Overcoming the design, build, test bottleneck for synthesis of nonrepetitive protein-RNA cassettes

We apply an oligo-library and machine learning-approach to characterize the sequence and structural determinants of binding of the phage coat proteins (CPs) of bacteriophages MS2 (MCP), PP7 (PCP), and Q beta (QCP) to RNA. Using the oligo library, we generate thousands of candidate binding sites for each CP, and screen for binding using a high-throughput dose-response Sort-seq assay (iSort-seq). We then apply a neural network to expand this space of binding sites, which allowed us to identify the critical structural and sequence features for binding of each CP. To verify our model and experimental findings, we design several non-repetitive binding site cassettes and validate their functionality in mammalian cells. We find that the binding of each CP to RNA is characterized by a unique space of sequence and structural determinants, thus providing a more complete description of CP-RNA interaction as compared with previous low-throughput findings. Finally, based on the binding spaces we demonstrate a computational tool for the successful design and rapid synthesis of functional non-repetitive binding-site cassettes. Phage-coat proteins can be used to build synthetic biology parts. Here the authors use an oligo library and machine learning to predict and verify sequences based on binding scores.

Automated summary

In this study, an oligo library and machine learning approach were used to predict and validate binding sequences of phage coat proteins to RNA. By expanding the binding site space, critical structural and sequence features for each protein's binding to RNA were identified. The computational tool developed based on the binding spaces successfully facilitated the design and rapid synthesis of functional non-repetitive binding-site cassettes.