Model-guided engineering of DNA sequences with predictable site-specific recombination rates

Site-specific recombination (SSR) is an important tool in synthetic biology, but its applications are limited by the inability to predictably tune SSR reaction rates. Here, using quantitative high-throughput experiments and machine learning, the authors achieve rational control of a DNA attachment site sequence to predictably modulate site-specific recombination rates both in vitro and in cells. Site-specific recombination (SSR) is an important tool in synthetic biology, but its applications are limited by the inability to predictably tune SSR reaction rates. Facile rate manipulation could be achieved by modifying the DNA substrate sequence; however, this approach lacks rational design principles. Here, we develop an integrated experimental and computational method to engineer the DNA attachment sequence attP for predictably modulating the inversion reaction mediated by the recombinase Bxb1. After developing a qPCR method to measure SSR reaction rate, we design, select, and sequence attP libraries to inform a machine-learning model that computes Bxb1 inversion rate as a function of attP sequence. We use this model to predict reaction rates of attP variants in vitro and demonstrate their utility in gene circuit design in Escherichia coli. Our high-throughput, model-guided approach for rationally tuning SSR reaction rates enhances our understanding of recombinase function and expands the synthetic biology toolbox.

Automated summary

In this study, the authors achieved rational control of DNA attachment site sequence to predictably modulate site-specific recombination rates using quantitative high-throughput experiments and machine learning. This research provides an important tool for gene circuit design in synthetic biology.