Cecilia Clementi Department of Chemistry Rice University Houston, Texas Prediction of protein functional states by multi-resolution protein modeling

Cecilia ClementiCecilia ClementiDepartment of Chemistry

Rice UniversityHouston, Texas

Prediction of protein functional states by Prediction of protein functional states by multi-resolution protein modelingmulti-resolution protein modeling

The challenges in molecular biophysics:The “middle way”, in between a few small molecules and bulk

Large water clusters

Wet/Dry interfaces

Interaction with solutesquantum chemistry gives

molecular orbitals

one water molecule

what are the relevant variables?what is the intrinsic dimensionality?

…in between…

thermodynamicsdescribes the system

bulk water

C.Clementi, Curr. Opin. Struct. Biol. 2008, vol.18(1), 10-15

Empirical approach Theoretical approach

Biochemist view: Physicist view:

Protoporphyrin ring

Central Iron

1 nm

Example: representation of a Heme group

Biophysics should reconcile the two!

Physicists and biochemists often perceive molecular structure and function differently

Outline

Our toolbox to explore protein landscapes

at multiple resolutions

Application to characterizea protein functional state

Photoactive Yellow Protein

PYP is believed to be responsible for H.halophila's ability to respond to

blue light.

PYPHow?

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 rhodopsinPYP

PYP transforms light into biological signal

PYP’s native state.

Basic outline of the photocycle

How?We know the structure of

these states.

But the structure of this state is unkown.

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?

How?

The signalling process can be characterized using a multiscale

approach:

1) Coarse Graining2) All atom reconstruction

3) All atom / quantum calculations

P.Das, S.Matysiak & C.Clementi PNAS 102, 10141-10146 (2005)

The signaling state ensemble can be characterized using a multiscale approach:

1) Coarse graining

What’s 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

Building a coarse-grained protein model

2

2

i

j

Building a coarse-grained protein model

20 aminoacid “colors”

1-bead per residue (Cmodel)

P.Das, S.Matysiak & C.Clementi PNAS 102, 10141-10146 (2005)

A realistic coarse-grained protein model

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.


It is interesting to compare the results of this model (DMC) to a simpler model (GO)

The difference is in the inclusion of non-native interactions

DMCGO


GO

mod

elD

MC

mod

elDark PYP

Photoactivated PYP

Comparison with available experimental data (on D25)


experimental data from Bernard, et al.

Structure, 13, 953–962 (2005)

Flu

ctua

tions

(A

)

How much can we push

a prediction from a

protein coarse-grained model?

How accurate is the prediction?How can we test it quantitatively ?

Energy

foldedminimum

“activated”minimum?

unfoldedminimum

folded state ensemblechromophore in

trans configuration

folded state ensemblechromophore in cis configuration

activated statechromophore in cis configuration

photo-isomerization

protein“quake”

recovery

A.P.Heath, L.E.Kavraki & C.Clementi, Proteins 2007, 68, 646-661

Reconstruct backbone atoms

Reconstruct side-chain atoms

Start from only C-alpha atoms

Optimize structure(locally and globally)


2) All atom reconstruction



Along backbone… Along backbone…

Alpha-carbon

LysineLysine

An example rotational isomer (rotamer)

Different rotamers can be obtained by twisting around all the residue

bonds.




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 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.


3) All atom/quantum computations

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

Goal: Goal: Converge to parameters which approximate the molecule’s free energy


New Parameter Fitting Procedure

MD SimulationsMD Simulations

What: What: With initial parameters, run very long

molecular dynamics simulations.

Goal: Goal: Generate an ensemble large

enough for statistical properties

to converge


ClusterClusterWhat: What: Select sub-

ensembles by clustering the MD trajectory, using its

size to estimate as a measure of free energy.

Goal: Goal: Choose a few structures on which to calculate the quantum

chemical energy.


Quantum CalculationsQuantum CalculationsWhat: What: Use Gaussian to calculate the quantum chemical energy of the molecule. (PBE1PBE 6-311G**)

Goal: Goal: Calculate the energy of the molecules

in a reliable way.



New ParametersNew ParametersPerform 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.


New Parameter Fitting ProcedureResultsResults


The signalling process can be characterized using a multiscale

approach:

1) Coarse Graining

3) QM parameter fitting for chromophore force field

2) All-atom reconstruction

All-atom structures of 25 most populated intermediate structures

Diffusion dynamics from the 25 reconstructed structures

P. J. Ledbetter, B.P. Lambeth & C.Clementi, unpublished results (2011)

Lowest energy structures

are solvated

Structural Analysis of the Results

Native (dark) state Photoactivated ensemble


How accurate is the prediction?How can we test it quantitatively ?

foldedminimum

“activated”

minimum?

unfoldedminimu

m

folded state ensemblechromophore in

trans configuration

folded state ensemblechromophore in cis configuration

activated statechromophore in cis configuration

comparable energy

pG

pR pB

Conformational entropy in pB

much largerthan pR

P. J. Ledbetter, B.P. Lambeth & C.Clementi, unpublished results (2011)

Next: design experimental tests(collaboration with Thomas Kiefhaber)

Clementi’s groupDr. 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)

$$ 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

Cecilia Clementi’s research groupCecilia Clementi’s research grouphttp://leonardo.rice.edu/~cecilia/research/

Graduate Students and Postdoctoral Positions

Available

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Cecilia Clementi Department of Chemistry Rice University Houston, Texas Prediction of protein functional states by multi-resolution protein modeling