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Protein Folding

Protein Folding. Atlas F. Cook IV & Karen Tran. Overview. What is Protein Folding? Motivation Experimental Difficulties Simulation Models: Configuration Spaces Triangular Lattice models Pull Moves Probabilistic Roadmaps Map-Based Master Equation (MME) Map-Based Monte Carlo (MMC)

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Protein Folding

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  1. Protein Folding Atlas F. Cook IV & Karen Tran

  2. Overview • What is Protein Folding? • Motivation • Experimental Difficulties • Simulation Models: • Configuration Spaces • Triangular Lattice models • Pull Moves • Probabilistic Roadmaps • Map-Based Master Equation (MME) • Map-Based Monte Carlo (MMC) • Conclusion

  3. Motivation • What is protein folding? • Folding/Morphing process • 1D Amino Acid Chain  3D Folded protein

  4. Motivation • Why study protein folding? • Proteins regulate almost all cellular functions • 1D chain dictates 3D shape (NP-Hard) • 3D Shape determines protein’s function 1D amino acid chain 3D folded protein

  5. Kill Cancer Cells Required Amino Acid Chain Required Shape Desired Function Motivation • Holy grail of Protein Folding • Build amino acid chain that: • folds into a desired shape • and has a nice function (e.g., kill cancer cells) • How would we do this?

  6. Motivation • Another reason to study protein folding: • Unfolded protein = vulnerable protein

  7. Motivation • Misfolded proteins cause diseases: • Alzheimer’s • Mad Cow • Parkinson’s • Understand protein folding  cure diseases!

  8. Terminology • Primary Structure • 1D Amino Acid Chain (string) • MGDVEKGKKIFIMKCSQCH • Secondary Structure • Local patterns in a global folding • Helices and Strands • Tertiary Structure • Global 3D folded shape

  9. Experimental Difficulties • Levinthal Paradox • Exponentially many ways to fold, yet • folding occurs rapidly (milliseconds to seconds) • Why is folding so fast? • Unfolded protein = vulnerable protein • Experimental observation • Too slow to capture all significant motions • Our Goal: • Simulate protein folding on computer! 

  10. Simulation Models • HP Lattice Model: [Böckenhauer08] • HP = Hydrophobic-Polar • Models forces between Hydrophobic amino acids

  11. Amino Acid Chain Simulation Models • HP Lattice Model: [Böckenhauer08] • Amino acid  vertex in a grid • Protein  self-avoiding chain in a grid

  12. Simulation Models • HP Lattice Model: [Böckenhauer08] • Spring-like forces are modeled between neighboring amino acids. • Sum of forces for a state  Energy. +3 +10 +2 4 5 1 2 Energy = 16 +0 +1 3 +2 +2 5 4 2 1 Energy = 8 +1 +1 +2 3

  13. Simulation Models • HP Lattice Model: [Böckenhauer08] • Global min energy • “native state” = final folded state • Native state is stable. • Global minimum is MUCH smaller than local minima. +2 +2 5 Global min Energy = 8 4 2 1 +1 +1 +2 3

  14. Simulation Models • HP Lattice Model: [Böckenhauer08] • A state is defined by the position of every amino acid in the chain A State Another State

  15. Simulation Models • HP Lattice Model: [Böckenhauer08] • Configuration space = set of all possible states • Exponential to protein length • Protein folding simulation: • “Move” from start state  goal state.

  16. Simulation Models • HP Lattice Model: [Böckenhauer08] • Move Properties: • Complete – moves can reach all feasible states • Reversible – every move has an inverse

  17. Simulation Models • HP Lattice Model: [Böckenhauer08] • Forward Pull Move • Pull vertex5 to a new position Before move After move

  18. Simulation Models • HP Lattice Model: [Böckenhauer08] • Tabu Search • Greedy, heuristic search • Simulates protein folding • Pull moves transform start state  local minimum • Records recent moves in a Tabu list • Fast backtracking to different paths • Summary of HP Lattice Model: • Input: Amino acid sequence • Output: Heuristically folded protein

  19. Probabilistic Roadmap Model

  20. Simulation Models • Probabilistic Roadmap [Song04] • 2D Graph (Configuration space): • Each point represents an entire state (all amino acids). • Obstacles are infeasible states

  21. Simulation Models • Probabilistic Roadmap [Song04] • Goal: • Given start & goal states • Find “best path” from start  goal

  22. Simulation Models • Probabilistic Roadmap [Song04] • 3 Steps: • Node generation: • Generate points randomly (dense near the goal state) • Roadmap Construction • Connect nearest neighbors  graph • Query roadmap • Dijkstra’s algorithm  shortest path • Shortest path = set of states • Describes the dynamic folding process

  23. Simulation Models • Probabilistic Roadmap [Song04] • Node generation: • Generate random points • “Obstacles” are infeasible (self-overlapping) states

  24. Simulation Models • Probabilistic Roadmap [Song04] • Roadmap Construction • Connect nearest neighbors  graph

  25. Simulation Models • Probabilistic Roadmap [Song04] • Query roadmap • Dijkstra’s algorithm  shortest path • Path = set of states that describes the folding process

  26. Molecular Dynamics Model

  27. Simulation Models • Molecular Dynamics Models [Tapia07] • Model forces based on Newton’s laws of motion • Very accurate • Very slow! • Simulating one microsecond of folding for a 36 residue protein = Months of supercomputer time! • Cannot handle full length proteins

  28. Simulation Models • Map-based Master Equation (MME) [Tapia07] • Fast enough to study full length proteins • More accurate than simplistic lattice models • MME is an extension of a Probabilistic Roadmap • Probabilistic roadmap ≈ Viterbi algorithm • returns one optimal path • MME ≈ Baum-Welch algorithm • Maintains transition probabilities for every state • Learning is executed until probabilities stabilize. • Can return the probability of any state at time t.

  29. Simulation Models • Map-based Monte-Carlo (MMC) [Tapia07] • MMC = Probabilistic Roadmap + Monte-Carlo • Monte-Carlo [Wiki08_MC] • random sampling + algorithms = result • Example: Battleship • Make random shots • Apply prior knowledge • Battleship = 4 vertical/horizontal dots • Apply algorithms to quickly sink the ship

  30. Simulation Models • Map-based Monte-Carlo (MMC) [Tapia07] • Fast & reasonably accurate • Models the protein as an articulated figure • Each joint = set of angles • Movement-based (kinetic) statistics • Results suggest that: • Local helix structures form first • Folding occurs around hydrophobic core

  31. Conclusion • Protein Folding: • 1D Amino acid chain folds into 3D structure • Misfolding  Alzheimer’s, Parkinson’s, Mad Cow diseases • Folding is too fast to observe experimentally • Four Simulation Models: • Triangular Lattice model (2D Graph) • Vertex = one amino acid • “Moves” transition between states

  32. Conclusion • Four Simulation Models (cont.) • Probabilistic Roadmaps • Vertex represents state of entire protein • Random sampling + Dijkstra’s alg  Best folding route • ≈ Viterbi (returns one path) • Map-Based Master Equation (MME) • Learn probabilities • ≈ Baum-Welch (confidence level for each state) • Map-Based Monte Carlo (MMC) • Articulated figures with joints model proteins

  33. References: • [Böckenhauer08] • Hans-Joachim Böckenhauer, Abu Zafer M. Dayem Ullah, Leonidas Kapsokalivas, and Kathleen Steinhöfel. A local move set for protein folding in triangular lattice models. In Keith A. Crandall and Jens Lagergren, editors, WABI, volume 5251 of Lecture Notes in Computer Science, pages 369–381. Springer, 2008. • [Dobson99] • C. Dobson and M. Karplus. The fundamentals of protein folding: bringing together theory and experiment. Current Opinion in Structural Biology, 9:928–101, 1999.

  34. References: • [Song04] • G. Song and N. M. Amato. A motion planning approach to folding: From paper craft to protein folding. Proc. IEEE Transactions on Robotics and Automatics, 20:60–71, 2004. • [Tapia07] • Lydia Tapia, Xinyu Tang, Shawna Thomas, and Nancy M. Amato. Kinetics analysis methods for approximate folding landscapes. Bioinformatics, 23(13):i539–i548, 2007.

  35. References: • [˘Sali94] • A., E. Shakhnovich, and M. Karplus. How does a protein fold? Nature, 369:248–251, 1994. • [Wiki08] • Wikipedia. Protein folding — Wikipedia, the free encyclopedia, 2008. http://en.wikipedia.org/wiki/Protein_folding. • [Wiki08_MC] • Wikipedia. Monte-Carlo method — Wikipedia, the free encyclopedia, 2008. http://en.wikipedia.org/wiki/Monte_Carlo_method

  36. Thank you for your attention.Questions

  37. Extra Slides

  38. Simulation Models • Map-based Master Equation (MME) [Tapia07] • MME = Probabilistic roadmap + Master Equation • Master Equation – set of equations defining the probability of a system to be in a discrete set of states at a given time.

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