Yoni Savir, Elad Noor, Ron Milo & T.T.

Weizmann Institute

* Rubisco, n. ribulose bisphosphate carboxylase oxygenase.

An enzyme present in plant chloroplasts and involved in the fixing

of atmospheric carbon dioxide in photosynthesis (OED) .

(PNAS 2010)

Outline

• Intro: Rubisco, an enzyme essential for life yet inefficient (?)

• Function of Rubisco in photosynthesis: Cross-species analysis.

• Evolution constrained to low dimensional landscapes:

phenomenological power laws.

• Is Rubisco optimal to its habitat?

• Outlook:

Confined plasticity - generic phenomenon?

D. Goodsell (PDB)

Rubisco catalyzes carbon fixation

D. Goodsell (PDB)

• Photosynthesis fixates carbon into organic forms.

• Rubisco participates in the Calvin cycle:

captures CO2 and releases C3 sugars.

• Complex of 8 large + 8 small subunits (540 kDa).

Impact of Rubisco’s (in)efficiency on the biosphere

• Possibly the most abundant protein on Earth.

• Catalyzes most carbon fixation.

• Very slow catalysis rate (~ 3-10 CO2 /sec).

• Specificity: confuses O=C=O and O=O

(KC = 10-100 and 1000 μM).

• Photorespiration instead of photosynthesis

(wasteful release of CO2 in daylight).(Kannapan & Gready 2008)

(D. Goodsell (PDB))

* Calvin cycle

Can Rubisco be improved?

• Improve specificity and carboxylation rate?

• Ongoing effort (genetic manipulation, directed evolution)

– little success so far.

• Already optimized by evolution? (or relic of old atmosphere?)

• Inefficiency due to biochemical constraints?

• Hint : rate-specificity tradeoff.

• Is Rubisco optimal,

constrained, or both?

specificity rate

?O=C=O O=O

sugars

Rubisco has several forms

More abundant hexadecimeric

form: eight large and small

subunit (L8S8)

Found in bacteria: only large

subunit dimmer (L2)

Rubisco fixates carbon in a multistage process

carboxylation

oxygenation

RuBP = Ribulose bi-phosphate PGA = 3-phosphoglyceratePGY =2-phosphoglycolate )

Rubisco kinetics is effectively Michaelis-Menten

2

2

carboxylation =

per Rubisco [O ]1 1

[CO ]

C

C

OK

v

K

2

2

oxygenation

per Rubisco [CO ]1 1

[O ]

O

O

C

v

K

K

4 parameters: C C O CK v K v

oxygenation

carboxylation

Energy landscape from MM Kinetics

Specificity ,

1, 1,

,

expon C

O C

on O

kS G G

k

, 1,

, 1,

/ exp( )

/ exp( )

on C C C C

on O O O O

k v K G

k v K G

Gas addition

2,

2,

exp( )

exp( )

C C

O O

v G

v G

Catalysis

* Rubisco fixates carbon in a multistage process

Rubisco

Binding

KC+

Khyd,C

Kdhyd,C

Kcleav,C

Kc-

Specificity of Rubisco is rather low

• Specificity = ratio of gas addition rates

• Why oxygenation? Biochemical constraints due to O2-CO2 similarity?

• Still, variation in specificity – selection pressures?

• Negative S - vC correlation (interplay between selection and constraints ?)

low [CO2]/[O2+ → high S, low vC and vice versa.

• [CO2]/[O2] determined by environment + concentration mechanism (CCM).

,2

2 ,

carboxylation / [CO ] /.

oxygenation / [O ] /

on CC C

O O on O

kv KS

v K k Range: 40-160

Kinetic parameters of Rubisco are correlated

• Analysis of data from 28 eukaryotes and prokaryotes:

Bacteria, cyanobacteria, green/non-green algae, C3/C4 plants.

• 4 kinetic parameters (S, vC , KC , KO ).

CO2 /O2 sugar

2

2

2

2

carboxylation = , [O ]

1 1[CO ]

oxygenation[CO ]

1 1[O ]

O

O

O

C

C

C

K

K

K

v

K

v

Cross-species analysis exhibits strong correlation

• Data resides in 4D space (S, vC , KC , KO ).

• But constrained to 1D power law (linear in log scale).

• PCA analysis ( >90% of variability is 1D)→

clear power law correlations:

Parameter space is approximately 1D

2.0 0.2

1.5 0.2

0.5 0.1

M

M

C C

CC

O

C

K v

Kv

K

S v

Effective 1D landscape for Rubisco evolution

• All organisms scattered around a straight line in 4D parameter space.

• Outliers: R. rubrum& R. sphaeroides

(form II of Rubisco).

Correlations indicate related energy barriers

easier CO2 addition

↕

harder hydration & cleavage

easier O2 addition

↕

much easier CO2 addition

“Conformational proofreading” (Savir & TT)

1, 2, constantC CG G

1, 1,2C OG G

Tradeoff between specificity and carboxylation rate

• Specificity S = kon,C /kon,O.

• S↔vC tradeoff results from these

two basic tradeoffs.

• Hints for possible optimality of Rubsico.

• Evolution in constrained ~1D landscape.

• Possible mechanistic picture: partition of deformation energy between two stages of carboxylation, binding and catalysis.

1, 2, constantC CG G

Look for optimality in 1D landscape (coordinate vC ) in given environment ([CO2], [O2] ).

Is Rubisco optimal? (under design constraints)

• Performance measure

net photosynthesis rate:

CO2fixed − CO2lost by photoresp:

12

carboxylation oxygenationf

3 3/2

2 2

3 3

2

/2 2

2 2

,1

3 10 [O ]/[CO ]

5 10 [O ]/[C1.3 / [C O ]O ]C C

C Cvf

v

v

v

• “Design constraints”:2.0 0.2

1.5 0.2

0.51 0.1

C C

CC

O

C

K v

Kv

K

S v

Environments - CO2:O2 content

• Atmosphere: 21% O2, 0.04% CO2 (volume).

• C3 Plants: No CCM. Aqueous solution in equilibrium with atmosphere.Gradient due to CO2 consumption. [CO2 ] ~7 μM, [O2]~ 250μM.

• C4 Plants: 10 fold CCM. Rubisco resides in bundle sheath cells where [CO2]is raised by pumping mechanism (CCM): [CO2 ] ~80 μM [O2] ≤ equilibrium.

• Aquatic species (Algae, Cyanobacteria): 100 fold CCM. [Ci ] is ~ 1-100 foldrelative to passive concentration in algae and may reach a 1000 fold forHCO3

- in cyanobacteria. Thus, [CO2 ]~ 1-100 mM. “typical” [CO2]~ 250μM.

• Photosynthetic bacteria: (uncertainty ~ CCM 10 fold). prosper in theanaerobic parts of all kinds of aquatic environments.

Rubisco are nearly optimal to their habitats

[O ] 3/22

[CO ]2

[O ]1 2 3/22

[CO ] [CO ]2 2

0.003,

1 1.3 0.005

C C

C C

v vf

v v

• Max( f ) as function of vC

in given habitat (O2 and CO2).

• All organism classes are nearly optimal.

Simple “design rules” of optimal Rubisco

• By maximizing photosynthesis rate:

• The optimal photosynthesis rate (per Rubisco):

* 1/2

2

*

2

* 1/4

2

0.86 [CO ] 1

[CO ] 1 2

164 [CO ] 1 0.5 .

C O

C O

C O

v

K

S

1/2* *

2

1

20.43· CO 1 2· ~O Cf v

1/43

2 2 (   10 · O CO is the oxygen effect 0-15%)O

Simple power laws for optimality

* 1/2

20.86 [CO ] 1C Ov

• Possible experimental tests:Evolution in response tochanges in habitat or translocation of foreign Rubisco

Rubisco effect on fitness

95%

34%

99%

Interplay between evolution and constraints

• Kinetic parameters with negative correlation such as S and vC.

• Hint: fluctuations around the line stronger for parameters that affect NPR weakly (KO vs. KC).

• Possible test: point mutation survey.

Conclusions

• Rubisco evolve in 1D landscape of simple power laws.

• Rubisco is slow and inefficient

yet nearly optimal in this landscape.

• Simple optimality relations (f ~ vC ~ [CO2]1/2 , KC ≈ *CO2])

• Suggests co-evolution of CCM and kinetic parameters.

• Possible experimental tests:

- evolution of C3 plants in changing environment.

- “ switching” Rubisco between organisms.

Yoni Savir, Elad Noor, Ron Milo & TT

(PNAS 2010)

Questions, future directions, generalization

• Form II outliers: Could specificity and carboxylation activity be localized?

• Response of Rubisco to long term climate changes?

• Can one improve Rubisco? CCM?

• Structural Mechanism?

• Constrained plasticity in low dimensional landscapes:A generic phenomenon in proteins? (preliminary data).

• Examining other strongly selected proteins