Transcript Simulation of Self-Assembly of Ampiphiles Using Molecular Dynamics Reza Banki, Misty Davies,

Simulation of Self-Assembly of
Ampiphiles Using Molecular
Dynamics
Reza Banki, Misty Davies,
Haneesh Kesari
Final Project Presentation ME346
Stanford University
Overview
• Introduction and Background
• Methodology
– Bead & Spring Model
– Potential Models
• Implementation
• Results
• Conclusions and Future Work
Introduction
Ampiphiles--large molecule
with one or more hydrophilic
“head” groups and
hydrophobic “tail” groups
Lipids, “fat molecules” which
create cell membranes and
micelles, do so because they
are ampiphiles
Images from Nielsen and Klein
Motivation
• Cell membranes are
composed of lipids
– Drug delivery
– Protobiological evolution
library.thinkquest.org/.../cell_membranes.html
• Nanomechanical
Synthesis by SelfAssembly
mrsec.uchicago.edu/Nuggets/Nanostructures/
Bead and Spring Model
• Replace hydrophilic “head”
groups with one kind of bead
and hydrophobic “tail” groups
with another kind of bead.
Water as a third kind of bead.
• Model bond interactions within
the lipid as springs
Top image from Nielsen and Klein
Bottom image: www.ahd.tudelft.nl/~frank/showcase.html
Potential
Models:
LJ 6-12
Used for all unbonded
non-hydrophobic
reactions
•hh
•tt
•ww
•hw
www.lsbu.ac.uk/water/models.html
Potential
Models: LJ 9
Used for all
unbonded
hydrophobic (purely
repulsive) reactions
•ht
•tw
Potential
Models: Bond
Stretching and
bending energies in
the bonds (modeled
as springs)
Top image: www.ahd.tudelft.nl/~frank/showcase.html
Bottom image from Goetz and Lipowsky
Implementation: makelipids
• Created as a function within
MD++
• Allows for creation of lipids
with multiple heads, multiple
number of beads per tail, and
allows you to specify which
heads are connected to tails
• Each lipid is randomly placed,
and then water molecules are
created based on specified
density and concentration.
• System is relaxed using CG
method to begin simulation at
equilibrium
Implementation: Connectivity
• Each bead is assigned an index
corresponding to a row in an array
that lists neighbor beads that it is
connected to. The columns of the
array identify the structure and the
bead type.
• Also identifies which lipid each bead
belongs to. This allows the entire
molecule to be moved across a
periodic boundary for visualization.
6
5
7
0
8
1
9
2
10
3
4
Implementation:
lennard_jones_bond
• Created as a function within MD++
• Calculates bond and bending
energies for bonded particles (LJ
potentials for bonded particles are
neglected.)
• Calculates appropriate LJ potential
energy for unbonded particles.
• Calculates and sums forces between
particles within the cutoff radius
(used same cutoff radius for all
particles). Uses neighbor list
implementation within MD++
Results: Current Model
• Used molecules with completely
flexible tails (ht4) and semi-rigid tails
(HT4)
• =0.006 particles/Å3
• Cs=0.069, 0.208, 0.347, 0.417
• Lx=Ly=40Å, Lz=50Å
• t=0.001ps, total simulation
time=100ps
• 0=3.321e-24 kJ
• =3.33 Å, rep=1.05 
• rc=2.5 
• kbond=5000* 0 /sqrt(), kbend=50* 0
Results: Conjugate Gradient
• Conjugate gradient failed more often
for higher densities. Current model
approximately 1/3 the density of the
desired model.
• Conjugate gradient converged much
more slowly for HT4.
• Much faster simulation times than
those reported in previous
simulations may be due to conjugate
gradient creating excellent initial
conditions.
Results: 0.069 Concentration
ht4
HT4
Results: 0.208 Concentration
ht4
HT4
Results: 0.347 Concentration
ht4
HT4
Results: 0.417 Concentration
ht4
HT4
Conclusions
•Using very simple models for the molecular structures
and for the potential interactions it is possible to
simulate lipid self-assembly
•More complicated structures are formed with higher
lipid concentration
•Bending potentials assist aggregate formation
•Relaxation may speed total simulation times
•CG Relaxation may not be suitable for high density
simulations
Suggestions for Future Work
•Implement bending energies in bonds between heads
•Implement a function that allows for more than one
kind of lipid
•Model the different masses of each particle--instead
of using the average
•Implement a detection algorithm to determine the time
of self-assembly and to place the center of mass of the
structure at the center of the simulation cell for
visualization
•Implement a DPD model so that water molecules do
not have to be simulated--this may allow CG to relax
higher density simulations