1 / 33

Complex Network Approach to predicting Mutations on Cardiac Myosin

This research project explores a novel approach to predict mutations in cardiac myosin that lead to various cardiomyopathies using complex network theory. We review previous findings and methodologies, focusing on protein interactions and their impacts on disease emergence. By applying a Markov state model, we analyze existing dilated cardiomyopathy mutations to characterize protein networks and their behaviors. This study aims to offer insights into the molecular basis of cardiac diseases through advanced network metrics, facilitating the understanding of protein folding and interaction patterns in biological systems.

starr
Télécharger la présentation

Complex Network Approach to predicting Mutations on Cardiac Myosin

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Complex Network Approach to predicting Mutations on Cardiac Myosin Del Jackson CS 790G Complex Networks - 20091202

  2. Outline • Introduction • Review previous two presentations • Background • Comparative research • Methods • Novel approach • Results • Conclusion

  3. Discussion Goals • Share results of my research project

  4. 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)

  5. Introduction • Fundamental Question • Motivation

  6. Fundamental Questions How did this fold?

  7. 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

  8. 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.”

  9. Examples of biological networks • Macroscopic level Food web Disease propagation

  10. Examples of biological networks • Microscopic level Metabolic network Protein interaction Protein

  11. 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

  12. Hypothesis (OLD) • Utilize existing techniques to characterize a protein network • Explore for different motifs based upon all aspects of molecular modeling

  13. 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!

  14. Revised (new) hypothesis • Complex network theory can predict sequences in cardiac myosin that give rise to cardiomyopathies

  15. Background • Markov State Model • Bowman @ Stanford • Repeated Random Walk • Macropol

  16. 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

  17. 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

  18. Methods • PDB File • Conversion • Experimental Data • General approach • Established tools • FIRST • Flexserv

  19. PDB

  20. Converting PDB to network file • VMD • Babel

  21. Experimental Data • Cardiac myopathies

  22. DCM mutations • 13 known dilated cardiomyopathy mutations

  23. 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)

  24. General approach • Connection characterization • Combinationof tools • Nodes • Alpha carbons • Edges • Combine flexibility with collectivity (crude)

  25. 1st Tool: Flexweb

  26. 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)

  27. 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

  28. 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.

  29. General approach • Topological view of molecular dynamics/simulations • Node value = Flexibility*Collective value Flexibility Flexibility Collective value

  30. Results • Progress • Current Data: • 13 known dilated cardiomyopathy mutations • 91 combinations • WT networks • 2 different tools (FIRST & Flexserv) • 184 Networks • Conversion is stalling progress

  31. (Hoped for) Results • Connected components • Strong vs weak • Degree distribution • Path length • Average path length • Network diameter • Centrality • Betweeness • Closeness

  32. Conclusion • Have data for Monday (!!) • May reduce number of networks to test

  33. Questions/Comments

More Related