Simulations of Lipid Droplets and Cell Membranes Showing How They Fuse

To prevent payloads such as RNA from degrading in highly acidic environments, LNPs must escape before the endosome matures into the lysosome [12]. LNPs are considered feasible for endosomal escape via membrane fusion not only because they have cell-like structures but also due to the interaction between positively charged lipids within the LNP and the anionic characteristics of the endosomal membrane [32]. However, the molecular mechanism governing the endosomal escape of LNPs remains poorly understood. Therefore, we focused on the membrane fusion process to gain a deeper understanding of this mechanism at the molecular level.

To explore the endosomal escape mechanism of LNPs with ILs, we first designed LNDs using LNP-mimicking lipid mixtures with ILs in a CG representation, and separately designed endosomal membranes. We then employed CG MD simulations to uncover the molecular-level dynamics occurring during the fusion of LNDs with endosomal membranes. We constructed the LNDs using two distinct ILs: MC3 and ALC-0315. The lipid compositions were designed to mimic those of the Pfizer-BioNTech mRNA vaccine [33] and a benchmark MC3-LNP-siRNA formulation [16]. The ILs, 1,2-distearoyl-sn-glycero-3-phosphocholine (DSPC), and cholesterol (CHOL) were used to construct the LNDs (Figure 2).

Additionally, to aid in understanding a realistic LNP endosomal escape mechanism, we designed LNDs and endosomal membranes that reflect critical characteristics such as pH changes and compositional changes in the endosomal membrane during endosomal maturation along the endosomal and lysosomal maturation pathway. To account for changes in the fraction of protonated ILs within LNDs due to the pH variations inside the endosome, we designed 11 types of LNDs for both ALC-0315-containing LNDs and MC3-containing LNDs, incrementing the fraction of protonated ILs from 0% to 100% in increments of 10%. Detailed methods of LNDs design are described in. Section 3.1

Next, we designed the endosomal membranes by mimicking the in vivo composition [34], varying the ratios of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE), and CHOL to create early endosomal membrane (EEM) and late endosomal membrane (LEM). Conversely, reflecting the complete composition of endosomal membranes is challenging due to the lack of precise knowledge about the asymmetry in composition between the inner and outer leaflets of the lipid bilayer and its variation with endosomal stage or cell type. Thus, we designed endosomal membranes that reflect the anionic characteristics while simplifying the membrane into a form commonly used in simulation studies [24]. This involved using 80 mol% POPC as the major phospholipid and 20 mol% of the anionic lipid 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-L-serine (POPS) to simplify the endosomal membrane. We define this simplified endosomal membrane as the simple lipid bilayer (SLB) and will refer to this throughout the paper. The endosomal membranes used in this study are all in the form of lipid bilayers. We chose this form because surrounding endosomes with LNDs would be computationally costly, given their large size, and our goal is to understand the interactions between LNDs and immediately adjacent parts of the endosomal membrane upon fusion. Additionally, other computational studies investigating the interactions between bio-membranes and LNDs also designed cellular membranes as lipid bilayers [23]. The detailed method for designing endosomal membranes is described in Section 3.2.

In the previous section, we created a total of 22 LNDs by combining both ALC-0315-containing and MC3-containing LNDs with three types of endosomal membranes, namely EEM, LEM, and SLB, thereby producing the LND–endosomal membrane systems. Using the known pH ranges of early endosomes (6.8–5.9) [35] and late endosomes (5.5–5.0) [36], along with the apparent pK_a values of ALC-0315 (6.09) [37] and MC3 (6.44) [38], we calculated the protonated ratios of ALC-0315 and MC3 within LNDs in each endosomal environment using the Henderson–Hasselbalch equation. Consequently, the protonated ratio of ALC-0315 was found to be 16.3–60.6% in early endosomes and 79.5–92.5% in late endosomes. Similarly, MC3 exhibited a protonated ratio of 30.4–77.6% in early endosomes and 89.7–96.5% in late endosomes. For the protonated cases of LNDs, designed in 10% increments, LNDs with a protonated ratio of ALC-0315 of 0–70% were combined with EEM, and those with a ratio of 70–100% were coupled with LEM, resulting in 12 LND–endosomal membrane complexes. For the case where ALC-0315 had a 70% protonated ratio, the uncertainty surrounding its clear assignment to EEM or LEM led to its inclusion in both designs. Similarly, LNDs with a protonated ratio of MC3 of 0–80% were coupled with EEM, while 90–100% were associated with LEM, resulting in 11 complexes. Additionally, combinations of ALC-0315 and MC3 protonated ratios of 0–100% with SLB resulted in a total of 22 LND–endosomal membrane complexes. On all 45 complexes, we performed 135 unbiased CG MD simulations for 2000 ns in three replicas. Using the resulting simulation trajectories, we analyzed the structural changes in lipids during the fusion process between LNDs and endosomal membranes from multiple perspectives (Figure 3). This analysis allowed us to uncover the characteristics and roles of lipids and elucidate the fusion process between LNDs and endosomal membranes. Although these simulations provided meaningful results, the conventional CG MD approach used in this study employs fixed protonation states for ILs. Consequently, it cannot capture the protonation changes that may occur due to the local lipid environment [39], such as during the fusion of LNDs with endosomal membranes. To address this limitation, constant-pH MD simulations or other adaptive protonation approaches, which allow protonation states to respond to the surrounding environment, could offer a more accurate description of lipid dynamics during the fusion process [40,41]. Despite their high computational cost and the challenges associated with achieving convergence in lipid systems with pH-dependent structural transitions and force field sensitivity [42], such methods may reveal additional mechanistic details in future studies.

Based on the well-known role of ILs in promoting RNA endosomal escape [13,14,15,16], we hypothesized that ILs initiate fusion by strongly binding to the endosomal membrane as LNPs begin to interact with it. Our first goal was to illustrate this from a structural perspective at the molecular level. To achieve this, we analyzed 135 CG MD simulation trajectories, each lasting 2000 ns, from the previous section. This analysis aimed to capture the moment when LNDs start to fuse with the endosomal membrane and to identify the lipids that first penetrate the endosomal membrane during this process. To pinpoint the onset of fusion, we calculated the average z-coordinate of PO4 CG beads from POPC molecules in the upper leaflet of the lipid bilayer corresponding to the endosomal membrane. We then determined the difference (ΔZ) between the lowest z-coordinate among CG beads within the LNDs (h_L) and the calculated upper leaflet's average z-coordinate (h_u) (Figure 4A). Fusion was identified as starting when ΔZ shifted from positive to negative and maintained a negative value for at least 1 ns thereafter (Figure 4B). The onset of fusion varied from 1.4 to 1458 ns across different systems (Figure 4C). In 117 out of 135 systems (86.7%), the residue in the LND showing the smallest ΔZ was identified as IL. In the remaining 18 systems (13.3%), 14 systems had IL as the residue with the second smallest ΔZ just before fusion occurred, while in the last 4 systems, IL was the third smallest ΔZ residue. Although the IL exhibited the second or third smallest ΔZ among the 18 systems, visual inspection using VMD [43] confirmed that the residues with the smallest ΔZ began fusing with the endosomal membrane nearly simultaneously. Consequently, our simulation results indicate that in nearly all instances, IL is the first component to enter the membrane when LNDs begin to fuse with the endosomal membrane, playing a significant role in the fusion process. This suggests that the pH-dependent charge of ILs within LNDs might facilitate strong electrostatic interactions with endosomal membranes, thereby overcoming the hydration repulsion barrier, which is theorized to prevent spontaneous lipid membrane fusion [44]. Unlike natural bio-membrane components, the inclusion of ILs in LNDs appears to aid this process.

Once LNDs have fused with the endosomal membrane, lipids can exhibit dynamics such as rotation, lateral diffusion, and flip-flop [45]. For LNDs to successfully escape the endosomal membrane, fusion must occur not only with the inner leaflet but also with the outer leaflet of the endosomal membrane. Thus, we hypothesized that flip-flopping, which involves lipid exchange between the two leaflets of the bilayer (Figure 5), would be crucial. Furthermore, given that ILs play a pivotal role in LNP endosomal escape, we considered that the flip-flop process of ILs is particularly important for the LNDs' endosomal escape mechanism.

To address this, we analyzed 135 sets of 2000 ns CG MD trajectories to calculate the population of ILs located in the outer leaflet of the membrane following the fusion of LNDs with the endosomal membrane. For precise analysis, snapshots of the LND–endosomal membrane complex were taken at 200 ps intervals from the CG MD trajectories, specifically within the 1500–2000 ns range where complete fusion was determined for all systems. The position of ILs in the endosomal membrane was based on the location of the terminal beads of the head groups of ALC-0315 and MC3, which were used as ILs. The terminal CG beads for the head groups of ALC-0315 and MC3 are OH and NC3, respectively (Figure 6A). These beads were defined as belonging to the inner leaflet if their minimum distance to any PO4 bead of a POPC molecule forming the surface of the inner leaflet was less than or equal to 1.2 nm. Similarly, they were defined as belonging to the outer leaflet if their minimum distance to any PO4 bead of a POPC molecule forming the surface of the outer leaflet was less than or equal to 1.2 nm. Beads that did not belong to either the inner or outer leaflets were identified as being in the midplane (Figure 6B). Using the population of head group beads located on the outer leaflet surface, we were able to determine the flip-flop rate of ILs. This is supported by two observations. First, ILs containing head groups positioned on either the inner or outer leaflet surfaces were aligned nearly parallel to the normal vector of the lipid bilayer (Figure 6C). Second, when the movement of ILs with head groups eventually found on the outer leaflet surface was tracked over the 0–2000 ns CG MD trajectories using VMD [43], ILs were visually observed to flip-flop only once from the inner leaflet to the outer leaflet. Additionally, ILs with head groups located in the midplane were confirmed to exist in a disordered state (Figure 6D).

Utilizing the previously described method, we calculated the migration rates of protonated and deprotonated ILs to the outer leaflet and midplane across 45 distinct LND–endosomal membrane systems. With three replicas for each system, we computed the average and standard deviation to determine these rates. To further assess the robustness of our simulations, we examined the migration rates obtained from the three replicas and confirmed that they exhibit consistent trends across systems (Tables S1–S4). In addition, time-evolution plots of fusion onset (ΔZ) and IL migration fractions for a representative system are provided in Figure S2, illustrating the temporal behavior leading to the post-fusion regime. The 45 systems were designed based on the type of ILs, the percentage of protonated ILs within LNDs, and the specific type of endosomal membrane. In systems where the proportion of protonated ALC-0315 varied from 0 to 100% in increments of 10%, and paired with either EEMs or LEMs, we found that a significant fraction of protonated ALC-0315, ranging from 0.29 to 0.44, migrated to the outer leaflet. This rate was markedly higher than that of deprotonated ALC-0315, which moved to the outer leaflet at a rate of 0.02 to 0.11 (Table 1). In contrast, deprotonated ALC-0315 predominantly migrated to the midplane, with rates ranging from 0.65 to 0.97, compared to 0.10 to 0.48 for protonated ALC-0315. Most deprotonated ALC-0315 remained centrally within the endosomal membrane, exhibiting minimal movement to the inner or outer leaflets (Table 1). These findings highlight that, regardless of the protonated state of LNDs, the flip-flop of ILs plays a crucial role in membrane fusion for LNP endosomal escape, with protonated ILs significantly contributing to the flip-flop process.

Similarly, in scenarios where the endosomal membrane is SLB, the dynamics resemble those observed with EEMs or LEMs. Protonated ALC-0315 demonstrates a high migration rate to the outer leaflet, ranging from 0.44 to 0.59, while showing a lower migration rate to the midplane, between 0.06 and 0.17. Conversely, deprotonated ALC-0315 exhibits a lower migration rate to the outer leaflet, ranging from 0.11 to 0.34, and a higher rate to the midplane, between 0.47 and 0.76 (Table 2). This indicates that, irrespective of the endosomal membrane's composition, ILs actively undergo flip-flop, with protonated ILs playing a crucial role in this process.

However, when the endosomal membrane transitions from EEM or LEM to SLB, the behavior of protonated ALC-0315 shows a notable shift. The migration rate to the outer leaflet increases from 0.29–0.44 to 0.44–0.59 across all protonated cases (Figure 7A). Conversely, the rate of migration to the midplane decreases from 0.10–0.48 to 0.06–0.17 across all protonated instances (Figure 7B). For deprotonated ALC-0315, the migration rate to the outer leaflet rises from 0.02–0.11 to 0.11–0.34 in all protonated cases (Figure 7C). Simultaneously, the rate to the midplane declines from 0.65–0.97 to 0.47–0.76, exhibiting a similar trend to that seen with protonated ALC-0315 (Figure 7D).

These differences are likely attributed to variations in endosomal membrane composition. Specifically, SLB lacks cholesterol, whereas EEM contains approximately 36%, and LEM contains about 18%. As the cholesterol content in the membrane increases, the migration of both protonated and deprotonated ILs to the outer leaflet tends to decrease (Figure 7A,C), while their migration to the midplane increases (Figure 7B,D). Considering the established fact that cholesterol interacts with other lipids in the cellular membrane to promote clustering and enhance lipid lateral packing [46,47,48,49], it is inferred that increased cholesterol levels may inhibit the flip-flop of ILs and elevate their clustering in the midplane. Moreover, during the fusion of LNDs with the endosomal membrane, the cholesterol within LNDs may also influence the dynamics of ILs. Therefore, adjusting the composition of LNDs based on the endosomal membrane's composition appears important. Although our endosomal models do not explicitly include sphingomyelin (SM) or bis(monoacylglycerol)phosphate (BMP), incorporating these lipids would likely reinforce the trends observed in our simulations across SLB, EEM, and LEM. SM is relatively enriched in EEM, where its strong interaction with cholesterol promotes lateral packing and reduces membrane fluidity [50,51]. In contrast, BMP is uniquely localized to the inner leaflet of late endosomes and lysosomes and is known to enhance the activity of lipid transfer proteins such as saposins and Niemann–Pick disease type C2, thereby facilitating cholesterol mobilization and extraction [50,51]. These properties are consistent with the lower cholesterol content and higher membrane fluidity observed in LEM. Therefore, even when SM and BMP are considered, the simulation-derived conclusion that reduced cholesterol content increases IL flip-flop would remain unchanged. Furthermore, BMP is a negatively charged, cone-shaped phospholipid restricted to the inner leaflet of late endosomes [52]. Owing to these structural and electrostatic features, BMP-rich membranes are thought to facilitate membrane fusion [53]. Consequently, in BMP-rich and highly protonated late endosomal environments, the flip-flop of protonated ILs would be expected to increase.

MC3-containing LNDs exhibited the same trends observed with ALC-0315-containing LNDs. Regardless of the protonated state of LNDs and the composition of the endosomal membrane, protonated MC3 had a higher migration rate to the membrane's outer leaflet and a lower rate to the midplane. Conversely, deprotonated MC3 showed a lower migration rate to the outer leaflet and a higher rate to the midplane (Tables S5 and S6). Additionally, when the endosomal membrane transitions from EEM or LEM to SBL, both protonated and deprotonated MC3 generally increased their migration rate to the outer leaflet while decreasing their rate to the midplane (Figure 8). Specifically, for protonated MC3, the migration rate to the outer leaflet was 0.35–0.55 with EEM or LEM, and 0.31–0.58 with SLB, increasing in all protonated cases except the 10% protonated case. In contrast, the rate at which protonated MC3 occupied the midplane decreased from 0.07–0.29 with EEM or LEM to 0.05–0.06 with SLB across all protonated cases (Figure 8, Tables S5 and S6). Similarly, deprotonated MC3's migration rate to the outer leaflet increased from 0.01–0.12 with EEMs or LEMs to 0.13–0.19 with SLB in all protonated cases. Conversely, the rate at which deprotonated MC3 occupied the midplane decreased from 0.79–0.97 with EEM or LEM to 0.71–0.75 with SLB across all protonated cases (Figure 8, Tables S5 and S6).

Namely, MC3-containing LNDs exhibit the same trends as those observed with ALC-0315-containing LNDs. However, it was noted that, under identical protonated conditions and even at the same pH levels, protonated MC3 tends to show a higher proportion in the outer leaflet compared to protonated ALC-0315 (Figure 9). Figure 9 presents the population ratios of protonated ALC-0315 and protonated MC3 in the outer leaflet of the endosomal membrane during the fusion of ALC-0315-containing and MC3-containing LNDs with EEM or LEM. These ratios are shown according to the protonated case and pH of the LNDs. Except for cases where the fraction of ILs in the LNDs is 80% or 100%, the number of protonated MC3 molecules in the outer leaflet was greater than that of ALC-0315 (Figure 9A). Additionally, by determining the pH corresponding to specific protonated cases for both ALC-0315-containing and MC3-containing LNDs (Section 3.5), and comparing LNDs with similar pH levels, it was found that the proportion of protonated MC3 migrating to the outer leaflet was higher than that of protonated ALC-0315 across all pH environments (Figure 9B). For deprotonated ILs, there is minimal difference in the ratios of deprotonated ALC-0315 and deprotonated MC3 migrating to the outer leaflet across all protonated cases of LNDs, with both exhibiting low migration rates (Figure S3). Thus, considering the overall IL population, the number of MC3 molecules reaching the outer leaflet is greater than that of ALC-0315.

We hypothesized that a higher number of ILs flopping to the outer leaflet would facilitate more efficient membrane fusion during endosomal escape for LNDs. Consequently, we anticipated that MC3-containing LNDs, which have a higher proportion of ILs in the outer leaflet, would exhibit greater endosomal escape efficiency compared to ALC-0315-containing LNDs. This aligns with experimental results showing that MC3-containing LNPs have higher endosomal escape efficiency than ALC-0315-containing LNPs in mRNA delivery studies, with MC3 achieving 5% cytosolic delivery compared to 4% for ALC-0315 [54]. Although the numerical difference is modest, endosomal escape is widely regarded as a major bottleneck in intracellular drug delivery, with typically fewer than 5% of nucleic acid cargo reaching the cytosol. Therefore, even a 1% difference can represent a biologically meaningful distinction. While our current study does not address membrane perturbation energy directly, future computational studies that map the free-energy landscape of lipid insertion or quantitatively estimate fusion rates could provide a deeper mechanistic understanding of LNP–membrane interactions and endosomal escape.

In the previous section, we calculated the positions of ILs in the membrane following the fusion of LNDs with the endosomal membrane, revealing that deprotonated ILs predominantly reside in the midplane. However, the degree of clustering among these lipids remained unclear, leading us to conduct a clustering analysis on both protonated and deprotonated ILs. To achieve this, radial distribution functions (RDFs) were calculated for each type of IL. The analysis was performed using snapshots taken at 200 ps intervals from the 135 CG MD trajectories of LND–endosomal membrane complexes, specifically within the 1500–2000 ns range. The RDFs were computed using only the NC3 CG beads of the ILs' head groups, utilizing GROMACS 2024 [55]. The results revealed that deprotonated ILs tend to cluster closely in all systems, whereas protonated ILs are generally more dispersed. Figure 10 depicts the RDFs for protonated and deprotonated ALC-0315 in a system where LNDs and EEM are complexed with 50% protonated ALC-0315. Despite having equal numbers of deprotonated and protonated ALC-0315 in the membrane, deprotonated ALC-0315 showed an RDF peak at 0.52 nm with a maximum value of 43.6, while protonated ALC-0315 had a peak at 0.86 nm with a maximum value of 8.9. This indicates that deprotonated ALC-0315 clusters closely, whereas protonated ALC-0315 does not. The same trend was observed across the remaining 134 systems, even when deprotonated ALC-0315 was markedly less abundant (Figure S4).

We further analyzed the maintenance of cluster size in LNDs during fusion with the endosomal membrane, focusing on the presence of protonated and deprotonated ILs within the largest clusters. This analysis utilized homemade code integrated with MDAanalysis [56,57], assuming that molecules are clustered if the minimum distance between them is 1.2 nm or less. Analyzing the maximum clustering size from snapshots extracted at 200 ps intervals from 135 sets of 2000 ns CG MD trajectories, we observed that initially, the number of lipids forming LNDs was around 100, but decreased after fusion with the endosomal membrane (Figure 11). As the proportion of protonated ILs in LNDs increased, the maximum cluster size reduced. Notably, while the number of deprotonated ILs in the max cluster remained stable over time, the number of protonated ILs decreased significantly. For instance, LNDs with 10% protonated ALC-0315 contained 45 deprotonated ALC-0315, matching the count of protonated ALC-0315 in LNDs with 90% protonation. Over time, the average number of deprotonated ALC-0315 in the max cluster of LNDs with 10% protonation was 20, whereas the average number of protonated ALC-0315 in the max cluster of LNDs with 90% protonation was merely 5, indicating poor clustering maintenance by protonated ALC-0315 (Figure 11A,B). Similarly, in LNDs with 50% protonated ALC-0315, both protonated and deprotonated ALC-0315 initially numbered 25 each, but differed in retention within the max cluster after fusion with the endosomal membrane (Figure 11C). MC3-containing LNDs yielded similar results to those observed with ALC-0315-containing LNDs (Figure 11D–F). Based on previous findings that deprotonated ILs tend to cluster while protonated ILs disperse, and deprotonated ILs predominantly occupy the membrane's midplane whereas protonated ILs are located near the surface, we conclude that deprotonated ILs help maintain LND shape by clustering in the midplane, while protonated ILs detach and spread near the membrane surface. Our simulation results are consistent with earlier studies using cryo-TEM, SAXS, NMR, and MD simulations, which reported that neutral ionizable aminolipids reside between the two membrane leaflets, while protonated lipids remain at the bilayer surface [58,59]. Furthermore, cryo-TEM analyses of Lipid-5-containing LNPs have revealed a single lamellar region surrounding an amorphous core [60], and MD simulations have captured the formation of amorphous droplets composed of neutral Lipid-5 within the hydrophobic core [61]. Together, these experimental and computational observations support our finding that deprotonated ILs preferentially cluster in the hydrophobic midplane region of the membrane.

To assess the ordering of IL lipid tails during LND fusion with the endosomal membrane, we calculated the second-rank order parameter, P₂, for IL tails. P₂ is defined as ⟨(1/2)(3 cos²θ − 1)⟩, where θ is the angle between the vector formed by two beads of the lipid tails and the normal vector of the lipid bilayer. P₂ value of 1 indicates that lipid tails are perfectly parallel to the bilayer's normal vector, 0 indicates a completely disordered orientation, and negative values suggest that the tails are perpendicular to the bilayer's normal vector. Using snapshots taken every 200 ps from the 135 CG MD trajectories within the 1500–2000 ns range, we calculated the bond order parameter for consecutive tail bonds and the tail order parameter for non-consecutive bead vectors of ALC-0315 and MC3 tails, combining these into a segmental order parameter. As illustrated in Figure 12, in a system where LNDs with 50% protonated ALC-0315 fuse with EEM, the segmental order parameters for beads composing the sn1 and sn2 tails of ALC-0315 are nearly identical due to their same chemical structure (Figure 12A). Across all segments, protonated ALC-0315 tails exhibited higher segmental order parameters than deprotonated tails. Notably, deprotonated ALC-0315 segments showed values close to 0, indicating a relatively disordered arrangement compared to protonated segments (Figure 12B). For segments T1–GL1 (T6–GL2), T2–T3 (T7–T8), T4–T5 (T9–T10), the segmental order parameters were 0.16 (0.16), 0.09 (0.09), and 0.09 (0.09), respectively (Figure 12B). When the endosomal membrane was SLB, segments T1–GL1 (T6–GL2), T2–T3 (T7–T8), T4–T5 (T9–T10) had order parameters of 0.25 (0.25), 0.12 (0.13), and 0.12 (0.12), respectively (Figure 12C), indicating lower values for ALC-0315 tails in EEM compared to SLB. Similar trends were observed for MC3 (Figure 12D), where protonated MC3 segments showed higher segmental order parameters than deprotonated segments, which had values close to 0, indicating a highly disordered orientation (Figure 12E,F). Additionally, when comparing hydrophilic head-adjacent segments CA1–CA2 (CB1–CB2), the segmental order parameter was 0.24 (0.24) in EEM and increased to 0.29 (0.29) in SLB, showing a 0.5 increase in SLB for MC3 tails (Figure 12E,F). Comparing protonated ALC-0315's tail order parameter to protonated MC3's, protonated ALC-0315's T1–T3 (T6–T8) segments had order parameters of 0.15 (0.15) in EEM (Figure 12B) and 0.22 (0.22) in SLB (Figure 12C), whereas protonated MC3's CA1–CA5 (CB1–CB5) segments were 0.24 (0.24) in EEM (Figure 12E) and 0.28 (0.28) in SLB (Figure 12F), indicating MC3's tail order parameter was higher than that of ALC-0315.

Across all CG MD trajectories, we consistently found that protonated ILs have larger segmental order parameter values and are more ordered compared to deprotonated ILs, which tend to have values near 0, indicating a more disordered arrangement. Additionally, when the endosomal membrane composition is EEM or LEM, the segmental order parameters for ILs are lower than those observed in SLB. This suggests that in SLB, where the POPC ratio is higher and the cholesterol ratio is lower, IL tails achieve better packing and exhibit more order than in EEM or LEM. Furthermore, the tail order parameter for MC3 is greater than for ALC-0315, suggesting that MC3 achieves better lipid packing and order compared to ALC-0315. We also calculated the segmental order parameter for POPC, the most abundant lipid in the endosomal membrane, to assess differences in packing and order influenced by IL types. When LNDs with 50% protonated ILs fused with EEM, the segmental order parameter values for POPC tails, sn1 and sn2, were higher in the presence of MC3 compared to ALC-0315 (Figure 13A,B). This result was consistent when LNDs with 50% protonated ILs fused with SLB (Figure 13C). These results suggest that ALC-0315 contributes to membrane fusion by slightly increasing membrane disorder and fluidity compared to MC3. However, this difference is minor, and MC3 is known to have higher endosomal escape efficiency than ALC-0315 in in vitro studies [54]. Thus, the variations in disorder and fluidity between ALC-0315 and MC3 do not significantly impact the trend of MC3's superior endosomal escape efficiency. Instead, these findings highlight the critical role of the flip-flop process in the endosomal escape mechanism. Additionally, regardless of the IL type used, the segmental order parameter for POPC's saturated chain (sn1) was higher than for the unsaturated chain (sn2), indicating more regular packing in saturated chains (Figure 13B,C). This observation aligns with previously established knowledge [62].

We designed two distinct types of LNDs utilizing ALC-0315 and MC3, which are ILs employed in FDA-approved LNP-formulated RNA medicines. All LNDs consisted of 100 lipids with a molar ratio of IL:CHOL:DSPC set to 50:40:10. In fact, the actual formulation for RNA vaccines and therapeutics uses a molar ratio of IL:CHOL:DSPC:PEGylated lipid at 50:38.5:10:1.5 [16,33]. However, due to the small proportion of PEGylated lipids potentially introducing artifacts, they were excluded, simplifying the molar ratio to IL:CHOL:DSPC at 50:40:10 for constructing LNDs. For each type of IL, we designed 11 types of LNDs by varying the proportion of protonated ILs from 0 to 50 in increments of 5. The initial coarse-grained structures of CHOL and DSPC were obtained using the INSert membrane tool (https://github.com/Tsjerk/Insane.git↗ (accessed on 11 April 2024)) [29,63], and CG MD simulations were performed using the MARTINI 3 force field [27]. However, since the CG force field (CG FF) parameters for protonated and deprotonated ALC-0315 and MC3 are not available in the Martini version, we first performed AA MD simulations using known AA force fields (AA FF). Simultaneously, the CGbuilder tool [64] was employed to convert the AA structures of ILs into corresponding CG representations (Figure S5). Using exploratory CG FF parameters for ILs, we performed CG MD simulations. Subsequently, the structural information obtained from AA MD simulation trajectories was used as a reference to fit the structural data derived from CG MD simulation trajectories, allowing us to determine optimal CG FF parameters [65] (Figures S6–S8). The initial structures and AA FF parameters for protonated and deprotonated ALC-0315 were based on Petra Čechová et al., 2024 [23], while those for protonated and deprotonated MC3 were obtained using CHARMM-GUI (https://www.charmm-gui.org↗ (accessed on 21 September 2021)) [66,67,68,69]. Detailed procedures for obtaining the initial CG structures and CG FF parameters are provided in the Supporting Information under the section "CG force field parameterization for ALC-0315 and MC3." To strengthen the reliability and transferability of the newly developed CG parameters, we additionally performed a quantitative validation of the CG models against AA reference simulations. Specifically, we compared mass density, radius of gyration, and the root mean square deviation of RDFs (RMS Δg) between AA and CG simulations for both protonated and deprotonated ALC-0315 and MC3. All metrics showed AA–CG agreement with deviations within reasonable CG–AA accuracy ranges, and the detailed numerical values are provided in Tables S7–S9. After obtaining the initial CG structures and CG FF parameters for each lipid, we used GROMACS [55] tools to randomly place the lipids in a 12 × 12 × 12 nm³ box and solvate them with standard MARTINI water particles. Then, we neutralized the system and achieved a physiological concentration of 0.15 M by adding Na⁺ and Cl⁻ ions. Subsequently, all systems underwent energy minimization using GROMACS 2024 [55] for refinement. The energy minimization process was terminated when the maximum force convergence criterion reached below 100 kJ mol⁻¹ nm⁻¹. Following this, CG MD simulations were conducted under NPT conditions for 1700 ns to achieve equilibration, using a time step of 20 fs.

To simulate the early and late endosome membranes, we mimicked the composition found in vivo [34]. The early endosome membrane was designed with a composition of POPC, DOPE, and Cholesterol in the ratios of 43%, 21%, and 36%, respectively. For the late endosome membrane, the ratios of POPC, DOPE, and CHOL were adjusted to 65%, 17%, and 18%, respectively. Although SM and BMP are also present in endosome lipid compositions found in vivo, they were not included in our models to maintain a simplified and representative lipid composition based primarily on POPC, DOPE, and CHOL. Additionally, we designed a simplified endosomal membrane model reflecting the anionic characteristics of endosomes with a composition of 80% POPC and 20% POPS. All lipid bilayer systems were constructed using the INSert membrane tool [29,63]. Each system was built within a simulation box of 12 × 12 × 10 nm³, with lipids solvated using standard MARTINI water beads. The early endosome membrane model comprises 192 POPC, 94 DOPE, and 162 CHOL molecules, along with 6676 MARTINI water particles. The late endosome membrane model includes 292 POPC, 76 DOPE, and 80 CHOL molecules, solvated with 6508 water particles. The simple lipid bilayer system consists of 360 POPC and 90 POPS molecules, together with 6311 MARTINI water particles. To neutralize the system, 0.15 M NaCl was added to all systems. Subsequently, the systems underwent minimization until the maximum force convergence was less than 10 kJ mol⁻¹ nm⁻¹. Finally, to relax the solvent and lipid bilayer, the systems were equilibrated for 2200 ns under NPT conditions with semi-isotropic pressure coupling using 20 fs integration time steps.

Based on the 22 LND types constructed in the previous section and the three types of endosomal membranes, a total of 45 LND–endosomal membrane complexes were generated by combining each LND with the appropriate endosomal environment as described in Section 2.2. The LND was manually positioned above the endosomal membrane using VMD [43] such that the minimum distance between the two components was approximately 1 nm. To avoid artifacts arising from periodic boundary conditions along the membrane normal, simulation boxes were constructed such that the minimum distance between the bottom of the complex and the edge of the box was 10 Å, and the minimum distance between the top of the complex and the upper box boundary was at least 100 Å, allowing the complex to be solvated in standard MARTINI water particles. Specifically, complexes with EEM were placed in a simulation box with dimensions of 10.7 × 10.7 × 25.0 nm³, those with LEM in 11.5 × 11.5 × 25.0 nm³, and those with SLB in 12.2 × 12.2 × 25.0 nm³. Each system was neutralized by adding NaCl at a concentration of 0.15 M. Energy minimization was performed using the steepest descent method [70] until the maximum force was below 100 kJ mol⁻¹ nm⁻¹. For each of the 45 systems, three independent replicas were generated, resulting in a total of 135 systems. All systems were simulated for 2000 ns under NPT conditions with isotropic pressure coupling, using a time step of 20 fs.

All coarse-grained simulations were performed using GROMACS 2024 [55] with the Martini 3 force field [27], along with custom-developed CG parameters for the ILs. Periodic boundary conditions were applied in all three dimensions. The verlet cutoff scheme was used with a neighbor list update every 20 steps and a buffer tolerance of 0.005 nm. Non-bonded interactions were calculated using a 1.1 nm cutoff for both van der Waals and Coulombic interactions, employing potential-shift modifiers to ensure a smooth decay to zero at the cutoff distance. A relative dielectric constant of 15 was used to account for electrostatic screening in the Martini water model. The system temperature was maintained at 310 K using the velocity-rescaling thermostat [71] with a time constant of 1.0 ps. Pressure was controlled isotropically using the C-rescale barostat [72], with a reference pressure of 1 bar, a coupling time constant of 4.0 ps, and a compressibility of 4.5 × 10⁻⁵ bar⁻¹.

The pH environments of early and late endosomes are known to be 6.8–5.9 and 5.5–5.0, respectively. The apparent pK_a values for ALC-0315-containing LNPs and MC3-containing LNPs are 6.09 and 6.44, respectively. Using the Henderson–Hasselbalch equation, we calculated the range of protonated ratios for LNDs in each endosomal environment. The Henderson–Hasselbalch equation is defined as pH = pK_a + log([A⁻]/[HA]), where HA is the acid and A⁻ is the conjugate base; here, HA represents protonated IL and A⁻ represents deprotonated IL. Results indicated that the protonated ratio of ALC-0315 in LNDs ranged from 16.3% to 60.6% in early endosomes and from 79.5% to 92.5% in late endosomes. Similarly, MC3 in LNDs showed a protonated ratio of 30.4% to 77.6% in early endosomes and 89.7% to 96.5% in late endosomes. Furthermore, the pH values corresponding to each protonated case for ALC-0315-containing and MC3-containing LNPs were calculated using the Henderson–Hasselbalch equation (Table 3).