Automated physics-based design of synthetic riboswitches from diverse RNA aptamers

Abstract

Riboswitches are shape-changing regulatory RNAs that bind chemicals and regulate gene expression, directly coupling sensing to cellular actuation. However, it remains unclear how their sequence controls the physics of riboswitch switching and activation, particularly when changing the ligand-binding aptamer domain. We report the development of a statistical thermodynamic model that predicts the sequence-structure-function relationship for translation-regulating riboswitches that activate gene expression, characterized inside cells and within cell-free transcription-translation assays. Using the model, we carried out automated computational design of 62 synthetic riboswitches that used six different RNA aptamers to sense diverse chemicals (theophylline, tetramethylrosamine, fluoride, dopamine, thyroxine, 2,4-dinitrotoluene) and activated gene expression by up to 383-fold. The model explains how aptamer structure, ligand affinity, switching free energy and macromolecular crowding collectively control riboswitch activation. Our model-based approach for engineering riboswitches quantitatively confirms several physical mechanisms governing ligand-induced RNA shape-change and enables the development of cell-free and bacterial sensors for diverse applications.

Figure 1.

A biophysical model of riboswitch regulation. (A) A biophysical model was developed to predict the function of translation-regulating riboswitches. The model uses the riboswitch's mRNA sequence, the aptamer's ligand-bound structure, the concentrations of the mRNA and ligand, and their molar volumes to calculate the riboswitch's translation initiation rates in the ON and OFF states (r_ON, r_OFF) as well as its maximum, concentration-limited and actual activation ratios (AR_max, AR_conc, AR_actual). (B) A reaction coordinate diagram showing the states, energies, and transition paths during mRNA co-transcriptional folding, ligand-binding and translation initiation. Without ligand, the ribosome binds to a folded mRNA (state 1) with free energy ΔG_total,OFF. When excess ligand is present, the co-transcriptional folding of mRNA and ligand through paths a and b ends with a mRNA–ligand complex (state 3) that binds the ribosome with free energy ΔG_total,ON. The stability of the mRNA–ligand complex is controlled by the switching free energy (ΔΔG_mRNA + ΔG_ligand).

Figure 2.

Biophysical model calculations. (A) Without ligand, when a ribosome binds to a folded mRNA, it undergoes several structural changes with corresponding free energy changes in the mRNA–ribosome interactions. Here, the ribosome binds poorly to the mRNA's initial state because it has an inaccessible standby site and sequestered (brown) Shine-Dalgarno (SD) sequence. In the final mRNA state, the ribosome's 16S rRNA and tRNA^fMet hybridize to the SD sequence and start codon, respectively, with significant refolding of the mRNA. The model uses the mRNA sequence to calculate the total free energy change. (B) When the ligand is present, the same free energy calculation is performed, while constraining the (blue) aptamer sequence to its ligand-bound structure. (C) Fifteen synthetic theophylline riboswitches from Ref. (14) were characterized to test the model's AR_max prediction. (D) Predicted ΔG_total values in the (white circles) OFF and (orange circles) ON states are well-correlated to the measured luminescences according to the expected log-linear relationship (R² = 0.68, P = 2 × 10⁻⁸). (E) The calculated free energy differences ΔΔG_total are well-correlated to the measured ARs according to the expected log-linear relationship (R² = 0.68, P = 1.5 × 10⁻⁴). Each data point and bar represents the mean and s.d. of three measurements.

Figure 3.

Automated design of synthetic riboswitches. (A) An optimization algorithm converts RNA aptamers into synthetic riboswitch sequences through rounds of mutation, prediction, selection and recombination. (B) The ligand-bound structures of the theophylline, TMR, fluoride, dopamine (dopa1.3/c.3), thyroxine (ApT4-J-min) and 2,4-dinitrotoluene aptamers are shown (see also Supplementary Figure S16). Pseudoknotted base pairs are indicated by dotted lines. Pre- and post-aptamer sequences varied after optimization. (C) The error in the model's calculated AR_conc is compared to the calculated switching free energy, showing that the result of Equation 2 loses accuracy as the switching free energy grows. Riboswitches are colored according to their aptamer: (dark blue) 12 designed theophylline riboswitches; (red) 12 theophylline riboswitches with mutated aptamer; (brown) 6 theophylline riboswitches with different mRFP1 coding sequences; (green) 15 previously engineered theophylline riboswitches (14); (yellow) 12 designed fluoride riboswitches; (light blue) 10 TMR riboswitches. The boxed outliers are riboswitches Theo-42 and Theo-44, suggesting a systematic malfunction in their function. (D) Model predictions using Equation 3 (AR_actual) are in good agreement with the measured ARs for 59–67 riboswitches (Spearman R = 0.69, P = 1.2 × 10⁻¹⁰, N = 67; Pearson R² = 0.61, P = 2.6 × 10⁻¹³, N = 59). (Shaded blue) The model predicted the ARs of 37 riboswitches to within 2-fold. (E) The predicted translation initiation rates in the riboswitches’ (white circles) OFF and (orange circle) ON states are compared to their measured fluorescence or luminescence levels (Spearman R = 0.68, P = 3.7 × 10⁻¹⁹, N = 134; Pearson R² = 0.44, P = 4.3 × 10⁻¹⁸, N = 134). Each data point and bar represents the mean and s.d. of 2–4 measurements.

Figure 4.

In vivo and cell-free characterization of designed riboswitches. (A) For each aptamer, the (circles) measured expression levels in the ON and OFF states and (bars) the ARs for the two best riboswitch variants are shown. To characterize their ON states, added ligand concentrations were 2 mM theophylline, 30 μM TMR, 150 mM fluoride, 1 mM DNT, 1 mM dopamine supplemented with 5 mM ascorbic acid and 150 μM thyroxine supplemented with 100 mM NaCl and 1.5 mM NaOH (see Supplementary Figure S15). Each data point and bar represents the mean and standard deviation of 2–4 measurements. (B) Cell-free transcription–translation assays are used to characterize riboswitch function during (yellow) the absence of ligand, (blue) post-transcriptional ligand binding and (red) co-transcriptional ligand binding. (C) Using cell-free transcription–translation assays, the expression levels of three TMR riboswitches and a no-aptamer control were measured to determine the role of co-transcriptional folding. As described in Ref. (14), expression levels are normalized to the highest luminescence level during the 10 min assay for each individual construct. Each data point and bar represents the mean and s.d. of 3–4 measurements.

Figure 5.

Macromolecular crowding controls riboswitch activation inside cells. (A) The relationship between riboswitch mRNA level and the volume fractions (ν) of free mRNA, free ligand, mRNA–ligand complex and free volume in either dilute or crowded systems were calculated according to Equation (2) for the TMR-10 riboswitch. (B) Cell-free transcription–translation assays were performed for three TMR riboswitches using increasing DNA template concentrations (0.1, 1, 10 and 100 ng/μl). Colors, symbols and ligand concentrations are the same as Figure 4. (C) The TMR riboswitches’ in vivo expression levels were measured at zero and 20 μM TMR after increasing the plasmid copy numbers, using the pBAC, p15A, pColE1 and pUC19 replication origins. (D) Model-predicted AR_actual for the TMR-10 riboswitch when varying the total concentrations of mRNA and ligand. The effect of changing the plasmid's copy number on mRNA level is shown. (E) The in vivo expression levels and ARs for theophylline and fluoride riboswitches were measured after increasing riboswitch mRNA levels, using three promoters with steadily increasing transcription rates. Colors, symbols and ligand concentrations are the same as Figure 4. Each data point and bar represents the mean and s.d. of 2–4 measurements.

Figure 6.

A ‘perfect’ riboswitch tests the limits of sensing. (A) Optimization was used to design a ‘perfect’ riboswitch using a hypothetical aptamer, which is shown in its ligand-free and ligand-bound states. (B) Model calculations show the effects of the aptamer's binding free energy (affinity) and maximum ligand concentration on the ‘perfect’ riboswitch's actual AR to illustrate the best possible expression changes under potential sensing scenarios.