Hierarchical Breakdown of RNA Structure Prediction in CASP16: From Reliable Local Helices to Speculative Multimer Assembly

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ID: 328904
2026
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Abstract
MOTIVATION: CASP16 provided a community-wide benchmark for assessing RNA structure prediction, including the first large-scale blind assessment of RNA-RNA multimer prediction. CASP16 results showed that accurate three-dimensional modeling, especially for RNA-RNA multimers, remains a major challenge across the field. RESULTS: In this work, we use the submissions of our group (LCBio) as a diagnostic case study to examine the current limits of RNA structure prediction. In the official CASP16 best-of-submitted-models analysis, our workflow ranked first in the RNA-RNA multimer category and remained competitive for monomers. This makes the submitted model set useful for examining why high-ranking multimer predictions can still deviate substantially from experimental structures. We combine hierarchical analysis with representative case studies to connect this field-wide limitation to specific structural failure modes, showing that prediction accuracy decreases from relatively reliable canonical base-pairing and local helical organization to less reliable non-canonical interactions, stacking geometry, tertiary motifs, and assembly-level features. In RNA-RNA multimers, errors in monomer structure can combine with uncertainty in interface geometry and model selection, reducing the accuracy of the assembled complexes. These findings point to monomer structure accuracy, interface modeling, and model selection as key areas for improving RNA-RNA multimer prediction. AVAILABILITY AND IMPLEMENTATION: The scripts used for feature extraction, scoring, bootstrap confidence-interval estimation, and figure generation are available at Zenodo: https://doi.org/10.5281/zenodo.21393731. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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openalex_W7213403720 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chandran Nithin, Smita P. Pilla, Sebastian Kmiecik
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag689
URL
https://doi.org/10.1093/bioinformatics/btag689
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