ORCID

https://orcid.org/0000-0001-6905-5411

Date of Award

12-17-2024

Author's School

Graduate School of Arts and Sciences

Author's Department

Biology & Biomedical Sciences (Computational & Molecular Biophysics)

Degree Name

Doctor of Philosophy (PhD)

Degree Type

Dissertation

Abstract

Mutations in the sequence of proteins associated with a disease often increase its risk or severity. They can also be a barrier to developing effective therapeutics for such diseases. One way in which mutations affect function is by altering dynamics that elude structural studies. Observing these changes requires methods that can report on movements of the protein on various timescales and is an ongoing challenge in biophysics and biochemistry. In this study I will use both experimental and computational techniques that can report on such dynamics to explore how small sequence changes shift the conformational landscape and consequently the function in two families of proteins implicated in human diseases, Ebola virus VP35 and human Apolipoprotein E. I used molecular dynamics simulations analyzed using Markov State Models to identify differences in the conformational dynamics in VP35 orthologs, and used stopped-flow kinetic assays to verify these differences in vitro. I monitored the binding of the proteins to RNA via fluorescence polarization and observed that differences in the probabilities of the two structural states across orthologs regulate the preference of these proteins to bind to either the blunt end or the backbone of the RNA. We were also able to design single amino acid substitutions that modulate the pocket opening and consequently change the preference of the proteins for the blunt end or the backbone. This preference is known to cause a trade-off between different modes of immune inhibition. I also co-developed a computational approach to compare single molecule Förster Resonance Energy Transfer (smFRET) experiments with simulations and applied it ApoE isoforms to get detailed information about their conformational dynamics. Analyzing the dynamics of the protein in simulations using mutual information metrics, we observe differences in interactions that propagate across the protein originating from the single amino acid substitutions that distinguish the isoforms. This also helps us identify key residues in the allosteric pathway that may help understand the effects of variants of unknown significance.

Language

English (en)

Chair and Committee

Gregory Bowman

Committee Members

Andrea Soranno; Alex Holehouse; Eric Galburt; Jay Ponder

Available for download on Saturday, December 13, 2025

Included in

Biophysics Commons

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