# RNA secondary structure prediction

Predict RNA base pairing, minimum-free-energy structures, alternative folds, and local accessibility from sequence.

[Open this workflow](https://proteiniq.io/app/workflows/templates?template=rna-secondary-structure-panel)[Contact sales](https://proteiniq.io/contact)

## More RNA structure prediction workflows

## Tools for RNA secondary structure prediction

Use these methods to prepare inputs, run the core analysis, inspect outputs, and validate the evidence described in this workflow.

[![RNAfold](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fviennarna.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### RNAfold Predict minimum-free-energy secondary structures and optional ensemble metrics](https://proteiniq.io/app/rnafold)[![ViennaRNA](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fviennarna.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### ViennaRNA Run curated ViennaRNA folding, interaction, plotting, and analysis methods](https://proteiniq.io/app/viennarna)[![RNAsubopt](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fviennarna.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### RNAsubopt Enumerate suboptimal structures near the minimum free energy](https://proteiniq.io/app/rnasubopt)[![RNAalifold](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fviennarna.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### RNAalifold Predict a consensus secondary structure from aligned homologous RNAs](https://proteiniq.io/app/rnaalifold)[![RNALfold](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fviennarna.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### RNALfold Find locally stable secondary structures along long RNA sequences](https://proteiniq.io/app/rnalfold)[![RNAplfold](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fviennarna.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### RNAplfold Calculate local base-pair probabilities and accessibility](https://proteiniq.io/app/rnaplfold)[![RNAeval](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fviennarna.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### RNAeval Evaluate the free energy of a supplied sequence–structure pair](https://proteiniq.io/app/rnaeval)[![RNAplot](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fviennarna.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### RNAplot Render a supplied RNA sequence and dot-bracket structure](https://proteiniq.io/app/rnaplot)[![RNAcofold](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fviennarna.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### RNAcofold Predict the joint secondary structure of two interacting RNAs](https://proteiniq.io/app/rnacofold)[![RNAduplex](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fviennarna.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### RNAduplex Calculate an optimal intermolecular duplex and hybridization energy](https://proteiniq.io/app/rnaduplex)[![RNAup](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fviennarna.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### RNAup Predict RNA–RNA interactions with opening-energy and accessibility terms](https://proteiniq.io/app/rnaup)[![RNAplex](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fviennarna.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### RNAplex Screen query–target RNA interactions with coordinates and energies](https://proteiniq.io/app/rnaplex)

## What is RNA secondary structure prediction?

RNA secondary structure prediction is the computational inference of base-pairing patterns within an RNA molecule, usually represented as stems, hairpins, bulges, internal loops, junctions, and unpaired regions. Thermodynamic methods estimate low-free-energy structures and ensembles, while comparative methods use conserved pairing and covariation across homologous sequences.

Use secondary-structure prediction when the scientific question concerns which nucleotides pair, whether a region is accessible, how a mutation changes a local fold, or which alternative structures are thermodynamically plausible. Minimum free energy is one summary of a structural ensemble, not proof that the RNA adopts one rigid conformation.

Sequence length, temperature, salt assumptions, pseudoknots, ligand binding, modifications, cotranscriptional folding, and cellular partners can change the observed structure. Add probing constraints or comparative evidence when available, and compare ensemble-level outputs instead of relying only on a single drawing.

### When to use rna secondary structure prediction

- Base-pair maps, local accessibility, regulatory motifs, construct design, and mutation comparison
- RNA sequence plus relevant temperature, alignment, constraints, modifications, and experimental context

### Strengths and limitations

Sequence-based folding rapidly provides explicit base-pair hypotheses, free-energy estimates, and, where supported, alternative structures or pairing probabilities. These outputs help identify stems, loops, and potentially accessible regions.

Predictions depend on sequence boundaries, temperature, and the energy model, and some methods omit pseudoknots. Proteins, modifications, and cellular conditions can redirect folding; use probing, covariation, or perturbation data to test important pairings.

## RNA secondary structure prediction methods

Nearest-neighbor thermodynamic models score loops and stacked base pairs, then use dynamic programming to find a minimum-free-energy structure or calculate a partition function over an allowed structure space. [RNAsubopt](https://proteiniq.io/app/rnasubopt) samples or enumerates structures near the optimum, while [RNAplfold](https://proteiniq.io/app/rnaplfold) focuses on local pairing and opening probabilities.

Comparative prediction asks whether homologous sequences preserve a common structure, including compensatory substitutions that maintain pairing. [RNAalifold](https://proteiniq.io/app/rnaalifold) is appropriate for a real multiple alignment; it should not be substituted for single-sequence folding or used with unrelated sequences.

## RNA secondary structure prediction applications

Common applications include evaluating untranslated regions, riboswitches, aptamers, guide RNAs, RNA therapeutics, splice-regulatory elements, primers, and mutations that may alter local structure or accessibility.

For long transcripts, local methods can identify stable windows and accessible regions without claiming a unique global fold. For interaction studies, secondary structure is often an input to accessibility-aware target prediction rather than the final answer.

## How to run RNA secondary structure prediction online

Open the connected workflow, review the input roles and model assumptions, then keep every method’s native result separate during comparison.

1. **Prepare sequence.** Paste or upload the RNA sequence in [FASTA](https://proteiniq.io/guides/how-to-make-a-fasta-file) format and confirm orientation, alphabet, length, and construct boundaries.
2. **Set conditions.** Set temperature and other supported folding options; add an alignment or experimental constraints only when the selected method accepts them.
3. **Run predictors.** Run [RNAfold](https://proteiniq.io/app/rnafold) for the minimum-free-energy structure and ensemble metrics, then RNAsubopt or [ViennaRNA](https://proteiniq.io/app/viennarna) for alternative structures when needed.
4. **Compare ensembles.** Compare dot-bracket structures, energies, pairing probabilities, ensemble diversity, and locally accessible regions rather than selecting by appearance.
5. **Export evidence.** Export structures, tables, plots, settings, sequence identifiers, and software versions for downstream review.

## How to interpret RNA secondary structure prediction results

Report the sequence, temperature, parameter set, constraints, and whether energies refer to a minimum structure, an ensemble, or an interaction. A small energy difference between structures can indicate competing conformations rather than a decisive winner.

Pairing probabilities and ensemble diversity provide uncertainty information that a single dot-bracket string omits. Validate important motifs with covariation, chemical probing, mutational evidence, or structural experiments whenever the decision is consequential.

## How rna secondary structure prediction works

Run RNAfold, RNAsubopt, and the curated ViennaRNA interface in parallel so minimum-energy, ensemble, and alternative-fold evidence remain distinguishable.

1. **Prepare sequence.** Paste or upload the RNA sequence in FASTA format and confirm orientation, alphabet, length, and construct boundaries.
2. **Set conditions.** Set temperature and other supported folding options; add an alignment or experimental constraints only when the selected method accepts them.
3. **Run predictors.** Run RNAfold for the minimum-free-energy structure and ensemble metrics, then RNAsubopt or ViennaRNA for alternative structures when needed.
4. **Compare ensembles.** Compare dot-bracket structures, energies, pairing probabilities, ensemble diversity, and locally accessible regions rather than selecting by appearance.
5. **Export evidence.** Export structures, tables, plots, settings, sequence identifiers, and software versions for downstream review.

## Inputs and outputs

Check formats before running, then inspect and download the result from every workflow step.

### Inputs

#### RNA inputs

`FASTA``TXT`

One or more RNA sequences in FASTA, with optional alignment or method-supported constraints.

### Outputs

#### Prediction evidence

`CSV``JSON``TXT``SVG``PDB``mmCIF``ZIP`

Dot-bracket structures, free energies, ensemble metrics, tables, plots, and downloadable native files.

## Frequently asked questions

AI answers from our docs. No sign-in needed.

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