What is RNAfold?

RNAfold predicts the secondary structure of a single RNA strand by finding the base-pairing pattern with the lowest folding free energy. It is the core structure-prediction program of the ViennaRNA Package, and it reports its answer as a minimum free energy (MFE) structure in dot-bracket notation along with the energy in kcal/mol.

The prediction is thermodynamic, not evolutionary or learned. RNAfold scores every possible nested pairing using the Turner nearest-neighbor energy parameters (stacking, hairpin, bulge, and loop contributions) and uses dynamic programming to find the global optimum without enumerating structures one by one. Because the model assumes nested base pairs, RNAfold does not predict pseudoknots, and accuracy drops for long sequences where tertiary contacts and cofactors matter.

With the partition function turned on, RNAfold goes beyond the single best structure and describes the whole Boltzmann ensemble of structures: how dominant the MFE structure is, how diverse the ensemble looks, and two alternative representative structures (centroid and MEA).

How to use RNAfold online

Paste one or more RNA sequences in FASTA or plain text, adjust the folding temperature or other options if needed, and run the job. ProteinIQ folds each sequence on the ViennaRNA engine and returns the MFE secondary structure in dot-bracket notation, the free energy in kcal/mol, and downloadable structure plots. No installation, compilation, or command-line flags required.

A short worked example shows the format. The sequence below is the canonical RNAfold example; at the default 37°C it folds into a single hairpin:

Input:
CGCAGGGAUACCCGCG

Output:
CGCAGGGAUACCCGCG
(((.(((...))))))   (-5.60)

The dots are unpaired bases and each matched ( / ) pair is a base pair. This sequence forms six base pairs, a three-nucleotide hairpin loop, and one unpaired bulge, with a free energy of -5.60 kcal/mol.

For batch jobs, give each sequence a FASTA header and the results table fills one row per record:

>tRNA_acceptor
GGGCGAAUUCCUUUUU
>hairpin
GGGGAAAACCCC

Inputs

Sequences may contain A, C, G, U (and T, read as U). Modified-base characters are tolerated. Each strand is folded on its own; for two strands that pair with each other, use RNA-RNA cofolding instead. Sequences up to roughly 32,700 nucleotides are supported, though folding time and memory grow quickly with length.

Settings

Results

Without the partition function, the results table reports one row per sequence:

Turning on the partition function adds these columns:

The Files tab holds structure plots of the MFE structure rendered as _ss.eps (PostScript), _ss.svg (vector image), and _ss.gml (graph for Cytoscape and other graph tools). To re-render or restyle a structure from its dot-bracket string, use RNAplot.

RNAfold examples

Canonical 16 nt hairpin with default MFE settings

This short sequence demonstrates the standard RNAfold workflow: one linear RNA folded at 37°C with the default energy model and no partition-function calculation.

• Input: CGCAGGGAUACCCGCG as the FASTA record canonical_hairpin

RNAfold returns a six-base-pair hairpin with one unpaired bulge and an MFE of -5.600 kcal/mol. The negative energy means this is the lowest-energy nested secondary structure found under these settings; it does not establish that this is the only structure populated in solution or that the RNA adopts it in vivo.

Human pre-miR-21 ensemble analysis

This example folds the 72 nt human pre-miR-21 precursor from RNAcentral (URS00000AF93C) and enables the partition function to compare the MFE prediction with representative structures from the full thermodynamic ensemble.

• Input: hsa_mir_21_precursor_URS00000AF93C, a 72 nt RNA sequence
• Non-default setting: Compute partition function = enabled to return ensemble, centroid, and MEA metrics

The MFE structure has an energy of -34.600 kcal/mol, while the complete ensemble has a lower free energy of -36.070 kcal/mol. An MFE Frequency of 0.092 means the exact MFE structure accounts for about 9.2% of the Boltzmann ensemble under the model. The centroid and MEA structures retain the same broad stem-loop pattern but differ in several base pairs, consistent with the reported Ensemble Diversity of 3.510. These values describe a thermodynamic model, not experimental confirmation of the precursor's cellular structure or processing.

Two-sequence FASTA batch with lonely pairs disallowed

This batch example shows how RNAfold processes two independently named FASTA records in one job and returns one result row per sequence.

• Inputs: viennarna_seq1 (66 nt) and viennarna_seq2 (56 nt)
• Non-default setting: Disallow lonely pairs = enabled to exclude isolated base pairs

The two records are folded independently rather than as an interacting pair. Under the same 37°C settings, viennarna_seq1 returns an MFE of -5.400 kcal/mol and viennarna_seq2 returns -2.400 kcal/mol. The more negative value indicates a lower predicted folding free energy for the first sequence in this job, but energies across different sequence lengths should not be treated as a normalized measure of structural quality or biological function.

Reading dot-bracket notation

Dot-bracket notation encodes a secondary structure as a string the same length as the sequence:

• . is an unpaired base.
• ( and the matching ) are the two partners of a base pair. Pairs nest like balanced parentheses, so the first ( closes with the last ).

In the example (((.(((...)))))), the outer three pairs and the inner three pairs form a stem interrupted by a single unpaired bulge, capping a loop of three unpaired bases. The notation cannot represent crossing pairs, which is why pseudoknots never appear in RNAfold output. To compute the free energy of a structure you wrote yourself in this notation, use RNAeval.

How RNAfold works

The MFE prediction uses the Zuker dynamic programming recursion. Instead of scoring each candidate structure separately, RNAfold builds the optimal structure for every subsequence and combines those optima, which makes the global minimum reachable in $O(n^3)$ time for a sequence of length $n$. Energies come from the Turner 2004 nearest-neighbor model, where the stability of a helix depends on which base-pair steps stack on each other, and loops carry length- and sequence-dependent penalties.

The partition function uses McCaskill's algorithm over the same energy model. Rather than the single lowest-energy fold, it computes the Boltzmann-weighted sum over all structures, which yields base-pairing probabilities and the ensemble free energy. From those probabilities RNAfold derives the two alternative structures:

• Centroid: the structure closest, on average, to every other structure in the ensemble. It tends to keep only pairs the ensemble strongly agrees on.
• MEA: the maximum expected accuracy structure, assembled to maximize the sum of probabilities of the pairs (and unpaired positions) it includes.

Interpreting the ensemble metrics

The MFE structure is the single best guess, but a thermodynamic prediction is only as trustworthy as the ensemble around it. The partition-function metrics tell you how much to trust that single structure.

• A high MFE Frequency (the MFE structure carries a large share of the ensemble probability) and a low Ensemble Diversity together mean the molecule has one dominant, well-defined fold. Reported structures from RNAfold, the centroid, and MEA usually agree in this case.
• A low MFE frequency with high diversity means many structures compete within a few kcal/mol. The single MFE structure is then a weak summary, and the centroid or MEA structure, or a sample of suboptimal structures, gives a more honest picture.

The Ensemble Free Energy is always lower than or equal to the MFE because it sums over every structure, not just the best one. The gap between them widens as more structures contribute.

When to use RNAfold vs other ViennaRNA tools

RNAfold folds one full-length strand into its global MFE structure. Several sibling tools cover cases it is not built for:

For a deep-learning alternative that predicts base pairing without the nested-structure assumption, including some pseudoknots, see SMRTNet. If you have a DNA template and need the transcript first, convert it with DNA to RNA.

FAQ

What does RNAfold predict?

RNAfold predicts the minimum free energy secondary structure of an RNA sequence: which bases pair with which, returned as a dot-bracket string and a free energy in kcal/mol. With the partition function enabled it also reports ensemble metrics and the centroid and MEA structures.

Is this the real ViennaRNA RNAfold?

Yes. ProteinIQ runs ViennaRNA 2.7.2 with the standard Turner energy parameters, the same engine and defaults as the command-line RNAfold, so results match what you would get locally.

What is minimum free energy in RNA folding?

The minimum free energy is the lowest folding free energy among all possible secondary structures for a sequence. The structure that achieves it is the MFE structure, the single most thermodynamically stable fold under the chosen temperature and energy model.

Does RNAfold predict pseudoknots?

No. RNAfold only models nested base pairs, so crossing interactions and pseudoknots are excluded by design. For pseudoknot-capable prediction, use a deep-learning model such as SMRTNet.

How long can the RNA sequence be?

Sequences up to about 32,700 nucleotides are accepted, with file uploads up to 10 MB. Runtime and memory scale steeply with length because the algorithm is $O(n^3)$ in time, so very long sequences fold slowly.

What is the difference between the MFE, centroid, and MEA structures?

The MFE structure is the single lowest-energy fold. The centroid is the structure closest on average to all structures in the ensemble. The MEA structure maximizes the expected number of correctly assigned pairs and unpaired bases. The centroid and MEA only appear when the partition function is enabled, and they are most useful when the ensemble has several competing structures.