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A complex network approach to following the path of energy in protein conformational changes

A complex network approach to following the path of energy in protein conformational changes. Del Jackson CS 790G Complex Networks - 20091019. Outline. Background Related Work Methods. Hypothesis. Utilize existing techniques to characterize a protein network

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A complex network approach to following the path of energy in protein conformational changes

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  1. A complex network approach to following the path of energy in protein conformational changes Del Jackson CS 790G Complex Networks - 20091019

  2. Outline • Background • Related Work • Methods

  3. Hypothesis • Utilize existing techniques to characterize a protein network • Explore for different motifs based upon all aspects of molecular modeling

  4. Proteins • Biopolymer • From 20 amino acids • Diverse range of functions • Sequence  Structure  Function

  5. Protein Structure • Primary • Sequence of amino acids • Secondary • Motifs

  6. Protein Structure • Tertiary • Domains • Quaternary • “Hinges” exist between domains

  7. Fundamental Questions How did this fold?

  8. Motivation • Misfolded proteins lead to age onset degenerative diseases • Pharmaceutical chaperones • Fold mutated proteins to make functional

  9. Simulation Methods/Techniques • Energy Minimization • Molecular Dynamics (MD) • Simulation Langevin Dynamics (LD) • Simulation Monte Carlo (MC) Simulation • Normal Mode (Harmonic) Analysis • Simulated Annealing

  10. Molecular Dynamics • Computer simulation using numerical methods • Based on math, physics, chemistry • Initial value problem

  11. Molecular Dynamics Limitations • Long simulations inaccurate • Cumulative errors in numerical integration • Huge CPU cost • 500 µs simulation ran in 200,000 CPUs • Without shared memory and continuous communication • Coarse-graining • Empirical method but successful

  12. Elastic Network Model • Representing proteins mass and spring network • Nodes: • Mass • α-carbons • Edges: • Springs • Interactions

  13. Complicated and the Complex • Emergent phenomenon • “Spontaneous outcome of the interactions among the many constituent units” • Forest for the trees effect • “Decomposing the system and studying each subpart in isolation does not allow an understanding of the whole system and its dynamics” • Fractal-ish • “…in the presence of structures whose fluctuations and heterogeneities extend and are repeated at all scales of the system.”

  14. Network Metrics • Betweenness • Closeness • Graph density • Clustering coefficient • Neighborhoods • Regular network in a 3D lattice • Small world • Mostly structured with a few random connections • Follows power law

  15. PDB

  16. Converting PDB to network file • VDM • Babel

  17. Test Approach How to characterize connections?

  18. Flexweb

  19. Flexweb - FIRST • Floppy Inclusions and Rigid Substructure Topography • Identifies rigidity and flexibility in network graphs • 3D graphs • Generic body bar (no distance, only topology) • Full atom description of protein (PDB)

  20. FIRST • Based on body-bar graphs • Each vertex has degrees of freedom (DOF) • Isolated: 3 DOF • x-, y-, z-plane translations • One edge: 5 DOF • 3 translations (x, y, z) • 2 rotations • Two+ edges: 6 DOF • 3 translations • 3 rotations

  21. FIRST – body bar • Bar represents each degree of freedom • 5 bars more rigid than node with 2 bars • 6 bars (5 bars per site with only 1 atom)

  22. Pebble game algorithm • Determines how bars affect degrees of freedom in system • Each DOF is represented by a pebble

  23. Pebble game algorithm • Small set of rules for moving pebbles on and off bars • One per bar • Game ends when no more valid moves exist • Determines if possible to rotate around edge (flexible) or if it is locked (rigid)

  24. Flexible hinges Hyperstatic Pebble Game results

  25. Other tools to incorporate • FRODA • Framework Rigidity Optimized Dynamics Algorithm • Maintains a given set of constraints, • Covalent bonds, hydrogen bonds and hydrophobic tethers • Bonding- or contact-based, with no long-range interactions in the system • TIMME • FlexServ

  26. Other tools to incorporate • FRODA • TIMME • Tool for Identifying Mobility in Macromolecular Ensembles • Identifies rigidity and flexibility in snapshots of networks • Agglomerative hierarchy based on standard deviation of distances between pairs of sites from mean value over 2 or more snapshots • FlexServ

  27. Other tools to incorporate • FRODA • TIMME • FlexServ • Coarse grained determination of protein dynamics using • NMA, Brownian Dynamics, Discrete Dynamics • User can also provide trajectories • Complete analysis of flexibility • Geometrical, B-factors, stiffness, collectivity, etc.

  28. Experimental Data • Cardiac myopathies

  29. Experimental Data • Access to 15 mutations in skeletal myosin • Affects on function are characterized

  30. Combine all approaches

  31. Derived Topology • Nodes • Alpha carbons • Edges • Weight determined by results of other algorithms • Topological view of molecular dynamics/simulations

  32. First Step • Create one-all networks • Try different weights on edges • Start removing edges • Apply network statistics • Betweenness, closeness, graph density, clustering coefficient, etc • See if reflect changes in function (from experimental data)

  33. Questions?

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