Fig. 1: Deep learning for ribonucleic acid (RNA) synthetic biology pipeline.

RNA tool selection is followed by library synthesis and characterization with analysis using deep neural networks (DNN) to provide functionality predictions and biological insights. We used a high-throughput toehold-switch library as a canonical model for the general investigation of RNA synthetic biology tools. The original toehold-switch architecture from Green et al.² was used, containing a 12-nucleotide toehold (a/a′) and an 18-nucleotide stem (b/b′) fully unwound by the trigger (left-bottom). We selected to fuse the RNA trigger to the 5′ end of the switch by an unstructured linker to facilitate library synthesis. A flow-sequence (seq) pipeline was used to characterize the fluorescence signal of individual toehold switches in a pooled sequential assay, including pooled induction, fluorescence-activated cell sorter (FACS) sorting, next-generation sequencing (NGS), and count frequency analysis. Finally, various DNN architectures were used to predict data outputs, while features contributing to DNN predictions were intuitively visualized to elucidate biological insights. Center panel adapted from Peterman et al.²⁴.