Prediction of protein functional states by
multi-resolution protein modeling
Cecilia Clementi
Department of Chemistry
Rice University
Houston, Texas
The challenges in molecular biophysics:
The “middle way”, in between a few small molecules and bulk
bulk water
…in between…
one water molecule
Large water clusters
Wet/Dry interfaces
Interaction with solutes
quantum chemistry gives
molecular orbitals
what are the relevant variables?
what is the intrinsic dimensionality?
thermodynamics
describes the system
Empirical approach
Theoretical approach
C.Clementi, Curr. Opin. Struct. Biol. 2008, vol.18(1), 10-15
Physicists and biochemists often
perceive molecular structure and function differently
Example: representation of a Heme group
Biochemist view:
Physicist view:
Protoporphyrin ring
Central Iron
1 nm
Biophysics should reconcile the two!
Outline
Our toolbox to explore
protein landscapes
at multiple resolutions
Application to characterize
a protein functional state
Photoactive Yellow Protein
PYP transforms light into
biological signal
PYP is believed to be responsible for
H.halophila's ability to respond to
blue light.
How?
PYP
PYP transforms light into
biological signal
PYP is interesting to
study because:

It is the prototype for the
PAS domain
(a ubiquitous domain in signaling proteins)

Its photochemistry is directly
analogous to rhodopsin
PYP
PYP’s
native state.
Basic outline of the photocycle
How?
But the structure of this state is
unkown.
We know the structure of
these states.
How?
The signaling state is
elusive:
It’s difficult to observe
experimentally
(because it partially unfolds)
It’s difficult to predict
computationally
(broad range of time scales)
PYP’s
signaling state?
The signalling process can be
characterized using a multiscale
approach:
1) Coarse Graining
2) All atom reconstruction
3) All atom / quantum calculations
The signaling state ensemble can be
characterized using a multiscale approach:
1) Coarse graining
P.Das, S.Matysiak & C.Clementi PNAS 102, 10141-10146 (2005)
What’s the role of a
protein coarse-grained model?
 Simplified models are largely used to test
general ideas and principles on toysystems
 Recently they have been applied to make
predictions on real protein systems
At what extent can protein coarse-grained models
be used as predictive tools on real systems?
C.Clementi, Curr. Opin. Struct. Biol. 2008, vol.18(1), 10-15
Building a coarse-grained protein model
Building a coarse-grained protein model
2
2
i
j
A realistic coarse-grained protein model
 1-bead per residue (Ca model)
 20 aminoacid “colors”
P.Das, S.Matysiak & C.Clementi PNAS 102, 10141-10146 (2005)
We “photoactivate” the coarse grained model by
perturbing the coarse grained forcefield
at the chromophore.
Dark PYP
Photoactivated PYP
The free energy is computed as a function of the “Diffusion Coordinates”
[“Determination of reaction coordinates via locally scaled diffusion map”,
M.A.Rohrdanz, W.Zheng, M.Maggioni & C.Clementi, J.Chem.Phys. 134, 124116 (2011)]
P.J. Ledbetter, B.P. Lambeth & C.Clementi, unpublished results (2011)
We “photoactivate” the coarse grained model by
perturbing the coarse grained forcefield
at the chromophore.
Dark PYP
Photoactivated PYP
This perturbation has a strong effect on the free energy
landscape, creating an on pathway intermediate.
P.J. Ledbetter, B.P. Lambeth & C.Clementi, unpublished results (2011)
It is interesting to compare the results of this
model (DMC) to a simpler model (GO)
DMC
GO
The difference is in the inclusion of non-native interactions
GO model
DMC model
Dark PYP
Photoactivated
PYP
P.J. Ledbetter, B.P. Lambeth & C.Clementi, unpublished results (2011)
Comparison with available experimental data (on D25)
Fluctuations (A)
experimental data from
Bernard, et al.
Structure, 13, 953–962 (2005)
P.J. Ledbetter, B.P. Lambeth & C.Clementi, unpublished results (2011)
How much can we push
a prediction from a
protein coarse-grained model?
How accurate is the prediction?
How can we test it quantitatively ?
“activated”
minimum?
folded state ensemble
chromophore in
cis configuration
protein
“quake”
activated state
chromophore in
cis configuration
folded state ensemble
chromophore in
trans configuration
unfolded
minimum
folded
minimum
recovery
Energy
photo-isomerization
The signaling state ensemble can be
characterized using a multiscale approach:
2) All atom reconstruction
Start from
only C-alpha atoms
Reconstruct
backbone atoms
Reconstruct
side-chain atoms
Optimize structure
(locally and globally)
A.P.Heath, L.E.Kavraki & C.Clementi, Proteins 2007, 68, 646-661
The signaling state ensemble can be
characterized using a multiscale approach:
2) All atom reconstruction
An example rotational isomer (rotamer)
Different rotamers can
be obtained by twisting
around all the residue
bonds.
Alpha-carbon
Along backbone…
Along backbone…
Lysine
The signaling state ensemble can be
characterized using a multiscale approach:
2) All atom reconstruction
P.J. Ledbetter, B.P. Lambeth & C.Clementi, unpublished results (2011)
Problem:
photo-isomerization changes the electronic
structure of the chromophore
Solution:
use quantum chemistry to correct the force field
(collaboration with Gustavo Scuseria’s
group at Rice)
The signaling state ensemble can be
characterized using a multiscale approach:
3) All atom/quantum computations
The chromophore is
responsible for triggering
conformational change.
But there are no standard
force fields for this residue.
The forcefield needs to be
derived from quantum
chemical computations, for cis,
trans and protonated forms.
Existing parameters are ineffective at
producing the isomerization energy
Trans (ground state) results
Cis results
Amber predicts
~ 14 kcal/mol,
while pbe1pbe/6-31++G** predicts ~ 6 kcal/mol
P.J. Ledbetter & C.Clementi, unpublished results (2011)
Parameter Fitting Procedure
MD
Simulations
Cluster
New
parameters
Quantum
Calculations
Goal: Converge to parameters which
approximate the molecule’s free energy
P.J. Ledbetter & C.Clementi, unpublished results (2011)
New Parameter Fitting Procedure
MD Simulations
What: With initial
parameters, run very long
molecular dynamics
simulations.
Goal: Generate an
ensemble large
enough for
statistical properties
to converge
New Parameter Fitting Procedure
Cluster
What: Select subensembles by clustering
the MD trajectory, using its
size to estimate as a
measure of free energy.
Goal: Choose a few
structures on which to
calculate the quantum
chemical energy.
New Parameter Fitting Procedure
Quantum Calculations
What: Use Gaussian to
calculate the quantum
chemical energy of the
molecule. (PBE1PBE 6-311G**)
Goal: Calculate the
energy of the molecules
in a reliable way.
P.J. Ledbetter & C.Clementi, unpublished results (2011)
New Parameter Fitting Procedure
New Parameters
Perform a least squares fit
on the energy of the
structures weighted by the
free energy estimate by
varying the parameters.
If the parameters
are realistic
enough, stop.
P.J. Ledbetter & C.Clementi, unpublished results (2011)
New Parameter Fitting Procedure
Results
P.J. Ledbetter & C.Clementi, unpublished results (2011)
The signalling process can be
characterized using a multiscale
approach:
2) All-atom reconstruction
All-atom structures
of 25 most populated
intermediate structures
1) Coarse Graining
3) QM parameter fitting
for chromophore force field
Diffusion dynamics from the 25 reconstructed structures
Lowest energy
structures
are solvated
P. J. Ledbetter, B.P. Lambeth & C.Clementi, unpublished results (2011)
Structural Analysis of the Results
Native (dark) state
Photoactivated ensemble
P.J. Ledbetter, B.P. Lambeth & C.Clementi, unpublished results (2011)
How accurate is the prediction?
How can we test it quantitatively ?
“activated”
minimum?
folded state ensemble
chromophore in
cis configuration
pR
activated state
chromophore in
cis configuration
comparable
energy
pB
Conformational
entropy
in pB
much larger
than pR
pG
unfolded
minimum
folded
minimum
folded state ensemble
chromophore in
trans configuration
Next: design experimental tests
(collaboration with Thomas Kiefhaber)
P. J. Ledbetter, B.P. Lambeth & C.Clementi, unpublished results (2011)
Cecilia Clementi’s research group
http://leonardo.rice.edu/~cecilia/research/
Clementi’s group
Dr. Mary Rohrdanz
Paul Ledbetter
Brad Lambeth
Wenwei Zheng
Amarda Shehu
Payel Das
Silvina Matysiak
Collaborators:
Prof. Kathy Matthews
Prof. Lydia Kavraki
Prof. Gustavo Scuseria
Prof. Kurt Kremer
Prof. Mauro Maggioni
(Rice Chemistry)
(Rice Applied Physics)
(Rice Chem. Eng.)
(Rice Chemistry)
(now: GMU)
(now: IBM Watson)
(now: U Maryland)
(Rice - Biochemistry)
(Rice - Computer Science)
(Rice - Chemistry)
(MPIP Mainz)
(Duke - Math)
Graduate Students and
Postdoctoral Positions
Available
$$ NSF (CAREER CHE-0349303, CCF-0523908, CNS-0454333)
$$ Texas Advanced Technology Program (003604-0010-2003)
$$ Norman Hackerman Welch Young Investigator Award
$$ Welch Foundation C-1570
$$ Hamill Innovation Award