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JAR3D scores RNA hairpin and internal loop sequences against motif groups from the RNA 3D Motif Atlas, by exact sequence match for sequences already observed in 3D and by probabilistic scoring and edit distance for novel sequences.

RNA hairpin and internal loops are often represented on secondary structure diagrams as if they are unstructured, but in fact most are structured by non-Watson-Crick basepairs, base stacking, and base-backbone interactions. Analysis of 3D structures shows that different RNA sequences can form the same RNA 3D motif, as is apparent in many motif groups in the RNA 3D Motif Atlas.

JAR3D scores sequences to motif groups based on the ability of the sequences to form the same pattern of interactions observed in 3D structures of the motif. As RNA 3D Motif Atlas incorporates new RNA 3D structures, the performance of JAR3D will improve over time.

Inferring the 3D structures of hairpin and internal loops is a step on the way toward correctly predicting full RNA 3D structures starting from sequence.


Learn more about JAR3D in the tutorial.


JAR3D accepts single or multiple sequences having one or many loops (see examples above).

One loop: To specify the break between strands in internal loops, use an asterisk *. Sequence(s) without an asterisk are interpreted as hairpins. Internal and hairpin loops should include closing Watson-Crick basepairs, with nucleotides running in 5' to 3' order within each strand. Individual loops do not need the nucleotides to be aligned.

Many loops: JAR3D will extract internal and hairpin loops from longer sequences if a dot-bracket secondary structure is provided as the first line of the input. Multiple sequences need to be aligned to one another.


The output shows the best-scoring motif groups from the RNA 3D Motif Atlas including representative instance from each motif group. It also possible to align input sequences to known 3D instances of a motif.


  1. We extract all hairpin and internal loops from a non-redundant set of RNA 3D structures from PDB/NDB and cluster them in geometrically similar families.

  2. For each recurrent motif, we build a probabilistic model for sequence variability based on a hybrid Stochastic Context-Free Grammar/Markov Random Field (SCFG/MRF) method.

  3. To parameterize each model, we use all instances of the motif found in the non-redundant dataset and knowledge of RNA nucleotide interactions, especially isosteric basepairs and their substitution patterns.

  4. For each motif group, we form an acceptance region that is consistent with the geometry and basepairing of that group. If the score is in the cutoff region, we infer that the new sequence can form the same 3D structure.

For more information please see:

Identifying novel sequence variants of RNA 3D motifs. Craig L. Zirbel, James Roll, Blake A. Sweeney, Anton I. Petrov, Meg Pirrung, and Neocles Leontis. Nucl. Acids Res. (2015) doi: 10.1093/nar/gkv651 Pubmed

Standalone version of JAR3D is also available.