embio meeting vienna, 2006

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22/5/2006 EMBIO Meeting 1 EMBIO Meeting Vienna, 2006 Heidelberg Group IWR, Computational Molecular Biophysics, University of Heidelberg Kei Moritsugu MD simulation analysis of interprotein vibrations and boson p Kinetic characterization of temperature-dependent protein int motion by essential dynamics Langevin model of protein dynamics

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EMBIO Meeting Vienna, 2006. Heidelberg Group IWR, Computational Molecular Biophysics, University of Heidelberg Kei Moritsugu. MD simulation analysis of interprotein vibrations and boson peak Kinetic characterization of temperature-dependent protein internal - PowerPoint PPT Presentation

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Page 1: EMBIO Meeting  Vienna, 2006

22/5/2006 EMBIO Meeting 1

EMBIO Meeting Vienna, 2006

Heidelberg GroupIWR, Computational Molecular Biophysics, University of Heidelberg

Kei Moritsugu MD simulation analysis of interprotein vibrations and boson peak Kinetic characterization of temperature-dependent protein internal motion by essential dynamics Langevin model of protein dynamics

Page 2: EMBIO Meeting  Vienna, 2006

Langevin Model of Protein Dynamics

EMBIO Meeting

Vienna, May 22, 2006

IWR, University of HeidelbergKei Moritsugu and Jeremy C. Smith

- Introduction Dynamical model for understanding protein dynamics Langevin equation

- Direct application of Langevin dynamics:

Velocity autocorrelation function model

- Extension of the Langevin model:

Coordinate autocorrelation function model

Page 3: EMBIO Meeting  Vienna, 2006

22/5/2006 EMBIO Meeting 3

Physical interest: multi-body (> ~1000 atoms)

inhomogeneous system

Why Protein Dynamics?

Anharmonic motion on rough potential energy surface

Understand a “molecular machine”from physical point of view

Biological/chemical interest: expression and regulation of function mediated by anharmonic protein dynamics

conformationaltransition

Page 4: EMBIO Meeting  Vienna, 2006

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Protein Dynamics: How to Analyze?

Molecular Dynamics Simulation

Neutron Scattering Experiment- low resolution- large, complex system with surrounding environments

Dynamical Model

Data Analysis

Simplification- harmonic approximation- two-state jump model

- Langevin model

….- atomic motions with fs-ns timescales- limited time < s, system size < ~100 Å

Settles et.al., Faraday Discussion 193, 269 (1996)

Model Parameters

Protein Dynamics

Page 5: EMBIO Meeting  Vienna, 2006

22/5/2006 EMBIO Meeting 5

Dynamical Model

Langevin Equation2

2

)( )

(ij j ii

ji j

jiijF

d Vm q q

dtq R t

q

q

1

( ) 0

(0) ( ) 2 ( )

i

i j ij

R t

R R t t

Random forceFriction

PES roughness = Friction curvature = Frequency

,

1( ) ( )

2( ) i j

i jijV FV V q q q 0q

Harmonic Approximation of Potential Energy

Page 6: EMBIO Meeting  Vienna, 2006

22/5/2006 EMBIO Meeting 6

Mode Analysis Simplifying Protein Dynamics

Normal Mode/Principal Component

Apply Dynamical Model for Each Mode

collective motion high frequency vibration

1 1 1( ; , )x f t 2 2 2( ; , )x f t 3 3 3( ; , )x f t 4 4 4( ; , )x f t

Page 7: EMBIO Meeting  Vienna, 2006

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Calculations of Langevin ParametersMD Simulations Normal Mode Analysis

2

(0) ( )exp( / 2)(cos sin )

2n n

n nnn

nnnn

v v tt t t

v

2 2 / 4nn n n

120 K in vacuum

300 K in solution ( )

( )i

i

r t

r t

( )

( )

n in ii

n in ii

x t u r

v t u r

2FU U

Temperature dependence

Solvent effects

Velocity Autocorrelation Function (VACF) Model

n , nn

by each normal mode, n

Langevin Parameters

Page 8: EMBIO Meeting  Vienna, 2006

22/5/2006 EMBIO Meeting 8

Computations 1

Molecular Dynamics Simulations

Normal Mode Analysis

myoglobin   (1A6G, 2512 atoms, 153 residues) equilibrium conditions at 120K and 300K 1-ns MD simulation with CHARMM vacuum: microcanonical MD solution: rectangular box with 3090 TIP3P waters, NPT, PME

vacuum force field minimization of 1-ns average structure in vacuum calculate the Hessian matrix and its diagonalization

independent atomic motion,

with vibrational frequency, n

1, 2, ,( , , )Tn n n N nu u u u

Page 9: EMBIO Meeting  Vienna, 2006

22/5/2006 EMBIO Meeting 9

Langevin Friction

in water > in vacuum 300K > 120 K

300K water300K vacuum 120K water120K vacuum

Page 10: EMBIO Meeting  Vienna, 2006

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Langevin Frequency

(anharmonicity) < 0 : low >high 300 K > 120 K

vacuum NMA

NMA

water vacuum

NMA

(solvation) > 0 : low >high 300 K = 120 K

Page 11: EMBIO Meeting  Vienna, 2006

22/5/2006 EMBIO Meeting 11

Potential Energy Surface via Langevin Model

NMAvacuumsolution

: roughness (anharmonicity) < 0

intra-protein interaction solvation: collisions with waters suppress protein vibrations

increase of : increased roughness (solvation) > 0, independent of T

Normal Mode Water MDVacuum MD

Page 12: EMBIO Meeting  Vienna, 2006

22/5/2006 EMBIO Meeting 12

Dynamic Structure Factors1

( , ) ( , ) e2

i tS F t dt

q q

(0) ( )2,

1

( , ) i i

Ni i t

MD inc ii

F t b e e

q r q rq

MD Trajectory

Langevin Model3 6

2 ( ) 2, 2

1 1

( , ) exp | [1 ( )]N N

iBL inc i n n

i n i n

k TF t b t

m

q | q u

/ 2

2

(0) ( )( ) (cos sin )

2nnn n t nn

n n nnn

x x tt e t t

x

Langevin Model + Diffusion

(q, ) (q, ) (q, )corr L DF t F t F t2(q, ) exp( )DF t Dq t

120K water120K vacuum 300K water300K vacuum

q = 2Å-1

Page 13: EMBIO Meeting  Vienna, 2006

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Conclusion 1

Langevin model via VACF Protein vibrational dynamics

Friction: anharmonicity low > high high T > low T increase via solvation Frequency shift: (anharmonicity) < 0 (solvation) > 0

Svib(q,)

Page 14: EMBIO Meeting  Vienna, 2006

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Modified Model for Diffusion

Extended Langevin model1) CACF model2) Add diffusional contribution

vibration

t

x(t)

v(t)

diffusionPCA mode 1 PCA mode 100PCA mode 1 PCA mode 100300K

water

Page 15: EMBIO Meeting  Vienna, 2006

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Probabilistic Vibration/Diffusion Model

20

0

exp( / 2)(co(0) ( )

1s1 exp(

))

2(sin )v

v v vv

t tx x t t

txt

diffusion0

Langevin vibration

,v v

Coordinate Autocorrelation Function (CACF) Model

2 2 / 4v v v

PCA mode 1 PCA mode 100

MDmodel

MDmodel

Page 16: EMBIO Meeting  Vienna, 2006

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Computations 2

Molecular Dynamics Simulations

myoglobin   (1A6G, 2512 atoms, 153 residues) in solution: rectangular box with 3090 TIP3P waters equilibrium conditions under NPT ensemble T = 120, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 280, 300 K 1-ns MD simulation with CHARMM PME

Principal Component Analysis

Fitting: Calculation of model parameters

variance-covariance matrix: ij i jC x xindependent atomic motion,

with square fluctuation, n

1, 2, ,( , , )Tn n n N nu u u u

MD trajectories (0) ( )n n MDx x t

least square fit to model functiont = 0 ~ 5, 10, 20 ps

diagonalization

Page 17: EMBIO Meeting  Vienna, 2006

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Mean Square Fluctuations: Decomposition

85% 85%

85% 85%

2 2

1 1

2 2

1 1

(1 )

N N

n n nvib vibn n

N N

n n ndiff diffn n

x x

x x

n: eigenvalue of PCA: model parameter

Page 18: EMBIO Meeting  Vienna, 2006

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300

1

/ 300nn

300

,1

/ 300v v nn

Temperature Dependence: Dynamical Transition

0.375v v

Vibrational FrictionVibrational Frequency Ratio of Vibration

Page 19: EMBIO Meeting  Vienna, 2006

22/5/2006 EMBIO Meeting 19

Height of Vibrational Potential Wells via Model

230 K250 K280 K300 K

E

v

2

vibx 2 2

2

( ) / 2

/ 2

v v vib

v

E x

for < 1

Page 20: EMBIO Meeting  Vienna, 2006

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Diffusion Constant via Model

E ,v v

k Kramers Rate Theory

2 2/ 4 / 2exp[ ]

2v v vk E

2

vibx

MDKramers theory

2 2

vibD ka k x : diffusion on 1D lattice

a a a

kkk

v

~ ~

Page 21: EMBIO Meeting  Vienna, 2006

22/5/2006 EMBIO Meeting 21

S(q,w)

MDCACF modelVACF model

q = 2Å-1

300 K in water

Page 22: EMBIO Meeting  Vienna, 2006

22/5/2006 EMBIO Meeting 22

Conclusion 2

Langevin-vibration&diffusion model via CACF Protein dynamics

Simulation-based probabilistic description

Vibration: linear scheme with T v

Diffusion: nonlinear scheme with T , v ,

Diffusion constant via the present model using Kramers theory

2

vibx

2

diffx

S(q,)

Page 23: EMBIO Meeting  Vienna, 2006

22/5/2006 EMBIO Meeting 23

Acknowledgement

Thanks for your attention!

Vandana Kurkal-Siebert

Fellowship by JSPS