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Complex Network Approach to predicting Mutations on Cardiac Myosin

Complex Network Approach to predicting Mutations on Cardiac Myosin. Del Jackson CS 790G Complex Networks - 20091202. Outline. Introduction Review previous two presentations Background Comparative research Methods Novel approach Results Conclusion. Discussion Goals.

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Complex Network Approach to predicting Mutations on Cardiac Myosin

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  1. Complex Network Approach to predicting Mutations on Cardiac Myosin Del Jackson CS 790G Complex Networks - 20091202

  2. Outline • Introduction • Review previous two presentations • Background • Comparative research • Methods • Novel approach • Results • Conclusion

  3. Discussion Goals • Share results of my research project

  4. Discussion Goals (2) • Share results of my research project • Show progress on research project and what to expect to see on Monday • Overall view of complex network theory applied to biological systems (small scale)

  5. Introduction • Fundamental Question • Motivation

  6. Fundamental Questions How did this fold?

  7. Motivations • Misfolded proteins lead to age onset degenerative and proteopathic diseases • Alzheimer's, familial amyloid cardiomyopathy, Parkinson's • Emphysema and cystic fibrosis • Pharmaceutical chaperones • Fold mutated proteins to make functional

  8. 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.”

  9. Examples of biological networks • Macroscopic level Food web Disease propagation

  10. Examples of biological networks • Microscopic level Metabolic network Protein interaction Protein

  11. 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

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

  13. Valid Hypothesis but… “..a more structured view  of transient protein interactions will ultimately lead to a better understanding of the molecular bases of cell regulatory networks. “ Too large in scope!

  14. Revised (new) hypothesis • Complex network theory can predict sequences in cardiac myosin that give rise to cardiomyopathies

  15. Background • Markov State Model • Bowman @ Stanford • Repeated Random Walk • Macropol

  16. Markov State Model • Divides a molecular dynamics trajectory into groups • Identifies relationships between these states • Results in a Markov state model (MSM) • Adds kinetic insights

  17. Repeated Random Walk • RRW makes use of network topology • edge weights • long range interactions • More precise and robust in finding local clusters • Flexibility of being able to find multi-functional proteins by allowing overlapping clusters

  18. Methods • PDB File • Conversion • Experimental Data • General approach • Established tools • FIRST • Flexserv

  19. PDB

  20. Converting PDB to network file • VMD • Babel

  21. Experimental Data • Cardiac myopathies

  22. DCM mutations • 13 known dilated cardiomyopathy mutations

  23. Original approach • 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)

  24. General approach • Connection characterization • Combinationof tools • Nodes • Alpha carbons • Edges • Combine flexibility with collectivity (crude)

  25. 1st Tool: Flexweb

  26. 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)

  27. 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

  28. 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.

  29. General approach • Topological view of molecular dynamics/simulations • Node value = Flexibility*Collective value Flexibility Flexibility Collective value

  30. Results • Progress • Current Data: • 13 known dilated cardiomyopathy mutations • 91 combinations • WT networks • 2 different tools (FIRST & Flexserv) • 184 Networks • Conversion is stalling progress

  31. (Hoped for) Results • Connected components • Strong vs weak • Degree distribution • Path length • Average path length • Network diameter • Centrality • Betweeness • Closeness

  32. Conclusion • Have data for Monday (!!) • May reduce number of networks to test

  33. Questions/Comments

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