Abstract
Landscapes exhibiting multiple secondary structures arise in natural RNA molecules that modulate gene expression, protein synthesis, and viral. We report herein that high-throughput chemical experiments can isolate an RNA’s multiple alternative secondary structures as they are stabilized by systematic mutagenesis (mutate-and-map, M2) and that a computational algorithm, REEFFIT, enables unbiased reconstruction of these states’ structures and populations. In an in silico benchmark on non-coding RNAs with complex landscapes, M2-REEFFIT recovers 95% of RNA helices present with at least 25% population while maintaining a low false discovery rate (10%) and conservative error estimates. In experimental benchmarks, M2-REEFFIT recovers the structure landscapes of a 35-nt MedLoop hairpin, a 110-nt 16S rRNA four-way junction with an excited state, a 25-nt bistable hairpin, and a 112-nt three-state adenine riboswitch with its expression platform, molecules whose characterization previously required expert mutational analysis and specialized NMR or chemical mapping experiments. With this validation, M2-REEFFIT enabled tests of whether artificial RNA sequences might exhibit complex landscapes in the absence of explicit design. An artificial flavin mononucleotide riboswitch and a randomly generated RNA sequence are found to interconvert between three or more states, including structures for which there was no design, but that could be stabilized through mutations. These results highlight the likely pervasiveness of rich landscapes with multiple secondary structures in both natural and artificial RNAs and demonstrate an automated chemical/computational route for their empirical characterization.
Highlights
RNAs are deeply involved in gene expression, gene regulation, and structural scaffolding and are forming the basis of novel approaches to control these processes [1,2,3]
RNA is a versatile macromolecule that underlies core natural processes throughout living systems and new strategies to re-engineer these systems. This versatility is due to the ability of an RNA molecule to adopt multiple conformations: the full description of the molecule involves a ‘landscape’ of alternative structures that are present at equilibrium and whose interconversions are critical for function
To leverage the signals of stabilized alternative structures present in M2 measurements, we developed a new analysis framework, RNA Ensemble Extraction From Footprinting Insights Technique (REEFFIT), to simultaneously infer multiple structures and their population fractions across the mutant RNAs
Summary
RNAs are deeply involved in gene expression, gene regulation, and structural scaffolding and are forming the basis of novel approaches to control these processes [1,2,3]. Several of RNA’s natural and engineered roles rely on its ability to fold into and interconvert between multiple functional structures. Dissecting and re-engineering these landscapes depends on knowledge of the alternative states of an RNA’s structural ensemble [10,11]. Empirical portraits of such landscapes are missing for the vast majority of natural and engineered RNAs and it is unclear whether a rich multi-state landscape is a property specially selected by evolution or an intrinsic feature of RNA that can arise without explicit design or selection. Some studies have suggested that conformational switches are special hallmarks of biological function rather than an intrinsic feature of generic RNA sequences [18,19,20,21]
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