Protein Design at Library Scale

Recent advances in de novo protein design have made it increasingly feasibleto create proteins with novel functions, driven by rapid progress in both com- putational modeling and high-throughput experimentation. Modern tools can explore vast sequence-structure spaces and evaluate biomolecular interactions, while experimental assays can now screen billions of variants in parallel. Yet, a key limitation remains: our current predictive models still struggle to capture the complex physical and dynamical factors that underlie enzyme function. My the- sis addresses this gap by developing an integrated experimental–computational framework for enzyme design that couples large-scale protein library construc- tion with data-driven model development. I design and test extensive libraries of enzyme variants to both optimize catalytic activity and generate training data for next-generation predictors of protein function. Ultimately, this approach ad- vances our ability to connect sequence, structure, and function.

Author dc:creator

• Gershon, Jacob

Advisor dc:contributor.advisor

• Baker, David

• ai4science
• protein
• protein design
• protein engineering
• protein modeling
• protein optimization
• Biochemistry
• Computer science

Statement dc:rights

• none

Language dc:language.iso

en_US

Handle dc:identifier.uri

https://hdl.handle.net/1773/55120

OAI identifier oai:identifier

oai:digital.lib.washington.edu:1773/55120