ORCID

http://orcid.org/0000-0003-1914-4955

Date of Award

Winter 1-15-2021

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

Protein conformational landscapes contain much of the functionally relevant information that is useful for understanding biological processes at the chemical scale. Understanding and mapping out these conformational landscapescan provide valuable insight into protein behaviors and biological phenomena, and has relevance to the process of therapeutic design.

While structural biology methods have been transformative in studying protein dynamics, they are limited by technicallimitations and have inherent resolution limits. Molecular dynamics (MD) simulations are a powerful tool for exploring conformational landscapes, and provide atomic-scale information that is useful in understanding protein behaviors. With recent advances in generating datasets of large timescale simulations (using Folding@home) and powerful methods to interpret conformational landscapes such as Markov State Models (MSMs), it is now possible to study complex biological phenomena and long-timescale processes. However, inferring communication between residues across long distances, referred to as allosteric communication, remains a challenge.

Allostery is a ubiquitious biological phenomena by which two distant regions of a protein are coupled to one anotherover large distances. Allosteric coupling is the mechanism through which events in one region (such as ligand binding) alter the conformation or dynamics of another region (ie. large conformational domain motions). For example, allostery plays a critical role in cellular signaling, such as in the transfer of a signal from outside the cell to cytosolic proteins for generating a cellular response.

While many methods have made tremendous progress in inferring and measuring allosteric communication usingstructures or molecular simulations, they rely on a structural view of allostery and do not account for the role of conformational entropy. Furthermore, it remains a challenge to interpret allosteric coupling in large, complex biomolecules relevant to physiology and disease.

In this thesis, I present a method to measure the Correlation of All Rotameric and Dynamical States (CARDS) whichis used to construct and interpret allosteric networks in biological systems. CARDS allows us to infer allostery both via concerted changes in protein structure and in correlated changes in conformational entropy (dynamic allostery). CARDS does so by parsing trajectories into dynamical states which reflect whether a residue is locally ordered (ie. stable in a single rotameric basin) or disordered (ie. rapidly hopping between rotamers).

Here I explain the CARDS methodology (chapter 2) and demonstrate applications to a variety of disease-relevantsystems. In particular, I apply CARDS and other sophisticated computational methods to understand the process of G protein activation (chapter 3), a protein whose mutations are linked to cancers such as uveal melanoma. I further demonstrate the utility of CARDS in the study a potentially druggable pocket in the ebolavirus protein VP35 (chapter 4). The analyses and models constructed in this work are supported by experimental testing. Lastly, I demonstrate how integrating MD with experiments, sometimes with the help of citizen-scientists around the world, can provide unique insight into biological systems and identify potentially useful targets. In particular, I highlight our recent effort converting Folding@home into an exascale computer platform to hunt for potentially druggable pockets in the proteome of SARS-CoV-2 (chapter 7) (the cause of the COVID19 pandemic).

Language

English (en)

Chair and Committee

Gregory R. Bowman

Committee Members

Kendall J. Blumer, Rohit V. Pappu, Linda J. Pike, Jay W. Ponder,

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