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.
Recommended Citation
Jordan, E. Joseph, "Simulation & Experiment Learning From Kinases In Cancer" (2017). Publicly Accessible Penn Dissertations. 2680.
https://repository.upenn.edu/edissertations/2680