This review explains how classical thermodynamic RNA folding models can be improved with chemical probing data, and why that combination remains one of the most reliable routes to biologically useful structure prediction.
BarMap models RNA folding on changing energy landscapes by linking macrostates between landscape snapshots, enabling efficient analysis of co-transcriptional and externally perturbed folding scenarios.
Kinwalker predicts folding trajectories of large RNAs by combining locally optimal substructures and kinetic heuristics, making co-transcriptional folding analysis feasible for molecules up to about 1500 nucleotides.
This paper shows how barrier trees and numerical integration can approximate RNA folding dynamics efficiently enough to analyze bistable molecules and RNA switches on biologically relevant timescales.