Proteins are molecular machines that living things use to eat, grow, and reproduce; they evolve over time and yet we have no tools to predict their adaptation to novel environments. Protein evolution is key to how germs become resistant to drugs, for example. As another example, engineers can apply the evolution of new protein functions to help break down oil spills. Predicting these adaptive changes in advance would greatly enhance our understanding of adaptation but would also open the door for new biological tools to confront environmental challenges. This project will develop computational tools to help scientists both predict and understand how proteins evolve. To do so, the researchers are experimentally studying the evolution of brightly colored proteins isolated from coral. They are then using the information from these experimental studies to develop general computational models to understand and predict protein evolution. Such tools will be powerful, enabling both more effective protein engineering and intervention in the evolution of undesired features such as drug-resistance. Finally, the project is designed to provide opportunities for high school STEM teachers in Oregon to directly participate in the research. Teacher research experiences demonstrably improve educational outcomes in STEM classrooms.
As an experimental model for protein evolution, the researchers are studying a collection of possible evolutionary trajectories between an ancestral and modern green fluorescent protein (GFP)-like protein from a coral in the genus Favia. The ancestral protein was green; the modern protein is red. This phenotypic change was achieved through 15 historical substitutions. The researchers are constructing all combinations of these mutations and characterizing the fluorescence of each genotype. This will allow them to map the collection of possible trajectories connecting the starting and end states. In parallel, they are developing computational tools to dissect epistasis, to describe connectivity, and to predict unmeasured phenotypes. This work will provide: 1) A publicly available, high-dimensional genotype-phenotype map covering the evolution of a new protein function; 2) Powerful software for studies of evolutionary trajectories; and 3) Deep insight into the determinants of protein evolutionary trajectories in high-dimensional genotype-phenotype maps.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
