Transcript Protein Folding - The Robinson Group – University of

Protein Folding & Biospectroscopy
Lecture 4
F14PFB
David Robinson
Protein Folding
1. Introduction
2. Protein Structure
3. Interactions
4. Protein Folding Models
5. Biomolecular Modelling
6. Bioinformatics
Protein Folding
• Protein folding considers the question of how
the process of protein folding occurs, i. e.
unfolded  native state.
• This very challenging problem has been
described as the second half of the genetic
code, and as the three-dimensional code, as
opposed to the one-dimensional code involved
in nucleotide/amino acid sequence.
• Importance:
– Predict 3D structure from primary sequence
– Avoid misfolding related to human diseases
– Design proteins with novel functions
Anfinsen Experiment
• Denaturation of ribonuclease
A (4 disulfide bonds), with 8
M Urea containing bmercaptoethanol, leads to
random coil and no activity
Anfinsen Experiment
• After renaturation, the refolded protein has native activity,
despite 105 ways to renature the protein.
• Conclusion: All the information necessary for folding into
its native structure is contained in the amino acid sequence
of the protein.
Anfinsen Experiment
• Remove b-mercaptoethanol only,
oxidation of the sulfhydryl group,
then remove urea → scrambled
protein, no activity
• Further addition of trace amounts
of b-mercaptoethanol converts the
scrambled form into native form.
• Conclusion: The native form of a
protein has the thermodynamically most stable structure.
The Levinthal paradox
 Many proteins fold in seconds or less: how is this possible?
 Cyrus Levinthal tried to estimate how long it would take a protein to
do a random search of conformational space for the native fold.
 Imagine a 100-residue protein with three possible conformations per
residue. Thus, the number of possible folds = 3100 = 5 x 1047.
 Let us assume that protein can explore new conformations at the
same rate that bonds can reorient (1013 structures/second).
 Thus, the time to explore all of conformational space = 5 x 1047/1013 =
5 x 1034 seconds = 1.6 x 1027 years >> age of universe
 This is known as the Levinthal paradox.
Models of Protein Folding
Framework model of protein folding
C
N
Supported by experimental observation of rapid formation
of secondary structure during protein folding process
Framework model of protein folding
C
N
Formation of individual secondary structure elements
Framework model of protein folding
N
C
Coalescence and rearrangement of
individual secondary structure elements
Nuclear condensation model
C
N
Supported by protein engineering studies
and various theoretical calculations
Nuclear condensation model
N
C
Formation of a nucleus of hydrophobic residues
Nuclear condensation model
N
C
Expansion of nucleus
Framework and Condensation
models are extremes on a continuum
Framework (or diffusion-collision):
2º structure forms independently and “dock” to form 3º structure
Nucleation condensation:
Concerted consolidation of 2º and 3º structure as nucleus expands
Condensation when 2º structure inherently unstable in absence of 3º
Structure. Framework becomes more probable as 2º elements become
more stable
Molten Globule State
• Collapsed, with native-like 2º structure (far UV CD)
• Weak or transient side-chain interactions (near UV CD)
• Binds hydrophobic dyes
• Many proteins form molten globules at low pH
• Model for early stages of protein folding (hydrophobic collapse)
The Folding Funnel
• A new view of protein folding suggested that there is no single route,
but a large ensemble of structures follow a many dimensional funnel
to its native structure.
• Progress from the top to the bottom of the funnel is accompanied by
an increase in the native-like structure as folding proceeds.
Levinthal & Landscapes
• Structure space
3100 conformations
• Sequence space
20100 sequences
Figure from Englander & co-workers,
Proc Natl Acad Sci 98 19104 (2001)
Boltzmann distribution
Boltzmann distribution
Distribution of conformations over the
available energy levels.
Which is the most probable?
Boltzmann distribution is the outcome
of blind chance occupation of energy
levels, subject to the requirement that
the total energy has a particular value
N exp( Ei / kT )
Ni 
q
q = partition function; N total number
Occupancy (Ni) of level i
Partition function
q is the sum of Boltzmann factors
Reflects the number of thermally accessible
states at the temperature of interest
q   Boltzm ann factors
q   exp( Ei / kT )
i
Toy protein model
Red – Hydrophobic (H)
Black – Polar (P)
HP model
1 conformation: E = -e
4 conformations: E = 0
Partition function – toy model
Q = 4 exp(-E0/kT) + exp(-E1/kT)
Let E0 = 0 and E1 = -e, then
Q = 4 + exp(e/kT)
Prob (Native state), P = qNative/Q
P = exp(e/kT)/{4 + exp(e/kT)}
EHH = -e
DG = -kT {Prob(Nat)/Prob(Unf)}
1
Prob (Nat)
UNFOLDED
0
Tm
T
Folding landscapes and the Levinthal paradox
Flat landscape
(Levinthal paradox)
Tunnel landscape
(discrete pathways)
Realistic landscape
(“folding funnel”)