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

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

Benefits of RNA structure prediction

• Question-led method choice. Separates base-pairing, atomic-coordinate, and intermolecular-binding questions before analysis.
• Complementary evidence. Supports thermodynamic, comparative, ensemble, and foundation-model evidence where appropriate.
• Inspectable outputs. Preserves structures, coordinates, energies, confidence, settings, and native files.

Primary limitations

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

Types of RNA structure prediction

These three searched use cases are the maintained RNA structure-prediction spokes. Choose the one whose output matches the next scientific decision.

RNA secondary structure prediction

Predicts intramolecular base pairs, stems, loops, local accessibility, and alternative folds from one sequence or an alignment.

Best for: Folding maps, regulatory elements, accessibility, and construct comparison
Requires: An RNA sequence, optional alignment or probing constraints, and justified thermodynamic conditions

RNA 3D structure prediction

Predicts atomic coordinates and ranked three-dimensional hypotheses for an RNA chain or RNA-containing complex.

Best for: Tertiary geometry, structural hypotheses, complexes, and downstream modeling
Requires: RNA sequence and any known stoichiometry, partners, modifications, templates, or restraints

RNA–RNA interaction prediction

Predicts intermolecular base pairing, binding sites, hybridization energies, accessibility costs, or a joint two-strand fold.

Best for: miRNA targets, antisense design, sRNA regulation, and candidate RNA pairs
Requires: Two RNA sequences and a declared interaction model, temperature, and screening objective

RNA structure prediction levels

Secondary structure describes a base-pairing graph, not a complete spatial arrangement. A 3D model places atoms in space. An interaction model 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.

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 RNA structure prediction 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

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.