University of Illinois Urbana-Champaign

Dimensionality reduction and multiscale modeling for the understanding of protein folding and hierarchical self-assembly

Mansbach, Rachael Alexandra

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https://hdl.handle.net/2142/101496

Description

Title
Dimensionality reduction and multiscale modeling for the understanding of protein folding and hierarchical self-assembly
Author(s)
Mansbach, Rachael Alexandra
Issue Date
2018-06-29
Director of Research (if dissertation) or Advisor (if thesis)
Ferguson, Andrew L.
Doctoral Committee Chair(s)
Goldenfeld, Nigel D.
Committee Member(s)
  • Mason, Nadya
  • Kuehn, Seppe
Department of Study
Physics
Discipline
Physics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2018-09-27T16:17:28Z
Keyword(s)
  • biophysics
  • molecular simulation
  • machine learning
  • dimensionality reduction
  • coarse-graining
Abstract
The monomeric and assembled structures of proteins significantly influence their function. In order to rationally design proteins for specific applications, it is necessary to understand the ways in which those proteins fold and aggregate. In this thesis, I consider problems of protein folding and aggregation with a focus on two specific applications and investigate different methods for understanding the effects of chemistry and external environment on their monomeric and assembled conformations. First, I employ molecular dynamics and nonlinear dimensionality reduction to study a family of antimicrobial peptides with different side chain lengths and demonstrate a critical side chain length that determines backbone secondary structure in solution. Second, I study the effects of environment and chemistry upon oligopeptides that spontaneously assemble into bioelectronic nanostructures. By employing coarse-grained molecular dynamics to reach sufficient length and time scales to observe salient properties of assembly, I demonstrate that aggregation proceeds hierarchically, that flow has little effect on the early stages of assembly, and that aggregation in a specific pH range improves peptide alignment. I also identify regions of model parameter space defining particular peptide chemistries that are expected to rapidly agglomerate into fibrils with desirable optoelectronic properties. In sum, this work establishes new computational methods and machine learning techniques, deepens understanding of how to control the conformations of antimicrobial peptides in solution, and presents a multiscale model for the rational design of peptides for bioelectronic applications such as organic photovoltaic cells, organic field effect transistors, and biocompatible pH sensors.
Graduation Semester
2018-08
Type of Resource
text
Permalink
http://hdl.handle.net/2142/101496
Copyright and License Information
Copyright 2018 by Rachael A. Mansbach

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Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois
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