a brownian dynamics interpretation of membrane protein ... · fusion 2. experiments 1: cluster...
TRANSCRIPT
Carsten Kutzner, Helmut Grubmüller
Jochen J. Sieber1, Katrin I. Willig2, Claas Gerding-Reimers1, Benjamin Harke2, Gerald Donnert2, Burkhard Rammner, Christian Eggeling2, Stefan W. Hell2, Thorsten Lang1
Departments of 1Neurobiology, 2NanoBiophotonics
A brownian dynamics interpretation of membrane protein clustering
1
‣ fluid mosaic model of cell membrane (Singer, Nicolson 1972): individual proteins diffuse freely in a sea of lipids
‣ however, most membrane proteins are organized in clusters
‣ no satisfactory explanation! (subplasmalemmal fences that form compartment boundaries?)
‣ cluster formation is likely functionally important since syntaxin clusters represent sites for docking & fusion of vesicles
Extracellular Fluid
Cytoplasm
Cholesterol Integral protein(Globular protein)
Peripherial protein
Glycoprotein
Carbohydrate
Glycolipid
Phospholipid bilayer
Alpha-Helix protein(Integral protein)
Surface protein
Protein channel(transport protein)
Globular protein
Filaments of cytoskeleton
Phospholipidmolecule
Hydrophilic heads
Hydrophobic tails
Questions & motivation
‣ physical principles underlying most membrane protein clusters poorly understood
‣ explain the experimentally accessible properties of the syntaxin-1 (Sx1) clusters
SNARE protein involved in membrane
fusion
2
Experiments 1: cluster density and cluster diameter
‣ STED microscopy on plasma sheets yields density of 19.6(5.7) clusters / μm2
‣ average cell surface area is 460 μm2 ⇒ 9000 clusters per cell
‣ quantitative immunoblotting: 830 000 Sx1 per cell ⇒ max. 90 Sx1 per cluster (if all Sx1 in
clusters)
‣ STED: average cluster diameter 50-60 nm ⇒ dense package
‣ overexpression ⇒ more clusters
STEDConfocal
2 µm
500 nm
Histogram of measured Sx1 cluster size
0 20 40 60 80 100 120 140 1600
40
80
120
160
F WHM (nm)
num
bero
fclu
ster
s
100 nm
3
‣ FRAP measurements with green fluorescent protein (GFP)–labeled Sx1 overall mobility
‣ recovery half times t1/2 range from 40–60 s, much longer than expected for a freely diffusing protein with a single transmembrane region (TMR)
Experiments 2: syntaxin mobility
Sx1A-GFP
0 s 25 s 100 s 256 spre5 µm
S x1A, T MR -G FP (T MR )
S x1A-G FP
0 50 100 150 200 2500.0
0.2
0.4
0.6
0.8
1.0
norm
alized
s igna l
time (s )
TMR
60
40
20
t(s)
1/2
S x1A, T MR -G FP (T MR )
S x1A-G FP
0 50 100 150 200 2500.0
0.2
0.4
0.6
0.8
1.0
norm
alized
s igna l
time (s )
TMR
60
40
20
t(s)
1/2
0
N-term.
S NAR Emotif
TMR
linker
4
‣ study mutants and deletion constructs of Sx1 C-term TMR – SNARE – linker – N-term domain
‣
‣ SNARE motifs are responsible for reduced syntaxin mobility (whereas closed conformation plays no role)
‣ reduced mobility is caused by the assembly of syntaxin into clusters
Experiments 3: which region of Sx1 is responsible for mobility restriction?
0
20
40
60
111G F P b lo ckI 0 11G F P 0 0 1G F P0
20
40
60
111G F P b lo ckII o pen111G F P 111multG F P
S x1A, T MR -G F P (T MR )
S x1A-G F PS x1A, S NAR E motif-T MR -G F P ( S NAR E )
0 50 100 150 200 2500.0
0.2
0.4
0.6
0.8
1.0
norm
aliz
edsi
gna l
time (s )0 50 100 150 200 250
0.0
0.2
0.4
0.6
0.8
1.0
norm
aliz
edsi
gnal
time (s )
S x1AmutT MR -G F P ( mut)
S x1A-G F Popen S x1A-G F P ( open)
60
40
20t
(s)
1/2
0
open mut T MRS NAR E
60
40
20
t(s
)1/
2
0
N-term.
S NAR Emotif
T MR
linker
5
‣ FRAP recovery du to ...
➊ diffusion of entire clusters? ➋ exchange of individual molecules?
‣ syntaxin spots do not change over minutes ⇒ Clusters are immobile. ⇒ Equilibrium of free and clustered syntaxins.
Experiments 4 – molecule exchange or whole cluster diffusion?
103 s 199 s 304 s0 s1 µm
0 s 304 s overlay5 µm
membrane Sx1A-GFP
S x1A-G FP
0 50 100 150 200 2500.0
0.2
0.4
0.6
0.8
1.0
norm
alized
s igna l
time (s )
6
The BD model‣ can size and dynamics of the syntaxin clusters be explained by simple physical
principles? (rather than by elaborate layers of biological regulation)
‣ MD not feasible, since we eventually need 250 s trajectories to compare with the FRAP experiments
‣ no need to describe in detail the collisions of syntaxins with surrounding lipids, consider them as a heat bath only
‣ MD becomes BD (Newtons eq. of motion ⇒ Langevin):
‣ Position Langevin eq. (Lax 1966, Zwanzig 1969):
positions r, diffusion coefficient D, random velocity process drS/dt
‣ Recursive update of molecular positions with algorithm by Ermak (1975):
‣ lateral diffusion coefficient of TMR construct D = 0.075(25) μm2 / s determined from half time of recovery according to Ficz et al. (2005)
drdt
= DF(t)+drS
dt
ri(t +!t) = ri(t)+D!tFi(t)+"!
2D!t
random number drawn from a 2d normal distribution
with variance 1
7
The BD model‣ diffusion on a 2d plane, periodic boundary conditions
‣ interaction potential between individual molecules i, j at distance r = rij :
(1) (2) and (3)
‣ (1) mutal repulsion of strength E1 and range σ that prohibits molecules to run into each other (Pauli repulsion)
‣ (2) effective attraction of strength E2 and range 2σ between the molecules, mediated by the SNARE motifs
‣ (3) fs steric hindrance due to crowding
‣ force
‣ σ is chosen such that an approximate area per TM helix of 1.5 nm2 is obtained (Takamori et al. 2006)
V (ri j) = E1 · e!r2/(2!2)!E2 · fs · e!r2/{2(2!)2}
0.5 1 1.5 2 2.5
-2
0
2
r (nm)
pote
ntia
l(kT
)
nmax=140
nc =175140
10570
350
fs = 1! nc
nmax
!F(!ri) =!!iU(!r1, . . .!rn)
U = !i< jV (ri j)
min(V ) =!
1.5 nm
8
Global algorithm
‣ generate starting configuration
‣ calculate forces
‣ make neighbourlists (every 10th step)
‣ detect clusters (every 10th step)
‣ calculate forces for particles within cutoff radius
‣ update positions
‣ calculate fluorescence recovery
‣ output
‣ ri = (x, y)i, bleached?
‣ potential energy
‣ FRAP signal intensity
‣ cluster size distribution
0.5 1 1.5 2 2.5
-2
0
2
r (nm)
pote
ntia
l(kT
)
nmax=140
nc =175140
10570
350
gridded neighbour searching with cutoff >3.5 nm
rc
9
Cluster detection‣ needed for cluster penalty term
‣ if distance d between 2 molecules < rd =1.5 nm they belong to same cluster
‣ HOW TO AUTOMATICALLY DETECT THAT?
‣ if 2 molecules belong to same cluster, they must be in same neighbourlist, since rd < rcutoff
‣ go through all molecules
‣ (if not already assigned to a cluster), build the network that connects all molecules with a mutual distance of less than 1.5 nm;mark each of those molecules with a unique cluster-number.
fs = 1! nc
nmax
0.5 1 1.5 2 2.5
-2
0
2
r (nm)
pote
ntia
l(kT
)
nmax=140
nc =175140
10570
350
neighbourlist cutoff
distance criterion
snapshots every 250 steps
10
Time step length
0 0.5 1 1.5 2 2.5 3 3.50
100
200
300
400
500
600
700
800
900Radial distribution of syntaxins (E2 = 10.5 kT)
radius (nm)
coun
t (ar
bitra
ry u
nits
)
dt= 3.05 nsdt= 15.25 nsdt= 30.5 nsdt= 61.0 nsdt= 91.5 nsdt=122 ns
0.5 1 1.5 2 2.5
-2
0
2
r (nm)
pote
ntia
l(kT
)
nmax=140
nc =175140
10570
350
11
Parallel force calculation
#include <omp.h>
...
/*******************************************************************************//* start omp parallel region *//*******************************************************************************/#pragma omp parallel for \ default(none) \ shared(signal, dvdx, dvdy, stderr) \ firstprivate(bleachedarr, cluster_size, cluster_no, numneighbours, ind, x, y) \ private(xoffset, yoffset, signalindex, xdum, ydum, xblock, yblock, blockindex, \ j, jsize, ijsize, isize, neighbour_no, index, r2, dx, dy, fac, a, \ exp1, exp2) \ reduction(+ : etot) \ schedule(static)
/* loop over molecules */for (i = 0; i < nsyntaxins; i++){
(calculate potential & force)...
}
Benchmark on Kea:CPUs time/s speedup scaling
1 165.1 1.00 1.00 serial code 1 170.6 0.97 0.97 threaded code 2 93.4 1.77 0.88 4 54.0 3.06 0.76
‣ fastest CPUs (roc) need ≈3 days for 80M time steps (250 s)
‣ icc ./synsim.c -O3 -openmp -o synsim.x
12
Simulation protocol‣ make a parameter study:
vary E2 and nmax
‣ E2 ⇒ depth of well
‣ nmax ⇒ at which cluster size force becomes
repulsive
‣ start with 1391 random positions ri
‣ iterate until equilibrated (potential energy)
‣ if cluster density matches exp. density of 19.6(5.7) clusters / μm2
‣ bleach (simulate FRAP experiment)
‣ record 250 s of fluorescence recovery
‣ now match experimental FRAP curve
‣ extract number of molecules per cluster, fraction of free molecules
0.5 1 1.5 2 2.5
-2
0
2
r (nm)
pote
ntia
l(kT
)
nmax=140
nc =175140
10570
350
bleach size 293 nm (1:10)
13
Equilibration
0 50 100 150 200 250 300 350 400 4500
500
1000
1500
time [seconds]
num
ber o
f Sx1
mol
ecul
es
0 50 100 150 200 250 300 350 400 450−2.5
−2
−1.5
−1
−0.5
0x 104
time [seconds]
pote
ntia
l ene
rgy
[kT]
single Sx1
clusters 10+ (*10)
Sx1in clusters
14
Parameter study
experimental density of 19.6(5.7) clusters/μm2
}
11 12 13 140
10
20
30
40
50
60
E2 (kT)
clus
ters
/ (
m)2
Cluster number (min/av/max)
11 12 13 140
10
20
30
40
50
60
70
80
90
E2 (kT)
<nc>
Average cluster size
Counting only clusters nc 10
nmax = 40
60
80
100
120140
nmax = 40
60
80
100
120
140
15
Example 0-11s
‣ experiment measures intensity of white molecules in central area
‣ bleached molecules (orange) are invisible
0 5 100
0.05
0.1
0.15
0.2
time [seconds]
norm
aliz
ed s
igna
l
16
Example 0-257 s
0 100 2000
0.5
1
time [seconds]
norm
aliz
ed s
igna
l
‣ experiment measures intensity of white molecules in central area
‣ bleached molecules (orange) are invisible
17
Simulation of FRAP data
‣ each unbleached molecule in the middle area adds one count to the intensity each time step
‣ bleach central area
‣ bleach 9 areas: assign each syntaxin a number from 1-9 depending on where it is at bleach time
‣ displace by half a bleach box size in x, y, x&y directions
‣ ⇒ altogether 36 bleach areas
0 50 100 150 2000
0.5
1
1.5
time [seconds]
norm
aliz
ed s
igna
l
36 signals from 1 area (9 shown)4 signals from 9 areasaverage signal from 36 areas
18
0
20
40
60
111G F P b lo ckI 0 11G F P 0 0 1G F P0
20
40
60
111G F P b lo ckII o pen111G F P 111multG F P
S x1A, T MR -G F P (T MR )
S x1A-G F PS x1A, S NAR E motif-T MR -G F P ( S NAR E )
0 50 100 150 200 2500.0
0.2
0.4
0.6
0.8
1.0
norm
aliz
edsi
gna l
time (s )0 50 100 150 200 250
0.0
0.2
0.4
0.6
0.8
1.0
norm
aliz
edsi
gnal
time (s )
S x1AmutT MR -G F P ( mut)
S x1A-G F Popen S x1A-G F P ( open)
60
40
20
t(s
)1/
2
0
open mut T MRS NAR E
60
40
20
t(s
)1/
2
0
N-term.
S NAR Emotif
T MR
linker
Image acquisition correction
‣ FRAP experiments: bleach fraction s of molecules during each image acquisition
s = 1! (Iexpa ! Iexp
b )1/10 " 0.005
19
Image acquisition correction‣ FRAP experiments: bleach fraction s of molecules during each image acquisition
‣
‣ include this bleaching effect in the simulated curves by simulating 68 data acquisitions
s = 1! (Iexpa ! Iexp
b )1/10 " 0.005
image acquisition
time
FRAP signal
correctedoriginal
I(ti) · (1! s)
I(ti) · (1! s) } s · I(ti)
R(t) = s · I(ti) · I(t! ti) t > ti
20
Image acquisition correction
0 50 100 150 200 2500
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
time (s)
norm
aliz
ed s
igna
l
experimental Sx1without correctionwith correctionimage acquisition
21
Simulated fluorescence recovery
0 50 100 150 200 2500
0.2
0.4
0.6
0.8
1
time (s)
norm
aliz
ed s
igna
l
free Sx1 nmax E2 100% ---- 0.055% 60 11.0
21% 120 11.518% 140 11.516% 140 11.810% 140 12.54% 140 13.01% 140 14.0
---experiment
‣ simulated curves are average of 2 trajectories each
‣ 1:10 scale ⇒ 102 x faster diffusion and FRAP recovery
for simulations that match experimentally observed cluster density
clustersize 70-80
22
Snapshots87
9 nm
293
nm
a
start positions in equilibriumin equilibrium
0 s0 s 129 s129 s
start positions
b
23
Conclusions
‣ experimental data on composition and dynamics of Sx1 clusters can be explained by simple physical principles
protein-protein attraction ↔ steric hindrance
‣ Sx1 molecules are quasi-immobile when in clusters
‣ a fraction of ≈16% of the molecules diffuse freely between the clusters which contain 70–80 Sx1
‣ Clustering via self-assembly likely applies to a variety of membrane protein clusters
‣ presented a framework with syntaxin-1 as an example
24
Acknowledgments
‣ Jochen Sieber & Thorsten Lang – experiments, experiments, ...
‣ Helmut Grubmüller – Idea & model layout
‣ 10500 – thanks to all of you!
Let‘s make a model!
25
0 0.01 0.02 0.03residence time in cluster [s]
# di
ssoc
iatio
n ev
ents
(log
sca
le)
c( ) = g( ) −1 e− /
simulationfit c( )
10−6 10−5 10−4 10−3 10−2 10−1
ampl
itude
s g(
)
dissociation time constant [s]
fast dissociation
slow dissociation
26