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COMPLEX NETWORK APPROACH TO PREDICTING MUTATIONS ON CARDIAC MYOSIN Del Jackson CS 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
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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
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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. “
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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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PDB
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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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1 st 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 Collective 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
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