Cecilia Clementi

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Prediction of protein functional states by
multi-resolution protein modeling

• Cecilia Clementi
• Department of Chemistry
• Rice University
• Houston, Texas

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The challenges in molecular biophysicsThe
middle way, in between a few small molecules
and bulk
in between
what are the relevant variables? what is the
intrinsic dimensionality?
Empirical approach
Theoretical approach
C.Clementi, Curr. Opin. Struct. Biol. 2008,
vol.18(1), 10-15
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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!
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Outline
Our toolbox to explore protein landscapes at
multiple resolutions
Application to characterize a protein functional
state
Photoactive Yellow Protein
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PYP transforms light into biological signal
PYP is believed to be responsible for
H.halophila's ability to respond to blue light.
How?
PYP
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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
PYPs native state.
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Basic outline of the photocycle
How?
We know the structure of these states.
But the structure of this state is unkown.
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How?

• The signaling state is elusive
• Its difficult to observe experimentally
• (because it partially unfolds)
• Its difficult to predict computationally
• (broad range of time scales)

PYPs signaling state?
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The signalling process can be characterized using
a multiscale approach
1) Coarse Graining
2) All atom reconstruction
3) All atom / quantum calculations
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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)
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Whats the role of a protein coarse-grained
model?

• Simplified models are largely used to test
  general ideas and principles on toy-systems
• 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
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Building a coarse-grained protein model
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Building a coarse-grained protein model
i
j
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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)
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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)
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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)
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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
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Photoactivated PYP
Dark PYP
DMC model
GO model
P.J. Ledbetter, B.P. Lambeth C.Clementi,
unpublished results (2011)
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Comparison with available experimental data (on
D25)
experimental data from Bernard, et al.
Structure, 13, 953962 (2005)?
Fluctuations (A)
P.J. Ledbetter, B.P. Lambeth C.Clementi,
unpublished results (2011)?
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• How much can we push
• a prediction from a
• protein coarse-grained model?

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How accurate is the prediction?How can we test
it quantitatively ?
activated minimum?
protein quake
folded state ensemble chromophore in cis
configuration
Energy
photo-isomerization
activated state chromophore in cis configuration
folded state ensemble chromophore in trans
configuration
recovery
unfolded minimum
folded minimum
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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
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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
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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)
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• Problem
• photo-isomerization changes the electronic
  structure of the chromophore
• Solution
• use quantum chemistry to correct the force
  field
• (collaboration with Gustavo Scuserias
• group at Rice)

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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.
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Existing parameters are ineffective at producing
the isomerization energy
Trans (ground state) results
Cis results
Amber predicts 14
kcal/mol, while pbe1pbe/6-31G predicts 6
kcal/mol
P.J. Ledbetter C.Clementi, unpublished results
(2011)
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Parameter Fitting Procedure
Goal Converge to parameters which approximate
the molecules free energy
P.J. Ledbetter C.Clementi, unpublished results
(2011)
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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
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New Parameter Fitting Procedure
Cluster
What Select sub-ensembles 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.
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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)
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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)
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New Parameter Fitting Procedure Results
P.J. Ledbetter C.Clementi, unpublished results
(2011)
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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
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Diffusion dynamics from the 25 reconstructed
structures
Lowest energy structures are solvated
P. J. Ledbetter, B.P. Lambeth C.Clementi,
unpublished results (2011)?
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Structural Analysis of the Results
Native (dark) state
Photoactivated ensemble
P.J. Ledbetter, B.P. Lambeth C.Clementi,
unpublished results (2011)
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How accurate is the prediction?How can we test
it quantitatively ?
folded state ensemble chromophore in cis
configuration
activated state chromophore in cis configuration
comparable energy
activated minimum?
pR
pB
Conformational entropy in pB much larger than
pR
pG
folded state ensemble chromophore in trans
configuration
folded minimum
unfolded minimum
Next design experimental tests (collaboration
with Thomas Kiefhaber)
P. J. Ledbetter, B.P. Lambeth C.Clementi,
unpublished results (2011)?
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Cecilia Clementis research group http//leonardo.
rice.edu/cecilia/research/
Clementis group Dr. Mary Rohrdanz (Rice
Chemistry) Paul Ledbetter (Rice Applied
Physics) Brad Lambeth (Rice Chem. Eng.) Wenwei
Zheng (Rice Chemistry) Amarda Shehu (now
GMU) Payel Das (now IBM
Watson) Silvina Matysiak (now U
Maryland) Collaborators Prof. Kathy Matthews
(Rice - Biochemistry) Prof. Lydia Kavraki
(Rice - Computer Science) Prof. Gustavo
Scuseria (Rice - Chemistry) Prof. Kurt Kremer
(MPIP Mainz) Prof. Mauro Maggioni
(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