues can further reduce the rate of diffusion (Figure 2d). We note that the slower diffusion of the lipids that are near the protein have been discussed in previous papers (Javanainen et al., 2017; Niemelä et al., 2010). The mean squared displacement of the lumenal PLs trapped inside the seipin ring, referred to as proteinized PLs, leveled off at later simulation times due to confinement (Figure 2—figure supplement 3). Such confinement can increase the bending modulus of this area (Schachter et al., 2020), thereby working as a rigid base to ensure the direction of LD budding to the cytosolic side along with the rigid lumenal domain of seipin.

LD biogenesis is a microscopic/mesoscopic process, having its time and length scales beyond those accessible by present day AA MD simulations. For instance, during our 3-µs-long AA MD simulation we observed recruitment of TG inside the seipin complex, but not TG nucleation. To access the relevant time and length scales, we instead developed CG lipid and seipin models (Figure 3a–c). Linear, four-site models were used for lipids (Grime and Madsen, 2019; Kim et al., 2022b). Every four protein residues were linearly mapped to one CG atom to match the resolution with CG lipids. We also placed 24 PL molecules inside the HH ring with the orientation consistent with other PL molecules in the lumenal leaflet and considered these as a part of protein. This is based on the AA MD simulation that demonstrated PLs inside the HH ring are trapped. We constructed an elastic network model (ENM) (Haliloglu et al., 1997) by connecting a pair of seipin CG atoms via an effective harmonic bond with a spring constant (sc) of 0.2 kcal/mol/Ų if the distance is less than 1.5 nm (Figure 3b). Such a choice was made to best reproduce the fluctuations from AA MD simulation data. To understand the impact of the stiffness of harmonic bonds, we also made a model with a sc of 2 kcal/mol/Ų. To achieve the known stability of seipin in a bilayer membrane, nonbonded protein–lipid interactions were based on the lipid–lipid interactions. Attraction scaling factors that change the force and potential depth linearly are shown in Figure 3c (Kim et al., 2022b). Although it is not obvious how to quantitatively incorporate the AA MD simulation data into phenomenological CG models, higher scaling factors between TG-HH and TG-TM can be qualitatively justified by the high attractions of those pairs indicated in the analysis of the AA MD simulation (Figure 2d).

Figure 3

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Seipin is thought to catalyze TG nucleation, thereby decreasing the critical concentration (Chung et al., 2019). To test this hypothesis, we performed the CG simulations of spherical bilayers with a diameter of 40 nm. The radius of the spherical bilayer is comparable that of the actual ER tubule (Georgiades et al., 2017). The initial structures had evenly distributed 2 mol% TG molecules. Because the TG concentration is below the critical concentration for its phase transition (Hamilton and Small, 1981; Khandelia et al., 2010; Zoni et al., 2021a), TG nucleation did not occur in the lipid system (Figure 3d). In contrast, the system that includes the seipin complex showed a nucleated TG lens inside the complex due to the attractions between TG-TM and TG-HH (Figure 3d). As a control simulation, we included a single seipin subunit in the lipid system and carried out the CG MD simulation. TG nucleation did not happen in the system (Figure 3d), indicating that a single subunit falls well short of TG nucleation. Therefore, high protein density at the seipin site provides for collective and cooperative interactions with TG, catalyzing TG nucleation. To support our findings of seipin’s role in facilitating TG nucleation, we also performed MARTINI CG model simulations in a bilayer, containing 2% TG (Figure 3e). While TG was dissolved in the reference lipid system without seipin, TG formed its distinct phase inside the seipin complex. Such seipin-driven TG nucleation was observed in the recent other computational studies using MARTINI or SDK CG force fields (Klug et al., 2021; Prasanna et al., 2021; Zoni et al., 2021b). Given that these studies used different approaches to model the TM segments but came to the same conclusion, it is expected that the lumenal domain of seipin, especially the HH, is critical in TG nucleation, while the locations and orientations of the TM segments contribute less to TG nucleation. Interestingly, yeast seipin that lacks the HH could not facilitate TG nucleation in the simulations (Klug et al., 2021), consistent with the experimental observations in which yeast seipin alone is not functional (Wang et al., 2014).

Exploiting the computationally highly efficient nature of our low-resolution CG models, we also investigated LD maturation. In particular, to study the impact of the cage-like structure of the seipin oligomer and their TM segments in LD biogenesis, we simulated various geometries of seipin in the spherical bilayers containing 6% TG (Figure 4). In the first model, we removed the TM segments, and the resulting model only contained the lumenal domain. In the second model, we removed six continuous subunits from the seipin oligomer, and the resulting model contained five subunits. As a reference, we also carried out simulations of the pure lipid system and the 11mer-containing system. Because the TG concentration was above the critical concentration, TG nucleation occurred in those systems even in the system without seipin. However, the resulting morphologies of oil lenses of those systems were different as shown in the final snapshots and characterized by anisotropy. First, in the lipid system, a nucleated TG lens was flat and had high anisotropy to minimize the membrane deformation penalty (Kim et al., 2022b). In contrast, in the 11-subunit model, a nucleated TG lens was located on top of the seipin lumenal domain, surrounded by seipin TM segments. This resulted in a significant change in the shape of the oil lens from high anisotropy in the lipid system, minimizing the membrane deformation penalty, to low anisotropy in the seipin-containing systems. Given the nucleation percentages were comparable in those simulations, a change in anisotropy can be attributed to the seipin TM, not to the amount of nucleated TG molecules. Importantly, the seipin TM segments constrained the XY area where TG can be in the bilayer, pushing excessive TG molecules to the budding LD. This results in the formation of the ER-LD neck structure, consistent with an experimental tomogram (Salo et al., 2019). An equilibrated diameter of the ER-LD structure (Figure 4—figure supplement 1), approximated by a diameter of the circle formed by the end residues of N- and C-terminal TM segments, also agreed well with the experimentally measured data, which is in the range of 13–17 nm (Salo et al., 2019). We note the TM segments tilted away from the oligomeric center during LD growth; therefore, the diameter of the seipin ring increased with simulation time. We also simulated the 11-subunit model with a spring constant of 2.0 kcal/mol/Ų (Figure 4—figure supplement 2). The higher spring constant reduced the diameter of the ER-LD neck structure. However, the nucleation percentage and morphology of the formed oil lens had little difference with the previous system that contained the ENM with a spring constant of 0.2 kcal/mol/Ų.

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The 5-subunit model can be considered a mixture of the lipid system and 11-subunit model because one end is occupied with seipin subunits while the other end is exposed to lipids. The resulting morphology of an oil lens from the CG MD simulations was also between those results. The TG oil lens was elongated to the region where there was no seipin subunit; however, it was constrained in the region of seipin subunits, especially by their TM segments. The equilibrated anisotropy was also between the lipid and 11-subunits systems. Finally, we simulated the seipin model that only contained the lumenal domain. Such a complex does not form a mobile focus in cells likely because it fails to form an oligomer, it is not stable in bilayers, or it is degraded (Chung et al., 2019). However, simulating this system can provide further valuable insight on the roles of the TM segments. The resulting oil lens showed little difference from the lipid system without seipin. The anisotropy was high, and the formation of the ER-LD neck structure was abolished.

It is worth noting that the mechanisms of oil growth can be predicted from analysis of the nucleation percentage and anisotropy. A sharp increase in the nucleation percentage indicates oil coalescence, as shown in ~70 M MD time steps in the 5-subunit system and ~100 M MD time steps in the 11-subunit system (Figure 4). In contrast, the nucleation percentage grew slowly in the seipin model without the TM segments from ~200 M MD time steps, indicating Ostwald ripening. Finally, a sudden increase followed by a sharp decrease in anisotropy in the 11-subunit system implies a slow coalescence. When any two TG molecules in distinct oil lenses are within 2 nm, those oil lenses are considered one oil lens, as shown by a step increase in the nucleation percentage at 100 M MD time steps (Figure 4). Such a snapshot resembles two humps, increasing anisotropy. After ~110 M MD time steps, the oil lenses are fully merged into one spherical lens, reducing anisotropy to zero. Controlled coalescence at the seipin site will be discussed later.

To investigate the more advanced biogenesis steps, we simulated a larger spherical bilayer with a diameter of 60 nm and 6% TG. We also constructed a hENM model (Lyman et al., 2008) using the fluctuations obtained from the AA simulation of seipin in the bilayer membrane (Figure 5a). The calculated root-mean-square fluctuations (RMSF) from the AA and CG simulations agreed well in bilayers (Figure 5b). Consistent with the previous results, the seipin TM segments defined the oil boundary, facilitating the transport of TG into the LD (Figure 5c). The equilibrated anisotropy was close to zero, indicating a spherical shape of the forming oil lens (Figure 5d). Collectively, our tests demonstrate that the ring geometry of seipin TM segments is key to defining the boundary of the forming oil lens and creating the unique ER-LD structure.

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The coevolutionary sequence analysis (Hopf et al., 2019) of human seipin indicated that seipin’s lumenal domain and two TM segments are evolutionarily conserved (Cartwright and Goodman, 2012; Figure 6—figure supplement 1). In contrast, the N- and C-terminal tail regions, exposed to the cytosol, are not conserved (Figure 6—figure supplement 1). To test whether the nonconserved, cytosolic tails are important for LD formation, we experimentally constructed seipin-ΔTERM, which lacks the N-terminal region (1–22 amino acids) and C-terminal region (268–398 amino acids). First, we confirmed that seipin-ΔTERM has the same membrane topology with wildtype protein (Figure 6a) and assembles discrete seipin foci in the ER. We next tested whether seipin-ΔTERM is functional by examining its ability to rescue the LD phenotypes in seipin knockout (KO) SUM159 cell line we previously reported (Wang et al., 2016). Consistent with the previous experiments (Chung et al., 2019; Fei et al., 2008; Salo et al., 2016; Szymanski et al., 2007; Wang et al., 2016), seipin KO cells had massive accumulation of small nascent LDs at the early time (~1 hr) of LD formation after oleate treatment (Figure 6b, c). Seipin-ΔTERM rescued the defective LD phenotype of seipin KO cells (Figure 6b, c), indicating that the cytosolic nonconserved N-/C-terminal regions of human seipin are dispensable for the seipin function. This is comparable with previous experiments that demonstrated the removal of N- or C-terminal region of fly seipin in Drosophila S2 cells did not change the rescue efficiency of the seipin deletion phenotype (Wang et al., 2016).

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We also investigated if the positively charged residues located at the beginning of the N-terminal TM segment (R23, R24, and R26) and the end of the C-terminal TM segment (R265, H266, and R267) are essential for the seipin function. Those charged residues located at the borders of the TM segments are conserved (Figure 6—figure supplement 1), and in our CG trajectories, they were exposed to the outer leaflet of the membrane (Figures 4 and 5c). To test the importance of those residues, we experimentally made the two mutant seipin constructs, seipin-FL-tipA and seipin-ΔTERM-tipA, in which their arginine and histidine residues at the ends of seipin’s TM segments were mutated to alanine. Despite the absence of charged residues, those seipin constructs are correctly inserted into the ER bilayer and stably formed foci as same as wildtype seipin (Figure 6a). However, seipin tipA mutants were not fully functional and resulted in an intermediate LD phenotype with more and irregular shaped LDs compared to wild-type cells (Figure 6b, c).

We further hypothesized that the reduced attraction between the borders of seipin’s TM segments and the membrane interface caused defective LD maturation in the cells that contained the mutant constructs. To investigate this possibility, we carried out CG simulations with variable attraction scaling factors (r) between the residues at the membrane interface and PL interfacial and head groups (Figure 6d). When r is small, it could be thought of as the mutant construct that does not have the charged residues at the ends of TM segments (seipin-ΔTERM-tipA). The resulting CG simulations demonstrated that those residues no longer interacted with the interface of the cytosolic leaflet (Figure 6d). Instead, the whole TM segments were immersed in the forming oil lens, leading to the destruction of the ER-LD neck structure. In particular, the residues at the end of TM segments lost contact with the outer membrane during oil coalescence when a seipin-free oil lens approached the seipin ring and merged with the oil lens contained within it. The loss of the ER-LD neck structure resulted in a flat oil lens, as shown in our CG system that did not include seipin or that had seipin without TM segments (Figure 4). In contrast, the ER-LD neck structure was maintained during oil coalescence when r was large, as in the previous CG simulations. TG molecules in a seipin-free oil lens were transferred to the seipin in a controlled manner without disrupting the ER-LD neck structure. Collectively, the cell experiments and CG simulations suggest that the conserved, charged residues located at the borders of seipin TM segments maintain the ER-LD neck structure during oil coalescence and promote LD growth.

Seipin is a critical protein that orchestrates LD formation (Arlt et al., 2022; Chung et al., 2019; Klug et al., 2021; Prasanna et al., 2021; Salo et al., 2020; Salo et al., 2019; Wang et al., 2016; Zoni et al., 2021b). Recent joint computational and experimental studies reported that the HH of seipin facilitates TG nucleation (Klug et al., 2021; Prasanna et al., 2021; Zoni et al., 2021b). However, little is known about the roles of seipin TM segments despite their experimentally confirmed importance for seipin’s function (Arlt et al., 2022; Chung et al., 2019). Capitalizing on highly CG models of lipids and seipin, we investigate LD maturation, showing that a cage-like geometry of seipin TM segments forms a constricted neck, converting a planar oil lens into a unique ER-LD, facilitating LD growth. In contrast, the system that lacked seipin or contained seipin without TM segments resulted in a flat oil lens with high anisotropy.

Using cell-based experiments and CG simulations, we further identified certain essential and dispensable parts of human seipin. We show that the nonconserved N- and C-terminal cytosolic regions of human seipin are not required for LD formation. Therefore, truncated seipin models used in the current and previous computational studies are reasonable due to the dispensability of the cytosolic tails (Klug et al., 2021; Prasanna et al., 2021; Zoni et al., 2021b). We also provide evidence that the conserved, positively charged residues located at the borders of the seipin TM segments are crucial for LD maturation. Mutating those residues to alanine resulted in an intermediate LD phenotype with more and nonuniform shaped LDs than those formed in wild-type cells. However, these mutations did not alter seipin’s membrane topology. In the CG simulations, seipin TM segments were immersed in an oil lens when interactions between those residues and PL interfacial and head groups became reduced.

Based on our data, we propose a model in which the positively charged residues, located at the borders of seipin TM segments, anchor the TM segments at the cytosolic side of the membrane (Figure 7). This was particularly important in maintaining the ER-LD neck structure during oil coalescence. When a seipin-free oil lens approaches a seipin-positioned oil lens, the TM segments should keep their positions in the bilayer to maintain the ER-LD neck structure. Strong electrostatic interactions between the seipin residues at the borders of seipin TM segments and the membrane interface of the cytosolic leaflet inhibited rapid coalescence. Instead, TG in the seipin-free lens was transported to the seipin-positioned oil lens in a controlled manner. A slow coalescence at the seipin site is implicated in anisotropy analysis in Figure 4. If the interactions between the seipin TM tip residues and the cytosolic leaflet are small, the seipin TM segments become immersed in the lens during swift coalescence, affecting LD maturation (Figure 6d).

Figure 7

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We investigated protein–lipid interactions and lipid dynamics using the AA simulation. Hydrophobic interactions between seipin TM segments and HH with TG are the main driving force of TG nucleation, in conjunction with hydrophilic interactions between the TG glycerol moiety and protein residues. It is worth noting that different force fields consistently report a strong attraction between S166 and TG glycerol moieties (Prasanna et al., 2021; Zoni et al., 2021b). During LD growth, lipids or proteins such as LiveDrop migrate from the ER onto LD through the populated TM region (Olarte et al., 2020; Wang et al., 2016). The diameter of the ER-LD neck structure during the LD growth phase is larger than during the initial nucleation stage as demonstrated in the CG simulations. The widely spread TM segments will promote lipid and protein influx to LDs.

Finally, we discuss the limitations of our study. First, LDAF1 is not included in our simulations as its structure and number in the seipin oligomer are not identified yet. LDAF1 is predicted to have a double hairpin structure, which increases the density of TM segments inside the seipin oligomer. Therefore, including LDAF1 in simulations will likely change protein–lipid interactions in the system and lipid diffusion. Second, the CG simulations benefited from highly CG models to simulate large systems and study advanced LD biogenesis steps. However, seipin sequence-specific features are missing in the seipin model. Also, since this is a phenomenological model, the quantities calculated are not directly related to the underlying AA systems.

Taken together, we suggest a model in which seipin TM segments are key for LD biogenesis by assisting TG nucleation, controlling lipid diffusion, defining the boundary of the forming oil lens, maintaining the ER-LD neck structure, and controlling oil coalescence. Our study thus provides a broader and deeper understanding of the roles of the TM segments, critical for seipin function.

Numerical data represented as a graph in this manuscript are available at https://github.com/ksy141/seipin.

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Author details

1. Siyoung Kim

   Pritzker School of Molecular Engineering, University of Chicago, Chicago, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4670-9747

2. Jeeyun Chung

   1. Department of Molecular Metabolism, Harvard T H Chan School of Public Health, Boston, United States
   2. Department of Cell Biology, Harvard Medical School, Boston, United States

   Contribution

   Formal analysis, Investigation, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4617-7181

3. Henning Arlt

   1. Department of Molecular Metabolism, Harvard T H Chan School of Public Health, Boston, United States
   2. Department of Cell Biology, Harvard Medical School, Boston, United States
   3. Howard Hughes Medical Institute, Harvard T H Chan School of Public Health, Boston, United States

   Contribution

   Resources, Writing - review and editing

   Competing interests

   No competing interests declared

4. Alexander J Pak

   Department of Chemical and Biological Engineering, Colorado School of Mines, Golden, United States

   Contribution

   Resources, Writing - review and editing

   Competing interests

   No competing interests declared

5. Robert V Farese Jnr

   1. Department of Molecular Metabolism, Harvard T H Chan School of Public Health, Boston, United States
   2. Department of Cell Biology, Harvard Medical School, Boston, United States
   3. Broad Institute of Harvard and MIT, Cambridge, United States

   Contribution

   Funding acquisition, Investigation, Supervision, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8103-2239

6. Tobias C Walther

   1. Department of Molecular Metabolism, Harvard T H Chan School of Public Health, Boston, United States
   2. Department of Cell Biology, Harvard Medical School, Boston, United States
   3. Howard Hughes Medical Institute, Harvard T H Chan School of Public Health, Boston, United States
   4. Broad Institute of Harvard and MIT, Cambridge, United States

   Contribution

   Funding acquisition, Investigation, Supervision, Writing - review and editing

   Competing interests

   No competing interests declared

7. Gregory A Voth

   Department of Chemistry, Chicago Center for Theoretical Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, United States

   Contribution

   Conceptualization, Funding acquisition, Investigation, Supervision, Writing - review and editing

   For correspondence

   gavoth@uchicago.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3267-6748

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