the control of enzyme activity - cheric · pdf file1 the control of enzyme activity enzyme...
TRANSCRIPT
1
The control of enzyme activity
Enzyme Engineering
Two ways to control enzyme activity
1. Controlling concentration of enzyme : Regulation of transcription and translation →Long-term
2. Controlling activities of enzyme → Rapid responses
6.2 Control of the activities of single enzymes
1. By covalent bonds
2. By reversible binding of ‘regulator’
3. Inhibitor, [S], product inhibition, etc…
2
6.2 Control of the activities
1. Control by covalent bondIrreversible, mostly intracellular regulation
Example: phosphorylation, ubiquitination, …
1.1 Irreversible changes in covalent bond (Proteolysis)
(mostly for extracellular regulation)
6.2.1.1 Control by irreversible changes in covalent bond
Signal amplification mechanism of proteolysis
1) Pancreatic proteases
2) Blood coagulation Fibrinogen
prothrombin → thrombin →
Fibrin
3
6.2.1.2 Control by reversible changes in covalent bond
Phosphorylation-dephosphorylation
Adenylation or ADP-ribosylation also regulate enzyme activity
Methylation, acetylation, tyrosinolation, etc…do not regulate activity
1. Phosphorylation
Kinase : phosphorylation of other protein
Protein phosphatase : dephosphorylation
Human has 2000 kinase and 1000 protein phosphatase
Phosphorylation occurs on serine/threonine, tyrosine using ATP(GTP) as a substrate
4
6.2.1.2 Control by reversible changes in covalent bond
Ser/Thr phosphorylationinvolves in metabolic control, while Tyrphosphrylation in cell growth/differentiation
6.2.1.2 Control by reversible changes in covalent bond
Adenylylation on Tyr of glutamine synthase →Reducing enzyme activity
Nitrogenase from nitrogen-fixing bacteria is regulated by ADP-ribosylation on Arg →Reducing enzyme activity
Features
1. Rapid response and signal amplification
2. Continuous response
Ser/thr phos → in balance
Tyr phos → shift to dephosphorylation,
meaning the transient response
5
6.2.2 Control by Ligand binding
Conformation changes by ligand binding regulates enzyme activity
Firstly found in biosynthetic pathway
6.2.2 Control by Ligand binding
From Monod and Jacob
1. Effectors are structurally distinct from substrate or product, so they are unlikely bound to active site (allosteric)
2. Enzyme kinetics are sigmoidal rather than hyperbolic
3. Some treatments desensitize the enzymes to effector without losing enzyme activity
4. In general, the regulated enzymes are multimeric (structurally complex)
6
6.2.2.2 Kinetics of Ligand binding
Starting from Hb model
Allosteric effect
P + A PA
][]][[
PAAPK =
KAA
PAPPAY
+=
+=
][][
][][][
6.2.2.2 Kinetics of Ligand binding
Hill eq. (1910)
KAA
PAKPAY h
h
n
n
+=
+=
][][
][][
h = 1
h > 1
P + nA PAn
][]][[
n
n
PAAPK =
7
6.2.2.2 Kinetics of Ligand binding
KA
YY h][
1=
−
h : cooperativity
h is generally smaller than nh can be obtained by experiment
KAhY
Y log]log[)1
log( −=−
6.2.2.2 Kinetics of Ligand binding
Monod, Wyman, and Changeux (MWC) model
1. At lease one axis of symmetry
2. The conformation of each subunit is affected by others
3. Two conformation states, R and T
4. Either in R or T state, the symmetry is conserved - no ‘hybrid’ state
Fraction of subunitbound to substrate
8
6.2.2.2 Kinetics of Ligand binding
Two parameters are defined
a; conc. Free ligand, c=KR/KT, L=[T0]/[R0]
6.2.2.2 Kinetics of Ligand binding
L and c can be obtained experimentally (in hemoglobin, L=9050, c=0.014)
K system and V systema) No Substrate, no Effector
b) Adding substrate
c) Adding effector
9
6.2.2.2 Kinetics of Ligand binding
Koshland, Nemethy, and Filmer (KNF) model
“Induced-fit” hypothesis
1. Without substrate, the protein exist in one state
2. The conformational change is sequential
3. The intxns b/n subunits can be positive/negative
6.2.2.2 Kinetics of Ligand binding
Differences between MWC and KNF models
MWC model
KNF model
10
6.2.2.2 Kinetics of Ligand binding
The significance of the cooperativity in enzyme kinetics : small changes in [S] induce large changes in v
6.3 Control of metabolic pathways
Intrinsic control vs extrinsic control
Intrinsic control : Control of metabolic activity by metabolite concentrations (Unicellular organism)
Extrinsic control : Control of metabolic activity by extracellular signals, such as hormones or nervous stimulation (Multicellular organism)
Secondary signaling molecules : cyclic AMP, Ca2+, inositol 1,4,5-triphosphate, and diacylglycerol
11
6.3 Control of metabolic pathways
1. Amplification
2. Signaling depending on the # of receptors/cell
3. One protein kinase activating many different enzymes
6.3 Control of metabolic pathways
6.3.1 General aspects of intrinsic control
Finding most economic way to regulate the flux
12
6.3 Control of metabolic pathways
Carbamoyl-phosphate synthase is inhibited by pyrimidines, but not by arginineCarbamoyl-phosphate synthase is activated by orinithineOther methods of regulation include isoenzymes(ex. Aromatic amino acid pathway)
6.3 Control of metabolic pathways
6.3.2 Amplification of signals
1. Substrate cycles
If E2 and E-2 are catalyzed by different enzymes,
AMP activates 1 and inhibits 2
13
6.3 Control of metabolic pathways
Control of glucose/glycogen metabolism in liver
Other examples include PEP/pyruvateinterconversion, fatty acid/triglycerol in adipose tissue, etc…Sometimes working as futile cycle
6.3 Control of metabolic pathways
2. Interconvertible enzyme cycle
Control of enzyme activities by covalent modification
0.5% changes of modifier can regulate enzyme activity from 10 to 90%
High ratios of [NADH]/[NAD+] and [acetylCoA]/[CoA] activate kinase and inactivates protein phosphatase
14
6.3 Control of metabolic pathways
1% increase of [cAMP] can convert 50% of phosphorylase b to a within 2 seconds
6.3 Control of metabolic pathways
3. Theoretical approach to analyze the metabolic pathways
1. Metabolic flux analysis
2. Metabolic control analysis
15
Metabolic Flux Analysis
X1 X2 X3v1 v2 v3 v4
211 vv
dtdX
−=
322 2vv
dtdX
−=
433 vv
dtdX
−=
vSdtdX
⋅=
⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
−−
−=
110002100011
S
V1 V2 V3 V4
X1
X2
X3
Example 1
322 XX →
Quasi Steady State Assumption
0=⋅= vSdtdX
01100
02100011
4
3
2
1
=
⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢
⎣
⎡
⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
−−
−
vvvv
16
X1
X2X3
X4
v2v3
v4
v5
v6
v1
4322 XXX →+
0=⋅= vSdtdX
0
011000001100102010
000111
6
5
4
3
2
1
=
⎥⎥⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢⎢⎢
⎣
⎡
⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢
⎣
⎡
−−
−−−−
vvvvvv
Example 2
0
011000001100102010
000111
6
5
4
3
2
1
=
⎥⎥⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢⎢⎢
⎣
⎡
⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢
⎣
⎡
−−
−−−−
vvvvvv
0
001100000110100201
010011
6
1
5
4
3
2
=
⎥⎥⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢⎢⎢
⎣
⎡
⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢
⎣
⎡
−−
−−−−
vvvvvv
Type I Operation
17
0
001100000110100201
010011
6
1
5
4
3
2
=
⎥⎥⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢⎢⎢
⎣
⎡
⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢
⎣
⎡
−−
−−−−
vvvvvv
[ ] 0=⎥⎦
⎤⎢⎣
⎡
m
umu V
VSS
0=+ mmuu VSVS
mmuu VSVS −=
mmuu VSSV 1−−=
Example 3
Acetate(internal)
Glucose
G6P
F6P
Ru5P
X5P R5P
GAP
PEP
2 NADPHCO2
PEP
PYR
GAP S7P
PYR
AcCoA
ICT
SUC KG
OAA
F6P E4P
ATP
NADHATP
ATP
NADHCO2
ATP
Acetate(external)
Acetatefrom proteinproduction
CO2
CO2(internal)
CO2(external)
Biomass
NADH
AcCoA
ATPNADHCO2
NADHCO2
NADHFADH2
ATPNAD(P)H 2 ATP
*
qglc
v3
vpyr
vpdhvppc
vacev8
vtal
vtkt
v16v15
v14
v12
v11
vglyoxvict
vbiomass
vatp
vresp
qCO2
qace
vpgi
vpep
18
Jglc Jpgi J3 Jpep Jpyk Jpdh Jace J8 Jict J11 J12 Jppc J14 J15 J16 Jtkt Jtal Jresp Jatp Qco2 Qace Jbiomass
G6P 1 -1 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 -205
F6P 0 1 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 -70.9
GAP 0 0 2 -1 0 0 0 0 0 0 0 0 0 0 0 1 -1 0 0 0 0 -1625
PEP -1 0 0 1 -1 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 -519.1
PyR 1 0 0 0 1 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -2832.8
AcCoA 0 0 0 0 0 1 -1 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 -4028.8
OAA 0 0 0 0 0 0 0 -1 0 0 1 1 0 0 0 0 0 0 0 0 0 -1786.7
ICT 0 0 0 0 0 0 0 1 -1 0 0 0 0 0 0 0 0 0 0 0 0 0
KG 0 0 0 0 0 0 0 0 1 -1 0 0 0 0 0 0 0 0 0 0 0 -1078.9
SUC 0 0 0 0 0 0 0 0 0 1 -1 0 0 0 0 0 0 0 0 0 0 0
Ru5P 0 0 0 0 0 0 0 0 0 0 0 0 1 -1 -1 0 0 0 0 0 0 0
R5P 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 -1 0 0 0 0 0 -897.7
X5P 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 -1 0 0 0 0 0 0
S7P 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 -1 0 0 0 0 0
E4P 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 -361
NAD(H)P 0 0 0 1 0 1 0 0 1 1 1 0 2 0 0 0 0 -1 0 0 0 -14678
ATP 0 0 -1 1 1 0 1 0 0 1 1 0 0 0 0 0 0 2 -1 0 0 -18485
CO2 0 0 0 0 0 1 0 0 1 1 0 -1 1 0 0 0 0 0 0 -1 0 1793
acetate 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 387
Assumption : Vglyox = 0, Otherwise, Su is singular
mmuu VSSV 1−=
⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡=
⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
=2.23.0
1
2CO
ace
glc
m
qqJ
V
⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢
⎣
⎡
=
⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢
⎣
⎡
=
0000788.056.735.3
0285.00285.00285.00992.0128.0226.0466.0466.0551.0551.0271.014.1363.063.1879.0856.0
16
15
14
12
11
8
3
Biomass
atp
resp
tal
tkt
ppc
ict
ace
pdh
pyr
pep
pgi
u
JJJJJJJJJJJJJ
JJJJJ
J
V
19
⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢
⎣
⎡
=
⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢
⎣
⎡
=
0000788.056.735.3
0285.00285.00285.00992.0128.0226.0466.0466.0551.0551.0271.014.1363.063.1879.0856.0
16
15
14
12
11
8
3
Biomass
atp
resp
tal
tkt
ppc
ict
ace
pdh
pyr
pep
pgi
u
JJJJJJJJJJJJJ
JJJJJ
J
V
Acetate(internal)
Glucose
G6P
F6P
Ru5P
X5P R5P
GAP
PEP
2 NADPHCO2
PEP
PYR
GAP S7P
PYR
AcCoA
ICT
SUC KG
OAA
F6P E4P
ATP
NADHATP
ATP
NADHCO2
ATP
Acetate(external)
Acetatefrom proteinproduction
CO2
CO2(internal)
CO2(external)
Biomass
NADH
AcCoA
ATPNADHCO2
NADHCO2
NADHFADH2
ATPNAD(P)H 2 ATP
*
qglc
v3
vpyr
vpdhvppc
vacev8
vtal
vtkt
v16v15
v14
v12
v11
vglyoxvict
vbiomass
vatp
vresp
qCO2
qace
vpgi
vpep
mmuu VSSV 1−=
⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡=
⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
=35.3
237.01
resp
ppc
glc
m
JJJ
V
⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢
⎣
⎡
=
⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢
⎣
⎡
=
0000829.0204.032.242.7
0299.00299.00299.0104.0134.0500.0500.0590.0590.0174.010.1331.061.1873.0849.0
2
16
15
14
12
11
8
3
Biomass
ace
CO
atp
tal
tkt
ict
ace
pdh
pyr
pep
pgi
u
JqqJJJJJJJJJJ
JJJJJ
J
V
20
Metabolic Flux Analysis I
Mass Balance Equations
Quasi Steady State Approximation
Removing the Linearly Dependent
Reactions
Measure the Fluxes as many as Degree
of Freedom
Metabolic Flux Analysis II
Mass Balance Equations
Quasi Steady State Approximation
Removing the Linearly Dependent
Reactions
Measure the Fluxes as many as
Degree of Freedom
• More Constraints
• Optimal Solution
21
0=⋅= vSdtdX
max,0 ii vv ≤≤
max,0 jj vv ≤≤
max,0 kk vv ≤≤
Jbiomass Maximum
::
42 =+ yx )0,0( ≥≥ yx
x
y
Maxyx →+ 32
4
2
1
2
22
From Edwards and Palsson, PNAS, 97:5528
Summary : Metabolic Flux Analysis
Metabolic Flux Distribution
Maximum Yield
Predicting Growth Rates (wild type and
mutant)
Relying on Biochemical Knowledge
No Kinetics and Regulatory
Information
23
Metabolic Control Analysis
X1 X2 X3e1 e2 e3 e4
i
k
i
k
k
iJi Ed
JddEdJ
JEC k
lnln
==
Flux control coefficient
i
kvi dS
dvE k =Enzyme
FluxkJ
iCSlope =
Steady StateElasticity coefficient
Theorems 1=∑ JnC
0=∑ iS
Ji EC
Metabolic Control Analysis
Rate limiting enzyme
How the activity change in one enzyme can
affect on the overall pathway
Hard to estimate parameters