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Protein Folding & Biospectroscopy

Lecture 4

F1CPFB

Jonathan Hirst

Protein Folding

1. Introduction

2. Protein Structure

3. Interactions

4. Protein Folding Models

5. Biomolecular Modelling

6. Bioinformatics

Protein FoldingAnfinsen

Experiment

Anfinsen ExperimentAnfinsen Experiment

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The Levinthal paradox Models of Protein Folding

Framework model of protein folding

N C

Framework model of protein folding

N

C

Framework model of protein folding

N

C

Nuclear condensation model

N C

3

Nuclear condensation model

N C

Nuclear condensation model

N

C

Framework and Condensationmodels are extremes on a continuum Molten Globule State

The Folding Funnel

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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

Occupancy (Ni) of level i

q = partition function; N total number q

kTENN i

i

)/exp(−=

Partition function

∑

∑−=

=

i

i kTEq

factorsBoltzmannq

)/exp(

Toy protein model

Red – Hydrophobic (H)

Black – Polar (P)

HP model

1 conformation: E = -ε

4 conformations: E = 0

Partition function – toy model

Q = 4 exp(-E0/kT) + exp(-E1/kT)

Let E0 = 0 and E1 = -ε, then

Q = 4 + exp(ε/kT)

Prob (Native state), P = qNative/Q

P = exp(ε/kT)/{4 + exp(ε/kT)}

EHH = -ε

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Tm0 T

1

Prob (Nat)

UNFOLDED

∆G = -kT {Prob(Nat)/Prob(Unf)}

Folding landscapes and the Levinthal paradox

Flat landscape

(Levinthal paradox)

Tunnel landscape

(discrete pathways)

Realistic landscape

(“folding funnel”)