Phospholipid Monolayer Simulations using GROMACS

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Phospholipid Monolayer Simulations
using GROMACS
Matthew Storey
Dept. of Engineering Science, Penn State
Prof. Zuo and Prof. Kobayashi
Dept. of Mechanical Engineering, University of Hawaii – Manoa
HARP REU Program
August 3rd, 2011
Overview of Presentation
 Introduction to Prof. Zuo’s research
 GROMACS introduction and general simulation
methodology
 Specific steps to simulate DPPC monolayer
 Results of energy minimization and equilibrium parameter
optimization
 Major errors/problems encountered
 Conclusion and recommended future work
One focus of Prof. Zuo’s research is the characterization
of lung surfactants.
Experimental AFM images of lung surfactants during lateral compression
[2]
GROMACS and VMD were used this summer to set up
and run MD simulations of DPPC monolayers.
GROMACS

is a free publically licensed software for protein and lipid simulations
 Fast calculations for non-bonded interactions
 capable of being run in parallel with MPI
VMD

(Visual molecular dynamics) is a free publication-quality MD structure and
trajectory viewer
 no limit on number of atoms in system
The majority of MD simulations in GROMACS follow
the same general approach.
1. Select property of interest and appropriate force fields
2. Generate the raw initial layout of your system (file.gro)
3. Make a coordinate file (file.top) forming the connection
between force field parameters and system structure
4. Generate box size for simulation, add solvent, and counter
ions (if needed).
General approach continued
5. Run energy minimization on solvated system
6. Determine run parameters for equilibrium simulations
•
•
First NVT is run to equilibrate temperature
Then run NPT to relax to density required for simulation
7. Select appropriate simulation parameters for the Production
simulation
8. Analyze/Visualize the resulting data to get property of interest
The first step in my simulation was to determine the
more appropriate force field: Coarse or Fine Grain.
 FG more detailed, but computationally
expensive time step (2 fs)
 CG is faster (4:1 atom mapping)
large time steps (20-40 fs)
 Monolayer conformations and surface
pressure readings require long equilibration
times, so CG model was chosen
 MARTINI force field is free and most widely
used CG field for GROMACS simulations
[13]
Both the Coarse and Fine Grain simulation systems
were built using the following procedure.
5.
generate
large
sample
size MARTINI
2. Start
1.
Remove
with
4.Randomly
Remove
all
pre-equilibrated
solvent
3. Solvate
solvent
andentire
top
above
DPPC
half
system
of
monolayer
bilayer
equilibrated
from
bilayer
Simulation parameters were then determined for the
minimization and equilibration steps.
 Need large scale sample (+1000 DPPC)
 Large CG time step = 30 fs
 Constant temperature above or below
314 K Temp = 300 K
 Semi isotropic pressure coupling with
1atm normal pressure
 Simulation time: 100ns- 1us for
equilibration
Poor PBC setup from literature
Periodic Boundary Conditions
x/y PBC with 2 walls
Walls – 9-3 LJ potential with a density of 110 nm-3 /nm-2
[1]
The first results were a performance study between the
constructed CG and FG monolayer systems.
CG model
dt = 40fs
FG model
dt = 2fs
96,650 atoms
431,900 atoms
Performance at 120 processors
~ 1200 ns/day
~9 ns/day
During the energy minimization step, a density study
was performed to determine the optimal wall density.
Density (nm-3/nm-2)
2750
2000
1500
1000 750
500
250
110
100
EM steps
20,720 16,26
9
11,37
3
1,41
8
2,47
3
716
4,13
6
5,42
9
5,82
4
Norm. Force
4.2
4.1
10.4
7.1
12.
9
2.73
2.42
2.38
(kJ/mol*nm)
4.3
After the EM procedure, the time step and wall density
were examined together during the NVT equilibration.
Density (nm /nm ) 275
0
-3
-2
200
0
150
0
1000 75
0
50
0
25
0
11
0
10
0
Performance
X
X
X
XXXXXXX
dt = 40 fs
X
X
X
X
X
X
dt = 30 fs
X
X
X
X
X
X
dt = 20 fs
X
X
X
X
X
(24cpu)
270 ns/day
180 ns/day
The 110 density had the most stable potential curve and as
expected didn’t blow up for the stable time steps
Since 30 fs is the largest stable time step at the optimal wall
density, it was chosen for the equilibrium simulations
The first major error of this project occurred
immediately after installing GROMACS on HOSC.
 Mhpcc help desk said only thing I needed in my path was
correct compilers for GROMACS
 After a week of email exchanges with help desk 2 compilers
were found in path
 OpenFOAM third party compiler found in path; got rid of
OpenFOAM source in .bashrc and solved problem (since I
never use it).
After deciding to simulate with the CG model, realistic
solvation of the system became a problem.
 Since MARTINI atoms are
approximate shells, VDW radii
were inaccurate in the solvation
executable
 Adjusted vdw radii (trial and
error) until looked right
 Compared to FG model of same
size to see if water level was too
high in CG model
The most recent problem occurred during the NVT
simulation when the system kept blowing up.
 Density too high in walls defined at the top and bottom of
system
 Time step too large and energies not updated frequently
enough
 Ran density and time step study to find fastest, most stable
case, which had a density of 110 nm-3/nm-2 and a time step
of 30 fs
Conclusion / Recommended future work
 While there were some problems during the project, an
overall understanding of GROMACS and a foundation for
simulating lipid monolayers were accomplished
 Future work can be done on generating detailed isotherms for
pure DPPC monolayers
 Afterwards, slight adjustments can be made to the
composition of this DPPC monolayer to create other
surfactant models, which can be simulated and analyzed in a
similar fashion
Acknowledgements
I would like to thank the following:
 Prof. Zuo and Prof. Kobayashi
 Dr. Brown
 Sheree Hashimoto
"This material is based upon work supported by the National Science Foundation under
Grant No. 0852082. Any opinions, findings, and conclusions or recommendations
expressed in this material are those of the author(s) and do not necessarily reflect the
views of the National Science Foundation."
References
[1] Baoukina, S., Monticelli, L., Risselada, H., Marrink, S., & Tieleman, D. (2008). The molecular mechanism of lipid
monolayer collapse. The National Academy of Sciences, 105(31), 10803-10808.
[2] Zhang, H., Fan, Q., Wang, Y., Neal, C., & Zuo, Y. (2011). Comparative study of clinical pulmonary surfactants using
atomic force microscopy. Biochimica Et Biophysica Acta, 1808, 1832-1842.
[3] Duncan, S., & Larson, R. (2008). Comparing Experimental and Simulated Pressure-Area Isotherms for DPPC. The
Biophysical Society, 1, 1-46.
[4] Wong-Ekkabut, J., Baoukina, S., Triampo, W., Tang, I., Tieleman, D., & Monticelli, L. (2008). Computer simulation
study of fullerene translocation through lipid membranes. Nature Publishing Group, 3, 363-368.
[8] Scott, H. (2002). Modeling the lipid component of membranes. Current Opinion in Structural Biology, 12, 495-502.
[10] Schneemilch, M. and Quirke, N. (2010). Molecular dynamics of nanoparticle translocation at lipid interfaces.
Molecular Simulation, 36(11), 831 — 835.
[11] Engin, O., Villa, A., Sayar, M., & Hess, B. (2010). Driving Forces for Adsorption of Amphiphilic Peptides to the AirWater Interface. Journal of Physical Chemistry, 114, 11093–11101.
[13] http://md.chem.rug.nl/cgmartini/index.php/about
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