Liam Bartie

Abstract Protein engineering is limited by the inefficient search through a high-dimensional sequence space to find combinations of synergistic mutations. Traditional approaches use stepwise mutation stacking, whereas machine learning methods require extensive datasets or multiple experimental rounds and are bottlenecked by costly, length-limited gene synthesis. We present MULTI-evolve, a rapid evolution framework that systematically engineers multimutants.

Abstract Insertions of large DNA sequences into the genome are broadly enabling for research and therapeutic applications. Large serine recombinases (LSRs) can mediate direct, site-specific genomic integration of multi-kilobase DNA sequences without a pre-installed landing pad, albeit with low insertion rates and high off-target activity. Here we present an engineering roadmap for jointly optimizing their DNA recombination efficiency and specificity.

Abstract Bridge recombinases are naturally occurring RNA-guided DNA recombinases that we previously demonstrated can programmably insert, excise, and invert DNA in vitro and in Escherichia coli. In this study, we report the discovery and engineering of the bridge recombinase ortholog ISCro4 for universal rearrangements of the human genome. We defined strategies for the optimal application of bridge systems, leveraging mechanistic insights to improve their targeting specificity.