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

dc.contributor.author

Nisonoff, Hunter

dc.date.accessioned

2015-05-07T18:03:31Z

dc.date.available

2016-11-12T05:30:04Z

dc.date.issued

2015-05-07

dc.department

Mathematics

dc.description.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.

dc.identifier.uri

https://hdl.handle.net/10161/9746

dc.language.iso

en_US

dc.subject

Protein Design

dc.subject

Partition Function

dc.subject

Probabalistic Algorithm

dc.title

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

dc.type

Honors thesis

duke.embargo.months

18

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