| Abstract: |
The Inverse Protein Folding problem, also called Protein Design, spans the boundaries of both the computer and biological sciences. The problem consists of determining a sequence of amino acids to compose a protein that will, due to their combined bio-chemical properties, fold into a predetermined three-dimensional structure. This three-dimensional structure, or conformation, determines the effect of the protein on its environment. Hence, success in the Inverse Protein Folding problem would allow scientists the ability to redesign existing proteins with additional or enhanced functionality or even to design new proteins with novel functionality.
Nevertheless, while the impact of a solution to the Inverse Protein Folding is apparent, the computational challenges are extraordinary, since the search space explodes exponentially as the length of the protein increases. We demonstrate a Monte-Carlo model for sampling the search space coupled with energy functions for evaluating conformations. These energy functions are based on statistical propensities mined from the Protein Data Bank. We further demonstrate a framework based on the Illinois Bio-Grid Toolkit which utilizes grid technologies to give the capabilities of engaging massive amounts of computational power to the problem.
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