Biology Reference
In-Depth Information
membrane bilayer, and, thus, the lipid composition of cell membranes is also
asymmetric within the membrane itself. Furthermore, certain pathologies such as
Alzheimer's (
Mart´n et al., 2010
) or Parkinson's disease (
Fabelo et al., 2011
) can
trigger a change in the membrane lipid composition. This finding could be
behind the particular behavior or certain GPCRs in such relevant diseases (
Guix`-
Gonz´lez, Bruno, Marti-Solano, & Selent, 2012
).
In this context, membrane simulations need to inevitably make room for an
adequate representation of such heterogeneity. The first step towards a native-like
representation of cell membranes is to incorporate cholesterol in membrane models.
Apart from being a key element of biological membranes, cholesterol concentration
is typically different across cells and tissues. This molecule is created in the endo-
plasmic reticulum but it is largely found within the plasma membrane (
Van Meer
et al., 2008
). Membranes enriched in cholesterol display different biophysical prop-
erties primarily in terms of membrane fluidity and condensation (
Hung, Lee,
Chen, & Huang, 2007
). This effect has an impact not only in lipid properties (
De
Meyer & Smit, 2009; Lindblom & Or
¨
dd, 2009
) but also in the dynamics of other
membrane components such as membrane proteins. In addition, the lateral segrega-
tion of cholesterol and specific membrane components into lipid microdomains,
known as lipid rafts, is today an exciting line of research in cell biology
(
Lingwood & Simons, 2010
). But along with cholesterol concentration, cells deli-
cately regulate important chemical aspects of other membrane lipids such as the type
of polar head or the carbon length and degree of unsaturation of lipid tails (
Van Meer
et al., 2008
). Although this might represent just subtle changes in the lipid structure,
these aspects highly influence the biophysical properties of cell membranes
(
Niemel¨, Hyv¨nen, & Vattulainen, 2009
).
Therefore, at least from the theoretical point of view, membrane simulations should
have a propensity to use native-like membranes, where reality is surely better repre-
sented. On the other hand, the rapid advances made in computer sciences during the last
years have greatly increased the timescale reachable by computer simulations and
have helped studying more complex lipid mixtures (
Bennett & Tieleman, 2013
).
Nevertheless, the increasing complexity of these realistic environments makes it still
an extremely challenging task where one has to be very cautious upon interpretation
of results.
4.1.2
Building and simulating membranes
Most simulations are done at constant temperature and pressure (
Tieleman, 2010
).
While constant volume may lead to artificial system dimensions and surely restrict
thickness fluctuations, constant pressure is frequently applied when simulating mem-
brane systems. The last update of the CHARMM force field (
Klauda et al., 2010
)is
an important step forward in membrane simulations. In the former study, the valida-
tion of this force field in the tensionless ensemble (NPT, number of particles (
N
),
pressure (
P
), and temperature (
T
) remain constant) yielded real experimental values.
Simulating a membrane system at constant temperature and pressure is a must if one
