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

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Physics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2018

Author dc:creator

• Mansbach, Rachael Alexandra

Contributors dc:contributor

• Ferguson, Andrew L.
• Goldenfeld, Nigel D.
• Mason, Nadya
• Kuehn, Seppe

• biophysics
• molecular simulation
• machine learning
• dimensionality reduction
• coarse-graining

Statement dc:rights

• Copyright 2018 by Rachael A. Mansbach

Language dc:language

en

Handle dc:identifier

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

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/101496