Date of Award

2012

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Biology

Abstract

Protein-protein interactions are part of all biological processes and are responsible for directing the development and maintenance of all systems in a species. Identifying such interactions provides insight into molecular processes in addition to their importance in understanding disease. Identifying protein-protein interactions experimentally is expensive, both in terms of cost and effort, and can generate erroneous results. Thus computational methods are key in reducing the scope of experimental assays, providing predictions for subsequent verification. Herein I present a new computational tool for the prediction of protein-protein interactions which, looking at sequence data alone, can identify putative interacting proteins as a result of their coordinated evolution. This new approach builds on previous molecular evolutionary methods and combines evolutionary information from individual proteins. As a proof of concept, the new approach was tested on the well-studied interaction networks of the visual and auditory systems. From this analysis, several protein clusters were identified warranting further experimental investigation. Furthermore, this effort also identified areas for future refinement of the software tool.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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