Automated RNA structure prediction uncovers a missing link in double glycine riboswitches

TL;DR

This study demonstrates that automated RNA structure prediction tools can identify previously overlooked structural elements in double glycine riboswitches, validated by experimental data, revealing their functional importance in ligand-binding cooperativity.

Contribution

The paper introduces a novel combination of prediction tools and experimental validation to uncover a missing structural element in glycine riboswitches, advancing RNA structural understanding.

Findings

Identified a new stem P0 and kink-turn motif in glycine riboswitches.

Disruption of the predicted structure significantly reduces ligand-binding affinity.

Automated prediction tools effectively detect critical RNA structural elements.

Abstract

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 remarkable class of double glycine riboswitches that exhibit ligand-binding cooperativity. A novel method (BPPalign), RMdetect, JAR3D, and Rosetta 3D modeling give consistent predictions for a new stem P0 and kink-turn motif. These elements structure the linker between the RNAs' double aptamers. Chemical mapping on the F. nucleatum riboswitch with SHAPE, DMS, and CMCT probing, mutate-and-map studies, and mutation/rescue experiments all provide strong evidence for the structured linker. Under solution conditions that separate two glycine binding transitions, disrupting this helix-junction-helix structure gives 120-fold and 6- to 30-fold poorer association constants for the two transitions, corresponding to an overall energetic impact of 4.3 \pm 0.5 kcal/mol. Prior biochemical and crystallography studies from several labs did not include this critical element due to over-truncation of the RNA. We argue that several further undiscovered elements are likely to exist in the flanking regions of this and other RNA switches, and automated prediction tools can now play a powerful role in their detection and dissection.