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Frank L. H. Brown
Department of Chemistry and Biochemistry, UCSB
Simple models for biomembranedynamics and structure
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Limitations of Fully Atomic Molecular Dynamics Simulation
A recent “large” membrane simulation(Pitman et. al., JACS, 127, 4576 (2005))
•1 rhodopsin, 99 lipids, 24 cholesterols, 7400 waters (43,222 atoms total)
•5.5 x 7.7 x 10.3 nm periodic box for 118 ns duration
Length/time scales relevantto cellular biology•ms, m (and longer)•A 1.0 x 1.0 x 0.1 m simulation for 1 ms
would be approximately 2 x 109 more expensive than our abilities in 2005
• Moore’s law: this might be possible in 46 yrs.
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Outline
• Elastic simulations for membrane dynamics– Protein motion on the surface of the red blood cell– Supported bilayers and “active” membranes
• Molecular membrane model– Flexible lipid, solvent-free
• Correspondence with experimental physical properties and stress profile
• Protein induced deformation of the bilayer
• Elastic model for peristaltic bilayer deformations– Fluctuation spectra– Protein deformations
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Linear response, curvature elasticity model
h(r)
LHelfrich bending free energy:
E =
Kc
2∇2h(
r r )( )
2
∫ dxdy=Kc
2L2 k4 hk
2
k∑
Linear response for normal modes:ddt
hk t( )=−ωkhk +ζ(t)
ωk =Kck
3
4η
Ornstein-Uhlenbeck process for each mode:
P(hk)=Kck
4
2πkBTL2 exp−Kck
4
2kBTL2 hk
2⎡
⎣ ⎢ ⎤
⎦ ⎥
P hk(t) |hk(0)( ) =Kck
4
2πkBTL2(1−e−2ωkt)exp−
Kck4
2kBTL2
hk(t) −hk(0)e−ωkt 2
(1−e−2ωkt)
⎡
⎣ ⎢
⎤
⎦ ⎥
Kc: Bending modulusL: Linear dimensionT: Temperature: Cytoplasm viscosity
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Relaxation frequencies
R. Granek, J. Phys. II France, 7, 1761-1788 (1997).
Solve for relaxation of membrane modes coupled to a fluid inthe overdamped limit:
Non-inertial Navier-Stokes eq:
∇p−η∇2r u = fext
∇ ⋅r u =0
Nonlocal Langevin equation:
˙ h (r r ,t) = d
r r ' H(
r r −
r r ' ) f(
r r ',t)∫ +ζ (
r r ,t)
H(r r )=
18πηr
f (r r ) =−
δEδh(
r r )
Oseen tensor(for infinite medium)
Bending force
˙ h k(t)=− Kck4
( )1
4ηk
⎛ ⎝ ⎜ ⎞
⎠ hk(t)+ˆ ζ (k,t)
ωk =Kck
3
4η
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Generalization to other fluid environments is possible
Different viscoscitieson both sides of membrane
Membrane near an Impermeable wall
Membrane near a semi-permeable wall
And various combinations
Seifert PRE 94, Safran and Gov PRE 04,
Lin and Brown (In press)
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Membrane Dynamics
QuickTime™ and a decompressor
are needed to see this picture.
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Extension to non-harmonic systems
Helfrich bending free energy + additional interactions:
€
E =Kc
2∇ 2h(
r r )( )
2
∫ dxdy + V[h(r)] =Kc
2L2k 4 hk
2
k
∑ + Vk[h(r)]
Overdamped dynamics:
€
˙ h (r r , t) = d
r r 'H(
r r −
r r ') f (
r r ', t)∫ + ζ (
r r , t)
f (r r ) = −
δE
δh(r r )
Solve via Brownian dynamics•Handle bulk of calculation in Fourier Space (FSBD)
•Efficient handling of hydrodynamics•Natural way to coarse grain over short length scales
L. Lin and F. Brown, Phys. Rev. Lett., 93, 256001 (2004).L. Lin and F. Brown, Phys. Rev. E, 72, 011910 (2005).
€
d
dthk t( ) = Λk Fk[h(r, t)] + ζ k (t){ }
Λk =1
4ηk(or generalizedexpressions)
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Protein motion on the surface of red blood cells
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S. Liu et al., J. Cell. Biol., 104, 527 (1987).
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S. Liu et al., J. Cell. Biol., 104, 527 (1987).
Spectrin “corrals” protein diffusion•Dmicro= 5x10-9 cm2/s (motion inside corral)
•Dmacro= 7x10-11 cm2/s (hops between corrals)
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Proposed Models
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Why membrane undulations?
• The mechanism has not been considered before.
• “Thermal” membrane fluctuations are known to exist in red blood cells (flicker effect).
• Physical constants necessary for a simple model are available from unrelated experiments.
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F. Brown, Biophys. J., 84, 842 (2003).
Dynamic undulation model
Kc=2x10-13 ergs=0.06 poiseL=140 nmT=37oCDmicro=0.53 m2/sh0=6 nm
Dmicro
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Explicit Cytoskeletal Interactions
• Additional repulsive interaction along the edges of the corral to mimic spectrin
∑∫ ++= −
i
/)( )( iiih
Arep cbyaxedF δε λrr
L. Lin and F. Brown, Phys. Rev. Lett., 93, 256001 (2004).
• Harmonic anchoring of spectrin cytoskeleton to the bilayer
E = dA∫ r
Kc
2 ∇2h(r) [ ]
2+
12
V(r)h2(r)⎧ ⎨ ⎩
⎫ ⎬ ⎭
V(r) =γ δ(r−Ri)i∑
L. Lin and F. Brown, Biophys. J., 86, 764 (2004).
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Dynamics with repulsive spectrin
QuickTime™ and a decompressor
are needed to see this picture.
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Information extracted from the simulation
•Probability that thermal bilayer fluctuation exceeds h0=6nm at equilibrium (intracellular domain size)•Probability that such a fluctuation persists longer than t0=23s (time to diffuse over spectrin)
•Escape rate for protein from a corral•Macroscopic diffusion constant on cell surface
(experimentally measured)
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Calculated Dmacro• Used experimental median value of corral size L=110 nm
∞
9×10−12
5×10−12
7×10−10
System Size Simulation
Type
Simulation
Geometry
L Free Membrane Square
L Pinned Square
Pinned Square
Pinned & Repulsive
Square
Pinned & Repulsive
Triangular
Dmacro (cm2s-1)
∞∞
€
2 ×10−10
€
3.4 ×10−10
Dmacro=6.6×10-11 cm2s-1• Median experimental value
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Fluctuations of supported bilayers
Y. Kaizuka and J. T. Groves, Biophys. J., 86, 905 (2004).Lin and Brown (submitted).
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Fluctuations of supported bilayers (dynamics)
x10-5
Impermeable wall (different boundary conditions) No wall
Timescales consistent withexperiment
Way off!
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Fluctuations of “active” bilayers
koff
J.-B. Manneville, P. Bassereau, D. Levy and J. Prost, PRL, 4356, 1999.
Off Push down
Off Push up
kon
koff
kon
Fluctuations of active membranes (experiments)
L. Lin, N. Gov and F.L.H. Brown, JCP, 124, 074903 (2006).
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Summary (elastic modeling)
• Elastic models for membrane undulations can be extended to complex geometries and potentials via Brownian dynamics simulation.
• “Thermal” undulations appear to be able to promote protein mobility on the RBC.
• Other biophysical and biochemical systems are well suited to this approach.
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Molecular membrane models with implicit solvent
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Why?
• Study the basic (minimal) requirements for bilayer stability and elasticity.
• A particle based method is more versatile than elastic models.– Incorporate proteins, cytoskeleton, etc.– Non “planar” geometries
• Computational efficiency.
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A range of resolutions...
Y. Kantor, M. Kardar, Y. Kantor, M. Kardar, and D.R. Nelson, Phys. and D.R. Nelson, Phys. Rev. A Rev. A 35, 35, 3056 (1987)3056 (1987)
J.M. Drouffe, A.C. J.M. Drouffe, A.C. Maggs, and S. Leibler, Maggs, and S. Leibler, Science Science 254254, 1353 , 1353 (1991)(1991) Helmut Heller, Helmut Heller,
Michael Schaefer, Michael Schaefer, and Klaus Schulten, and Klaus Schulten, J. Phys. Chem J. Phys. Chem 1993, 1993, 8343 (1993)8343 (1993)
R. Goetz and R. R. Goetz and R. Lipowsky, J. Chem. Lipowsky, J. Chem. Phys. Phys. 108, 108, 7397 7397 (1998)(1998)
W. Helfrich, Z. W. Helfrich, Z. Natuforsch, Natuforsch, 28c28c, 693 , 693 (1973)(1973)
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Motivation
D. Marsh, Biochim. Biophys. Acta, 1286, 183-223 (1996).
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A solvent free model
G. Brannigan, P. F. Philips and F. L. H. Brown, Phys. Rev. E, 72, 011915 (2005).
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Self-Assembly
QuickTime™ and aYUV420 codec decompressor
are needed to see this picture.
(128 lipids)
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Phase Behavior
Fluid (kBT = 0.9)
“Gel” (kBT = 0.5)
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Flexible (and tunable)
kc: 1 - 8 * 10-20 JkA: 40-220 mJ/m2
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Stress Profile(-surface tension vs. height)
R. Goetz and R.Lipowsky, JCP 108 p. 7397, (1998)
ExplicitSolvent
ImplicitSolvent
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Protein Inclusions
Inclusion is composed of rigid “lipid molecules”
Single protein inclusion in the bilayer sheet
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Deformation of the bilayer
r
€
δ =h(r) − h0
h0
data
fit to elastic
H. Aranda-Espinoza et. al., Biophys. J., 71, 648 (1996). G. Brannigan and FLH Brown, Biophys. J., 90, 1501 (2006).
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The fit is meaningfulDeformation profile has three independent
constants formed from
combinations of elastic properties
h0monolayer thickness
0area/lipid
kcbending modulus
kAStretching modulus
c0Spontaneous curvature (monolayer)
c0’ area
derivative of above
Direct from simulation
Thermal fluctuations
Infer from stressProfile & fluctuations
Using physical constant inferred from homogeneous bilayer simulations we can predict the “fit” values.
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A consistent elastic model
•Begin with standard treatment for surfactant monolayers (e.g. Safran)•Couple monolayers by requiring volume conservation of hydrophobic tails and equal local area/lipid between leaflets•Include microscopic protrusions (harmonically bound to z fields & include interfacial tension)
bendingprotrusion
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A consistent elastic model
€
dxdykc
2∇ 2z+
( )2
+ kλ λ+2+ γ int ∇λ+
( )2
+ 2γ int∇z+ ⋅∇λ+ ⎧ ⎨ ⎩A
∫
+kA
2t02 z−
( )2
+ 2kcc0∇2z− + 2kc c0 − c0
' Σ0( )z−
t0
∇ 2z− +kc
2∇ 2z−
( )2
+kλ λ−2+ γ int ∇λ−
( )2
+ 2γ int∇z− ⋅∇λ−}
Undulations (bending & protrusion)
Peristaltic bending
Peristaltic protrusions (and coupling to bending)
€
kc
2: Monolayer bending modulus
kλ : Protrusion binding constant
γ int: Interfacial (water/hydrocarbon) tension
kA
2: Monolayer compressibility modulus
c0 : Monolayer spontaneous curvature
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•Predicts thermal fluctuation amplitudes for both undulation and peristaltic modes
–Continuous as function of q–Spontaneous curvature included–Elastic constants are interrelated–5 fit constants over two data sets
•Predicts response to hydrophobic mismatch–Same elastic constants as fluctuations–Protrusion modes explicitly considered
A consistent elastic model
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Marrink & Mark
Scott & coworkers
Lindahl &Edholm
€
hq
2=
kBT
kcq4
+kBT
2 kλ + γ intq2
( )
tq
2=
kBT
kcq4 − 4
kc
t0
c0 − c0' Σ0( )q
2 + kA / t02
+kBT
2 kλ + γ intq2
( )
Fits to fluctuation spectra
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Positive spontaneous curvature & conservation of volume favors modulated thickness
Effect of spontaneous curvature
These molecules are
unhappy, but, there are
relatively few of them.
These molecules are happy
because they’re in their
preferred curvature.
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Fluctuation spectra: spontaneous curvature
Of the available simulation data, this effect is most pronounced for DPPC:
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
Lindahl, E. and O. Edholm. Biophysical Journal 79:426(2000)
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gramicidin A channel lifetimes
€
kd (z0(1))
kd (z0(2))
≈e−β Felas (TS )−Felas (D )( )
1
e−β (Felas (TS )−Felas (D ))2
Rate vs. monolayer thickness
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In progress…
Free energy calculations to establishpotential of mean force.
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Summary•Implicit solvent models can reproduce an array of bilayer properties and behaviors.
•Computation is significantly reduced relative to explicit solvent models, enabling otherwise difficult simulations.
•A single elastic model can explain peristaltic fluctuations, height fluctuations and response to a protein deformations (if you include spontaneous curvature and protrusions)
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Lawrence LinGrace Brannigan
Adele TamboliPeter Phillips
Jay Groves, Larry Scott, Jay MashlSiewert-Jan Marrink
Olle Edholm
NSF, ACS-PRF, Sloan Foundation,UCSB
Acknowledgements