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 orders of magnitude. Facile rate manipulation can in principle be achieved by modifying the nucleotide sequence of the DNA substrate of the recombinase, but the design principles for rationally doing so have not been elucidated. To enable predictable tuning of SSR reaction kinetics via 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 DNA attachment sequence attP for the inversion reaction mediated by the serine recombinase Bxb1. A quantitative PCR method was developed to measure the Bxb1 reaction rate in vitro. Then, attP sequence libraries were designed, selected, and sequenced to inform a machine-learning model, which revealed that the Bxb1 reaction rate can be accurately represented assuming independent contributions of nucleotides at key positions. Next, 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. Finally, we demonstrate that attP variants with predictable DNA recombination rates can be used in concert to achieve kinetic control in gene circuit design by coordinating the coexpression of two proteins in both their relative proportion and their total amount. Our high-throughput, data-driven method for rationally tuning SSR reaction rates through DNA sequence modification enhances our understanding of recombinase function and expands the synthetic biology toolbox.

中文翻译：

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具有可预测位点特异性重组率的 DNA 序列的模型引导工程

位点特异性重组 (SSR) 是基因组编辑和基因电路设计的重要工具。然而，它的应用受到无法简单且可预测地调整 SSR 反应速率跨数量级的限制。原则上可以通过修改重组酶的 DNA 底物的核苷酸序列来实现简便的速率操作，但尚未阐明合理这样做的设计原则。为了通过 DNA 序列对 SSR 反应动力学进行可预测的调整，我们开发了一种集成的实验和计算方法来解析单个核苷酸对整体反应速率的贡献，我们用它来分析和设计 DNA 附着序列 attP，用于由丝氨酸重组酶 Bxb1。体外。然后，设计、选择和测序 attP 序列库以告知机器学习模型，这表明假设关键位置的核苷酸的独立贡献，可以准确表示 Bxb1 反应速率。接下来，我们使用该模型来预测 DNA 位点变异在体外和大肠杆菌中的反应速率测定中的性能，翻转率范围为野生型 attP 序列的 0.01 到 10 倍。最后，我们证明了具有可预测 DNA 重组率的 attP 变体可以通过协调两种蛋白质的相对比例和总量的共表达来协同使用，以实现基因电路设计中的动力学控制。我们通过 DNA 序列修饰合理调整 SSR 反应速率的高通量数据驱动方法增强了我们对重组酶功能的理解并扩展了合成生物学工具箱。