Efficient Partition Function Estimation in Computational Protein Design: Probabalistic Guarantees and Characterization of a Novel Algorithm
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 thesisDepartment
MathematicsPermalink
https://hdl.handle.net/10161/9746Citation
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.Collections
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