Date of Award

2017

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Graduate Group

Biochemistry & Molecular Biophysics

First Advisor

Ravi Radhakrishnan

Abstract

The decreasing cost of genome sequencing technology has lead to an explosion of informa-

tion about which mutations are frequently observed in cancer, demonstrating an important

role in cancer progression for kinase domain mutations. Many therapies have been devel-

oped that target mutations in kinase proteins that lead to constitutive activation. However,

a growing body of evidence points to the serious dangers of many kinase ATP competitive

inhibitors leading to paradoxical activation in non-constitutively active proteins. The large

number of observed mutations and the critical need to only treat patients harboring activat-

ing mutations with targeted therapies raises the question of how to classify the thousands

of mutations that have been observed. We start with an in depth look at the state of knowl-

edge of the distribution and effects of kinase mutations. We then report on computational

methods to understand and predict the effects of kinase domain mutations. Using molecular

dynamics simulations of mutant kinases, we show that there is a switch-like network of la-

bile hydrogen bonds that are often perturbed in activating mutations. This is paired with a

description of a software platform that has been developed to streamline the execution and

analysis of molecular dynamics simulations. We conclude by examining a machine learning

method to demonstrate what kinds information derived from protein sequence alone have

the most value in distinguishing activating and non-activating mutations.

Embargoed

Available to all on Monday, January 18, 2021

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