complex network approach to predicting mutations on cardiac myosin del jackson cs 790g complex...

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

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)

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