# RNA 3D structure prediction Generate and compare atomic RNA structure hypotheses with multiple biomolecular foundation models. [Open this workflow](https://proteiniq.io/app/workflows/templates?template=rna-3d-structure-prediction)[Contact sales](https://proteiniq.io/contact) ## More RNA structure prediction workflows ## Tools for RNA 3D structure prediction Use these methods to prepare inputs, run the core analysis, inspect outputs, and validate the evidence described in this workflow. [![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)[![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)[![IntelliFold 2](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fintellifold-2.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### IntelliFold 2 Generate ranked RNA-containing structure predictions](https://proteiniq.io/app/intellifold-2)[![Protenix](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fprotenix.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### Protenix Predict RNA and multicomponent 3D structures with Protenix](https://proteiniq.io/app/protenix)[![LMI4Boltz](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2FLMI4boltz.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### LMI4Boltz Run low-memory Boltz inference for RNA-containing systems](https://proteiniq.io/app/lmi4boltz)[![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)[![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)[![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)[![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)[![PDBFixer](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fpdb-fixer-featured.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### PDBFixer Repair missing atoms and formatting in compatible RNA structure files](https://proteiniq.io/app/pdb-fixer) ## 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 - Atomic structural hypotheses, tertiary organization, RNA complexes, and downstream coordinate-based analysis - RNA sequence plus known partners, stoichiometry, modifications, templates, restraints, and biological assembly context ### Strengths and limitations Coordinate models make tertiary contacts and geometry inspectable and allow comparison of several structural hypotheses. RNA-containing complex models can also suggest interfaces when the input composition is represented correctly. Flexible RNA may occupy multiple states that one coordinate model cannot capture. Confidence scores have model-specific meanings, and omitted ions, modifications, ligands, or partners may alter the fold; compare models with structural or biochemical constraints. ## 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](https://proteiniq.io/app/chai-1), [Boltz-2](https://proteiniq.io/app/boltz-2), [OpenFold-3](https://proteiniq.io/app/openfold-3), and [Protenix](https://proteiniq.io/app/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, OpenFold3, and Protenix, 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, OpenFold3, 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. ## Frequently asked questions AI answers from our docs. 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