We propose a novel heuristic to predict RNA secondary structures. The algorithm is inspired by the kinetic partitioning mechanism, by which molecules follow alternative folding pathways to their native structure, some much faster than others. Similarly, our algorithm RAFFT generates an ensemble of concurrent folding pathways ending in multiple metastable structures for each given sequence; this is in contrast with traditional thermodynamic approaches, which are based on aim to find single structures with minimal free energies. When analyzing 50 predicted folds per sequence, we found near-native predictions (79% PPV and 81% sensitivity) for RNAs of length ≤ 200 nucleotides, matching the performance of recent deep-learningbased structure prediction methods. Our algorithm also acts as a folding kinetic ansatz, which we tested on two RNAs: the coronavirus frameshifting stimulation element (CFSE) and a classic bi-stable sequence. For the CFSE, an ensemble of 68 distinct structures computed by RAFFT allowed us to produce complete folding kinetic trajectories, whereas known methods require evaluating millions of sub-optimal structures to achieve this result. For the second application, only 46 distinct structures were required to reproduce the kinetics, whereas known methods required a sample of 20,000 structures. Thanks to the efficiency of the fast Fourier transform on which RAFFT is based, these computations are efficient, with complexity .
RAFFT: Efficient prediction of RNA folding pathways using the fast Fourier transform