Computational Design of Tunable Kinetic RNA Biosensors

Abstract

Bacterial metabolism is made up of complex gene networks that can be engineered to produce valuable chemicals from renewable resources such as sugar. However, these complex networks require sophisticated genetic engineering to optimize the production of these high-value compounds. For most biosynthetic pathways, it remains impossible to predict what combinations of gene expression levels will give rise to the most productive bacteria. Therefore, to identify the most productive pathway variants it is necessary to develop biosensors that can give a visible readout of how much of the target chemical each cell is producing. In nature, bacteria’s solution to measuring the concentrations of important chemicals is a class of RNA based biosensors called “riboswitches”. Riboswitches bind their target chemical and modulate gene expression levels in response. However, despite numerous design and selection methods published to date, there exists no common methodology to design high-performance, tunable, chemical-responsive RNA biosensors from first principles. By combining design lessons learned from nature with novel kinetic RNA folding predictions we present a new class of engineered RNA biosensors with increased activation ratios and tunable ligand sensitivities, both of which are essential for applying biosensors to real-world problems. We first demonstrated that our novel molecular architecture can be applied to the regulation of self-cleaving ribozymes in vitro, resulting in activation ratios exceeding 200-fold. By rationally modifying a single domain within the biosensors we were able to tune the sensitivity to its target molecule by more than 100-fold. We then demonstrated that the same architecture can be applied to regulate protein expression levels in E. coli with unprecedented sensitivity. Finally, we use our kinetic design rules to engineer a CRISPR-based transcriptional activation system, resulting in a completely novel biosensor, for an industrially-relevant chemical, able to sense the production of the target within an engineered bacterium.