Site-specific recombination (SSR) is an important tool in genome editing and gene circuit design. However, its applications are limited by the inability to simply and predictably tune SSR reaction rates across several orders of magnitude. Facile rate manipulation could in principle be achieved by modifying the sequence of the DNA substrate (rather than the recombinase), but the design principles for rationally doing so have not been elucidated. To enable predictable tuning of SSR reaction dynamics as a function of DNA sequence, we developed an integrated experimental and computational method to parse individual nucleotide contributions to the overall reaction rate, which we used to analyze and engineer the attP DNA sequence for the inversion reaction mediated by the serine recombinase Bxb1. A quantitative polymerase chain reaction (qPCR) method was developed to measure the relative Bxb1 reaction rate in vitro. Then, attP libraries were designed, selected, and sequenced to inform a machine-learning model, which revealed that the Bxb1 reaction rate can be represented by additive contributions of nucleotides at key positions. Finally, we used the model to predict the performance of DNA site variants in reaction rate assays, both in vitro and in Escherichia coli, with flipping rates ranging from 0.01- to 10-fold that of the wild-type attP sequence. Our high-throughput, data-driven method for rationally tuning SSR reaction rates through DNA sequence modification enhances mechanistic insights of recombinase function and expands the synthetic biology toolbox.

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

ACS Fall 2021

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