Energy-Filtered Branching Alternatives Improve RNA Secondary Structure Recall.

Abstract

Accurately predicting RNA secondary structure remains a central challenge in computational biology. Although standard methods based on minimum free energy (MFE) optimization often produce good predictions, their accuracy varies, and they can still miss helices present in known structures. Because the thermodynamics of multibranch loops strongly influence these predictions, we examine how changes to the multiloop initiation and branching penalties affect prediction performance, focusing specifically on recall. Using a recent algorithm that partitions the branching-parameter space, we generate all distinct optimal structures obtainable under different choices of the multiloop parameters. We then evaluate the recall of these alternative structures for the Archive II dataset. Our results show that many sequences admit multiple alternative structures with substantially higher recall than the MFE prediction, establishing the predictive potential of multiloop reparameterization. We next introduce an energy-based filtering method that retains only those structures whose adjusted residual energy is at least as good as that of the MFE structure. This produces a tractable number of candidates while preserving most of the achievable improvements in recall. Compared with Boltzmann sampling, the resulting ensemble typically provides a more favorable balance between recall and precision at the level of helix classes despite being much smaller, making it particularly useful for identifying lower-probability structural features. Overall, our results show that examining branching configurations optimal modulo the branching energy provides structural information beyond the standard MFE prediction. The proposed energy-filtering approach yields a compact set of alternative structural hypotheses that can complement Boltzmann sampling and provide a practical source of candidate helices for downstream computational or experimental analysis.