Proper treatment of nonbonded interactions
is essential for the
accuracy of molecular dynamics (MD) simulations, especially in studies
of lipid bilayers. The use of the CHARMM36 force field (C36 FF) in
different MD simulation programs can result in disagreements with
published simulations performed with CHARMM due to differences in
the protocols used to treat the long-range and 1-4 nonbonded interactions.
In this study, we systematically test the use of the C36 lipid FF
in NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM. A wide range of
Lennard-Jones (LJ) cutoff schemes and integrator algorithms were tested
to find the optimal simulation protocol to best match bilayer properties
of six lipids with varying acyl chain saturation and head groups.
MD simulations of a 1,2-dipalmitoyl-sn-phosphatidylcholine
(DPPC) bilayer were used to obtain the optimal protocol for each program.
MD simulations with all programs were found to reasonably match the
DPPC bilayer properties (surface area per lipid, chain order parameters,
and area compressibility modulus) obtained using the standard protocol
used in CHARMM as well as from experiments. The optimal simulation
protocol was then applied to the other five lipid simulations and
resulted in excellent agreement between results from most simulation
programs as well as with experimental data. AMBER compared least favorably
with the expected membrane properties, which appears to be due to
its use of the hard-truncation in the LJ potential versus a force-based
switching function used to smooth the LJ potential as it approaches
the cutoff distance. The optimal simulation protocol for each program
has been implemented in CHARMM-GUI. This protocol is expected to be
applicable to the remainder of the additive C36 FF including the proteins,
nucleic acids, carbohydrates, and small molecules.
CHARMM-GUI Membrane Builder,
http://www.charmm-gui.org/input/membrane, is a web-based user interface designed to interactively build all-atom protein/membrane or membrane-only systems for molecular dynamics simulation through an automated optimized process. In this work, we describe the new features and major improvements in Membrane Builderthat allow users to robustly build realistic biological membrane systems, including (1) addition of new lipid types such as phosphoinositides, cardiolipin, sphingolipids, bacterial lipids, and ergosterol, yielding more than 180 lipid types, (2) enhanced building procedure for lipid packing around protein, (3) reliable algorithm to detect lipid tail penetration to ring structures and protein surface, (4) distance-based algorithm for faster initial ion displacement, (5) CHARMM inputs for P21 image transformation, and (6) NAMD equilibration and production inputs. The robustness of these new features is illustrated by building and simulating a membrane model of the polar and septal regions of E. coli membrane, which contains five lipid types: cardiolipin lipids with two types of acyl chains and phosphatidylethanolamine lipids with three types of acyl chains. It is our hope that CHARMM-GUI Membrane Builder becomes a useful tool for simulation studies to better understand the structure and dynamics of proteins and lipids in realistic biological membrane environments.
On-line consumer reviews, functioning both as informants and as recommenders, are important in making purchase decisions and for product sales. Their persuasive impact depends on both their quality and their quantity. This paper uses the elaboration likelihood model to explain how level of involvement with a product moderates these relationships. The study produces three major findings: (1) the quality of on-line reviews has a positive effect on consumers' purchasing intention, (2) purchasing intention increases as the number of reviews increases, and (3) low-involvement consumers are affected by the quantity rather than the quality of reviews, but high-involvement consumers are affected by review quantity mainly when the review quality is high. These findings have implications for on-line sellers in terms of how to manage on-line consumer reviews.KEY WORDS AND PHRASES: Consumer involvement, elaboration likelihood model, electronic word-of-mouth, on-line consumer review, on-line word of mouth, product review.
Glycolipids (such as glycoglycerolipids, glycosphingolipids, and glycosylphosphatidylinositol) and lipoglycans (such as lipopolysaccharides (LPS), lipooligosaccharides (LOS), mycobacterial lipoarabinomannan, and mycoplasma lipoglycans) are typically found on the surface of cell membranes and play crucial roles in various cellular functions. Characterizing their structure and dynamics at the molecular level is essential to understand their biological roles, but systematic generation of glycolipid and lipoglycan structures is challenging because of great variations in lipid structures and glycan sequences (i.e., carbohydrate types and their linkages). To facilitate the generation of all-atom glycolipid/LPS/LOS structures, we have developed Glycolipid Modeler and LPS Modeler in CHARMM-GUI (http://www.charmm-gui.org), a webbased interface that simplifies building of complex biological simulation systems. In addition, we have incorporated these modules into Membrane Builder, so that users can readily build a complex symmetric or asymmetric biological membrane system with various glycolipids and LPS/LOS. These tools are expected to be useful in innovative and novel glycolipid/LPS/LOS modeling and simulation research by easing tedious and intricate steps in modeling complex biological systems, and shall provide insight into structures, dynamics, and underlying mechanisms of complex glycolipid-/LPS-/LOS-containing biological membrane systems.
Coarse-grained simulations are widely used to study large biological systems. Nonetheless, building such simulation systems becomes nontrivial, especially when membranes with various lipid types are involved. Taking advantage of the frameworks in all-atom CHARMM-GUI modules, we have developed CHARMM-GUI Martini Maker for building solution, micelle, bilayer, and vesicle systems as well as systems with randomly distributed lipids using the Martini force field. Martini Maker supports 82 lipid types and different flavors of the Martini force field, including polar and nonpolar Martini, Dry Martini, and ElNeDyn (an elastic network model for proteins). The qualities of the systems generated by Martini Maker are validated by simulations of various examples involving proteins and lipids. We expect Martini Maker to be a useful tool for modeling large, complicated biomolecular systems in a user-friendly way.
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