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Simple toy models
(An experimentalist’s perspective)
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Lattice Polymers
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Lattice Polymers
Do they predict absolute folding rates?
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Lattice Polymers
Do they predict relative folding rates?
Two-state folding rates
kf = 2 x 105 s-1 kf = 2 x 10-1 s-1
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Landscape Roughness
Energy Gap
Collapse Cooperativity
Putative rate-defining criterion
Bryngelson & Wolynes (1987) PNAS, 84, 7524
Landscape Roughness
Kinetics switch from single exponential:
A(t) = A0 exp(-t·kf)1/h
To stretched exponential:
A(t) = A0 exp(-t·kf)1/h
When Landscape Roughness Dominates Kinetics
Socci, Onuchic & Wolynes (1998) Prot. Struc. Func. Gen. 32, 136Nymeyer, García & Onuchic (1998) PNAS, 95, 5921Skorobogatiy, Guo & Zuckermann (1998) JCP, 109, 2528Onuchic (1998) PNAS, 95, 5921
The energy landscape of protein L
Gillespie & Plaxco (2000) PNAS, 97, 12014
0.0
0.5
1.0
0.00 0.05 0.10
37˚C
Fraction folded
Time (s)
h = 0.98 0.08
0.0
0.5
1.0
0.0 0.1 0.2
-15˚C
Fraction folded
Time (s)
h = 1.04±0.07
The pI3K SH3 domain
Gillespie & Plaxco (2004) Ann. Rev. Bioch. Biophy, In press
0
25
50
75
100
0 500 1000 1500 2000
Relative Fluorescence (-100%)
Time (s)
h = 1.004±0.008
The Energy Gap
“The necessary and sufficient condition for [rapid] folding in this
model is that the native state be a pronounced
global minimum [relative to other
maximally compact structures].”
Sali, Shakhnovich & Karplus (1994) Nature, 369, 248
Gap Size Correlates with theFolding Rates of Simple Models
Dinner, Abkevich, Shakhnovich & Karplus (1999) Proteins, 35, 34
The uniqueness of the native state indicates that it is significantly more stable than any other
compact state: the energy gap is generally too large to measure experimentally.
An Indirect Test
For many simple models,
Tm correlates with Energy Gap size
15-mers (B0 = -2.0) r = 0.73
15-mers (B0 = -0.1) r = 0.92
27-mers (B0 = -2.0) r = 0.89
27-mers (B0 = -0.1) r = 0.97
Dinner, Abkevich, Shakhnovich & Karplus (1999) Proteins, 35, 34Dinner & Karplus (2001) NSB, 7, 321
Gillespie & Plaxco (2004) Ann. Rev. Bioch. Biophy., In press
Collapse cooperativity
“The key factor that determines the foldability of
sequences is the single, dimensionless parameter
…folding rates are determined
by ””””
Thirumalai & Klimov (1999) Curr. Op. Struc. Biol., 9, 197
Thirumalai & Klimov (1999) Curr. Op. Struc. Biol., 9, 197
101
102
103
104
105
106
107
108
0 0.2 0.4 0.6 0.8
101
102
103
104
105
106
107
108
Cytochrome C
10
20
30
40
-30
-20
-10
0 2 4 6
R
g
(Å)
[GdnHCl] (M)
Ellipticity 288 nm (m
o
)
Protein Rate Reference
Cytochrome C 6400 s-1 Gray & Winkler, pers com.
Ubiquitin 1530 s-1 Khorasanizadeh et al., 1993
Protein L 62 s-1 Scalley et al., 1997
Lysozyme 37 s-1 Townsley & Plaxco, unpublished
Acylphosphatase 0.2 s-1 Chiti et al., 1997
See also: Jaby et al., (2004) JMB, in press
101
102
103
104
105
106
107
108
0 0.2 0.4 0.6 0.8
101
102
103
104
105
106
107
108
101
102
103
104
105
106
107
108
0 0.2 0.4 0.6 0.8
101
102
103
104
105
106
107
108
Millet, Townsley, Chiti, Doniach & Plaxco (2002) Biochemistry, 41, 321
All “foldability” criterion optimal
1. Energy landscapes unmeasurably smooth
2. Energy gaps unmeasurably large
3. All within error of zero
Plaxco, Simons & Baker (1998) JMB, 277, 985
-2
0
2
4
6
5 10 15 20 25
log(k)
Relative Contact Order (%)
When the energy gap dominates folding kinetics, none
of a long list of putatively important parameters,
including the “number of short- versus long-range
contacts in the native state*”, plays any measurable
role in defining lattice polymers folding rates.
*Sali, Shacknovich & Karplus (1994) “How does a protein fold?” Nature, 369, 248
Do subtle, topology-dependent kinetic
effects appear only in the absence of
confounding energy landscape issues?
Go Polymers
• Native-centric energy potential
• Extremely smooth energy landscape
• Topologically complex
Topology-dependence of Go folding
5.0
5.5
6.0
0.3 0.4 0.5
log(MFPT)
Relative Contact Order
r = 0.2; p = 0.06
The topomer search model
1. The chain is covalent
2. Rates largely defined by native topology
3. Local structure formation is rapid
4. Equilibrium folding is highly two-state
Local structure formationis not the rate-limiting step
-1 10
-2
0 10
0
1 10
-2
2 10
-2
3 10
-2
4 10
-2
5 10
-2
6 10
-2
7 10
-2
-100 0 100 200
∆ Absorbance
Time (ns)
Closure of10-residue loop
Oh, Heeger & Plaxco, unpublished
Protein folding is highly two-state
Fyn SH3 domain
∆Gu = -3 kcal/mol
55 residue protein
Kohn, Gillespie & Plaxco, unpublished
-10123
Chemical Shift
4 residue truncation
Kohn, Gillespie & Plaxco, unpublished
∆Gu ~ 2 kcal/mol
-10123
Chemical Shift
The Topomer Search Model
Makarov & Plaxco (2003) Prot. Sci., 12, 17
P(QD) <K>QD
kf = QD<K>QD
Testing the topomer search model
We can test the model if we assume that all sequence-
distant residues in contact in the NATIVE STATE
must be in proximity in the TRANSITION STATE
Sequence-distant: > 4-12 residues
Native contact: CCÅ
-2
0
2
4
0 25 50 75 100
( k
f
/Q
D
)
Q
D
log
kf QD<K>QD
r = 0.88
Crowding Effects
Real Polymers
Gaussian Chains
Persistence lengthExcluded volume
-2
0
2
4
0.0 0.5 1.0
( k
f
/Q
D
)
Q
D
N
-1
log
kf QD<K>QD/N
r = 0.92
Makarov & Plaxco (2003) Prot. Sci., 12, 17
“It is also a good rule not to put
overmuch confidence in
observational results that are put
forward until they have been
confirmed by theory.”
Paraphrasing Sir Arthur Eddington
theoretical
simulation
Minimum requirements for topology-dependent kinetics
1. Connectivity
2. Rapid local structure formation
3. Smooth landscapes
4. Cooperativity
Go polymers are not cooperative
-10
-5
0
5
10
15
0.70.80.91
Contacts (Fraction Native)
Experimental
SimulationGF
(kB
T)
Δ
-20
-10
0
0 10 20
Energy ( )
Number of Native Contacts
ε
=1 = =3 =5s s s s
-εQ
Q(1 - s)/QN + sQ
E =
-10
-5
0
5
10
15
0.70.80.91
GF
(kB
T)
Δ
( )Contacts Fraction Native
=3s
=s
=1s
Experimental
s = 2
6.5
7.0
7.5
0.3 0.4 0.5
log(MFPT)
Relative Contact Order
r = 0.71; p = 10-16
s = 3
7.5
8.0
8.5
9.0
0.3 0.4 0.5
log(MFPT)
Relative Contact Order
Jewett, Pande & Plaxco (2003) JMB, 326, 247
See also: Kaya & Chan (2003) Proteins, 52, 524
r = 0.76; p = 10-18
AcknowledgementsUCSBBlake GillespieLara TownsleyJonathan Kohn Andrew JewettHoria Metiu
UT AustinDima Makarov
StanfordSeb DoniachIan MilletVijay Pande
Universita di FirenzeFabrizio Chiti
NIH, UC BioSTAR, ONR
Acknowledgements
Dziekowac!