5.2 Coarse graining
Coarse-grained molecular dynamics simulations employ intermediate
resolution in order to balance chemical detail with system size. They
offer sufficient size to study membrane-remodeling events while
retaining the ability to self-assemble. Because they are capable of
simulating mesoscopic length scales, they make contact with a wider
variety of experiments. A complete coarse-grained model must include two
components: a mapping from atomistic structures to coarse-grained beads
and a set of potentials that describe the interactions between beads.
The former defines the geometry or length scale of the resulting model,
while the latter defines the potential energy function or the force
field. The parameterization of the force field is essential to the
performance of the model, which is only relevant insofar as it can
reproduce experimental observables. Here we will describe the
characteristic methods for developing CGMD models, namely the bottom-up
structure- and force-matching and top-down free energy-based approaches.
We note that excellent reviews have been written on coarse-grained
methods with applications in other fields such as polymer physics, see,
e.g., [72].
5.2.1 Structure and energy matching in the CMM-CG model: Klein
and co-workers developed a coarse-grained model for phospholipid
bilayers by matching the structural and thermodynamic properties of
water, hydrocarbons and lipid amphiphile to experimental measurements
and all-atom simulations [102]. The resulting force field, titled
CMM-CG, has been used to investigate a range of polymer systems as well
as those containing nonionic liquids and lipids. Classic coarse-grained
methods propose pair potentials between CG beads according to the
Boltzmann inversion method. In this method, a pair correlation function,
or radial distribution function (RDF) g(r) defines the probability of
finding a particle at distance r from a reference particle such that the
conditional probability of finding the particle is ρ(r)=ρg(r), where ρ
is the average number density of the fluid. The potential of mean force
(PMF) between CG beads is then estimated by (Eq. 18) where
gaa(r) is the RDF measured from atomistic simulation,
and αn is a scaling factor (corresponding to the nth
iteration of the estimate) designed to include the effect of
interactions with the (necessarily) heterogeneous environment.