Ph.D. Research Talk: Automated Selection Identification for Directed Evolution of Enzymes

Abstract

Directed evolution of enzymes consists of an iterative process of creating mutant libraries and choosing desired phenotypes through screening or selection until the enzymatic activity reaches the desired goal. This process can be used to develop biocatalysts for the production of novel bioactive compounds and fine chemicals. The biggest challenge in directed enzyme evolution is to identify an appropriate high-throughput screen or selection to isolate the variant(s) with the desired property. Automated techniques to identify selection mechanisms can significantly expedite experimental practices in directed evolution. Currently, there are no known computational techniques for identifying appropriate high throughput selections for enzyme engineering purposes. We describe in this talk a computational method, Automated Selection Finder (ASF), for constructing a selection pathway from a desired enzymatic product to a cellular host and couple the pathway for the cell’s survival. We have applied ASF to different test cases and some of our findings match ones that have been experimentally validated.