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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
Share results of my research project
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)
Introduction
Fundamental Question Motivation
Fundamental Questions
How did this fold?
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
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.”
Examples of biological networks Macroscopic level
Food web Disease propagation
Examples of biological networks
Microscopic level
Metabolic network Protein interaction Protein
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
Hypothesis (OLD)
Utilize existing techniques to characterize a protein network Explore for different motifs based upon all
aspects of molecular modeling
Derived Topology
Timme
FRODA
Flexserv
FIRST
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!
Revised (new) hypothesis
Complex network theory can predict sequences in cardiac myosin that give rise to cardiomyopathies
Background
Markov State Model Bowman @ Stanford
Repeated Random Walk Macropol
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
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
Methods
PDB File Conversion
Experimental Data General approach Established tools
FIRST Flexserv
Converting PDB to network file VMD Babel
Experimental Data
Cardiac myopathies
DCM mutations
13 known dilated cardiomyopathy mutations
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)
General approach
Connection characterization Combination of tools
Nodes Alpha carbons
Edges Combine flexibility with collectivity (crude)
1st Tool: Flexweb
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)
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
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.
General approach
Topological view of molecular dynamics/simulations
Node value = Flexibility*Collective value
Flexibility Flexibility
Collective value
Results
Progress Current Data:
13 known dilated cardiomyopathy mutations
91 combinations WT networks 2 different tools (FIRST & Flexserv) 184 Networks
Conversion is stalling progress
(Hoped for) Results
Connected components Strong vs weak
Degree distribution Path length
Average path length Network diameter
Centrality Betweeness Closeness
Conclusion
Have data for Monday (!!) May reduce number of networks to test
Questions/Comments