Understanding Polyglutamine Structure

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Understanding Polyglutamine Structure
Alfred Chung Michael McPhail Karis Stevenson Drs.
Finke Zohdy Oakland University June 26,
2009 NSF/NIH Grant 0609152
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Background Foundations of Protein Structure
Primary Structure

• 4 main types of amino acids
• Hydrophobic
• Polar
• Positively Charged
• Negatively Charged
• Peptides amino acid linkages
• N-terminal to C-terminal
• Dihedral Angles

Glutamine(Q)
http//www.molecularsciences.org/structural_bioinf
ormatics/protein_structures
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Background Foundations of Protein Structure
Secondary Structure

• 3 main categories of secondary structure
• Alpha-helices
• Beta-sheets
• Random Coil

www.bio.mtu.edu/campbell/401lec8all.pdf
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Background Foundations of Protein Structure
Higher-order Structure

• Interactions that stabilize structure
• Electrostatic Interactions
• Hydrophobic Effect
• H-bonds
• Disulfide Bonds
• Environment also effects structure
• pH
• Salts
• Composition

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Background The Theory of Protein Folding
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Background Potential for Misfolding
http//www.nature.com/nature/journal/v426/n6968/fu
ll/nature02261.html
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Problem Defining Polyglutamine Structure

• Monomeric structure not well-established
• Crystal structure of aggregates difficult to
  obtain
• Structural and folding information provide
  framework for therapeutics

http//www.nature.com/nature/journal/v426/n6968/fu
ll/nature02261.html
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MotivationDiseases Associated with PolyQ
aggregation
http//www.sciencedirect.com/science?_obArticleUR
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MotivationHuntingtons Disease Attributes

• Autosomal dominant mutation of chromosome 4
• Late onset 35-44 years old
• Symptoms progress faster down generations
• Neuronal loss in caudate nucleus
• Movement disorders
• Cognitive decline
• Behavioral disturbances

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SolutionIntegration of 3 Complementary Techniques
Polyglutamine Structure
MOLECULAR DYNAMICS (In-silico experiments)
FRET EXPERIMENTS (In-vitro experiments)
Q-Learning (Learning Algorithm)
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Fluorescence Resonance Energy Transfer (FRET)

• FRET is characterized by the transfer of energy
  from an excited donor chromophore to an acceptor
  chromophore, without associated radiation
  release.

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FRET
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FRETEnergy Transfer

• To measure distances or changes in distance, you
  need to specifically and uniquely label your
  molecule of interest with a donor and an acceptor
  probe.
• Excited donor fluorophore transfers its energy to
  an acceptor chromophore via dipole-dipole
  interactions

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FRETMeasurement

• Range of approx. 10 nm.
• FRET measurements can be utilized as an effective
  molecular ruler for determining distances between
  biomolecules.

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FRETThe Equations

• Ro is the Förster distance the distance
  between the donor and acceptor probe at which the
  energy transfer is (on average) 50 efficient
• The overlap integral J represents the degree of
  overlap between the donor fluorescence spectrum
  and the acceptor absorption spectrum

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FRETEfficiency

• FRET efficiency can be measured using the
  lifetime of the donor in presence (Tda) and
  absence of the acceptor probe (Td)

Td6.486133ns
Tda5.130811ns
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FRETMolecular Measurements

• Once FRET efficiency and Förster distance are
  calculated, the distance between the donor and
  acceptor ends can be calculated.

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FRETFRET and Molecular Dynamics

• FRET can then tell you how far apart two parts of
  a protein are. This can give you a rough idea of
  the dimension and shape of the protein.
• A check on the validity of molecular dynamics
  simulations

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Molecular DynamicsAMBER Molecular Dynamics

• Suite of programs for analysis of molecular
  dynamic simulations
• Analysis tool for protein folding,
  ligand-binding, and denaturation
• Validation of experimental findings

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Molecular DynamicsUsing AMBER

• 3 main procedures
• System Preparation
• LEaP
• Simulation
• Sander
• Trajectory Analysis
• Ptraj

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Molecular DynamicsForce Fields of AMBER

• Delineates the functional form for a system of
  atoms
• Incorporates parameters relevant to
• Bond lengths
• Bond angles
• Dihedral angles
• Requires careful selection to prevent bias

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Molecular DynamicsPreliminary Simulation for
Polyglutamine

• Sequence FK2Q16K2Y
• Force Fields 96 and 99SB
• Model Generalized Born
• Conditions 300K for 50 ns

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Molecular DynamicsMovie Illustrating
Equilibration
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Molecular DynamicsDifferences Between Force
Fields
Parm96
Parm99SB
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Molecular DynamicsResults
Distance (Å)
Steps
Parm99SB Distance 47.6 0.4Å

• Parm96
• Distance 33.8 3.4Å

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Molecular DynamicsImproved Simulation for
Polyglutamine

• Sequence (ABZ)-K2Q16K2-(YNO)
• Force Fields 96 and 99SB
• Model Generalized Born
• Conditions 300K for 50 ns

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Reinforcement Learning

• Agent learns autonomously
• What is learned?
• Focus on experience(explore/exploit)

Neuroscience Psychology
Artificial Intelligence
RL
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Q-LearningReinforcement Learning

• An agent takes actions in an environment
• Agent wants to maximize reward

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Example-Tower of Hanoi
http//http//brynnevans.com/blog/wp-content/uploa
ds/2009/03/tower_of_hanoi.jpg
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http//people.revoledu.com/kardi/tutorial/Reinforc
ementLearning/index.html
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http//people.revoledu.com/kardi/tutorial/Reinforc
ementLearning/index.html
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Q-LearningTower of Hanoi Learning Curve
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Q-LearningAlgorithm

• state
• repeat
• pick action from Q
• observe reward
• act in world s----gta---gts'
• update
• Q(s,a) (1-a)Q(s,a) aR ?maxQ(s,a)
• ss'

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Q-LearningExtended-Algorithm

• Q-initialization
• Small random values
• Boltzmann distribution

• state
• repeat
• pick action from Q
• observe reward
• act in world s----gta---gts'
• update
• Q(s,a) (1-a)Q(s,a) aR ?maxQ(s,a)
• ss'

• Reward Structre
• Gaussian distribution

• a and ? values
• Steepest descent

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Q-Learning2D Protein Folding
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MD and Q-Learning End Distances
Distance33.8 /- 3.4 angstroms Parm 96
Distance33.9 /- 10.4 angstroms
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Q-Learning3-D Model

• 3-D Model
• Ramachandran plots to select backbone angles
• Minimizing energy
• Flexibility of parameters

http//giantshoulders.files.wordpress.com/2007/10/
ramaplot.png?w250
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References

• C. J. C. H.Watkins and P. Dayan, Q-learning,
  Machine Learning, vol.8, pp. 279292, 1992.
• Warby, Graham, Hayden. Huntington Disease. 2007.
• Jieya Shao , and Marc I. Diamond. Polyglutamine
  diseases emerging concepts in pathogenesis and
  therapy. Hum. Mol. Genet. 16 R115-R123.
• D. Shortle, Propensities, probabilities, and the
  Boltzmann hypothesis, Protein Science, vol.12
  pp. 12981302.
• J. Finke, P Jennings, J Lee, J Onuchic, J
  Winkler, Equilibrium Unfolding of the
  Poly(glutamic acid) Helix Biopolymers, vol. 86,
  pp. 193-211.
• D.A. Case, T.E. Cheatham, III, T. Darden, H.
  Gohlke, R. Luo, K.M. Merz, Jr., A. Onufriev, C.
  Simmerling, B. Wang and R. Woods. The Amber
  biomolecular simulation programs. J. Computat.
  Chem. 26, 1668-1688 (2005).
• I.O.Bucak,M.A.Zohdy,Reinforcementlearningcontrolof
  nonlinearmulti-linksystem,Eng.Appl.Artif.Intell.14
  (5)(2001)563575.

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Questions