A COMPLEX NETWORK APPROACH TO FOLLOWING THE PATH OF ENERGY IN PROTEIN CONFORMATIONAL CHANGES Del Jackson CS 790G Complex Networks - 20091019
Dec 19, 2015
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
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?