Automated RNA Structure Prediction Uncovers a Kink-Turn Linker in Double Glycine Riboswitches

The tertiary structures of functional RNA molecules remain difficult to decipher. A new generation of automated RNA structure prediction methods may help address these challenges but have not yet been experimentally validated. Here we apply four prediction tools to a class of double glycine riboswitches that can bind two ligands cooperatively. A novel method (BPPalign), RMdetect, JAR3D, and Rosetta 3D modeling give consistent predictions for a new stem P0 and a kink-turn motif. These elements structure the linker between the RNAs' double aptamers. Chemical mapping on the Fusobacterium nucleatum riboswitch with N-methylisatoic anhydride, dimethyl sulfate and 1-cyclohexyl-3-(2-morpholinoethyl)carbodiimide metho-p-toluenesulfonate probing, mutate-and-map studies, and mutation/rescue experiments all provide strong evidence for the structured linker. Under solution conditions that permit rigorous thermodynamic analysis, disrupting this helix-junction-helix structure gives 120- and 6-30-fold poorer dissociation constants for the RNA's two glycine-binding transitions, corresponding to an overall energetic impact of 4.3 ± 0.5 kcal/mol. Prior biochemical and crystallography studies did not include this critical element due to over-truncation of the RNA. We speculate that several further undiscovered elements are likely to exist in the flanking regions of this and other functional RNAs, and automated prediction tools can play a useful role in their detection and dissection.