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. Alexey V. Krasnoslobodtsev, PhD 1 st Annual Unither Nanomedical and Telemedical Technology Conference

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1 st Annual Unither Nanomedical and Telemedical Technology Conference. AFM force spectroscopy as a nanotool for early detection of misfolded protein. Alexey V. Krasnoslobodtsev, PhD. Outline. Misfolding (conformational) diseases – background. - PowerPoint PPT Presentation

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

Page 1: AFM force spectroscopy as a nanotool for early detection of misfolded protein.

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

Alexey V. Krasnoslobodtsev, PhD

1st Annual Unither Nanomedical and Telemedical Technology Conference

Page 2: AFM force spectroscopy as a nanotool for early detection of misfolded protein.

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.

Page 3: AFM force spectroscopy as a nanotool for early detection of misfolded protein.

Protein folding, misfolding and aggregation

Protein aggregation

Chaperones

Protein fibrils

EnvironmentalStress

GenericPerturbations

ChemicalStress

Pathophysiological Stress

Disease

Native folded protein

Misfolded protein

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Protein Misfolding (Conformational) Diseases

These diseases include neurodegenerative disorders such as Alzheimer’s, Parkinson’s disease, Huntington’s 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

Claudio Soto, 2003

Many human diseases are now recognized to be conformational diseases associated with misfolding of the proteins and their consequent aggregation.

Alzheimer’s

Plaques and tangles

Parkinson’s

Lewy bodies

Huntington’s intranuclear inclusions

Prion amyloid plaques

Amyotrophic lateral sclerosis aggregates

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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

Misfoldedprotein

Large aggregates and fibrils

Oligomers

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Possible therapeutic interventions for protein

misfolding diseases

Skovronovsky D.M., et al., 2006, Annu. Rev. Pathol. Dis., 1:151-70

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Therapeutic approaches to misfolding diseases

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

Expression of the protein

Protein misfolding

Aggregation

Loss of neuronal function and cell death

Neurodegeneration

Prevent aggregation of misfolded proteins

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Rationale

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

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.

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Probing interactions between individual molecules by AFM force spectroscopy

AFM force spectroscopy allows studying:

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

Force

Distance

Dimerization of misfolded proteins is the very first step in aggregation process.

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AFM force spectroscopy

34

2

4)5)3)

1) Approaching 2)

Contact of the tip with sample surface

Tip retractionStretching the linkers Bond rupture

Rupture force

5

Rupture event

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Model system- 7 aa 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-β spine of amyloid-like fibrils. Nature. Vol. 435, No. 9, 773-778.

1 122 253 685

7GNNQQNY13

Misfolding – exposing “hot” regions Aggregation

Alzheimer’s: amyloid-beta peptide 1-40(42) -> Aβ16-22 is responsible for aggregation.

“Hot” regions are short stretches of peptide sequences.

Huntington’s: polyQ (>40) -> elementary Q7 shows maximal kinetics of aggregation.

Parkinson’s: α-synuclein -> 12 aa regions is the core domain for aggregation.

Prion diseases: short peptide from Sup35 yeast prion

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Sup35 Aggregation at different pHs (Environmental Stress)

pH 5.6

pH 3.7

pH 2

Misfolded 1

Misfolded 2

Misfolded 3

EnvironmentalStress

0 200 400 600 800 100002468

10121416 0 200 400 600 800 1000

0

2

4

6

8

pH 3.7

0 200 400 600 800 100005

10152025303540

pH 5.6

pH 2.0

Force (pN)

Co

un

tsMorphology of aggregation – different misfolding states that have different strength of interactions?

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AFM force spectroscopy – nanotool for detection of misfolded state.

• Parallel circular dichroism (CD) measurements performed for Aβ 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 β-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.

0 2 4 6 8 100

100

200

300

400

500

600

700

Fo

rce

, pN

pH

Amyloid -β peptide

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AFM force spectroscopy -High throughput screening machine

for detecting efficient therapeutic agents

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

Force of intermolecular interactions

Drug #1

Drug #2

Drug #3

Control

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Challenges

1. 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).

2. Automated exchange of buffers containing drugs of interest.

3. Automated data analysis.

OH Si

O

O

ONOH

N OO

O

N OO

OOO

SiN

N OO4

O

N OO

4

SiO

O

ONOH

N OO

O

N OO

S

DNA

4

DNA - SH

+

in water

SiSi

..

..

aqueous solution

Si

..

Peptide-SH

Peptide

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Beyond Force SpectroscopyDynamic Force Spectroscopy (DFS) measurements

Tkk

xr

x

TkF

Boff

B

ln1 2

F1 < F2

r – pulling velocity (loading rate)

DFS – measures kinetic parameters of dissociation reaction

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Dynamic Force Spectroscopy

ΔG‡

PP P + Pkoff

Tkk

xr

x

TkF

Boff

B

ln

0 100 200 30008

16

Force (pN)

Cou

nts

01224

05

1015

0102030

09

1827

02040

0 200 400 600 800 100007

14

Load

ing

Rat

e

ln r

ForceDissociation rate constant

Distance to transition state

Loading rate

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Dynamic Force Spectroscopy

101 102 103 104 1050

50

100

150

200

250

300

350

koff

Rup

ture

forc

e, p

N

Loading rate, pN/sec

A dynamic force spectrum at pH=2.0 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.

GNNQQNY – Sup35 yeast prion

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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.

Two barriers in the energy profile: Inner (second fit) and outer (first fit) activation barriers

Dynamic Force Spectroscopy

101 102 103 104 1050

50

100

150

200

250

300

350

koff

Rup

ture

forc

e, p

N

Loading rate, pN/sec

3.5 Å

0.2 Å

k2off

k1off

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.

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Summary

1. 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.

2. There is an intimate relationship between aggregation propensity (protein misfolding) and strength of interprotein interactions.

3. Force spectroscopy allows to study the mechanism of early dynamic events in the aggregation process which is not accessible by any other available method.

4. 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