IRfold: An RNA Secondary Structure Prediction Approach

Ribonucleic acid (RNA) sequences can be viewed as an ordered list of symbols representing the component nucleobases of the RNA sequence also known as its primary structure. The prediction of an RNA's secondary structure from its primary structure involves predicting which of its bases are most likely to pair. Computing the likelihood of each pair to obtain the set of pairings with the highest cumulative probability is computationally intractable. We propose a new approach, IRfold, which considers possible base pairings across secondary structures known as inverted repeats (IRs) and composes a secondary structure prediction. Our approach identifies the set of minimal thermodynamic free energy IRs that satisfies empirically determined thermodynamic and steric constraints. The proposed method is implemented as a constraint programming problem, which is benchmarked against state-of-the-art secondary structure prediction approaches on the bpRNA-1m dataset. Our results yield promising initial outcomes, and we discuss potential avenues for further investigation.