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Visual Analytics Methods for Analyzing Molecular Dynamics Simulations of Mutant Proteins

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Visual Analytics Methods for Analyzing Molecular Dynamics Simulations of Mutant Proteins Visual Analytics Methods for Analyzing Molecular Dynamics Simulations of Mutant Proteins Dennis N. Bromley A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2014 Reading Committee: Valerie Daggett, Chair James Brinkley Peter Myler Program Authorized to Offer Degree: Biomedical Informatics and Medical Education UMI Number: 3680140 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. UMI 3680140 Published by ProQuest LLC (2015). Copyright in the Dissertation held by the Author. Microform Edition © ProQuest LLC. All rights reserved. This work is protected against unauthorized copying under Title 17, United States Code ProQuest LLC. 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, MI 48106 - 1346 © Copyright 2014 Dennis N. Bromley University of Washington Abstract Visual Analytics Methods for Analyzing Molecular Dynamics Simulations of Mutant Proteins Dennis N. Bromley Chair of the Supervisory Committee: Professor Valerie Daggett Bioengineering The structural dynamics of proteins are integral to protein function; if these structural dynamics are altered by mutation, the function of the protein can be altered as well, potentially resulting in disease. Experimental structure-determination with x-ray crystallography and Nuclear Magnetic Resonance (NMR) can be useful in determining mutant protein structures, but detailed, high- resolution dynamics data can be difficult to ascertain. Molecular Dynamics (MD) simulation is a high temporal- and spatial-resolution in silico method for dynamic protein structure determination. Unfortunately, the data generated by MD simulations can be too large for standard analysis tools. Here I describe a novel visual-analytics tool called DIVE that was specifically created to handle large, structured datasets like those generated by MD simulations. Using DIVE, I analyzed MD simulation-data of disease-associated mutations to the α- Tocopherol Transfer Protein (α-TTP) and to the p53 tumor suppressor protein. In addition to mutant structural-analysis and characterization, I also used DIVE to develop an algorithm for identifying regions of mutant proteins that are amenable to ‘rescue’, or ligand-mediated stabilization that can suppress the destabilizing effect of mutations. The results of these investigations highlight the utility of big-data, visual-analytics approaches to exploring MD simulation data. 1 TABLE OF CONTENTS Chapter 1 ................................................................................................................................. 13 Visual Analytics Methods for Analyzing Molecular-Dynamics Simulations of Mutant Proteins .................................................................................................................................... 13 1.1 Introduction ............................................................................................................... 13 1.2 Visual Analytics ........................................................................................................ 15 1.3 Contact Analysis and the α-Tocopherol Transfer Protein ........................................... 17 1.4 The Tumor-Suppressor Protein p53 ........................................................................... 18 1.5 Conclusions and Future Work .................................................................................... 20 Chapter 2 ................................................................................................................................. 25 DIVE: A Graph-Based Visual Analytics Framework for Big Data ...................................... 25 2.1 Abstract ..................................................................................................................... 26 2.2 The DIVE Architecture .............................................................................................. 27 2.3 Object Parsing ........................................................................................................... 33 2.4 Scripting .................................................................................................................... 35 2.5 Data Streaming .......................................................................................................... 36 2.6 A Case Study ............................................................................................................. 38 2.7 Discussion ................................................................................................................. 40 Chapter 3 ................................................................................................................................. 51 DIVE: A Data Intensive Visualization Engine ....................................................................... 51 2 3.1 Abstract ..................................................................................................................... 51 3.2 Introduction ............................................................................................................... 51 3.3 System and Implementation ....................................................................................... 52 3.4 Results ....................................................................................................................... 53 3.5 Conclusions ............................................................................................................... 56 Chapter 4 ................................................................................................................................. 58 Structural Consequences of Mutations to the α-Tocopherol Transfer Protein Associated with the Neurodegenerative Disease Ataxia with Vitamin E Deficiency ............................... 58 4.1 Abstract ..................................................................................................................... 58 4.2 Introduction ............................................................................................................... 59 4.3 Methods ..................................................................................................................... 61 4.4 Results and Discussion .............................................................................................. 67 4.5 Conclusions ............................................................................................................... 76 Chapter 5 ................................................................................................................................. 88 Preliminary Results from a Proposed In Silico Algorithm for Identifying Stabilizing Pockets in the Y220C Mutant of the p53 Tumor Suppressor Protein ................................... 88 5.1 Abstract ..................................................................................................................... 88 5.2 Introduction ............................................................................................................... 89 5.3 Methods ..................................................................................................................... 92 5.4 Results and Discussion .............................................................................................. 98 5.5 Conclusions ............................................................................................................. 109 3 Chapter 6 ............................................................................................................................... 128 Insights into the Structural Dynamics of Cancer: Molecular Dynamics Simulations of Wild Type p53 and 20 Different Mutants Show Common Structural-Disruption Motifs Including Hypothesized Amyloidogenic Conformations ...................................................................... 128 6.1 Abstract ................................................................................................................... 128 6.2 Introduction ............................................................................................................. 129 6.3 Methods ................................................................................................................... 131 6.4 Results ..................................................................................................................... 134 6.5 Discussion ............................................................................................................... 147 6.6 Conclusions ............................................................................................................. 153 Appendix A ............................................................................................................................ 177 Supplementary Materials for DIVE: A Graph-Based Visual Analytics Framework for Big Data........................................................................................................................................ 177 A.1 Ontologies ............................................................................................................... 177 A.2 Molecular Dynamics ...............................................................................................
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