Realizing the Potential of Protein-Protein Interaction Prediction for Studying Single and Evolutionarily Similar Organisms and Engineering Inhibitory Proteins with InSiPS: The In Silico Protein Synthesizer

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Schoenrock, Andrew




Protein-protein interactions (PPIs) play a vital role in the life cycle of a cell and the elucidation of PPIs and PPI networks have become the interest of many over the past 15 years. There are a variety of experimental techniques to determine PPIs, however they are expensive, time and resource consuming and have relatively high error rates. To combat this, many computational tools have been produced to predict PPIs. These tools, however, have their own downfalls, including limited applicability, lack of supporting biological data, long running times and inadequately high error rates. The Protein-protein Interaction Prediction Engine (PIPE) is a computational tool used to predict PPIs which overcome the limitations of other methods and is able to perform proteome-wide PPI predictions within a wide range of organisms with a very low false positive rate (< 0.05%) in a reasonable amount of time. This thesis has four main areas of contribution. First, extensions to the PIPE method (including a method to predict PPI sites within a protein pair and a new scoring function used to determine PPIs) and performance improvements over the last PIPE implementation will be detailed. Secondly, the study of PIPE predicted PPIs will be used to produce novel biological insights in S. cerevisiae and H. Sapiens and the capability of PIPE to produce cross-species PPIs will be shown. Next, PIPE will be used to study the evolution of two families of organisms, starting with a family of green algae containing members at different stages of the transition from unicellularity to multicellularity followed by a group of five closely related yeast strains. In the latter, a novel null model used to compare PPI networks will also be introduced. Lastly, the PIPE method will be incorporated with a genetic algorithm to create a massively parallel computational tool used to generate novel, synthetic protein sequences designed to interact with a specific target while avoiding off-target interactions. Proteins with these properties can be used in an inhibitory manner and can form the basis for the development of various therapeutics. Wet-lab experiments were done to verify the generated proteins’ function in multiple cases.


Computer Science




Carleton University

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Computer Science

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Theses and Dissertations

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