# RNA structure prediction

Choose the RNA prediction workflow that matches the biological question: intramolecular base pairing, atomic 3D coordinates, or intermolecular RNA binding.

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

## More RNA structure prediction workflows

## Tools for RNA 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)[![Chai-1](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fchai-1.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### Chai-1 Predict RNA and biomolecular-complex 3D coordinates with Chai-1](https://proteiniq.io/app/chai-1)[![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)[![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)[![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)[![Boltz-2](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fboltz-2.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### Boltz-2 Predict RNA-containing biomolecular structures with Boltz-2](https://proteiniq.io/app/boltz-2)[![OpenFold3](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fopenfold-3.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### OpenFold3 Generate RNA 3D structure hypotheses with OpenFold3](https://proteiniq.io/app/openfold-3)[![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)[![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)[![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)

## What is RNA structure prediction?

RNA structure prediction is the computational inference of how RNA sequences form intramolecular base pairs, three-dimensional folds, or intermolecular RNA–RNA contacts. These outputs answer different questions and use different evidence, so the prediction type should be selected before choosing a tool or comparing scores.

Start with the desired observable. Secondary-structure prediction maps paired and unpaired nucleotides and can describe local accessibility or alternative folds. RNA 3D prediction generates atomic coordinates and confidence-ranked structural hypotheses. RNA–RNA interaction prediction focuses on binding sites, duplexes, accessibility costs, or a joint two-strand fold.

RNA behavior depends on sequence boundaries, temperature, salts, ions, modifications, ligands, proteins, concentration, and cellular context. Computational predictions narrow the hypothesis space, but important conclusions should retain uncertainty and be tested with comparative, biochemical, or structural evidence.

### When to use RNA structure prediction

- Use secondary structure for base-pair maps, 3D prediction for coordinates, and interaction prediction for contacts between two RNA molecules.
- Record construct boundaries, chain roles, partners, conditions, modifications, and the validation evidence required for the decision.

### Strengths and limitations

Separates base-pairing, atomic-coordinate, and intermolecular-binding questions before analysis. Supports thermodynamic, comparative, ensemble, and foundation-model evidence where appropriate. Preserves structures, coordinates, energies, confidence, settings, and native files.

Many predictors omit cellular partners, kinetics, localization, modifications, or condition-specific ensembles. Free energy, opening energy, and model confidence cannot be placed on one universal ranking scale. A computationally plausible result remains a hypothesis until supported by appropriate evidence.

## RNA structure prediction levels

[RNA secondary structure prediction](https://proteiniq.io/use-cases/rna-secondary-structure-prediction) describes a base-pairing graph, not a complete spatial arrangement. [RNA 3D structure prediction](https://proteiniq.io/use-cases/rna-3d-structure-prediction) places atoms in space. [RNA–RNA interaction prediction](https://proteiniq.io/use-cases/rna-rna-interaction-prediction) adds a second RNA and asks which intermolecular contacts are favorable under its assumptions.

A project may use more than one level in sequence, for example, secondary structure to assess accessibility before RNA–RNA interaction prediction, or a secondary-structure hypothesis to help interpret a 3D model. Preserve the boundary between those results.

## How to run RNA structure prediction online

The method panel routes each question to a separate prediction branch so an energy, coordinate model, and binding-site prediction retain their native meanings.

1. **Define the output.** Choose a base-pair map, atomic RNA structure, or interaction between a labeled target and query.
2. **Prepare inputs.** Confirm sequence orientation, construct boundaries, chain count, partners, modifications, and conditions.
3. **Select a branch.** Use [RNAfold](https://proteiniq.io/app/rnafold) for intramolecular secondary structure, [Chai-1](https://proteiniq.io/app/chai-1) for a 3D coordinate hypothesis, or [RNAcofold](https://proteiniq.io/app/rnacofold) for a joint two-RNA fold.
4. **Run and retain results.** Keep the settings, candidate structures, energy or confidence values, logs, and native files from the selected method.
5. **Review alternatives.** Compare plausible structures within the same output class and inspect sensitivity to boundaries or conditions.
6. **Validate the claim.** Seek covariation, probing, binding, or structural evidence that addresses the specific predicted feature.

## RNA structure prediction applications

Use base-pair maps to examine regulatory elements and accessibility, 3D models to inspect tertiary geometry or complex interfaces, and interaction predictions to prioritize candidate target sites. A project may combine these levels, but a favorable duplex energy cannot stand in for a measured binding constant or an atomic structure.

## Evidence and validation

Useful validation sources include covariation, chemical probing, mutational rescue, crosslinking, direct binding measurements, crystallography, NMR, cryo-EM, and known motif geometry. The right evidence depends on whether the claim concerns pairing, binding, or atomic structure.

Record every sequence, construct boundary, chain role, condition, model version, option, energy or confidence output, and candidate structure. Reproducibility is part of the scientific result.

## How the featured workflow works

The method panel routes RNA inputs into distinct secondary-structure, 3D-structure, and RNA–RNA interaction branches without combining unlike outputs.

1. **Define question.** Choose base-pair mapping, atomic coordinates, or an interaction between two RNAs.
2. **Prepare RNA.** Review sequences, boundaries, orientation, chain roles, partners, modifications, and conditions.
3. **Select branch.** Route inputs to RNAfold, Chai-1, or RNAcofold without relabeling one method as another.
4. **Run prediction.** Retain every candidate structure, energy or confidence value, setting, log, and native file.
5. **Validate result.** Compare alternative models and connect the prediction to comparative or experimental evidence.

## Inputs and outputs for the featured workflow

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

### Inputs

#### RNA sequences and context

`FASTA``TXT`

One RNA for secondary or 3D prediction, or a labeled target and query RNA for interaction prediction, plus relevant context.

### Outputs

#### Method-specific predictions

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

Base-pair structures, energies, accessibility, interaction sites, atomic coordinates, confidence, plots, and native files.

## Frequently asked questions

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

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