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$Id: surfactants.m4 174 2010-02-07 02:08:21Z akohlmey $ $Id: icms.m4 199 2010-02-20 01:34:18Z akohlmey $
Computational resources continue to rapidly increase allowing theoretical investigation of larger systems on longer timescales. Indeed the capacity of all-atom (AA) molecular dynamics (MD) has reached a level that permits somewhat routine exploration of systems containing on the order of hundreds of thousands of atoms for timescales approaching hundreds of nanoseconds. Nonetheless, with these spatial and temporal scales, many soft matter and biological systems of interest still extend far beyond this capability.
In order to overcome this issue, techniques such as enhanced sampling methods or reduced description models can be used, just to name a couple. Models using a reduced description of the system are typically referred to as coarse grain (CG) models. Recently there has been a renewed interest in CG techniques with numerous models now appearing in the literature. As well, there are numerous methods for deriving parameters for CG models.
However, each method has its limitations, which has limited greater adoption of these methods. One rather consistent characteristic of CG models is their dependence on AA MD simulations. This has the negative consequence of including any undesirable characteristics of the AA model into the CG model. We have recently developed a novel parameterization approach that relies heavily on experimental data including surface tension, density and free energy and reduces the dependence on atomistic molecular dynamics simulations. The resulting CG potential is based upon rather standard functional forms facilitating implementation in conventional MD codes. This approach has been applied to nonionic and anionic surfactants, biologically relevent lipid molecules and amino acids. The results demonstrate the ability to make modular transferable CG sites that are capable of accurately predicting the phase and surface behavior specific to a system.
The image on the left shows the CG (left) and AA (right) representations of a DMPC lipid. The images on the right show the transition from a hexagonal phase to a lamellar phase of a PEG-lipid/water mixture upon reduction of the water content. Both are snapshots from simulations containing >1 mio. CG-beads.
Current status (January 2011) of the force field development:CG models have been developed for non-ionic liquids which includes subsets of parameters for alkane, alcohol and ether based molecules. [1,2] Details of the water model can be found in this work as well. Our PC and PE based lipid model is available with flexibility in the hydrophobic chain length and degree of saturation. [3] Amino acids are currently in the development stage with some preliminary work reported. [4] We hope to bring the model up to speed by late 2011. A very accurate phenyl based model with applications to fullerenes is available and has been integrated into our PC lipid model. [5,6,7] Ionic surfactants LABS [8] and SDS [9] along with counterions are also available. A more elaborate analysis of water models (not only including our current model) can be found in [10].
References:
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Carbon nanospheres (CNS) can be used for pharmaceutical and biomedical applications, such as drug delivery or cell imaging. It has been observed that such objects can enter into the nucleus of cells without any specific surface active chemical molecules. On the other hand there is the question whether such particles are toxic for human cells. The translocation of carbon nanospheres through cell membranes is not fully understood on molecular scale, specifically nano-objects of sizes that are close or larger than the thickness of typical lipid membranes (around 3nm). In this study we investigate the mechanism of translocation of moderatly hydrophobic compounds through membranes without attaching any functional molecules on the translocated molecule. Fullerenes of different sizes serve as model system. We are specifically interested in the free energy change when a CNS enters the membrane. We use all-atom Molecular Dynamics (MD) simulations to address this question. For CNS with sizes exceeding the bilayer thickness we develop a coarse grain model. Coarse grain simulations help to to overcome the required length and time scales, allowing for example to study also structures of CNS inside the membranes. The image shows a simulation snapshot of a C540 fullerene at the lipid-water interface.
Contact: Russell DeVane, Arben Jusufi Go back to research index.
Surfactants are used in many industrial applications, such as detergents in cleaning products, as visco-elastic drilling fluids, or for solubilization of lipid molecules. Above the so-called critical micelle concentration (cmc), surfactants self-assemble into aggregates of characteristic morphologies (spherical or worm-like micelles, bilayers) depending, e.g., on the chemical architecture of the surfactant or the composition of the surfactant solution. It is computationally very challenging to model the self-assembly process of such systems, due to the required systems sizes and the long aggregation time (>1μs). Coarse grain simulations and implicit solvent models allow studies of self-assembly processes on the required time and length scales. A recently developed implicit solvent model of ionic surfactants is extended to study the effect of divalent salt ions (Ca2+ or Mg2+) on the micellization properties. Furthermore we investigate micellar properties when different surfactant types are mixed, such as sodium dodecyl sulfate (SDS) with polyethylene glycol (PEG) surfactants. It has been experimentally observed that the composition of the surfactant mixture and length of the PEG head group have significant impact on the morphologies of the micelles, e.g., the formation of spherical and worm-like micelles. We are particularly interested in such synergetic effects, and the ability to quantitatively predict the cmc and agggegate structures which is not feasible using conventional atomistic simulation approaches. We combine Grand Canonical Monte Carlo and atomistic Molecular Dynamics simulations to parameterize the model and to study the mentioned self-assembly properties. The image shows a simulation snapshot of an ellipsoidal micelle composed of a mixture of sodium dodecyl sulfate [SDS; grey spheres attached to light blue chain] and dodecyl polyethylene glycol surfactants [red and light blue chain molecules] in presence of NaCl salt [small dark blue and green particles].
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