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Efficient Partition Function Estimation in Computational Protein Design: Probabalistic Guarantees and Characterization of a Novel Algorithm

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Date
2015-05-07
Author
Nisonoff, Hunter
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Abstract
By computational protein design we mean the use of computer algorithms to design new proteins or redesign existing ones. A significant challenge in this field involves computing the partition function of the ensemble of conformations that a protein can adopt. Due to the exponentially large number of possible states, there are too many conformations to explicitly count. One solution is to employ a probabilistic algorithm to estimate the number of conformations instead. In this work we implemented such an algorithm, studied its mathematical guarantees and analyzed its properties. Additionally we proposed different approaches to improve the convergence of the algorithm.
Type
Honors thesis
Department
Mathematics
Subject
Protein Design
Partition Function
Probabalistic Algorithm
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https://hdl.handle.net/10161/9746
Citation
Nisonoff, Hunter (2015). Efficient Partition Function Estimation in Computational Protein Design: Probabalistic Guarantees and Characterization of a Novel Algorithm. Honors thesis, Duke University. Retrieved from https://hdl.handle.net/10161/9746.
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.

Rights for Collection: Undergraduate Honors Theses and Student papers


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