RNA structure prediction

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 describes a base-pairing graph, not a complete spatial arrangement. RNA 3D structure prediction places atoms in space. 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 for intramolecular secondary structure, Chai-1 for a 3D coordinate hypothesis, or 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

FASTATXT

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

Outputs

Method-specific predictions

TXTCSVJSONSVGPDBmmCIFZIP

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