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

Area of Science:

• Synthetic Biology
• Molecular Biology
• Biotechnology

Background:

• Site-specific recombination (SSR) is a valuable tool in synthetic biology but lacks predictable control over reaction rates.
• Current methods for modulating SSR rates by altering DNA substrates lack rational design principles.

Purpose of the Study:

• To develop an integrated experimental and computational method for engineering the attP DNA substrate sequence to predictably modulate the Bxb1 recombinase-mediated inversion reaction.
• To establish a qPCR-based assay for measuring SSR reaction rates.
• To create a machine-learning model for predicting Bxb1 inversion rates based on attP sequences.

Main Methods:

• Development of a quantitative PCR (qPCR) assay to measure SSR reaction rates.
• Design, selection, and sequencing of attP DNA libraries.
• Training a machine-learning model using attP sequence data to predict Bxb1 inversion rates.
• In vitro validation of predicted attP variant reaction rates.
• Demonstration of engineered attP variants in gene circuit design in Escherichia coli.

Main Results:

• A functional qPCR method was established for SSR rate measurement.
• A machine-learning model was developed capable of predicting Bxb1 inversion rates as a function of attP sequence.
• Predicted attP variants showed modulated inversion rates in vitro.
• Engineered attP sequences were successfully applied in designing gene circuits in E. coli.

Conclusions:

• The study presents a high-throughput, model-guided approach for rationally tuning SSR reaction rates.
• This method enhances the understanding of recombinase function and expands the synthetic biology toolbox.
• Predictable control over SSR rates opens new possibilities for designing complex genetic circuits.