Chain-Length-Dependent Partitioning of 1-Alkanols in Raft-Like Lipid Membranes

TL;DR

This study tackles the molecular basis of the chain-length–dependent anesthesia cutoff by simulating a raft-like lipid bilayer composed of DPPC, DOPC, and cholesterol. Using all-atom MD across alkanols with chain lengths $n=2$ to $16$, the authors show a pronounced partitioning shift at a cutoff $n_{cutoff}=12$: short-chain alkanols preferentially localize in DOPC-rich $l_d$ domains and soften the membrane, while long-chain alkanols accumulate in DPPC-rich $l_o$ domains with diminished mechanical perturbations. The work connects domain-selective partitioning to changes in the lateral pressure profile, area compressibility, and bending rigidity, providing a membrane-centric mechanism for the anesthetic cutoff and a framework that complements protein-centric explanations. The findings underscore the role of membrane phase behavior and elasticity in modulating anesthetic efficacy and offer predictive insight into how chain length governs membrane-mediated activity.

Abstract

Although 1-alkanols are widely used as anesthetics and membrane-active agents, the molecular basis of their chain-length-dependent cutoff behavior remains unclear. Here, we perform extensive atomistic molecular dynamics simulations to investigate the partitioning of 1-alkanols with varying chain lengths in a raft-like lipid bilayer composed of dipalmitoylphosphatidylcholine (DPPC), dioleoylphosphatidylcholine (DOPC), and cholesterol (Chol), which exhibits coexistence of liquid-ordered ($l_o$) and liquid-disordered ($l_d$) domains. We observe pronounced lateral heterogeneity in alkanol distribution, membrane thickness, number density, and lateral pressure profiles across coexisting phases. A distinct cutoff chain length, $n_{cutoff}=12$, is identified: alkanols with $n<n_{cutoff}$ preferentially partition into DOPC-rich $l_d$ domains, whereas alkanols with $n \ge n_{cutoff}$ preferentially localize within DPPC- and cholesterol-rich $l_o$ domains. This chain-length-dependent redistribution is accompanied by systematic reductions in the lateral pressure profile, membrane compressibility, and bending rigidity of the bilayer. The results provide a detailed molecular characterization of how alkanol chain length modulates membrane structure and mechanical response in laterally heterogeneous lipid membranes.

Figures (10)

• Figure 1: Lateral pressure profile and its moments for the composite membrane in the absence (upper panels) and presence (lower panels) of 1-hexadecanol. The pressure profile, surface tension ($\gamma$), first moment (product of bending rigidity $\kappa$ and spontaneous curvature $C_0$), and second moment (Gaussian curvature modulus $\kappa_G$) are calculated using a cylindrical coordinate representation, with the z-axis normal to the bilayer plane. Panels (a) and (e) show the spatial heterogeneity of the lateral pressure profile for the composite membrane without and with 1-hexadecanol, respectively, illustrating a reduction in pressure upon alkanol incorporation. The radial dependence of $\gamma$ is shown in panels (b) and (f), that of $\kappa C_0$ in panels (c) and (g), and that of $\kappa_G$ in panels (d) and (h), for the upper leaflet, lower leaflet, and the full bilayer. The corresponding radial averages of $\kappa C_0$ and $\kappa_G$ are summarized in Table-I, indicating a reduction in membrane rigidity in the presence of alkanols. Mechanistically, alkanol incorporation redistributes lateral stresses across the bilayer, leading to membrane softening through reduced elastic moduli.
• Figure 2: Density profiles of the headgroup and tail of 1-alkanols in the composite bilayer membrane composed of DOPC, DPPC, and cholesterol. The z-coordinate distributions of the 1-alkanol headgroup (–OH) and tail (acyl chain, $-(CH2)_{n-1} -CH_3$, with chain length $n$) are computed from the last $500$ ns of four independent $2000$ ns ($2\,\mu s$) simulations for each system. Panels (a) and (b) show the density profiles of the headgroups and tails, respectively, from which it is evident that 1-alkanols penetrate progressively deeper into the membrane with increasing chain length. Therefore, deeper insertion of longer-chain alkanols enhances coupling to the bilayer core, leading to redistribution of lateral stresses and a reduction in membrane elastic moduli.
• Figure 3: Representative side and top views of the final configurations ($t=2000$ ns) of symmetric composite bilayer membranes composed of DOPC (gray), DPPC (red), cholesterol (green), and water (cyan): (a) membrane without 1-alkanol; membranes containing (b) 1-Ethanol (blue), (c) 1-Pentanol (pink), (d) 1-Octanol (yellow), (e) 1-Decanol (orange), (f) 1-Dodecanol (black), (g) 1-Tetradecanol (dark orange), and (h) 1-Hexadecanol (purple). Thus, side views reveal progressively deeper membrane penetration with increasing alkanol chain length, while top views show domain-selective clustering of short-chain alkanols in DOPC-rich $l_d$ regions and long-chain alkanols in DPPC-rich $l_o$ regions.
• Figure 4: Clustering of 1-alkanols in the composite bilayer membrane, identified using the Voronoi tessellation technique for varying acyl chain lengths. Black circles denote individual 1-alkanol molecules. Cluster analysis is performed using the last $500$ ns of each trajectory ($5000$ frames) from four independent $2000$ ns simulations per system. For each frame, the (x,y) coordinates of the center of mass (COM) of all 1-alkanol molecules are extracted. The analysis proceeds as follows: (i) spatial distributions of the COMs at a representative time ($t=2000$ ns) are constructed using Voronoi tessellation (first column); (ii) Voronoi diagrams are generated, with distinct colors indicating individual Voronoi cells (second column); (iii) local number density is computed as the inverse of each Voronoi cell area, and cells with densities exceeding the mean density are selected (third column); and (iv) high-density cells are rendered in white, while low-density regions are shaded in black, yielding binary cluster maps (fourth column). High-resolution ($\sim 1024 p$ ) binary images are analyzed in MATLAB using the bwlabel function to quantify the area of polygonal white regions corresponding to individual 1-alkanol clusters. Briefly, the emergence of larger, more compact clusters with increasing chain length reflects enhanced membrane insertion and lateral pressure reduction, providing a membrane-mediated basis for the anesthetic cutoff phenomenon.
• Figure 5: Area of clustering, $A_{cluster}$, of 1-alkanols in the composite bilayer membrane, quantified using Voronoi tessellation–based cluster analysis. In this approach, clusters are defined as contiguous regions formed by Voronoi cells whose local number density exceeds the mean alkanol density, and $A_{cluster}$ is calculated as the total area of connected high-density Voronoi regions. The results demonstrate domain formation of 1-alkanols in all membranes, with short-chain alkanols preferentially clustering in the DOPC-rich liquid-disordered ($_d$) domains and long-chain alkanols aggregating in the DPPC-rich liquid-ordered ($l_o$) domains. Therefore, the growth and relocation of $A_{cluster}$ with chain length correlate with alkanol-induced reductions in lateral pressure and membrane elasticity, supporting a membrane-mediated origin of the anesthetic cutoff phenomenon.