complex network approach to predicting mutations on cardiac myosin
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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COMPLEX NETWORK APPROACH TO PREDICTING MUTATIONS ON CARDIAC MYOSIN Del JacksonCS 790G Complex Networks - 20091202
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Outline Introduction
Review previous two presentations Background
Comparative research Methods
Novel approach Results Conclusion
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Discussion Goals Share results of my research project
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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)
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Introduction Fundamental Question Motivation
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Fundamental Questions
How did this fold?
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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
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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.”
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Examples of biological networks Macroscopic level
Food web Disease propagation
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Examples of biological networks
Microscopic level
Metabolic network Protein interaction Protein
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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
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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
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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!
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Revised (new) hypothesis Complex network theory can predict
sequences in cardiac myosin that give rise to cardiomyopathies
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Background Markov State Model
Bowman @ Stanford Repeated Random Walk
Macropol
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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
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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
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Methods PDB File
Conversion Experimental Data General approach Established tools
FIRST Flexserv
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Converting PDB to network file VMD Babel
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Experimental Data Cardiac myopathies
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DCM mutations 13 known dilated cardiomyopathy
mutations
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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)
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General approach Connection characterization
Combination of tools Nodes
Alpha carbons Edges
Combine flexibility with collectivity (crude)
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1st Tool: Flexweb
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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)
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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
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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.
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General approach Topological view of molecular
dynamics/simulations
Node value = Flexibility*Collective value
Flexibility FlexibilityCollective value
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Results Progress Current Data:
13 known dilated cardiomyopathy mutations
91 combinations WT networks 2 different tools (FIRST & Flexserv) 184 Networks
Conversion is stalling progress
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(Hoped for) Results Connected components
Strong vs weak Degree distribution Path length
Average path length Network diameter
Centrality Betweeness Closeness
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Conclusion Have data for Monday (!!) May reduce number of networks to test
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Questions/Comments