Naturally evolving proteins must navigate a vast set of possible sequences to evolve new functions. This process depends on the genotype-phenotype map. Much effort has been directed at measuring protein genotype phenotype maps to uncover evolutionary trajectories that lead to new functions. Often, these maps are too large to comprehensively measure. Sparsely measured maps, however, are prone to missing key evolutionary trajectories. Many groups turn to computational models to infer missing phenotypes. These models treat mutations as independent perturbations to the genotype-phenotype map. A key question is how to handle non-independent effects known as epistasis. In this dissertation, we address two sources of epistasis: 1) global and 2) local epistasis. We find that incorporating global epistasis improves our predictive power, while local epistasis does not. We use our model to infer unknown phenotypes in the Plasmodium falciparum chloroquine transporter (PfCRT) genotype-phenotype map, a protein responsible for conferring drug resistance in malaria. From these predictions, we uncover key evolutionary trajectories that led high resistance. This dissertation includes previously published and unpublished co-authored material. / 2020-01-11
| Identifer | oai:union.ndltd.org:uoregon.edu/oai:scholarsbank.uoregon.edu:1794/24231 |
| Date | 11 January 2019 |
| Creators | Sailer, Zachary |
| Contributors | Harms, Michael |
| Publisher | University of Oregon |
| Source Sets | University of Oregon |
| Language | en_US |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Rights | Creative Commons BY 4.0-US |
Page generated in 0.0016 seconds