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

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 propagation
Disease

Examples of biological networks

Microscopic level
Metabolic network Protein interaction Protein

Network Metrics





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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

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. “

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


PDB File

Conversion

Experimental Data

General approach

Established tools

FIRST

Flexserv
Methods

PDB

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)

1 st 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
Collective value
Flexibility Flexibility

Node value = 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