Rich RNA Structure Landscapes Revealed by Mutate-and-Map Analysis

Area of Science:

• Molecular Biology
• Computational Biology
• Biochemistry

Background:

• Natural RNA molecules often adopt multiple secondary structures, influencing crucial biological processes like gene expression and protein synthesis.
• Characterizing these complex RNA structural landscapes has historically required intensive, specialized experimental techniques.
• Understanding RNA structural dynamics is key to deciphering its diverse functional roles.

Purpose of the Study:

• To develop and validate a high-throughput method for isolating and reconstructing RNA's multiple alternative secondary structures.
• To assess the capability of a combined mutagenesis and computational approach in characterizing complex RNA structure landscapes.
• To investigate the prevalence of complex RNA structural landscapes in both natural and artificial sequences.

Main Methods:

• Employed a high-throughput chemical probing technique combined with systematic mutagenesis (mutate-and-map, M2).
• Utilized a computational algorithm, REEFFIT, for unbiased reconstruction of RNA structural states and their populations.
• Validated the M2-REEFFIT approach through in silico benchmarks and experimental characterization of diverse RNA molecules.

Main Results:

• The M2-REEFFIT method accurately recovered RNA helices in silico (95% recovery, 10% false discovery rate).
• Successfully characterized complex structure landscapes of natural RNAs, including riboswitches and rRNA, with unprecedented efficiency.
• Discovered that artificial RNA sequences can exhibit multiple stable structural states, even without explicit design.

Conclusions:

• M2-REEFFIT provides an automated, efficient, and accurate route for empirical characterization of RNA structure landscapes.
• Complex RNA structural landscapes are likely widespread in both natural and artificial RNA molecules.
• This methodology opens new avenues for designing and understanding functional RNAs.