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Studying Proteins Implicated in Cancer with a Computational Toolbox

Abstract

Cancer formation is a complex, multi-step process that allows cells to grow abnormally and potentially invade and spread throughout the body. A single genetic or structural alteration of a single protein in a cellular physiological process is enough to stimulate cancer formation. In treating cancer, a ‘targeted therapy’ approach is becoming increasingly common, where we can develop drugs that specifically target these altered proteins implicated in cancer. Two proteins that are heavily involved in several human cancers are protein kinases and p53, which are the focus of this dissertation work. I chose to use molecular dynamics simulations and in silico virtual screening, two methods from the computational chemistry toolbox in studying protein kinases and p53. I demonstrate that performing molecular dynamics is worthwhile in conducting virtual screens against protein kinases, because it may result in that at least one conformation is more predictive than the crystal structure. I also reveal key insight into the transcriptional activation mechanism of p53, and show how this mechanism is altered as a result of the R175H cancer mutation.

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