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

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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

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Both the Coarse and Fine Grain simulation systems were built using the following procedure.

5. Randomly generate large sample size 4. Remove solvent above monolayer3. Solvate entire system 2. Remove all solvent and top half of equilibrated bilayer1. Start with pre-equilibrated DPPC bilayer from MARTINI

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 Conditionsx/y PBC with 2 walls Walls – 9-3 LJ potential with a density of 110 nm-3 /nm-2

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The first results were a performance study between the constructed CG and FG monolayer systems.

CG model FG model

dt = 40fs dt = 2fs

Performance at 120 processors

~ 1200 ns/day ~9 ns/day

431,900 atoms 96,650 atoms

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

16,269

11,373

1,418

2,473

716

4,136

5,429

5,824

Norm. Force (kJ/mol*nm)

4.2 4.3 4.1 10.4

7.1 12.9

2.73

2.42 2.38

After the EM procedure, the time step and wall density were examined together during the NVT equilibration.

Density (nm-3/nm-2)

2750

2000

1500

1000

750

500

250

110

100

Performance (24cpu)

dt = 40 fs X X X X X X X X X XXXXXXX

dt = 30 fs X X X X X X 270 ns/day

dt = 20 fs X X X X X 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 Air-Water Interface. Journal of Physical Chemistry, 114, 11093–11101.

[13] http://md.chem.rug.nl/cgmartini/index.php/about