RNA 3D structure prediction

What is RNA 3D structure prediction?

RNA 3D structure prediction is the computational generation of three-dimensional atomic coordinates for an RNA molecule from its nucleotide sequence and, when available, templates, secondary structure, interaction partners, modifications, or experimental restraints. The result is a structural hypothesis or ranked ensemble, not an experimental structure.

Use RNA 3D prediction when tertiary geometry matters: arranging helices and junctions, proposing ligandable pockets, modeling an RNA-containing complex, or generating coordinates for visualization and downstream simulation. A secondary-structure prediction alone does not specify the complete atomic fold.

RNA is conformationally flexible and the experimentally relevant state may depend on ions, ligands, proteins, modifications, temperature, or alternative assemblies. Compare independent models, inspect confidence and stereochemistry, and avoid treating a plausible-looking coordinate file as validated.

When to use RNA 3D structure prediction

• Best fit. Atomic structural hypotheses, tertiary organization, RNA complexes, and downstream coordinate-based analysis
• Required context. RNA sequence plus known partners, stoichiometry, modifications, templates, restraints, and biological assembly context

Benefits of RNA 3D structure prediction

• Produces atomic coordinates. Produces atomic coordinates while keeping the method and its native evidence explicit.
• Supports RNA-containing complexes. Supports RNA-containing complexes while keeping the method and its native evidence explicit.
• Enables multi-model comparison. Enables multi-model comparison while keeping the method and its native evidence explicit.

Primary limitations

• RNA flexibility remains difficult. RNA flexibility remains difficult; address it with appropriate context, controls, and orthogonal validation.
• Confidence is model specific. Confidence is model specific; address it with appropriate context, controls, and orthogonal validation.
• Missing context can change the fold. Missing context can change the fold; address it with appropriate context, controls, and orthogonal validation.

RNA 3D structure prediction methods

Modern biomolecular foundation models infer joint coordinates for proteins, nucleic acids, ligands, and complexes. Their architectures, training data, MSA handling, diffusion sampling, and confidence outputs differ, so scores should not be compared as if they shared one calibrated scale.

Template-based and physics-based RNA methods remain useful when homologous structures, reliable secondary structures, or specialized restraints are available. The connected ProteinIQ workflow focuses on supported end-to-end predictors and keeps each model’s output separate.

RNA 3D structure prediction applications

RNA 3D models can support hypothesis generation for ribozymes, riboswitches, aptamers, untranslated regions, viral RNAs, guide RNAs, RNA–protein complexes, and RNA-targeted small-molecule discovery.

Coordinates may also seed visualization, docking, or molecular simulation, but those downstream steps do not repair an incorrect starting fold. Carry model uncertainty and alternative conformations into every later analysis.

How to run RNA 3D 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. Define assembly. Define the biologically relevant RNA construct, chain count, partners, modifications, ions, ligands, and stoichiometry before creating inputs.
2. Prepare inputs. Enter each RNA chain in the correct 5′-to-3′ orientation and add only supported templates, MSAs, constraints, or partner molecules.
3. Run four models. Run Chai-1, Boltz-2, OpenFold-3, and Protenix independently with documented seeds and sampling settings.
4. Compare coordinates. Compare ranked coordinates, per-model confidence, chain interfaces, secondary-structure consistency, and agreement across independent predictors.
5. Validate hypotheses. Export every candidate model and validate important claims with stereochemical checks, known motifs, restraints, experiments, or downstream simulations.

How to interpret RNA 3D structure prediction results

Inspect global topology, local nucleotide geometry, clashes, chain breaks, base pairing, stacking, interfaces, and whether predicted confidence is concentrated in the region behind the claim. Compare models after accounting for chain identity and symmetry.

Agreement among predictors raises confidence only when the methods provide partly independent evidence. Shared training data and architectures can produce correlated errors, so use experimental restraints and known structural motifs whenever possible.

How RNA 3D structure prediction works

Submit the same RNA sequence independently to Chai-1, Boltz-2, OpenFold-3, and Protenix v2, then compare model confidence and coordinate agreement.

1. Define assembly. Define the biologically relevant RNA construct, chain count, partners, modifications, ions, ligands, and stoichiometry before creating inputs.
2. Prepare inputs. Enter each RNA chain in the correct 5′-to-3′ orientation and add only supported templates, MSAs, constraints, or partner molecules.
3. Run four models. Run Chai-1, Boltz-2, OpenFold-3, and Protenix independently with documented seeds and sampling settings.
4. Compare coordinates. Compare ranked coordinates, per-model confidence, chain interfaces, secondary-structure consistency, and agreement across independent predictors.
5. Validate hypotheses. Export every candidate model and validate important claims with stereochemical checks, known motifs, restraints, experiments, or downstream simulations.

Inputs and outputs

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

Inputs

• RNA inputs. FASTA TXT RNA sequences in FASTA, with optional supported partners, templates, MSAs, modifications, or constraints.

Outputs

• Prediction evidence. CSV JSON TXT SVG PDB mmCIF ZIP Ranked mmCIF or PDB coordinate models, confidence metrics, tables, logs, and downloadable files.