Applicability of a computational design approach for synthetic riboswitches

Abstract

Riboswitches have gained attention as tools for synthetic biology, since they enable researchers to reprogram cells to sense and respond to exogenous molecules. In vitro evolutionary approaches produced numerous RNA aptamers that bind such small ligands, but their conversion into functional riboswitches remains difficult. We previously developed a computational approach for the design of synthetic theophylline riboswitches based on secondary structure prediction. These riboswitches have been constructed to regulate ligand-dependent transcription termination in Escherichia coli. Here, we test the usability of this design strategy by applying the approach to tetracycline and streptomycin aptamers. The resulting tetracycline riboswitches exhibit robust regulatory properties in vivo. Tandem fusions of these riboswitches with theophylline riboswitches represent logic gates responding to two different input signals. In contrast, the conversion of the streptomycin aptamer into functional riboswitches appears to be difficult. Investigations of the underlying aptamer secondary structure revealed differences between in silico prediction and structure probing. We conclude that only aptamers adopting the minimal free energy (MFE) structure are suitable targets for construction of synthetic riboswitches with design approaches based on equilibrium thermodynamics of RNA structures. Further improvements in the design strategy are required to implement aptamer structures not corresponding to the calculated MFE state.

Evolving methods for rational de novo design of functional RNA molecules

“…For example, some studies did not use an optimization approach at all and instead enumerated all their candidates. Thus, they rely on such an analysis step to select solutions by ranking and filtering with respect to certain criteria and threshold boundaries [23,24]. Also, according to Garcia-Martin et al [62], an exhaustive quality determination is essential after the candidate enumeration step of the applied constraint programming approach.…”

Systematic optimization of L‐tryptophan riboswitches for efficient monitoring of the metabolite in Escherichia coli

“…Although computational designs (Domin et al, 2016;Espah Borujeni, Mishler, Wang, Huso, & Salis, 2016) or modular expression platforms (Ceres, Garst, MarcanoVelázquez, & Batey, 2013;Ceres, Trausch, & Batey, 2013) could be utilized for the construction of artificial riboswitches, we chose to take advantage of the in vivo selection system because the structural foundation of the L-tryptophan aptamer has not been fully investigated. In this design, binding of L-tryptophan to the aptamer results in expression of the selection-screening module depending on the sequence of the linker (Lynch, Desai, Sajja, & Gallivan, 2007).…”

“…Initially, an artificial L-tryptophan riboswitch was created by in vivo selection (Jang, Yang, Seo, & Jung, 2015;Muranaka, Sharma, Nomura, & Yokobayashi, 2009) of a riboswitch library composed of an L-tryptophan aptamer (70-727) (Majerfeld & Yarus, 2005), a randomized linker, a ribosome binding site (RBS), and a selection-screening module (tetA-sgfp) ( Figure 1b). Although computational designs (Domin et al, 2016;Espah Borujeni, Mishler, Wang, Huso, & Salis, 2016) or modular expression platforms (Ceres, Garst, Marcano-Velázquez, & Batey, 2013;Ceres, Trausch, & Batey, 2013) could be utilized for the construction of artificial riboswitches, we chose to take advantage of the in vivo selection system because the structural foundation of the L-tryptophan aptamer has not been fully investigated. In this design, binding of L-tryptophan to the aptamer results in expression of the selection-screening module depending on the sequence of the linker (Lynch, Desai, Sajja, & Gallivan, 2007).…”

Computational design of Small Transcription Activating RNAs (STARs) for versatile and dynamic gene regulation

“…Recently, there has been great progress towards this vision, with numerous demonstrations of synthetic RNA regulators that have been rationally engineered from natural versions [6][7][8][9][10][11] or designed de novo [12][13][14][15][16][17][18][19][20][21] . However, while computational design of RNA regulators has been possible for many years, a major challenge has been the design of high-performing mechanisms that exert large fold changes in gene expression comparable to protein-based regulators.…”