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Per user/month, billed annually
- 100 one-time credits
- Daily lightweight analysis
- Core sequence and format tools
RNA discovery workflows
From RNA sequence to testable structure.
Move from RNA sequence to secondary structure, three-dimensional models, interaction evidence, and small-molecule screening while keeping every energy, score, structure, and file available for review.
Yeast phenylalanine tRNA · PDB 1EHZ
Interactive result · PDB Viewer
RNA discovery models.Run the relevant methods in one consistent research workspace.
RNAfold
RNAfold predicts RNA secondary structure using minimum free energy (MFE) algorithms and optionally returns partition-function ensemble metrics when explicitly enabled.
Chai-1
Chai-1 is a multi-modal foundation model for molecular structure prediction. Predicts 3D structures for proteins, ligands, DNA, RNA, and multi-component complexes with high accuracy.
SMRTnet
Deep learning framework for predicting small molecule-RNA interactions using RNA secondary structure. Combines language models, CNNs, and graph attention networks for binding prediction.
RNAcofold
RNAcofold predicts the joint secondary structure of two interacting RNA molecules and optionally reports partition-function and concentration-dependent equilibrium metrics.
RNAplfold
RNAplfold computes local base pair probabilities using a sliding window approach. Useful for analyzing accessibility and identifying binding sites in long RNA sequences.
RNAinverse
RNAinverse designs RNA sequences for a specified target secondary structure using ViennaRNA inverse-folding semantics.
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- 24,000 credits/user/year
- More concurrent jobs
- Batch workflow runs
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$79/mo
For commercial research
Per user/month, billed annually
- 96,000 credits/user/year
- Commercial license
- Highest self-serve limits
Enterprise
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Questions & answers
ProteinIQ supports RNA secondary-structure prediction, ensemble and accessibility analysis, RNA three-dimensional structure prediction, RNA–RNA interaction prediction, RNA inverse folding, and RNA-targeted small-molecule screening. Each method returns its own scores, structures, tables, and files rather than collapsing unlike evidence into one result.
Most RNA workflows start from RNA sequence in FASTA or plain-text form. Depending on the method, you may also provide a second RNA sequence, a dot-bracket structure, an alignment, supported folding constraints, known molecular partners, or named compounds with SMILES strings.
Yes. RNAfold, RNAsubopt, RNAplfold, and related ViennaRNA methods return base-pairing and ensemble evidence, while Chai-1, Boltz-2, OpenFold 3, and Protenix v2 generate atomic-coordinate hypotheses. These outputs answer different questions and should be reviewed separately.
Yes. ProteinIQ can compare RNAcofold, RNAduplex, RNAup, and RNAplex outputs for two supplied RNA sequences. Results can include joint structures, duplex coordinates, accessibility terms, and interaction energies, depending on the selected method.
Yes. SMRTnet accepts named RNA sequences with dot-bracket structures and named compounds with SMILES strings, then returns binding probabilities, interaction calls, and the selected RNA window. These are prioritization signals, not measurements of binding affinity or activity.
Treat a predicted fold or coordinate model as a testable structural hypothesis. Review alternative folds, ensemble evidence, confidence, sequence context, known modifications, molecular partners, and agreement with probing, binding, cryo-EM, NMR, crystallography, or other experimental evidence when available.
Yes. Depending on the tool, ProteinIQ can export FASTA, dot-bracket structures, CSV or JSON tables, plots, CIF or PDB coordinates, confidence data, model files, and logs while preserving the identifiers and settings used for the run.
No. Computational RNA models support prioritization and experiment design. Structural probing, binding assays, activity measurements, cell-based studies, and high-resolution structural methods remain necessary before making biological or therapeutic claims.