A COMPLEX NETWORK APPROACH TO FOLLOWING THE PATH OF ENERGY IN PROTEIN CONFORMATIONAL CHANGES Del...

A COMPLEX NETWORK APPROACH TO FOLLOWING THE PATH OF ENERGY IN PROTEIN CONFORMATIONAL CHANGES 

Del Jackson

CS 790G Complex Networks - 20091019

Outline

Background Related Work Methods

Hypothesis

Utilize existing techniques to characterize a protein network Explore for different motifs based upon all

aspects of molecular modeling

Proteins

Biopolymer From 20 amino acids Diverse range of functions Sequence Structure Function

Protein Structure

Primary Sequence of amino acids

Secondary Motifs

Protein Structure

Tertiary Domains

Quaternary “Hinges” exist between domains

Fundamental Questions

How did this fold?

Motivation

Misfolded proteins lead to age onset degenerative diseases

Pharmaceutical chaperones Fold mutated proteins to make functional

Simulation Methods/Techniques Energy Minimization Molecular Dynamics (MD) Simulation Langevin Dynamics (LD) Simulation Monte Carlo (MC) Simulation Normal Mode (Harmonic) Analysis Simulated Annealing

Molecular Dynamics

Computer simulation using numerical methods

Based on math, physics, chemistry Initial value problem

Molecular Dynamics Limitations Long simulations inaccurate

Cumulative errors in numerical integration

Huge CPU cost 500 µs simulation ran in 200,000 CPUs

Without shared memory and continuous communication

Coarse-graining Empirical method but successful

Elastic Network Model

Representing proteins mass and spring network Nodes:

Mass α-carbons

Edges: Springs Interactions

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.”

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

PDB

Converting PDB to network file VDM Babel

Test Approach

How to characterize connections?

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

FIRST – body bar

Bar represents each degree of freedom 5 bars more rigid than node with 2 bars

6 bars (5 bars per site with only 1 atom)

Pebble game algorithm

Determines how bars affect degrees of freedom in system

Each DOF is represented by a pebble

Pebble game algorithm

Small set of rules for moving pebbles on and off bars One per bar

Game ends when no more valid moves exist

Determines if possible to rotate around edge (flexible) or if it is locked (rigid)

Pebble Game results

Flexible hinges

Hyperstatic

Other tools to incorporate

FRODA Framework Rigidity Optimized Dynamics

Algorithm Maintains a given set of constraints,

Covalent bonds, hydrogen bonds and hydrophobic tethers

Bonding- or contact-based, with no long-range interactions in the system

TIMME FlexServ

Other tools to incorporate

FRODA TIMME

Tool for Identifying Mobility in Macromolecular Ensembles

Identifies rigidity and flexibility in snapshots of networks

Agglomerative hierarchy based on standard deviation of distances between pairs of sites from mean value over 2 or more snapshots

FlexServ

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.

Experimental Data

Cardiac myopathies

Experimental Data

Access to 15 mutations in skeletal myosin Affects on function are characterized

Combine all approaches

Derived Topology

Timme

FRODA

Flexserv

FIRST

Derived Topology

Nodes Alpha carbons

Edges Weight determined by results of other

algorithms Topological view of molecular

dynamics/simulations

First Step

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)

Questions?