AFM force spectroscopy as a nanotool for early detection of misfolded protein.

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AFM force spectroscopy as a nanotool for early
detection of misfolded protein.
1st Annual Unither Nanomedical and Telemedical
Technology Conference

• Alexey V. Krasnoslobodtsev, PhD

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Outline

1. Misfolding (conformational) diseases
   background.
2. Single molecule approach (Force spectroscopy) to
   study misfolding phenomenon.
3. Force spectroscopy - advantages and applications.
   
4. Beyond measuring forces of intermolecular
   interactions Dynamic Force Spectroscopy.

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Protein folding, misfolding and aggregation
Environmental Stress
Misfolded protein
Chemical Stress
Chaperones
Native folded protein
Generic Perturbations
Pathophysiological Stress
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Protein Misfolding (Conformational) Diseases
Many human diseases are now recognized to be
conformational diseases associated with
misfolding of the proteins and their consequent
aggregation.
Alzheimers
Parkinsons

• These diseases include neurodegenerative
  disorders such as Alzheimers, Parkinsons
  disease, Huntingtons and prion diseases
  characterized by deposition of aggregates in
  Central Nervous System (CNS).
• Misfolded proteins are prone to aggregation
• Misfolded proteins and aggregates cause molecular
  stress and interfere with cellular function

Lewy bodies
Plaques and tangles
Huntingtons intranuclear inclusions
Prion amyloid plaques
Amyotrophic lateral sclerosis aggregates
Claudio Soto, 2003
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Mechanism of aggregation

• Stress (environmental) induced misfolding
  generates sticky aggregation prone conformation
• Normally folded protein interacts with misfolded
  protein
• Cycle multiplies copies of misfolded (diseased)
  proteins
• Goal - looking at the first stage of
  aggregation (dimerization) at a single molecule
  level

Normally folded protein
Misfolded protein
Oligomers
Large aggregates and fibrils
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Possible therapeutic interventions for protein
misfolding diseases

• Skovronovsky D.M., et al., 2006, Annu. Rev.
  Pathol. Dis., 1151-70

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Therapeutic approaches to misfolding diseases
Expression of the protein
Protein misfolding
Prevent aggregation of misfolded proteins
Aggregation
Loss of neuronal function and cell death
Neurodegeneration

• Small molecules that bind to specific regions of
  the misfolded protein and stabilize it.
• Chemical (pharmacological) Chaperones

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Rationale
Despite the crucial importance of protein
misfolding and abnormal interactions, very little
is currently known about the molecular mechanism
underlying these processes.
Initial stages of misfolding and aggregation are
very dynamic. High-resolution methods such as
x-ray crystallography, NMR, electron microscopy,
and AFM imaging have provided some information
regarding the secondary structure of aggregated
proteins and morphologies of self-assembled
aggregates. But they are unable to characterize
transient intermediates that can not be detected
by these bulk methods.
We propose a novel method for identification and
characterization of misfolded aggregation prone
states of a protein as well as conditions
favoring or disfavoring aggregation (misfolding).
Single molecule force spectroscopy is capable of
detecting interactions between transient species.

• Rationale A clear understanding of the
  molecular mechanisms of misfolding and
  aggregation will facilitate rational approaches
  to prevent protein misfolding mediated
  pathologies.

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Probing interactions between individual molecules
by AFM force spectroscopy
Force
AFM force spectroscopy allows studying
Distance

• Binding strengths - measures forces of
  interactions between individual molecules.

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AFM force spectroscopy
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Rupture event
Rupture force
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Tip retraction
Bond rupture
Stretching the linkers
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Model system- 7 aa peptide from Sup35 yeast prion
Misfolding exposing hot regions
Aggregation
Hot regions are short stretches of peptide
sequences.
Alzheimers amyloid-beta peptide 1-40(42) -gt
Aß16-22 is responsible for aggregation.
Huntingtons polyQ (gt40) -gt elementary Q7 shows
maximal kinetics of aggregation.
Parkinsons a-synuclein -gt 12 aa regions is the
core domain for aggregation.
Prion diseases short peptide from Sup35 yeast
prion

• A seven amino acid sequence within the N-terminal
  domain is responsible for the aggregation of the
  whole Sup35 protein
• Sequence GNNQQNY

Nelson, R.R., Sawaya, M.R., Balbirnie, M.,
Madsen, A.Ø., Riekel, C., Grothe, R., Eisenberg,
D. 2005. Structure of the cross-ß spine of
amyloid-like fibrils. Nature. Vol. 435, No. 9,
773-778.
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Sup35 Aggregation at different pHs
(Environmental Stress)
Morphology of aggregation different misfolding
states that have different strength of
interactions?
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AFM force spectroscopy nanotool for detection
of misfolded state.
Amyloid -ß peptide

• Parallel circular dichroism (CD) measurements
  performed for Aß peptide revealed that the
  decrease in pH is accompanied by a sharp
  conformational transition from a random coil at
  neutral pH to the more ordered, predominantly
  ß-sheet, structure at low pH.
• Importantly, the pH ranges for these
  conformational transitions coincide with those of
  pulling forces changes detected by AFM.
• In addition, protein self-assembly into
  filamentous aggregates studied by AFM imaging was
  shown to be facilitated at pH values
  corresponding to the maximum of pulling forces.
• Overall, these results indicate that proteins at
  acidic pH undergo structural transition into
  conformations responsible for the dramatic
  increase in interprotein interaction and
  promoting the formation of protein aggregates.

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AFM force spectroscopy -High throughput
screening machine for detecting efficient
therapeutic agents
Control
Drug 1
Drug 2
Drug 3

• Drug 2 is the best candidate for the
  development of effective therapeutic agents

Force of intermolecular interactions
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Challenges

• Robust system
• (for continuous measurements) We have recently
  developed surface chemistry which allows simple
  and reliable covalent attachment of biomolecules
  to the surfaces (AFM tip and mica).

Peptide
Peptide-SH

1. Automated exchange of buffers containing drugs of
   interest.
2. Automated data analysis.

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Beyond Force SpectroscopyDynamic Force
Spectroscopy (DFS) measurements
DFS measures kinetic parameters of dissociation
reaction
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2
r pulling velocity (loading rate)
F1 lt F2
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Dynamic Force Spectroscopy
Loading rate
Distance to transition state
Dissociation rate constant
Force
ln r
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Dynamic Force Spectroscopy
GNNQQNY Sup35 yeast prion
A dynamic force spectrum at pH2.0 reveals two
linear regimes distinguishable by differences in
slopes. This is usually attributed to a molecular
dissociation of a complex that involves
overcoming of more than one activation barrier.
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Dynamic Force Spectroscopy
k1off
k2off
0.2 Å
3.5 Å
Two barriers in the energy profile Inner
(second fit) and outer (first fit) activation
barriers
The estimated positions of inner and outer
barriers are 0.2 and 3.5 Å. The off rates are
286 and 0.9 s-1. Estimated lifetime of a dimer is
1.1 s which is much longer than nano/microsecond
conformational dynamics of a monomer.

• These data suggest that the ability of misfolded
  protein to form a stable dimer is a unique
  property of these conformational states for
  proteins suggesting a possible explanation for
  the phenomenon of the protein self-assembly into
  nanoaggregates.

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Summary

• Novel nanoprobing approach to study initial steps
  of misfolding and aggregation is proposed on the
  basis of AFM force spectroscopy operating on a
  single molecule level.
• There is an intimate relationship between
  aggregation propensity (protein misfolding) and
  strength of interprotein interactions.
• Force spectroscopy allows to study the mechanism
  of early dynamic events in the aggregation
  process which is not accessible by any other
  available method.
• A dimer formed by two misfolded peptides is very
  stable as compared to monomer conformational
  dynamics providing the explanation for the
  phenomenon of the protein self-assembly into
  nanoaggregates.

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Acknowledgements

• Yuri L. Lyubchenko, Ph.D., D. Sc.
• Lab Members
• Luda Shlyakhtenko, Ph.D.
• Alex Portillo
• Jamie Gilmore
• Junping Yu, Ph.D.
• Mikhail Karymov, Ph.D.
• Shane Lippold
• Nina Filenko, Ph.D.
• Igor Nazarov, Ph.D.
• Alexander Lushnikov, Ph.D

Supported by NIH and Nebraska Research
Initiative (NRI) grants to YLL