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

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. “

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  Combination of tools  Nodes  Alpha carbons  Edges Combine flexibility with collectivity (crude)

25. 1 st 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 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