yoni savir, elad noor, ron milotlusty/talks/rubisco2010.pdf · yoni savir, elad noor, ron milo...
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![Page 1: Yoni Savir, Elad Noor, Ron Milotlusty/talks/Rubisco2010.pdf · Yoni Savir, Elad Noor, Ron Milo & T.T. Weizmann Institute * Rubisco, n. ribulose bisphosphate carboxylase oxygenase](https://reader030.vdocuments.us/reader030/viewer/2022040703/5e86bb41ca9ae34da33da3ee/html5/thumbnails/1.jpg)
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)
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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)
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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).
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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))
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* Calvin cycle
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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
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Rubisco has several forms
More abundant hexadecimeric
form: eight large and small
subunit (L8S8)
Found in bacteria: only large
subunit dimmer (L2)
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Rubisco fixates carbon in a multistage process
carboxylation
oxygenation
RuBP = Ribulose bi-phosphate PGA = 3-phosphoglyceratePGY =2-phosphoglycolate )
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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
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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
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* Rubisco fixates carbon in a multistage process
Rubisco
Binding
KC+
Khyd,C
Kdhyd,C
Kcleav,C
Kc-
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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
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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
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Cross-species analysis exhibits strong correlation
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• 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
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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).
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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
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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
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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
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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.
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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.
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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
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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
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Rubisco effect on fitness
95%
34%
99%
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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.
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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)
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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