A Phage-Assisted Continuous Selection Approach for Deep Mutational Scanning of Protein–Protein Interactions
datasetposted on 05.12.2019 by Julia Zinkus-Boltz, Craig DeValk, Bryan C. Dickinson
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
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 processesand to potentially target themit 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 KD 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.