RAFFT: Efficient prediction of RNA folding pathways using the fast Fourier transform

Area of Science:

• Computational Biology
• Biophysics
• Molecular Biology

Background:

• Predicting RNA secondary structure is crucial for understanding gene regulation and function.
• Traditional thermodynamic models often identify single minimal free energy structures, potentially missing alternative folding pathways.
• Kinetic partitioning suggests molecules explore multiple pathways to reach their native state.

Purpose of the Study:

• To introduce a novel heuristic algorithm, RAFFT, for predicting RNA secondary structure formation pathways.
• To generate an ensemble of concurrent folding pathways and metastable structures, moving beyond single minimal energy predictions.
• To efficiently compute folding kinetics and trajectories for RNA molecules.

Main Methods:

• Developed RAFFT (RNA folding Algorithm with Fast Fourier Transform), a heuristic combining a folding algorithm and a kinetic ansatz.
• Generated an ensemble of concurrent folding pathways and metastable structures.
• Utilized Fast Fourier Transform for efficient computation, achieving a complexity of O(n log n).

Main Results:

• Identified near-native RNA secondary structures for molecules up to 200 nucleotides when predicting 50 structures per sequence.
• Successfully produced complete folding kinetic trajectories for the coronavirus frameshifting stimulation element (CFSE) using an ensemble of 68 structures.
• Demonstrated computational efficiency compared to methods requiring millions of sub-optimal structure evaluations.

Conclusions:

• RAFFT offers an efficient and effective approach for predicting RNA secondary structure formation pathways and kinetics.
• The ensemble-based heuristic captures complex folding dynamics missed by traditional thermodynamic methods.
• This novel method advances the study of RNA folding, particularly for complex biological elements like CFSE.