RNA Ensembles from Solvent Accessibility Data: Application to the SAM-I Riboswitch Aptamer Domain.
Jingru XieAaron T FrankPublished in: The journal of physical chemistry. B (2021)
Riboswitches are regulatory ribonucleic acid (RNA) elements that act as ligand-dependent conformational switches that recognize their cognate ligand via a binding pocket located in their aptamer domain. In the apo form, the aptamer domain is dynamic, requiring an ensemble representation of its structure. Here, as a proof-of-concept, we used solvent accessibility information to construct a pair of dynamical ensembles of the aptamer domain of the well-studied S-adenosylmethionine (SAM) class-I riboswitch in the absence (-SAM) and presence (+SAM) of SAM. To achieve this, we first generated a large conformational library and then reweighted conformers in the library using solvent-accessible surface area (SASA) data derived from recently reported light-activated structural examination of RNA (LASER) reactivities measured in the -SAM and +SAM states of the riboswitch. The differences in the resulting -SAM and +SAM ensembles are consistent with a SAM-dependent reshaping of the free-energy landscape of the aptamer domain. Within our -SAM ensemble, we identified a "transient" state that is missing a critical long-range contact, leading us to speculate that it may be representative of a folding intermediate. Further structural analysis also revealed that the transient state harbors a hidden binding pocket that is distinct from the SAM-binding pocket and is predicted by docking calculations to selectively bind small-molecule ligands. The SASA-based method we applied to the SAM-I riboswitch aptamer domain is general and could be used to construct dynamical ensembles for other riboswitch aptamer domains and, more broadly, other classes of structured RNAs.
Keyphrases
- gold nanoparticles
- sensitive detection
- small molecule
- molecular dynamics simulations
- molecular dynamics
- magnetic nanoparticles
- healthcare
- density functional theory
- ionic liquid
- single molecule
- label free
- electronic health record
- brain injury
- transcription factor
- mass spectrometry
- deep learning
- cerebral ischemia
- single cell
- high resolution