Statistical Mechanics Statistical Mechanics of Proteinsof Proteins

Equilibrium and non-equilibrium Equilibrium and non-equilibrium properties of proteinsproperties of proteins Free diffusion of proteinsFree diffusion of proteins

Coherent motion in proteins: temperature echoes

Simulated cooling of proteins

Ioan Kosztin Department of Physics & Astronomy

University of Missouri - Columbia

Molecular ModelingMolecular Modeling

1. Model building

2. Molecular Dynamics Simulation

3. Analysis of the• model • results of the simulation

Collection of MD DataCollection of MD Data

• DCD trajectory file coordinates for each atom velocities for each atom

• Output file global energies temperature, pressure, …

Analysis of MD DataAnalysis of MD Data1. Structural properties2. Equilibrium properties3. Non-equilibrium properties

Can be studied via both equilibrium and non-equilibrium MD simulations

Equilibrium Equilibrium (Thermodynamic) (Thermodynamic)

PropertiesPropertiesMD simulation

microscopic information

macroscopic properties

Statistical Statistical MechanicsMechanics

[r(t),p(t)]Phase space trajectory

Ensemble average overprobability density

()

Statistical Ensemble Statistical Ensemble

Collection of large number of replicas (on a macroscopic level) of the system

Each replica is characterized by the same macroscopic parameters (e.g., NVT, NPT)

The microscopic state of each replica (at a given time) is determined by in phase space

Time vs Ensemble Time vs Ensemble AverageAverage

For t, (t) generates an ensemble with tdd

t/lim(

Ergodic Hypothesis: Time and Ensemble averages are

equivalent, i.e., )(),( AprA t

Time average:

Ensemble average:

T

t ttAdtT

A0

)](),([1 pr

)()( AdA

Thermodynamic Thermodynamic Properties from MD Properties from MD

SimulationsSimulations

N

iitAA

N 1

)(1

MD simulationtime series

Thermodynamicaverage

Finite simulation time means incomplete

sampling!

Thermodynamic (equilibrium) averages can be calculated via time averaging of MD simulation time series

Common Statistical EnsemblesCommon Statistical Ensembles1. Microcanonical

(N,V,E):

2. Canonical (N,V,T):

3. Isothermal-isobaric (N,p,T)

])([)( EHNVE

}/)](exp{[)( TkHF BNVT

}/)](exp{[)( TkHG BNPT

Different simulation protocols [(t) (tt )] sample different statistical ensembles

Newton’s eq. of motion

Langevin dynamicsNose-Hoover method

Examples of Examples of Thermodynamic Thermodynamic

ObservablesObservables• Energies (kinetic, potential, internal,…)• Temperature [equipartition theorem]• Pressure [virial theorem]

• Specific heat capacity Cv and CP • Thermal expansion coefficient P

• Isothermal compressibility T

• Thermal pressure coefficient V

Thermodynamic derivatives are related to mean square fluctuations of thermodynamic quantities

Mean EnergiesMean EnergiesTotal (internal) energy:

Kinetic energy:

Potential energy:

You can conveniently use namdplot to graph the time evolution of different energy terms (as well as T, P, V) during simulation

Note:

N

iitEE

N 1

)(1

N

i

M

j j

ij

mt

KN 1 1

2

2)(1 p

TOTAL

KINETIC

BONDANGLEDIHEDIMPRPELECTVDW

KEU

TemperatureTemperature

From the equipartition theorem

KTBNk3

2

Note: in the NVTP ensemble NN-Nc, with Nc=3

TkpH/p Bkk

Instantaneous kinetic temperature

BNk

K

3

2T namdplot TEMP vs TS …

PressurePressureFrom the virial theorem TkrH/r Bkk

WTNkPV B

The virial is defined as

iji

ij

M

jjj rwW

,1

)(3

1

3

1 fr

Instantaneous pressure function (not unique!)

VWTkB /P

drrdrrw /)()( vpairwise

interactionwith

Thermodynamic Fluctuations Thermodynamic Fluctuations (TF)(TF)

N

ii AtAA

N 1

)(1

According to Statistical Mechanics, the probability distribution of thermodynamic fluctuations is

TkSTVP

Bfluct 2

exp

2222 )( AAAAA

Mean Square Fluctuations (MSF)

TF in NVT EnsembleTF in NVT EnsembleIn MD simulations distinction must be made between properly defined mechanical quantities (e.g., energy E, kinetic temperature T, instantaneous pressure P ) and thermodynamic quantities, e.g., T, P, …

VB CTkE 222 HFor example:

TB VTkP /22P

But:

22 )(2

3 TkK BN

)2/3(22BVB NkCTkU

)(2BVB kTkU P

Other useful formulas:

VV TEC )/( VV TP )/(

How to Calculate How to Calculate CCV V ??

VV TEC )/( 1. From definition

22 / TkEC BV

2. From the MSF of the total energy E

222 EEEwith

Perform multiple simulations to determine EEthen calculate the derivative of E(T) with respect to T

as a function of T,

Analysis of MD DataAnalysis of MD Data1. Structural properties2. Equilibrium properties3. Non-equilibrium properties

Can be studied via both equilibrium and/or non-equilibrium MD simulations

Non-equilibrium PropertiesNon-equilibrium Properties

1. Transport properties2. Spectral properties

Can be obtained from equilibrium MD simulations by employing linear response theory

Linear Response TheoryLinear Response Theory

Equilibriumstate

Non-equilibriumstate

)(tVext

Thermal fluctuationsautocorrelation function C(t)

External weak perturbationResponse function R(t)

Relaxation(dissipation)

Related through

the Fluctuation Dissipation Theorem

(FDT)

Time Correlation Time Correlation FunctionsFunctions

)0()'()'()()'( BttAtBtAttCAB

since eq is t independent !

BA BA

cross-auto- correlation function

Correlation time:

0

)0(/)( AAAAc CtCdt

Estimates how long the “memory” of the system lastsIn many cases (but not always): )/exp()0()( ctCtC

Response FunctionResponse Functionor generalized susceptibility

External perturbation: )()( tfAtV extext

Response of the system: )'()'(')(0

tfttRdttA ext

t

Response function: AtAAtAtR tPB )(}),({)(

with TkB/1Generalized susceptibility:

0

)()()( tRedtR ti

Rate of energy dissipation/absorption:

tieftfftAQdt

df 0

20 Re)(,||)(")(

2

1

Fluctuation-Dissipation Fluctuation-Dissipation TheoremTheorem

Relates R(t) and C(t), namely:

)()2/()(" C

Note: quantum corrections are important when TkB

In the static limit (t ): )0()0( 2 RTkAC B

)()2/tanh()(" 1 C

Diffusion CoefficientDiffusion Coefficient

Generic transport coefficient:

0

)0()( AtAdt tt

Einstein relation: 2)]0()([2 AtAt

Example: self-diffusion coefficient

0

)0()(3

1 vv tdtD

26 ( ) (0)Dt t r r

Free Diffusion (Brownian Motion) of Free Diffusion (Brownian Motion) of ProteinsProteins

in living organisms proteins exist and function in a viscous environment, subject to stochastic (random) thermal forces

the motion of a globular protein in a viscous aqueous solution is diffusive

2 ~ 3.2R nm

e.g., ubiquitin can be modeled as a spherical particle of radius R~1.6nm and mass M=6.4kDa=1.1x10-23 kg

Free Diffusion of Ubiquitin in Free Diffusion of Ubiquitin in WaterWater

ubiquitin in water is subject to two forces:• friction (viscous drag) force:

• stochastic thermal (Langevin) force:

f F v

( )L tF ( ) 0t often modeled as a “Gaussian white noise”

( ) (0) 2 ( )Bt k T t

6 R friction (damping) coeff

viscosity

(Stokes law)

231.38 10 ( ),Bk J K Boltzmann constant T temperature

The Dirac delta functionThe Dirac delta function

00 0

for tt

for t

In practice, it can be approximated as:

1( ) exp , 02

t t t as

Useful formulas:

0( ) ( ') ( ') '

tf t f t t t dt ( ) ( ) /a t t a

0.1 0.1

10

20

30

40

50

0.00

3.0

0.05

1

0

(t) describes =0 correlation time (“white noise”) stochastic processes

Equation of Motion and Equation of Motion and SolutionSolution

( )LfdvMa F M vdt

F t Newton’s 2nd law:

Formal solution (using the variation of const. method):

/0 0

/ '/( 1 ( ') ) 't tt tv e t e dtM

t v e / (/

0 0' ) /

0( ') 1 '( )

ttt t tx e t e dv eM

t tx M

= velocity relaxation (persistence) time

The motion is stochastic and requires statistical description formulated in terms of averages & probability distributions

Statistical AveragesStatistical Averages( ) 0t ( ) (0) 2 ( )Bt k T t

/0( ) 0tv t v e as t

Exponential relaxation of x and v with characteristic time

/0 0 0 0( ) 1 tx t x v e x v as t

22 /( ) ( ) 1 tD Dv t v t e as t

22

2

/

2

( ) (

)

) 3

(

2

2

tx t x t

x t x

Dt D

x D t

e

s

O

t a

Diffusion coefficient: (Einstein relation) /BD k T

Typical Numerical EstimatesTypical Numerical Estimatesexample: ubiquitin - small globular protein

2 10 21

3

3 / 29.60.560.16

25.4

1.6 10

6 0.

?

?

?

?9

?

?

B

T

T B

T

k Tv

v k T M m sps

d vpN s m

D m s

R mPa s

SimulationProperty Theory

ΜA

23

3 3

8.6 1.42 10 , 1.6 ,

10 , 310

M kDa kg R nm

M V kg m T K

mass : size: density: temperature:

Thermal and Friction ForcesThermal and Friction Forces

Friction force:

Thermal force:

94.5 10 4.5f TF v N nN

2 2 4.53

BT T f

k TF v F nN

For comparison, the corresponding gravitational force:14~ 10 ~g f TF Mg nN F F

Diffusion can be Studied by MD Diffusion can be Studied by MD Simulations!Simulations!

solvate

ubiquitin in water

1UBQPDB entry:

total # of atoms: 7051 = 1231 (protein) + 5820 (water) simulation conditions: NpT ensemble (T=310K, p=1atm), periodic BC, full electrostatics, time-step 2fs (SHAKE) simulation output: Cartesian coordinates and velocities of all atoms saved at each time-step (10,000 frames = 40 ps) in separate DCD files

How ToHow To: vel.dcd —> vel.dat: vel.dcd —> vel.dat namd2 produces velocity trajectory (DCD) file if in the

configuration file containsvelDCDfile vel.dcd ;# your file namevelDCDfreq 1 ;# write vel after 1 time-step

load vel.dcd into VMD [e.g., mol load psf ubq.psf dcd vel.dcd]note: run VMD in text mode, using the: -dispdev text option

select only the protein (ubiquitin) with the VMD commandset ubq [atomselect top “protein”]

source and run the following tcl procedure: source v_com_traj.tclv_com_traj COM_vel.dat

the file “COM_vel.dat” contains 4 columns: time [fs], vx, vy and vz [m/s] 70 12.6188434361 -18.6121653643 -34.7150913537

note: an ASCII data file with the trajectory of the COM coordinates can be obtained in a similar fashion

the v_COM_traj Tcl procedurethe v_COM_traj Tcl procedureproc v_com_traj {filename {dt 2} {selection "protein"} {first_frame 0} {frame_step 1} {mol top} args} { set outfile [open $filename "w"] set convFact 2035.4 set sel [atomselect $mol $selection frame 0] set num_frames [molinfo $mol get numframes] for {set frame $first_frame} {$frame < $num_frames} {incr frame $frame_step} { $sel frame $frame set vcom [vecscale $convFact [measure center $sel weight mass]] puts $outfile "$frame\t $vcom" } close $outfile}

Goal: calculate D and Goal: calculate D and

theory:

by fitting the theoretically calculated center of mass (COM) velocity autocorrelation function to the one obtained from the simulation

2 /0

20

( ) ( ) (0) tvv

B

C t v t v v ek T DvM

simulation: consider only the x-component

replace ensemble average by time average xv v

1

1( )N i

vv i n i nn

C t C v vN i

, , #i n nt t i t v v t N of frames in vel.DCD

(equipartition theorem)

Velocity Autocorrelation Velocity Autocorrelation FunctionFunction

0.1 ps

2 11 2 13.3 10B

x

D k Tv m s

0 200 400 600 800 1000

050

100150200250300

100 200 300 400

50100150200250300

100 200 300 400

50100150200250300

0 2 4 6 8 104020

02040

time [ps]

V x(t)

[m

/s]

( )vvC t

[ ]time fs

[ ]time fs

/( ) tvv

DC t e

Fit

Probability distribution of Probability distribution of

1/ 22 2 2

2

( ) 2 exp

exp

p v v v v

D v D

, ,x y zv

, ,x y zv vwith

Maxwell distribution of Maxwell distribution of vvCOMCOM

COM velocity [m/s]

3/ 2 2 2

3/ 2 22

( )

ex

(

p 4

2 ex

( )

2

( )

p

)x y z x y

B B

zP v

D v D

dv p v p v p v dv dv dv

dv

M Mvvk T

v

dk

vT

What have we learned ?What have we learned ?

2 10 2 10 21

3

3 / 29.6 31.60.56 0.10.16 0.03

25.4 141.6

1.6 10 0.3 10

6 0.9 4.7

B

T

T B

T

k Tv

v k T M m s m sps ps

d vpN s m pN s m

D m s m s

R mPa s mPa s

Property Theory Simulation

ΜA A

soluble, globular proteins in aqueous solution at physiological temperature execute free diffusion (Brownian motion with typical parameter values:

How about the motion of parts How about the motion of parts of the protein ?of the protein ?

parts of a protein (e.g., side groups, a group of amino acids, secondary structure elements, protein domains, …), besides the viscous, thermal forces are also subject to a resultant force from the rest of the protein

for an effective degree of freedom x (reaction coordinate) the equation of motion is ( ) ( )mx x f x t In the harmonic approximation ( )f x kx

and we have a 1D Brownian oscillator