RNA folding kinetics
An RNA molecule does not simply adopt its minimum free energy structure. It folds along paths through a landscape of alternative structures, can be trapped in metastable states, and in the cell the chain folds while it is being transcribed. Folding kinetics asks how fast and by which route a structure is reached, and for riboswitches and designed switches that answer matters more than the final structure.
One line of work runs through these posts: coarse-grained dynamics on barrier trees (Barriers and Treekin), the extension to landscapes that change over time (BarMap), co-transcriptional folding with and without ligands, and recent approaches that learn folding time distributions directly (KinPFN). The glossary explains folding kinetics, macrostates and metastable states.
Bayesian approximation of RNA folding times
This workshop paper introduces the core KinPFN idea: approximating RNA first-passage-time distributions with a prior-data fitted network trained on synthetic folding-time priors.
KinPFN for RNA folding kinetics
KinPFN uses prior-data fitted networks to approximate first-passage-time distributions for RNA folding kinetics orders of magnitude faster than direct simulation.
Co-transcriptional riboswitch modeling with ViennaRNA
A landscape-based method for modeling how cotranscriptional folding and ligand binding interact in kinetically controlled riboswitches, illustrated with the 2'dG riboswitch from Mesoplasma florum.
In silico design of ligand-triggered RNA switches
A computational workflow for designing ligand-triggered RNA switches, with emphasis on sequence design, folding kinetics, and candidate prioritization.
Predicting RNA structures from sequence and probing data
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.