1. Some
Surprises
in
the
Biophysics
of
Protein
Dynamics
Vijay
S.
Pande
Departments
of
Chemistry,
Structural
Biology,
and
Computer
Science
Program
in
Biophysics
Stanford
University
1
Friday, March 15, 13 1

2. Friday, March 15, 13 2

3. Crystallography
gives
a
wealth
of
informa>on
Collagen Helix
P53 Oligomerization Formation
(50% of cancers) (Osteogenesis Imperfecta)
Ribosome:
(Last step of
Central Dogma,
Antibiotic resistance)
Aβ peptide aggregation
(Alzheimer’s Disease) Chaperonin Assisted Folding
(relevant to cancer: HSP90 inhibitors)
Friday, March 15, 13 3

4. Ceci n’est pas une pipe.
Friday, March 15, 13 4

5. “This is not a GPCR”
(Hibert et al, TIPS Reviews, 1993)
Friday, March 15, 13 5

6. “This is not a cell”
Friday, March 15, 13 6

7. Age old challenges of molecular simulation
Friday, March 15, 13 7

8. Age old challenges of molecular simulation
1. Finding a sufficiently accurate
model
Friday, March 15, 13 7

9. Age old challenges of molecular simulation
1. Finding a sufficiently accurate
model
2. Sampling sufficiently long
timescales
Friday, March 15, 13 7

10. Age old challenges of molecular simulation
1. Finding a sufficiently accurate
model
2. Sampling sufficiently long
timescales
3. Learning something new from the
resulting flood of data
Friday, March 15, 13 7

11. How
do
you
break
a
billion-­‐fold
impasse?
Combine
mul=ple,
powerful,
complementary
technologies
8
Friday, March 15, 13 8

12. How
do
you
break
a
billion-­‐fold
impasse?
Combine
mul=ple,
powerful,
complementary
technologies
1)
Folding@home:
very
large-­‐scale
distributed
compu4ng
Most
powerful
computer
cluster
in
the
world
(~8
petaflops)
104x
to
105x
h#p://folding.stanford.edu
Voelz,
et
al,
JACS
(2010)
Ensign
et
al,
JMB
(2007)
Shirts
and
Pande,
Science
(2000)
8
Friday, March 15, 13 8

13. How
do
you
break
a
billion-­‐fold
impasse?
Combine
mul=ple,
powerful,
complementary
technologies
1)
Folding@home:
2)
OpenMM:
Very
very
large-­‐scale
fast
MD
(~1µs/
distributed
compu4ng day)
on
GPUs
Most
powerful
~1µs/day
for
implicit
computer
cluster
in
the
solvent
simulaton
of
world
(~8
petaflops) small
proteins
(~40aa)
104x
to
105x 102x
to
103x
h#p://folding.stanford.edu h#p://simtk.org/home/openmm
Voelz,
et
al,
JACS
(2010) Elsen,
et
al.
ACM/IEEE
conf.
on
Ensign
et
al,
JMB
(2007) Supercompu=ng
(2006)
Friedrichs,
et
al.
J.
Comp.
Chem.,
(2009)
Shirts
and
Pande,
Science
(2000) Eastman
and
Pande.
J.
Comp.
Chem.
8
(2009)
Friday, March 15, 13 8

14. How
do
you
break
a
billion-­‐fold
impasse?
Combine
mul=ple,
powerful,
complementary
technologies
1)
Folding@home:
2)
OpenMM:
Very
3)
Markov
State
Models:
very
large-­‐scale
fast
MD
(~1µs/ Sta4s4cal
mechanics
of
distributed
compu4ng day)
on
GPUs many
trajectories
Most
powerful
~1µs/day
for
implicit
very
long
4mescale
computer
cluster
in
the
solvent
simulaton
of
dynamics
by
combining
world
(~8
petaflops) small
proteins
(~40aa)
many
simula4ons
104x
to
105x 102x
to
103x 102x
to
103x
h#p://folding.stanford.edu h#p://simtk.org/home/openmm h#p://simtk.org/home/msmbuilder
Voelz,
et
al,
JACS
(2010) Elsen,
et
al.
ACM/IEEE
conf.
on
Bowman,
et
al,
J.
Chem.
Phys.
(2009)
Ensign
et
al,
JMB
(2007) Supercompu=ng
(2006) Singhal
&
Pande,
J.
Chem.
Phys.
Friedrichs,
et
al.
J.
Comp.
Chem.,
(2009) (2005)
Shirts
and
Pande,
Science
(2000) Eastman
and
Pande.
J.
Comp.
Chem.
(2009) Singhal,
et
al,
J.
Chem.
Phys.
(2004) 8
Friday, March 15, 13 8

15. How
do
you
break
a
billion-­‐fold
impasse?
Combine
mul=ple,
powerful,
complementary
technologies
1)
Folding@home:
2)
OpenMM:
Very
3)
Markov
State
Models:
very
large-­‐scale
fast
MD
(~1µs/ Sta4s4cal
mechanics
of
distributed
compu4ng day)
on
GPUs many
trajectories
Most
powerful
~1µs/day
for
implicit
very
long
4mescale
computer
cluster
in
the
solvent
simulaton
of
dynamics
by
combining
world
(~8
petaflops) small
proteins
(~40aa)
many
simula4ons
104x
to
105x 102x
to
103x 102x
to
103x
h#p://folding.stanford.edu h#p://simtk.org/home/openmm h#p://simtk.org/home/msmbuilder
Voelz,
et
al,
JACS
(2010) Elsen,
et
al.
ACM/IEEE
conf.
on
Bowman,
et
al,
J.
Chem.
Phys.
(2009)
Ensign
et
al,
JMB
(2007) Supercompu=ng
(2006) Singhal
&
Pande,
J.
Chem.
Phys.
Friedrichs,
et
al.
J.
Comp.
Chem.,
(2009) (2005)
Shirts
and
Pande,
Science
(2000) Eastman
and
Pande.
J.
Comp.
Chem.
(2009) Singhal,
et
al,
J.
Chem.
Phys.
(2004) 8
Friday, March 15, 13 8

16. What
are
Markov
State
Models
(MSMs)?
Markov
State
Models
(MSMs)
are
a
theoreOcal
scheme
to
build
models
of
long
Omescale
phenomena
(1)
to
aid
simulators
reach
long
Omescales
and
(2)
gain
insight
from
their
simulaOons
see
the
work
of:
Andersen,
Caflisch,
Chodera,
Deuflhard,
Dill,
Grubmüller,
Hummer,
Levy,
Noé,
Pande,
Pitera,
Singhal-­‐Heinrichs,
Roux,
SchüDe,
Swope,
Weber
Friday, March 15, 13 9

17. States
avoid
issues
with
projec>ons
and
R.C.’s
Synthesis
Degraded
fragments
U
Disordered Disordered
aggregate aggregate
Disordered
aggregate
I
Amyloid Prefibrillar Figure
adapted
from
fibril species Dobson,
et
al,
Nature
Oligomer
N
Fiber
Crystal
Friday, March 15, 13 10

18. States
avoid
issues
with
projec>ons
and
R.C.’s
Master
equaEon: Synthesis
dpi X
= [kl,i pl ki,l pi ] Degraded
dt fragments
U
l Disordered Disordered
aggregate aggregate
Disordered
aggregate
I
Amyloid Prefibrillar Figure
adapted
from
fibril species Dobson,
et
al,
Nature
Oligomer
N
Fiber
Crystal
Friday, March 15, 13 10

19. MSMs
coarse
grain
conformaEon
space
(to
~3Å)
to
build
a
Master
equaEon
Master
equaEon: Synthesis
dpi X
= [kl,i pl ki,l pi ] Degraded
fragments
dt U
l Disordered Disordered
aggregate aggregate
Build
from
MD:
derive
rate
matrix
Disordered
aggregate
from
simulaOon
w/
I
Bayesian
methods Amyloid
fibril
Prefibrillar
species
Figure
adapted
from
Dobson,
et
al,
Nature
Oligomer
N
Fiber
Crystal
11
Friday, March 15, 13 11

20. but
also
derive
a
coarser
view
for
human
consumpEon
Master
equaEon: Synthesis
dpi X
= [kl,i pl ki,l pi ] Degraded
fragments
dt U
l Disordered Disordered
aggregate aggregate
Build
from
MD:
derive
rate
matrix
Disordered
aggregate
from
simulaOon
w/
I
Bayesian
methods Amyloid
fibril
Prefibrillar
species
Coarse
grain
MSM: N
Oligomer
use
eigenvectors
to
idenOfy
Fiber
Crystal
collecOve
modes
Friday, March 15, 13 12

21. Heart
of
the
power
of
MSMs
Systema=cally
idenOfying
intermediate
states
allows
us
to
(1)
qualitaOvely
understand
and
(2)
quanOtaOvely
predict
chemical
mechanisms
Friday, March 15, 13 13

22. ogy to the quantum mechanical problem, an MSM
tor.” Suppose we would like to calculate the impact of a
matrix could also be augmented calculated with the
“perturbed” Hamiltonian can be by a “perturbation
m perturbation on the eigenspectrum of the transition ma-
(J.
Weber,
VSP)
eigenspectrum perturbation calculate the impact of a
like to theory [19].
Suppose we would can
tell
us
which
results
are
robust
The
quantum mechanical problem, an MSM
MSM
gy to the on the eigenspectrum of the transitionT (to first
We could define a perturbed transition matrix
turbation ma-
)atrix could also be augmented by a “perturbation
such that
ould define a perturbed transition matrix T (to first
0 ⇥
Suppose we would like⇥ T calculate the impact of a [ 3 ]
T
that PerturbaEons
to
to + ⇥T matrix
can
be
• transiEon
}
urbation on the eigenspectrum of the transition ma-
0 ⇥
0
the original T + ⇥T matrix theory
T ⇥ transition
e T define a perturbed transition matrix T ⇥ (to a matrix of
uld ishandled
like
QM
perturbaEon
and T is first ] slow [3
discrete
region
m noise. Transi4on
order correction (T0
=
to noise, ⇥ for each
that original transition matrixrror
due⇥ “real”matrix) of
• The first matrix
with
e and T is a matrix n
s the 0 of the transition matrix is given by the simple inner
value n
T ⇥ T0 + ⇥T⇥ due to noise, ⇥ for [ 3 ]
(rates
of
MSM
states)
se. The first order correction each
eigenvalue
spectrum
ct n
0
n of the transition matrix 0 |T⇥ |e0 ⌃ ⇥ the simple inner [ 4 ]
⇥ is given by
the original transition= ⇧en and T is a (ie
rates) of
matrix eigenvalues
matrix
• We
calculate
perturbed
n
n
e. The first order correction0due to noise, ⇥ for each
⇥
n n = ⇧e0 |T⇥ |en
e0 is e0 is the nth eigenvector⌃ of the zeroth-order transition
n
n
[4]
n of the transition matrix is given by the simple inner
x [19]. Corrected eigenvectors are given by the formula
}
is the • and
⇥perturbed
eigenvectors
(ie
mechanism)
nth eigenvector ⇥ of0the zeroth-order transition
= ⇧e0 |T ⇤⌃⇧e0 |T⇥ |e0 ⌃the formula 4 ]
con=nuous
region
. Corrected eigenvectors|en given by
n n are j [
n
en = e 0 +
n e0
j [5]
is the nth eigenvector of0 the zeroth-order transition
0
⇤ ⇧ej |T⇥ |e0 ⌃ 0 j
n
0
j⇤=n n
Corrected= e0 +
en eigenvectors are giveneby the formula [ 5 ]
n j
0 0
these Key
result ⇤n to n ⇥ 0 j
• corrections j⇤= due 0 perturbation, one could gauge the
ct of a n = e0 perturbs
(or a|en ⌃ e0 systematic) change infast
• error
+noise⇧ej |T more
e random n
eigenvalues
j [5] a
tion matrix on its=eigenspectrum. discrete
region
of
the appli-
corrections duewill
be
robust
in
the
We illustrate the
• results
to perturbation, one could gauge the
0 0
j⇤ n n j
n of this perturbationatheory by applying the above analysis
a randomeigenvalue
spectrum systematic) change in a
noise (or more
matrix Relevant
to perturbation, one matrix. the appli-
e eigenvalues eigenspectrum. We nd
could gauge the
corrections due ftheboth
theory
a illustrate
• on its of or
villin transition experiment
his perturbation (or a more systematic)above analysis
random noise theory by applying the change in a
nvalues of the villinPande. TheWe illustrate the appli- used
atrixJ.on its and V. S. transitionmethodismore extensively Biophys J. (2011)
for a Weber Framework. Proteinmatrix. mechanistically robust.
New eigenspectrum. folding
s Friday, March 15, 13analogous, though not identical, to classical per-
perturbation theory by applying the above analysis
study is 14

23. Folding
simulaEon
has
come
a
long
way
in
15
years
ACBP
10,000 Shaw (ANTON supercomputer)
Pande (Folding@home)
Schulten
Noe
NTL9
NTL9
Kollman
1000 blue = explicit solvent
Folding Time (microseconds)
Lambda
Lambda
red = implicit solvent
100 Protein G
Lambda
BBL NTL9
a3D
Pin1 WW GTT WW Lambda
BBA
10 BBA5 Trp Zip
Fip35 WW Fip35 Fip35 Trp-cage
Villin Protein B
Homeodomain
Trp Cage Villin
1 Villin Villin
Villin Chignolin
Fs
Peptide Fs Peptide
0.1
1998 2000 2002 2004 2006 2008 2010 2012
Year
Friday, March 15, 13 15

24. Folding
simulaEon
has
come
a
long
way
in
15
years
ACBP
10,000 Shaw (ANTON supercomputer)
Pande (Folding@home)
Schulten
Noe
NTL9
NTL9
Kollman
1000 blue = explicit solvent
Folding Time (microseconds)
Lambda
Lambda
red = implicit solvent
100 Protein G
Lambda
BBL NTL9
a3D
Pin1 WW GTT WW Lambda
BBA
10 BBA5 Trp Zip
Fip35 WW Fip35 Fip35 Trp-cage
Villin Protein B
Homeodomain
Trp Cage Villin
1 Villin Villin
Villin Chignolin
Fs
Peptide Fs Peptide
0.1
1998 2000 2002 2004 2006 2008 2010 2012
Year
Friday, March 15, 13 15

25. Can
we
quan>ta>vely
predict
experiment?
10,000
Implicit
Pande Explicit ACBP
NTL9
1000
Predicted folding time (μs)
Fip35 WW
100
10 WT Villin
Trp Zip BBA5
⋋-repressor
Trp-cage
1
0.1 Fs Peptide
0.01
0.1 1 10 100 1000 10,000
Experimental folding time (μs)
Friday, March 15, 13 16

26. What
has
the
community
done
so
far?
10,000
Noé Implicit
Pande Explicit ACBP
Schulten
NTL9
Shaw
1000
Predicted folding time (μs)
Fip35 WW
100 Protein G
⋋-repressor
BBL
α3D Fip35 NTL9
Pin1 WW
Trp-cage
10 WT Villin Fip35 WW
Protein B Trp Zip BBA5
⋋-repressor
Villin Nle
Trp-cage Homeodomain
Villin Nle
1
0.1 Fs Peptide
0.01
0.1 1 10 100 1000 10,000
Experimental folding time (μs)
Friday, March 15, 13 17

27. (Beauchamp,
Das,
VSP)
Experiments
can
now
probe
detailed
MSM
aspects
∆G
(kcal/mol)
RMSD
(Å)
Many
states
have
low
∆G
and
are
highly
structurally
related
Bowman,
Beauchamp,
Boxer,
Pande,
JCP
(2009);
Beauchamp,
Das,
Pande,
PNAS
(2011)
Friday, March 15, 13 18

28. (Beauchamp,
Das,
VSP)
Experiments
can
now
probe
detailed
MSM
aspects
∆G
(kcal/mol)
RMSD
(Å)
Many
states
have
low
∆G
and
are
highly
structurally
related
Bowman,
Beauchamp,
Boxer,
Pande,
JCP
(2009);
Beauchamp,
Das,
Pande,
PNAS
(2011)
Friday, March 15, 13 18

29. (Beauchamp,
Das,
VSP)
Experiments
can
now
probe
detailed
MSM
aspects
∆G
(kcal/mol)
RMSD
(Å)
Many
states
have
low
∆G
and
are
highly
structurally
related
Bowman,
Beauchamp,
Boxer,
Pande,
JCP
(2009);
Beauchamp,
Das,
Pande,
PNAS
(2011) from
Reiner,
Henklein,
&
Kie`aber
PNAS
(2010)
Friday, March 15, 13 18

30. The
challenge
of
simula>ng
vs
understanding
“It is nice to know that the
computer understands the
problem. But I would like to
understand it too.”
– Eugene Wigner, in response to
a large-scale quantum
mechanical calculation
Friday, March 15, 13 19

31. A
brief
history
of
protein
folding
kine>cs
theory
Friday, March 15, 13 20

32. A
brief
history
of
protein
folding
kine>cs
theory
• 1990:
Simple
kineEc
models
• Master
equa4on
approaches
(Shakhnovich
et
al;
Orland
et
al;
Wolynes
et
al)
• Ladce
model
simula4ons
(Dill;
many
others)
Friday, March 15, 13 20

33. A
brief
history
of
protein
folding
kine>cs
theory
• 1990:
Simple
kineEc
models
• Master
equa4on
approaches
(Shakhnovich
et
al;
Orland
et
al;
Wolynes
et
al)
• Ladce
model
simula4ons
(Dill;
many
others)
• 2000:
A
naEve-­‐centric
view
dominates
• Experiments
suggest
a
two-­‐state
model
for
protein
folding
kine4cs
(Fersht)
• Contact
order
(Plaxco,
Simmons,
Baker)
• Minimal
frustra4on/protein
design
approach
(Wolynes;
Shakhnovich;
Pande;
others)
• Consequence:
Go
model
simula4ons,
funnel
energy
landscape
paradigm
Friday, March 15, 13 20

34. A
brief
history
of
protein
folding
kine>cs
theory
• 1990:
Simple
kineEc
models
• Master
equa4on
approaches
(Shakhnovich
et
al;
Orland
et
al;
Wolynes
et
al) PHE35
• Ladce
model
simula4ons
(Dill;
many
others)
PHE11
• 2000:
A
naEve-­‐centric
view
dominates
• Experiments
suggest
a
two-­‐state
model
for
protein
folding
kine4cs
(Fersht) PHE18
• Contact
order
(Plaxco,
Simmons,
Baker)
• Minimal
frustra4on/protein
design
approach
(Wolynes;
Shakhnovich;
Pande;
others) TRP24
• Consequence:
Go
model
simula4ons,
funnel
energy
landscape
paradigm
• What
is
a
Go
model?
• Hα
=
-­‐ε
∑ij
Cαij
CNij
• interac4ons
present
in
the
folded
state
are
ajrac4ve
• all
others
are
repulsive
Friday, March 15, 13 20

35. A
brief
history
of
protein
folding
kine>cs
theory
• 1990:
Simple
kineEc
models
• Master
equa4on
approaches
(Shakhnovich
et
al;
Orland
et
al;
Wolynes
et
al) PHE35
• Ladce
model
simula4ons
(Dill;
many
others)
PHE11
• 2000:
A
naEve-­‐centric
view
dominates
• Experiments
suggest
a
two-­‐state
model
for
protein
folding
kine4cs
(Fersht) PHE18
• Contact
order
(Plaxco,
Simmons,
Baker)
• Minimal
frustra4on/protein
design
approach
(Wolynes;
Shakhnovich;
Pande;
others) TRP24
• Consequence:
Go
model
simula4ons,
funnel
energy
landscape
paradigm
• What
is
a
Go
model?
• 2010:
The
naEve
centric
view
is
unsaEsfying • Hα
=
-­‐ε
∑ij
Cαij
CNij
• Structure
in
the
unfolded
state
(eg
Raleigh) • interac4ons
present
in
the
• Slow
diffusion
(eg
Lapidus) folded
state
are
ajrac4ve
• non-­‐na4ve
interac4ons
(eg
Majhews) • all
others
are
repulsive
Friday, March 15, 13 20

36. A
key
ques>on
domina>ng
protein
folding
theory
How
important
are
non-­‐na=ve
(i.e.
not
present
in
the
folded
state)
interacOons?
Friday, March 15, 13 21

37. Folding
simulaEon
has
come
a
long
way
in
15
years
ACBP
10,000 Shaw (ANTON supercomputer)
Pande (Folding@home)
Schulten
Noe
NTL9
NTL9
Kollman
1000 blue = explicit solvent
Folding Time (microseconds)
Lambda
Lambda
red = implicit solvent
100 Protein G
Lambda
BBL NTL9
a3D
Pin1 WW GTT WW Lambda
BBA
10 BBA5 Trp Zip
Fip35 WW Fip35 Fip35 Trp-cage
Villin Protein B
Homeodomain
Trp Cage Villin
1 Villin Villin
Villin Chignolin
Fs
Peptide Fs Peptide
0.1
1998 2000 2002 2004 2006 2008 2010 2012
Year
Friday, March 15, 13 22

38. Folding
simulaEon
has
come
a
long
way
in
15
years
ACBP
10,000 Shaw
Pande
Schulten
Noe
NTL9
Kollman
1000 blue = explicit solvent
Folding Time (microseconds)
Lambda
red = implicit solvent
100 Protein G
NTL9
Lambda
BBL NTL9
a3D
Pin1 WW GTT WW Lambda
BBA
10
Lambda
Fip35 WW Fip35 Fip35 Trp-cage
BBA5 Trp Zip
Villin Protein B
Homeodomain
Trp Cage Villin
1 Villin Villin
Villin Chignolin
Fs Peptide
0.1
1998 2000 2002 2004 2006 2008 2010 2012
Year
Friday, March 15, 13 23

39. Friday, March 15, 13 24

40. Pathway
seen
in
the
movie:
Series
of
metastable
states
(Voelz,
Bowman,
Beauchamp,
VSP)
Voelz, Bowman, Beauchamp, Pande. JACS (2010)
snapshots
from
the
movie:
starts
in
helix collapse, final
part
of
folded
unfolded forms then
beta
beta
ready
to
structure
state early sheet
forms align forms
correspond
to
states
from
our
Markov
State
Model:
25
Friday, March 15, 13 25

41. RepeaEng
with
many
more
trajectories
yields
an
MSM:
coarse
visualizaEon (Voelz,
Bowman,
Beauchamp,
VSP)
f area
of
each
state
is
propor>onal
to
g macrostate
free
energy
l
d
a n
a→l→n
and
a→m→n
i comprise
10%
of
the
b total
flux
m width
of
each
arrow
is
c propor>onal
to
transi>on
flux
k
j h
Flux
calcula>on
method:
e TPT:
Vanden-­‐Eijnden,
et
al
(2006)
Berezhkovskii,
Hummer,
Szabo
(2009)
Top
10
folding
pathways
shows
us:
• A
great
deal
of
pathway
heterogeneity
exists
• non-­‐na4ve
structure
plays
a
key
role
in
many
states
• metastability
is
onen
structurally
localized
(analogous
to
the
foldon
concept) 26
Friday, March 15, 13 26

42. Contact
map
view
of
the
states
reveals
non-­‐naEve
structure
formaEon
along
the
pathway (Voelz,
Bowman,
Beauchamp,
VSP)
h
more alpha
a
k
m
n
more beta
unfolded basin transition state region
(committor) native basin
27
Friday, March 15, 13 27

43. Contact
map
view
of
the
states
reveals
non-­‐naEve
structure
formaEon
along
the
pathway (Voelz,
Bowman,
Beauchamp,
VSP)
h
more alpha
significant
a amount
of
non-­‐
k
naEve
structure,
even
in
high
m
pfold
states
n
more beta
unfolded basin transition state region
(committor) native basin
27
Friday, March 15, 13 27

44. (Bowman,
Voelz,
VSP)
Beta
sheet
states
slow
folding
in
helical
proteins?
Lambda
G. Bowman, V. Voelz, and V. S. Pande. Atomistic folding simulations of the five helix bundle protein !
λ6-85. Journal of the American Chemical Society 133 664-667 (2011)
Friday, March 15, 13 28

45. “Intramolecular
amyloids”?
ßsheets in unfolded state Lambda
A
B
C
D
E
F
“λ6-85 is not only thermodynamically, but
G
also kinetically protected from reaching
intramolecular analogs of beta sheet
H
aggregates while folding”
without helix5
xtal structure – Prigozhin & Gruebele
Friday, March 15, 13 29

46. (Voelz,
VSP)
Consequences
of
projec>ons
How
can
one
reconcile
this
with
the
simple
picture?
V. A. Voelz, et al. JACS (2012)
Friday, March 15, 13 30

47. (Voelz,
VSP)
Consequences
of
projec>ons
How
can
one
reconcile
this
with
the
simple
picture?
V. A. Voelz, et al. JACS (2012)
Friday, March 15, 13 30

48. (Voelz,
VSP)
Consequences
of
projec>ons
How
can
one
reconcile
this
with
the
simple
picture?
V. A. Voelz, et al. JACS (2012)
Friday, March 15, 13 30

49. (Voelz,
VSP)
Consequences
of
projec>ons
How
can
one
reconcile
this
with
the
simple
picture?
V. A. Voelz, et al. JACS (2012)
Friday, March 15, 13 30

50. (Voelz,
VSP)
Consequences
of
projec>ons
How
can
one
reconcile
this
with
the
simple
picture?
‘‘Regarded from two sides’’
by Diet Wiegman (1984)
Kruschela & Zagrovic.
V. A. Voelz, et al. JACS (2012) DOI:10.1039/b917186j
Friday, March 15, 13 30

51. Conclusions
Friday, March 15, 13 31

52. Conclusions
ACBP
10,000 Shaw
Pande
Schulten
Noe
NTL9
Kollman
1000
Folding Time (microseconds)
Lambda
100 Protein G
Lambda
BBL NTL9
a3D
Pin1 WW GTT WW Lambda
BBA
10 Trp Zip
Fip35 WW Fip35 Fip35
Trp-cage
BBA5
Villin Protein B
Homeodomain
Trp Cage Villin
1 Villin Villin
Villin Chignolin
Fs Peptide
0.1
1998 2000 2002 2004 2006 2008 2010 2012
Year
With MSMs, we can simulate
folding on the 10ms timescale
Friday, March 15, 13 31

53. Conclusions
ACBP
10,000
10,000 Shaw
Noé Implicit
Pande Pande Explicit ACBP
Schulten Schulten
NTL9
Shaw
Noe
Kollman
NTL9 1000
1000
Folding Time (microseconds)
Lambda
Predicted folding time (μs)
Fip35 WW
100 Protein G
⋋-repressor
BBL
100 Protein G
α3D Fip35 NTL9
Pin1 WW
Lambda Trp-cage
BBL NTL9 10 WT Villin Fip35 WW
a3D
GTT WW Lambda Protein B Trp Zip BBA5
Pin1 WW BBA ⋋-repressor
Villin Nle
10 Trp Zip
Fip35 WW Fip35 Fip35
Trp-cage
Villin Nle
Trp-cage Homeodomain
BBA5
Villin Protein B 1
Homeodomain
Trp Cage Villin
1 Villin Villin
0.1 Fs Peptide
Villin Chignolin
Fs Peptide
0.1 0.01
1998 2000 2002 2004 2006 2008 2010 2012 0.1 1 10 100 1000 10,000
Year Experimental folding time (μs)
With MSMs, we can simulate Simulation methods are sufficiently
folding on the 10ms timescale accurate to predict experiment
Friday, March 15, 13 31

54. Conclusions
ACBP
10,000
10,000 Shaw
Noé Implicit
Pande Pande Explicit ACBP
Schulten Schulten
NTL9
Shaw
Noe
Kollman
NTL9 1000
1000
Folding Time (microseconds)
Lambda
Predicted folding time (μs)
Fip35 WW
100 Protein G
⋋-repressor
BBL
100 Protein G
α3D Fip35 NTL9
Pin1 WW
Lambda Trp-cage
BBL NTL9 10 WT Villin Fip35 WW
a3D
GTT WW Lambda Protein B Trp Zip BBA5
Pin1 WW BBA ⋋-repressor
Villin Nle
10 Trp Zip
Fip35 WW Fip35 Fip35
Trp-cage
Villin Nle
Trp-cage Homeodomain
BBA5
Villin Protein B 1
Homeodomain
Trp Cage Villin
1 Villin Villin
0.1 Fs Peptide
Villin Chignolin
Fs Peptide
0.1 0.01
1998 2000 2002 2004 2006 2008 2010 2012 0.1 1 10 100 1000 10,000
Year Experimental folding time (μs)
With MSMs, we can simulate Simulation methods are sufficiently
folding on the 10ms timescale accurate to predict experiment
folding via parallel paths of
many metastable states
Friday, March 15, 13 31

55. Conclusions
ACBP
10,000
10,000 Shaw
Noé Implicit
Pande Pande Explicit ACBP
Schulten Schulten
NTL9
Shaw
Noe
Kollman
NTL9 1000
1000
Folding Time (microseconds)
Lambda
Predicted folding time (μs)
Fip35 WW
100 Protein G
⋋-repressor
BBL
100 Protein G
α3D Fip35 NTL9
Pin1 WW
Lambda Trp-cage
BBL NTL9 10 WT Villin Fip35 WW
a3D
GTT WW Lambda Protein B Trp Zip BBA5
Pin1 WW BBA ⋋-repressor
Villin Nle
10 Trp Zip
Fip35 WW Fip35 Fip35
Trp-cage
Villin Nle
Trp-cage Homeodomain
BBA5
Villin Protein B 1
Homeodomain
Trp Cage Villin
1 Villin Villin
0.1 Fs Peptide
Villin Chignolin
Fs Peptide
0.1 0.01
1998 2000 2002 2004 2006 2008 2010 2012 0.1 1 10 100 1000 10,000
Year Experimental folding time (μs)
With MSMs, we can simulate Simulation methods are sufficiently
folding on the 10ms timescale accurate to predict experiment
!
folding via parallel paths of intramolecular amyloid
many metastable states hypothesis
Friday, March 15, 13 31

56. Where
do
we
go
from
here?
Friday, March 15, 13 32

57. Petaflops
on
the
cheap
today,
exaflops
soon?
There
are
approximately
a
billion
computers
in
the
world
Folding@home
Friday, March 15, 13 33

58. Petaflops
on
the
cheap
today,
exaflops
soon?
There
are
approximately
a
billion
computers
in
the
world
How
many
GPUs?
How
many
GPU
flops?
Folding@home
Friday, March 15, 13 33

59. Petaflops
on
the
cheap
today,
exaflops
soon?
There
are
approximately
a
billion
computers
in
the
world
How
many
GPUs?
How
many
GPU
flops?
Folding@home
A
million
GPUs
pu]ng
out
1TFLOP
each
gets
us
to
an
exaflop:
we
could
do
this
today
Friday, March 15, 13 33

60. The
combinaOon
of
new
simulaOon
advances
and
chemically
detailed
models
has
suggested
a
paradigm
change
in
how
we
conceptualize
protein
folding.
Friday, March 15, 13 34

61. The
combinaOon
of
new
simulaOon
advances
and
chemically
detailed
models
has
suggested
a
paradigm
change
in
how
we
conceptualize
protein
folding.
We
are
now
looking
to
apply
MSM
approaches
to
new
areas:
1)
basis
of
signal
transducOon
2)
protein
misfolding
diseases
both
involving
issues
of
small
molecules
and
the
role
of
chemical
interacOons
Friday, March 15, 13 35

62. New
interest
in
my
lab:
probing
the
molecular
nature
of
the
mechanism
of
signal
transducEon
GPCRs kinases
Friday, March 15, 13 36

63. What
do
we
want
to
do?
kinases
Friday, March 15, 13 37

64. What
do
we
want
to
do?
• Understand
how
they
funcEon
• what
is
the
mechanism
of
ac4va4on
&
inac4va4on?
• how
is
the
signal
transduced?
• what
is
the
role
of
chemical
interac4ons
in
this
process?
kinases
Friday, March 15, 13 37

65. What
do
we
want
to
do?
• Understand
how
they
funcEon
• what
is
the
mechanism
of
ac4va4on
&
inac4va4on?
• how
is
the
signal
transduced?
• what
is
the
role
of
chemical
interac4ons
in
this
process?
• Use
this
understanding
to
modulate
their
funcEon
• design/predict
novel
small
inhibitors
&
ac4vators
• design/predict
protein
muta4ons
which
yield
new
func4ons
or
new
behaviors
kinases
Friday, March 15, 13 37

66. What
do
we
want
to
do?
• Understand
how
they
funcEon
• what
is
the
mechanism
of
ac4va4on
&
inac4va4on?
• how
is
the
signal
transduced?
• what
is
the
role
of
chemical
interac4ons
in
this
process?
• Use
this
understanding
to
modulate
their
funcEon
• design/predict
novel
small
inhibitors
&
ac4vators
• design/predict
protein
muta4ons
which
yield
new
func4ons
or
new
behaviors
• Connect
this
new
chemical
insight
to
kinases
basic
biology
and
aspects
of
disease
Friday, March 15, 13 37