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Why Does Polyglutamine Aggregate? Insights from studies of monomers. Xiaoling Wang, Andreas Vitalis, Scott Crick, Rohit Pappu Biomedical Engineering & Center for Computational Biology, Washington University in St.Louis pappu@biomed.wustl.edu http://lima.wustl.edu.

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Why Does Polyglutamine Aggregate? Insights from studies of monomers


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why does polyglutamine aggregate insights from studies of monomers

Why Does Polyglutamine Aggregate?Insights from studies of monomers

Xiaoling Wang, Andreas Vitalis, Scott Crick, Rohit Pappu

Biomedical Engineering & Center for Computational Biology,

Washington University in St.Louis

pappu@biomed.wustl.edu

http://lima.wustl.edu

slide2

Expanded CAG Repeat Diseases and Proteins

DISEASE

GENE

PRODUCT

NORMAL CAG

MUTANT CAG

REPEAT RANGE

REPEAT RANGE

Huntington’s

huntingtin

6

-

39

36-200

DRPLA

atrophin 1

35

3

49

-

88

SBMA

androgen rec.

33

9

38

-

65

SCA1

ataxin

-

1

6

44

39

-

83

SCA2

ataxin

-

2

13

33

32

-

200

SCA3/MJD

ataxin

-

3

3

40

54

-

89

SCA6

CACNA1A

4

19

20

-

33

SCA7

ataxin

-

7

4

35

37

-

306

SCA17

TBP

24

44

46

-

63

Bates, et al., Eds. (2002) Huntington's Disease, Oxford University Press

basic physics of aggregation
Basic physics of aggregation

n: denotes the number of peptide molecules in the system (concentration)

N: Length of each peptide molecule in the system

work done to grow a cluster

n*

Work done to grow a cluster
  • In vitro aggregation studies of synthetic polyglutamine peptides
  • Evidence for nucleation-dependent polymerization
  • Rates of elongation versus concentration are fit to a pre-equilibrium model
  • And fits to the model suggests that n*=1 for Q28, Q36, Q47
  • See Chen, Ferrone, Wetzel, PNAS, 2002
uv cd data q 5 d 2 q 15 k 2 q 28 q 45 chen et al jmb 311 173 2001
UV-CD data: Q5(-), D2Q15K2(-.-), Q28(…), Q45(---); Chen et al. JMB, 311, 173 (2001)
  • No major difference between different chain lengths
  • CD spectra for polyglutamine resemble those of denatured proteins
for given n there is a concentration n for which 0 why
For given N, there is a concentration (n) for which ∆ < 0. Why?
  • Hypothesis: Water is a poor solvent for polyglutamine:
    • Chain flexibility and attractions overwhelm chain-solvent interactions
    • Polymers form internally solvated collapsed globules
    • Rg and other properties scale with chain length as N0.34
    • Most chains aggregate and fall out of solution
  • CD data and heuristics counter our hypothesis:
    • For denatured proteins, Rg~ N0.59 - polymers in good solvents
    • Polyglutamine is polar – suggests that water is a good solvent
    • Requires new physics to explain polyglutamine aggregation

Let’s test our hypothesis

mrmd the algorithm
MRMD – the “algorithm”
  • Using a series of “short” simulations, estimate the time scale  over which :
    • Autocorrelation of “soft” modes decay
    • There are recurrent transitions between compact and swollen conformations
  • Use the estimate for , the time scale for each “elementary simulation” is tS~10
    • 60-100 independent simulations, each of “length” ts
  • Pool data from all simulations and construct conformational distributions using bootstrap methods
simulation engine
Simulation engine
  • Forcefield: OPLSAA for peptides and TIP4P for water
  • Constant pressure (P), constant temperature (T): NPT
  • T = 298K, P = 1atm
  • Thermostat and barostat: Berendsen weak coupling
  • Long-range interactions: Twin range spherical cutoffs
  • Periodic boundary conditions in boxes that contain > 4000 water molecules
  • Peptides: ace-(Gln)N-nme, N=5,15,20,…
  • Cumulative simulation times > 5s
  • We have an internal control – the excluded volume (EV) limit – to quantify conformational equilibria in good solvents
scaling of internal distances is consistent with behavior of chain in a poor solvent
Scaling of internal distances is consistent with behavior of chain in a poor solvent

Q5

Q15

Q20

Data for polyglutamine in EV limit

Data for polyglutamine in water

can we test our prediction yes
Can we test our “prediction”? Yes
  • Using Fluorescence Correlation Spectroscopy (FCS)
  • Peptides studied: -Gly-(Gln)N-Cys*-Lys2
    • * indicates fluorescent label, which is Alexa488
  • Solution conditions:
    • PBS: pH 7.3, 8.0g NaCl, 0.2g KCl, 1.15g Di-sodium orthophosphate, 0.2g Potassium di-hydrogen orthophosphate, dissolved in pure H2O
    • Approximately one molecule in beam volume
  • Is diffusion time, D N0.33 or is ln(D )  0.33ln(N)?
polyglutamine compact albeit disordered
Polyglutamine: Compact albeit disordered

Observation of disorder is consistent with CD data

quantifying topology
Quantifying topology

What is the length scale over which spatial correlations decay?

Compute <cos(θij)> as a function of |j-i|

residue i

C

θ

i

N

C

i

i+1

C

C

j

j+1

N

n

residue j

slide16

Q15

Q20

why collapse and what does it mean
Why collapse and what does it mean?
  • Summary – The ensemble for polyglutamine in water:
    • Is disordered albeit collapsed
    • Has a preferred up-down average topology
    • With a strong propensity for forming beta turns
    • And little to no long-range backbone hydrogen bonds
  • What drives collapse in water: Generic backbone?
  • Is there anything special about polyglutamine?
  • What does all this mean for nucleation of aggregation?
distributions for polyglycine
Distributions for polyglycine

Water

8M Urea

EV Limit

Mimics of polypeptide backbones prefer to be collapsed in water,

which appears to be a universal poor solvent for polypeptides

Polyglutamine is a chain of two types of amides: secondary and primary

primary and secondary amides
Primary and secondary amides

Propanamide (PPA)

N-methylformamide (NMF)

amides in water
Amides in water
  • Pure (primary or secondary) Amides in water:
    • N =nW + nA
    • NPT Simulations with varying nA implies varying A
    • T=300K, P = 1atm
    • OPLSAA forcefield for amides, TIP4P for H2O
    • nA = 16, 32, 64, etc. for 1, 2, 3, … molal solutions;
    • nW = 800
  • Amide (ternary) mixtures: Primary and secondary amides
    • N = nW + nP + nS
    • Keep nW and nP fixed and vary nS or nW and nS fixed, vary nP
    • Will show data for nP = nS = 32
pair correlations
Pair correlations
  • NMF prefers water-separated contacts over hydrogen bonded contacts
  • PPA prefers hydrogen bonded contacts over water-separated contacts
  • PPA donor - NMF acceptor hydrogen bonds are preferred in mixtures
typical large cluster in ppa nmf mixtures
Typical large cluster in PPA:NMF mixtures

Consistent with data of Eberhardt and Raines, JACS, 1994

in polyglutamine sidechains solvate the backbone in compact geometries
In polyglutamine, sidechains “solvate” the backbone in compact geometries

Q20: Rg=8.86Å,  =0.096

Q20: Rg=8.11Å,  =0.13

Q20: Rg=8.49Å, =0.16

hypothesis part i why is aggregation spontaneous
Hypothesis – part I: Why is aggregation spontaneous?
  • For a system of peptides of length N:
    • There is a finite concentration (n) for which ∆ < 0
  • ∆ < 0 if:
    • Aggregated state of intermolecular solvation via glutamine sidechains is preferred to the disordered state of intramolecular solvation whereby sidechains solvate their own backbones
  • It is our hypothesis that:
    • Peptide concentration at which ∆ becomes negative will decrease “rapidly” with increasing chain length
hypothesis part ii nucleation
Hypothesis – part II: Nucleation
  • Ensemble of nucleus is species of highest free energy for monomer
  • Nucleation must involve the following penalties:
    • DESOLVATION: Replace favorable sidechain-backbone contacts and residual water-backbone contacts with unfavorable backbone-backbone contacts
    • ENTROPIC BOTTLENECK: Replace disordered ensemble with ordered nucleus
  • Conformations in the nucleus ensemble?
    • β-helix-like (see work of Dokholyan group, PLoS, 2005)
    • -pleated sheet (see work of Daggett group, PNAS, 2005)
    • Antiparallel β-sheet (see fiber diffraction data)
thanks to
Thanks to…

THE LAB

  • Xiaoling Wang
  • Andreas Vitalis
  • Scott Crick
  • Hoang Tran
  • Alan Chen
  • Matthew Wyczalkowski

Collaborations

  • Ron Wetzel – UTK
  • Murali Jayaraman – UTK
  • Carl Frieden – WUSTL
ongoing work
Ongoing work…
  • Monomer distributions for N > 25
  • Free energies of nucleating intramolecular beta sheets
  • Influence of sequence context: In vivo, its not just a polyglutamine
  • Quantitative characterization of oligomer landscape
  • Generalizations to aggregation of other intrinsically disordered proteins rich in polar amino acids
  • Experiments: New FCS methods to study oligomers and nucleation kinetics