Towards a computational design of an oxygen tolerant H - PowerPoint Presentation

Slide1

Towards a computational design of an oxygen tolerant H2 converting enzyme

Jochen BlumbergerUniversity College London, UK

CCSWS4 workshop, IPAM Los Angeles, 18 May 2011Slide2

Methods and properties

Observables:-ionic structure

-electronic structure-reaction free energies-redox potentials-pKa values-reaction barriers-charge

mobilities

Ab-initio MD

Classical MD

QM/MM MD

Electronic structure

theory (DFT)

Statistical mechanicsSlide3

e

-

e

-

Charge transfer

in organic solar cell materials

Electron flow in biological wires

e

-

H

+

Redox and PT reactions in solution

Gas diffusion in proteinsSlide4

Acknowledgment

Po-hung Wang (UCL):

did all workRobert Best (University of Cambridge, UK) £££

Taiwanese government: PhD scholarshipUCL

: PhD studentship

Royal Society

: University Research Fellowship

Slide5

Outline

Defining the optimization problem:

hydrogenase & aerotolerance

A microscopic model for gas diffusion in proteins (forward problem)

Diffusion paths and rates of H

2

, O

2

and CO in WT hydrogenase

Optimization through amino acid mutations (reverse problem) Slide6

Hydrogenase

: Nature’s solution to H2

production and oxidation catalyses H2 production:

2H+ + 2e- + energy 

H

2

catalyses H

2 oxidation:

H2  2H+ + 2e

- + energy highly efficient: turnover

rate ~ 1000 s-1

O2

FeS clusters

NiFe

or

FeFe

active site

clusterSlide7

Applications in

Bio-energy

Catalyst in biofuel cellsH

2 photo-biological production(green algea, cyanobacteria)

enzyme is

renewable

as

active as Pt but less expensive

selective for substrates

simplified cell design as ion exchange membrane not neededSlide8

Problem: oxygen

sensitivity of Hases

Inhibited by

O2 (atmosphere) and CO

Inhibition is irreversible for

FeFe-hases

(best H

2 producers)

Intense research efforts world-wide (Armstrong, Leger, Fontecilla

-Camps, Ghirardi,…)



O

2

sensitivity hampers large

scale applications Slide9

Three strategies to make

Hase

oxygen-tolerant

1. Restricting access of O

2

molecules

2. Restrict binding of O

2

to active site

3. Facilitating removal of oxidation

products Slide10

Engineering a molecular filter into Hase

Can one modify

hase

by mutation so that

H

2

can diffuse into/out of the active site,

but O

2 and CO cannot ?

H

H

O

O

O

C

mass (

g

/mol)

2

32

28

van-

der

-Waals radius (A)

1.20

1.52

1.70

lowest

nonv

.

multipole

moment

quadrup

.

quadrup

.

dipole

Property to be optimized:

Diffusion rate of a gas molecule from the solvent to the enzyme active siteSlide11

Outline

Defining the optimization problem:

hydrogenase &

aerotolerance

A microscopic model for gas diffusion in proteins (forward problem)

Diffusion paths and rates of H

2

, O

2

and CO in WT

hydrogenase

Optimization through amino acid mutations (reverse problem)

Slide12

Previous work on gas diffusion in proteins

Elber

and co-workers: locally enhanced sampling (LES) Schulten and co-workers: LES, implicit

ligand sampling McCammon

and co-workers: very long MD simulations

Ciccotti

,

Vanden-Eijnden

and co-workers: temperature accelerated MD

valuable but

no

rates reportedSlide13

MD simulation of gas diffusion in

NiFe-haseSlide14

Trajectory of a single gas molecule

Gas transport by diffusive

`jumps’ between protein cavities

diffusive jumps Slide15

From trajectories to probability density

H

2 gas probability density (brown contour)

average over many trajectoriesSlide16

From probability density to clusters

clusters or cavities (red spheres)

clustering

algorithm Slide17

A coarse

master equation approach to gas transport

k

12

k

21

k

23

k

32

k

24

k

45

k

56

p

i

: population of cluster

i

k

ij

: transition rate between

cluster

i

and

j

Assuming detailed balance:

P. Wang, R. B. Best, JB

, J. Am. Chem. Soc

.

133

, 3548 (2011).

P. Wang, R. B. Best, JB,

Phys. Chem. Chem. Phys

.

13

, 7708 (2011). Slide18

Calculation of transition rates

Transition rates between clusters from long equilibrium MD simulation

Solvent-to-protein cluster transitions depend on gas concentration and are

pseudo-

unimolecular

at constant gas pressure:

 they must be multiplied by Vsim

(H2O)/V0(H2O).

Enhanced sampling methods for transitions that are poorly sampled in equilibrium MD.

N

ijsym: number of transitions from

j

to

i

(

symmetrised

)

T

j

: total time spent in

j

N. V.

Buchete

, G. Hummer,

J. Phys. Chem. B

.

112

, 6057 (2008). Slide19

Constant force pulling

Pulling of gas molecule from cluster

n to m. Average over initial conditions gives mean first passage time τmn

Obtain MFPT for different pulling forces, τmn

(

F

) = 1/

kmn(F)

Extrapolation to zero force using the Dudko-Hummer-Szabo model (Kramers theory)

Insert k

0mn= kmn

(0) into the rate matrix

O.

Dudko

, G. Hummer, A.

Szabo

,

Phys. Rev.

Lett

.

96

, 108101 (2006) Slide20

Solution of master equation

Initial conditions (

t = 0):pSOLVENT = 1, all other pi = 0

Destination cluster: G (geminate)

Then solve

to obtain

p

G

(t).

k

12

k

21

k

23

k

32

k

24

k

45

k

56

GSlide21

Link to phenomenological rate constants

diffusion only

F

it gives phenomenological

diffusion rates

k

+1

and k

-1 that can be compared to experiment.

P. Wang, R. B. Best, JB, J. Am. Chem. Soc. 133, 3548 (2011).P. Wang, R. B. Best, JB,

Phys. Chem. Chem. Phys. 13, 7708 (2011). Slide22

Summary of computational steps

Long equilibrium MD simulation of protein + gas

Clustering of gas probability density

Transition rates between clusters

Solve master equation for given initial conditions

Fit time-dependent population of destination cluster

to phenomenological rate equation

k

1

,

k

-1Slide23

Simulation details

Molecular models

Protein: Gromos96 43a1 (united atom) Water: SPC/EH2, O2 and CO: 3 interaction sites

charges to reproduce experimental quadrupole moment (H

2

, O

2

) and

dipole moment (CO) Lennard-Jones parameter to fit experimental

solvation structure

Experimental diffusion constant in water reproduced to within 7% and in

n-hexane to within 43 %. Slide24

Simulation details (

contd)

Equilibrium simulation of hase: 100 gas molecules initially placed outside protein (225 mM) 50 ns NVT, 300 K

Gromos clustering algorithm of probability density

protein RMSD ca. 2 A with and

without gas



most transitions between clusters

well sampled

Transitions into G not well sampled

Constant force pulling: 50-100 trajectories per force

mean first passage times fit well to DHS model relative stat. error in diffusion rates

(from block averaging): 30 %.

Slide25

Outline

Defining the optimization problem:

hydrogenase &

aerotolerance

A microscopic model for gas diffusion in proteins (forward problem)

Diffusion paths and rates of H

2

, O

2

and CO in WT hydrogenase

Optimization through amino acid mutations (reverse problem)

Slide26

Probability density maps of gas

molecules for

Hase P. Wang, R. B. Best, JB, J. Am. Chem. Soc. 133, 3548 (2011).

P. Wang, R. B. Best, JB, Phys. Chem. Chem. Phys. 13, 7708 (2011). Slide27

Clusters and diffusion pathways

H

2, O2

COSlide28

Constant force pulling 68

G

COSlide29

Diffusion kinetics

H

2, O2CO

H

2

O

2

CO

exp CO

k

+1

(10

4 s-1 dm3 mol

-1

)

99

17

11

10-20Slide30

Committors

and reactive flux

H2CO

CO

O

2

spheres =

committor

Φ

G

blue:

Φ

G

=

0, red:

Φ

G

=

1

tube diameter prop to flux

J

Slide31

Conclusions

We have developed a general microscopic model for gas diffusion in proteins (forward problem) Computed diffusion rate agrees well with experimental rates (same order of magnitude)

Simulations can suggest possible mutation sites to block access of inhibitor molecules (inverse problem)