Biosensors that transduce target chemical and biochemical inputs into genetic outputs are essential for bioengineering and synthetic biology. Current biosensor design strategies often suffer from a lack of signal-to-noise, requirements for extensive optimization for each new input, and poor performance in mammalian cells. Here we report the development of a proximity-dependent split RNA polymerase (RNAP) as a general platform for biosensor engineering. After discovering that interactions between fused proteins modulate the assembly of a split T7 RNAP, we optimized the split RNAP components for protein-protein interaction detection by phage-assisted continuous evolution (PACE). We then applied the resulting “activity-responsive RNAP” (AR) system to create light and small molecule activated biosensors, demonstrating the “plug-and-play” nature of the platform. Finally, we validated that ARs can interrogate multidimensional protein-protein interactions and trigger RNA nanostructure production, protein synthesis, and gene knockdown in mammalian systems, illustrating the versatility of ARs in synthetic biology applications.

Protein–protein interactions (PPIs) are critical for organizing molecules in a cell and mediating signaling pathways. Dysregulation of PPIs is often a key driver of disease. To better understand the biophysical basis of such disease processesand to potentially target themit is critical to understand the molecular determinants of PPIs. Deep mutational scanning (DMS) facilitates the acquisition of large amounts of biochemical data by coupling selection with high throughput sequencing (HTS). The challenging and labor-intensive design and optimization of a relevant selection platform for DMS, however, limits the use of powerful directed evolution and selection approaches. To address this limitation, we designed a versatile new phage-assisted continuous selection (PACS) system using our previously reported proximity-dependent split RNA polymerase (RNAP) biosensors, with the aim of greatly simplifying and streamlining the design of a new selection platform for PPIs. After characterization and validation using the model KRAS/RAF PPI, we generated a library of RAF variants and subjected them to PACS and DMS. Our HTS data revealed positions along the binding interface that are both tolerant and intolerant to mutations, as well as which substitutions are tolerated at each position. Critically, the “functional scores” obtained from enrichment data through continuous selection for individual variants correlated with K D values measured in vitro, indicating that biochemical data can be extrapolated from sequencing using our new system. Due to the plug and play nature of RNAP biosensors, this method can likely be extended to a variety of other PPIs. More broadly, this, and other methods under development support the continued development of evolutionary and high-throughput approaches to address biochemical problems, moving toward a more comprehensive understanding of sequence–function relationships in proteins.

Biosensors are an indispensable component of the synthetic biologist’s toolbox. Informed by biology and customized through rational design and evolutionary methods, these molecular protein scaffolds translate input signals into tractable outputs. By using biosensors to introduce synthetic gene circuits and cellular signaling pathways into host organisms, naturally-occurring biological processes can be harnessed and tailored for a wide range of applications. In this review, we have compiled recent innovations and advancements that have led to the generation of robust, reliable, and flexible biosensors for utility across basic biological research and synthetic biology tools, including biological engineering efforts and clinical applications.

Protein-protein interactions (PPIs) are critical for organizing molecules in a cell and mediating signaling pathways. Dysregulation of PPIs are often key drivers of disease. To better understand the biophysical basis of such disease processes – and to potentially target them - it is critical to understand the molecular determinants of PPIs. Deep mutational scanning (DMS) facilitates the acquisition of large amounts of biochemical data by coupling selection with high throughput sequencing (HTS). The challenging and labor-intensive design and optimization of a relevant selection platform for DMS, however, limits the use of powerful directed evolution and selection approaches. To address this limitation, we designed a versatile new phage assisted continuous selection (PACS) system using our proximity-dependent split RNA polymerase (RNAP) biosensors with the aim of greatly simplifying and streamlining the design of a new selection platform for PPIs. After characterization and validation using the model KRAS/RAF PPI, we generated a library of RAF variants and subjected them to PACS and DMS. Our HTS data revealed that amino acid (aa) positions 66, 84, and 89 on RAF, key residues in the KRAS/RAF PPI, are intolerant to mutations. We also identified a subset of residues with broad aa substitution tolerance, aa positions 52, 55, 76, and 79. Due to the plug and play nature of RNAP biosensors, this method can easily be extended to other PPIs. More broadly, this, and other methods under development, supports the application of evolutionary and high-throughput approaches to bear on biochemical problems, moving towards a more comprehensive understanding of sequence-function relationships in proteins.

Protein-protein interactions (PPIs) are critical for organizing molecules in a cell and mediating signaling pathways. Dysregulation of PPIs are often key drivers of disease. To better understand the biophysical basis of such disease processes – and to potentially target them - it is critical to understand the molecular determinants of PPIs. Deep mutational scanning (DMS) facilitates the acquisition of large amounts of biochemical data by coupling selection with high throughput sequencing (HTS). The challenging and labor-intensive design and optimization of a relevant selection platform for DMS, however, limits the use of powerful directed evolution and selection approaches. To address this limitation, we designed a versatile new phage assisted continuous selection (PACS) system using our proximity-dependent split RNA polymerase (RNAP) biosensors with the aim of greatly simplifying and streamlining the design of a new selection platform for PPIs. After characterization and validation using the model KRAS/RAF PPI, we generated a library of RAF variants and subjected them to PACS and DMS. Our HTS data revealed that amino acid (aa) positions 66, 84, and 89 on RAF, key residues in the KRAS/RAF PPI, are intolerant to mutations. We also identified a subset of residues with broad aa substitution tolerance, aa positions 52, 55, 76, and 79. Due to the plug and play nature of RNAP biosensors, this method can easily be extended to other PPIs. More broadly, this, and other methods under development, supports the application of evolutionary and high-throughput approaches to bear on biochemical problems, moving towards a more comprehensive understanding of sequence-function relationships in proteins.

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