Ensemble-based Computational Protein Design: Novel Algorithms and Applications to Energy Landscape Approximation, Antibiotic Resistance, and Antibody Design

dc.contributor.advisor

Donald, Bruce R

dc.contributor.author

Holt, Graham Thomas

dc.date.accessioned

2022-09-21T13:54:24Z

dc.date.issued

2022

dc.department

Computational Biology and Bioinformatics

dc.description.abstract

Proteins are incredibly varied in their biological function, and are therefore attractive targets for scientists and engineers to design new and improved functions. These functions are defined by a protein structure, which can be viewed as a probability distribution over a large conformation space. Many successful protein design methods construct and evaluate models of protein structure and physics in silico to design proteins. We apply the concept of protein structure as a probability distribution to design new protein design algorithms, study mechanisms of protein binding and antibiotic resistance, and design improved broadly-neutralizing antibodies This research highlights the utility of the distribution view of protein structure, and suggests future research in this direction.

dc.identifier.uri

https://hdl.handle.net/10161/25747

dc.subject

Computational chemistry

dc.title

Ensemble-based Computational Protein Design: Novel Algorithms and Applications to Energy Landscape Approximation, Antibiotic Resistance, and Antibody Design

dc.type

Dissertation

duke.embargo.months

23.86849315068493

duke.embargo.release

2024-09-16T00:00:00Z

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