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  1. Understanding Polyglutamine Structure Alfred Chung Michael McPhail Karis Stevenson Drs. Finke &Zohdy Oakland University June 26, 2009 NSF/NIH Grant #: 0609152

  2. 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_bioinformatics/protein_structures

  3. 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

  4. 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

  5. Background The Theory of Protein Folding

  6. Background Potential for Misfolding http://www.nature.com/nature/journal/v426/n6968/full/nature02261.html

  7. 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/full/nature02261.html

  8. MotivationDiseases Associated with PolyQ aggregation http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6T0V-4TCTGBK1&_user=1317309&_rdoc=1&_fmt=&_orig=s earch&_sort=d&view=c &_acct= C00005 2319&_version=1&_urlVersion=0&_userid=1317309&md5=b850ead20c472f6cc78687fbb9cb9ab2

  9. MotivationHuntington’s 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

  10. SolutionIntegration of 3 Complementary Techniques Polyglutamine Structure FRET EXPERIMENTS (In-vitro experiments) MOLECULAR DYNAMICS (In-silico experiments) Q-Learning (Learning Algorithm)

  11. 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.

  12. FRET

  13. 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

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

  15. 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

  16. FRETEfficiency FRET efficiency can be measured using the lifetime of the donor in presence (Tda) and absence of the acceptor probe (Td) Td=6.486133ns Tda=5.130811ns

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

  18. 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

  19. 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

  20. Molecular DynamicsUsing AMBER 3 main procedures: System Preparation LEaP Simulation Sander Trajectory Analysis Ptraj

  21. 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

  22. Molecular DynamicsPreliminary Simulation for Polyglutamine Sequence: FK2Q16K2Y Force Fields: 96 and 99SB Model: Generalized Born Conditions: 300K for 50 ns

  23. Molecular DynamicsMovie Illustrating Equilibration

  24. Molecular DynamicsDifferences Between Force Fields Parm96 Parm99SB

  25. Molecular DynamicsResults • Parm96 • Distance: 33.8 ±3.4Å Distance (Å) Steps Parm99SB Distance: 47.6 ± 0.4Å

  26. Molecular DynamicsImproved Simulation for Polyglutamine Sequence: (ABZ)-K2Q16K2-(YNO) Force Fields: 96 and 99SB Model: Generalized Born Conditions: 300K for 50 ns

  27. Reinforcement Learning • Agent learns autonomously • What is learned? • Focus on experience(explore/exploit) Neuroscience & Psychology RL Artificial Intelligence

  28. Q-LearningReinforcement Learning An agent takes actions in an environment Agent wants to maximize reward

  29. Example-Tower of Hanoi http://http://brynnevans.com/blog/wp-content/uploads/2009/03/tower_of_hanoi.jpg

  30. http://people.revoledu.com/kardi/tutorial/ReinforcementLearning/index.htmlhttp://people.revoledu.com/kardi/tutorial/ReinforcementLearning/index.html

  31. http://people.revoledu.com/kardi/tutorial/ReinforcementLearning/index.htmlhttp://people.revoledu.com/kardi/tutorial/ReinforcementLearning/index.html

  32. Q-LearningTower of Hanoi – Learning Curve

  33. Q-LearningAlgorithm state repeat{ pick action from Q observe reward act in world s---->a--->s' update: Q(s,a)= (1-α)Q(s,a) + α[R + γ*maxQ(s’,a’)] } s=s'

  34. Q-LearningExtended-Algorithm state repeat{ pick action from Q observe reward act in world s---->a--->s' update: Q(s,a)= (1-α)Q(s,a) + α[R + γ*maxQ(s’,a’)] } s=s' • Q-initialization • Small random values • Boltzmann distribution • Reward Structre • Gaussian distribution • α and γ values • Steepest descent

  35. Q-Learning2D Protein Folding

  36. MD and Q-Learning End Distances Distance:33.8 +/- 3.4 angstroms Parm 96 Distance=33.9 +/- 10.4 angstroms

  37. 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?w=250

  38. References • C. J. C. H.Watkins and P. Dayan, “Q-learning,” Machine Learning, vol.8, pp. 279–292, 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. 1298–1302. • 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,Reinforcementlearningcontrolofnonlinearmulti-linksystem,Eng.Appl.Artif.Intell.14(5)(2001)563–575.

  39. Questions