Analysis of Lipid Order States and Domains in Lipid Bilayer Simulations

We propose a general procedure to analyze lipid order states and domains in lipid bilayer simulations using surface areas and hydrophobic thicknesses of lipids. In our approach, the observable order states of individual lipids are inferred by a hidden Markov model analysis of their time series and by considering the deformation of a lipid in different packing environments. The assigned lipid order states are mapped onto the Voronoi tessellation of lipids, from which the ordered and disordered lipids are robustly clustered by the Getis–Ord local spatial autocorrelation statistics. The usefulness of this method is illustrated by its application to the quinary mixed bilayers consisting of cholesterol (Chol), 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC), 1,2-dimyristoyl-sn-glycero-3-phosphoethanolamine (DMPE), 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine (POPE), where any phospholipid type does not show strong preference over the other types to be enriched in lipid domains. The independent order state analysis for each lipid type allows straightforward applications of our method to arbitrarily complex bilayer simulations

Analysis of Lipid Order States and Domains in Lipid Bilayer Simulations

We propose a general procedure to analyze lipid order states and domains in lipid bilayer simulations using surface areas and hydrophobic thicknesses of lipids. In our approach, the observable order states of individual lipids are inferred by a hidden Markov model analysis of their time series and by considering the deformation of a lipid in different packing environments. The assigned lipid order states are mapped onto the Voronoi tessellation of lipids, from which the ordered and disordered lipids are robustly clustered by the Getis–Ord local spatial autocorrelation statistics. The usefulness of this method is illustrated by its application to the quinary mixed bilayers consisting of cholesterol (Chol), 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC), 1,2-dimyristoyl-sn-glycero-3-phosphoethanolamine (DMPE), 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine (POPE), where any phospholipid type does not show strong preference over the other types to be enriched in lipid domains. The independent order state analysis for each lipid type allows straightforward applications of our method to arbitrarily complex bilayer simulations

Analysis of Lipid Order States and Domains in Lipid Bilayer Simulations

We propose a general procedure to analyze lipid order states and domains in lipid bilayer simulations using surface areas and hydrophobic thicknesses of lipids. In our approach, the observable order states of individual lipids are inferred by a hidden Markov model analysis of their time series and by considering the deformation of a lipid in different packing environments. The assigned lipid order states are mapped onto the Voronoi tessellation of lipids, from which the ordered and disordered lipids are robustly clustered by the Getis–Ord local spatial autocorrelation statistics. The usefulness of this method is illustrated by its application to the quinary mixed bilayers consisting of cholesterol (Chol), 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC), 1,2-dimyristoyl-sn-glycero-3-phosphoethanolamine (DMPE), 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine (POPE), where any phospholipid type does not show strong preference over the other types to be enriched in lipid domains. The independent order state analysis for each lipid type allows straightforward applications of our method to arbitrarily complex bilayer simulations

Analysis of Lipid Order States and Domains in Lipid Bilayer Simulations

We propose a general procedure to analyze lipid order states and domains in lipid bilayer simulations using surface areas and hydrophobic thicknesses of lipids. In our approach, the observable order states of individual lipids are inferred by a hidden Markov model analysis of their time series and by considering the deformation of a lipid in different packing environments. The assigned lipid order states are mapped onto the Voronoi tessellation of lipids, from which the ordered and disordered lipids are robustly clustered by the Getis–Ord local spatial autocorrelation statistics. The usefulness of this method is illustrated by its application to the quinary mixed bilayers consisting of cholesterol (Chol), 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC), 1,2-dimyristoyl-sn-glycero-3-phosphoethanolamine (DMPE), 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine (POPE), where any phospholipid type does not show strong preference over the other types to be enriched in lipid domains. The independent order state analysis for each lipid type allows straightforward applications of our method to arbitrarily complex bilayer simulations

Theory of Adaptive Optimization for Umbrella Sampling

We present a theory of adaptive optimization for umbrella sampling. With the analytical bias force constant obtained from the constrained thermodynamic length along the reaction coordinate, the windows are distributed to optimize the overlap between neighbors. Combining with the replica exchange method, we propose a method of adaptive window exchange umbrella sampling. The efficiency gain in sampling by the present method originates from the optimal window distribution, in which windows are concentrated to the region where the free energy is steep, as well as consequently improved random walk

Role of Hydrogen Bonding and Helix−Lipid Interactions in Transmembrane Helix Association

To explore the role of hydrogen bonding and helix−lipid interactions in transmembrane helix association, we have calculated the potential of mean force (PMF) as a function of helix−helix distance between two pVNVV peptides, a transmembrane model peptide based on the GCN4 leucine-zipper, in a dimyristoylphosphatidylcholine (DMPC) membrane. The peptide name pVNVV represents the interfacial residues in the heptad repeat of the dimer. The free energy decomposition reveals that the total PMF consists of two competing contributions from helix−helix and helix−lipid interactions. The direct, favorable helix−helix interactions arise from the specific contribution from the helix-facing residues and the generic contribution from the lipid-facing residues. The Asn residues in the middle of the helices show the most significant per-residue contribution to the PMF with various hydrogen bonding patterns as a function of helix−helix distance. Release of lipid molecules between the helices into bulk lipid upon helix association makes the helix−lipid interaction enthalpically unfavorable but entropically favorable. Interestingly, the resulting unfavorable helix−lipid contribution to the PMF correlates well with the cavity volume between the helices. The calculated PMF with an Asn-to-Val mutant (pVNVV → pVVVV) shows a dramatic free energy change upon the mutation, such that the mutant appears not to form a stable dimer below a certain peptide concentration, which is in good agreement with available experimental data of a peptide with the same heptad repeat. A transmembrane helix association mechanism and its implications in membrane protein folding are also discussed

Quantitative Characterization of Cholesterol Partitioning between Binary Bilayers

We have devised a practical simulation protocol for quantitative characterization of cholesterol (Chol) partitioning between bilayers with different lipid types. The simulation model contains two patches of laterally contacting lipid bilayers, where the host lipids of each bilayer are allowed to self-adjust their packing. For two combinations of bilayers with different lipid types, 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC)/1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) and 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC), the simulation model has been verified by self-adjusted lipid packing in each bilayer, convergence of Chol partitioning between different Chol initial distributions, and relative diffusion coefficients consistent to those from experiments. The calculated Chol partition coefficient between POPC and DOPC bilayers from the Chol partitioning simulations in the POPC-DPPC and DOPC-DPPC binary bilayer systems shows an excellent agreement with that from available Chol exchange experiments between 1-stearoyl-2-oleoyl-sn-glycero-3-phosphocholine­(SOPC)/DOPC vesicles and β-cyclodextrins, which further validates the simulation protocol and illustrates its applicability to any molecular partitioning in the binary bilayer system

Analysis of Lipid Order States and Domains in Lipid Bilayer Simulations

We propose a general procedure to analyze lipid order states and domains in lipid bilayer simulations using surface areas and hydrophobic thicknesses of lipids. In our approach, the observable order states of individual lipids are inferred by a hidden Markov model analysis of their time series and by considering the deformation of a lipid in different packing environments. The assigned lipid order states are mapped onto the Voronoi tessellation of lipids, from which the ordered and disordered lipids are robustly clustered by the Getis–Ord local spatial autocorrelation statistics. The usefulness of this method is illustrated by its application to the quinary mixed bilayers consisting of cholesterol (Chol), 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC), 1,2-dimyristoyl-sn-glycero-3-phosphoethanolamine (DMPE), 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine (POPE), where any phospholipid type does not show strong preference over the other types to be enriched in lipid domains. The independent order state analysis for each lipid type allows straightforward applications of our method to arbitrarily complex bilayer simulations