Overcoming the design, build, test (DBT) bottleneck for synthesis of nonrepetitive protein-RNA binding cassettes for RNA applications

The design-build-test (DBT) cycle in synthetic biology is considered to be a major bottleneck for progress in the field. The emergence of high-throughput experimental techniques, such as oligo libraries (OLs), combined with machine learning (ML) algorithms, provide the ingredients for a potentialbig-data solution that can generate a sufficient predictive capability to overcome the DBT bottleneck. In this work, we apply the OL-ML approach to the design of RNA cassettes used in gene editing and RNA tracking systems. RNA cassettes are typically made of repetitive hairpins, therefore hindering their retention, synthesis, and functionality. Here, we used a strong repression effect that occurs when an RBP binds its binding site within the ribosomal initiation region of E. coli to generate using an OL-experiment thousands of new binding sites for the phage coat proteins of bacteriophages MS2 (MCP), PP7 (PCP), and Qβ (QCP). We then applied a connected neural network ML approach to vastly expand this space of binding sites to millions of additional predicted sites, which allowed us to identify the structural and sequence features that are critical for the binding of each RBP. To verify our approach, we designed new non-repetitive binding site cassettes of ten sites each containing either experimentally verified or predicted sites, and tested their functionality in U2OS mammalian cells. We found that all of our cassettes exhibited multiple trackable puncta. Additionally, we designed and verified a cassette containing predicted binding sites, where each site can bind both PCP and QCP. Consequently, we provide the scientific community with a novel resource for rapidly creating functional non-repetitive binding site cassettes using one or more of three phage coat proteins with a variety of binding affinities for any application spanning bacteria to mammalian cells.