Synergistic SHAPE/Single-Molecule Deconvolution of RNA Conformation under Physiological Conditions

Abstract

Structural RNA domains are widely involved in the regulation of biological functions, such as gene expression, gene modification, and gene repair. Activity of these dynamic regions depends sensitively on the global fold of the RNA, in particular, on the binding affinity of individual conformations to effector molecules in solution. Consequently, both the 1) structure and 2) conformational dynamics of noncoding RNAs prove to be essential in understanding the coupling that results in biological function. Toward this end, we recently reported observation of three conformational states in the metal-induced folding pathway of the tRNA-like structure domain of Brome Mosaic Virus, via single-molecule fluorescence resonance energy transfer studies. We report herein selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE)-directed structure predictions as a function of metal ion concentrations ([Mn+]) to confirm the three-state folding model, as well as test 2° structure models from the literature. Specifically, SHAPE reactivity data mapped onto literature models agrees well with the secondary structures observed at 0–10 mM [Mg2+], with only minor discrepancies in the E hairpin domain at low [Mg2+]. SHAPE probing and SHAPE-directed structure predictions further confirm the stepwise unfolding pathway previously observed in our single-molecule studies. Of special relevance, this means that reduction in the metal-ion concentration unfolds the 3′ pseudoknot interaction before unfolding the long-range stem interaction. This work highlights the synergistic power of combining 1) single-molecule Förster resonance energy transfer and 2) SHAPE-directed structure-probing studies for detailed analysis of multiple RNA conformational states. In particular, single-molecule guided deconvolution of the SHAPE reactivities permits 2° structure predictions of isolated RNA conformations, thereby substantially improving on traditional limitations associated with current structure prediction algorithms.