FoldARE, an RNA secondary structure analysis and prediction tool via generative pseudo-SHAPE modeling

RNA secondary structure prediction is limited by conformational heterogeneity and the scarcity of experimental data, as many RNAs populate ensembles of near-isoenergetic folds and SHAPE data are often unavailable. We present FoldARE (Folding and Analysis of RNA Ensembles), a two-step framework that derives pseudo-SHAPE constraints from in silico structural ensembles and uses them to guide SHAPE-aware secondary structure prediction. In the first step, an ensemble is generated and parsed nucleotide by nucleotide to estimate single-strandedness frequencies, which are converted into a pseudo-SHAPE reactivity profile using a weight-and-threshold scheme. In the second step, this profile is provided as a constraint to a SHAPE-compatible folding algorithm to improve the final prediction. We systematically evaluated all combinations of four ensemble-capable predictors, ViennaRNA, RNAstructure, LinearFold and EternaFold. After parameter optimization on a structurally diverse 25-RNA training set and validation using multiple scoring schemes, the best configuration combined EternaFold as ensembler and RNAstructure as predictor. Across external benchmark datasets (RNAstrand, ArchiveII and bpRNA) and the experimentally derived eFold dataset, FoldARE achieved the highest accuracy. Beyond prediction, FoldARE provides modules for ensemble-focused comparative analysis, including pairwise and multi-tool consensus assessment, per-nucleotide variability metrics, and interactive visualizations. Notably, it also supports the evaluation of m6A modification effects on structural ensembles. FoldARE is freely available on GitHub (https://github.com/TebaldiLab/FoldARE) and as a web accessible version (https://rdds.it/foldare/)