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RNAbridge: a database of extended and non-canonical helices

Zakrzewski, D.; Antczak, M.; Zok, T.

2026-09-17 bioinformatics
10.64898/2026.09.15.751775 bioRxiv
Show abstract

RNA function is dictated by 3D architecture. Although 2D structural models based on canonical Watson-Crick base pairs are widely used, they often fail to capture the non-canonical interactions, tertiary contacts, and coaxial stacking important for biological activity. We have developed RNAbridge, a comprehensive database and web application that systematically identifies, quantifies, and visualizes extended non-canonical helices and multi-way junctions. Using a geometry- and stacking-based pipeline, we analyzed the Protein Data Bank and compiled a catalog of 135,541 structural motifs. RNAbridge includes a user-friendly interface with interactive filters, synchronized 2D and 3D visualizations, and detailed structural data, including helical bend angles and stacking paths. By connecting simplified 2D topologies with complex 3D structures, RNAbridge serves as a valuable resource for structural biologists and lays the foundation for future machine learning applications in RNA structure prediction. The database, available at https://rnabridge.cs.put.poznan.pl/, is automatically updated once a week.

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The top 6 journals account for 50% of the predicted probability mass.

1
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3.3%
8
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Top 10%
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9
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● 5266 papers in training set
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3.1%
10
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Top 38%
2.6%
11
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○ 385 papers in training set
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2.4%
12
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Top 48%
1.9%
13
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Top 3%
1.9%
14
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○ 193 papers in training set
Top 1%
1.7%
15
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○ 64 papers in training set
Top 0.4%
1.7%
16
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● 242 papers in training set
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1.5%
17
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○ 494 papers in training set
Top 10%
1.3%
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19
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Top 0.8%
1.3%
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236 papers in training set
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1.1%
21
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22
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23
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188 papers in training set
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25
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Top 31%
0.8%
26
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● 105 papers in training set
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0.8%
27
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○ 286 papers in training set
Top 5%
0.8%
28
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● 285 papers in training set
Top 9%
0.6%
29
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● 238 papers in training set
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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.