Ensemble-based Computational Protein Design: Novel Algorithms and Applications to Energy Landscape Approximation, Antibiotic Resistance, and Antibody Design
| dc.contributor.advisor | Donald, Bruce Randall | |
| 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 | ||
| 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 |