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

Abstract CASP16 provided a community-wide benchmark for assessing RNA structure prediction, including the first large-scale blind assessment of RNA–RNA multimer prediction. The results showed that achieving high atomic precision remains a major challenge across the field. In this work, we use the performance of our group (LCBio) as a diagnostic case study to examine the current limits of RNA structure prediction. Our workflow ranked first in the RNA multimer category and remained competitive for monomers. We combine hierarchical analysis with representative case studies to identify a pattern of predictive breakdown, in which modeling fidelity degrades from reliable local features to increasingly speculative global architectures. Multi-helix junctions appear to mark a major transition boundary where 2D topological success often fails to translate into 3D geometric realism, leading to cascading errors in global architecture. This hierarchical breakdown is especially pronounced in RNA multimers, where limitations in the recovery of junction geometry and tertiary interactions propagate directly into errors in higher-order assembly, making multimer prediction increasingly speculative. By placing benchmark performance in a direct structural context, this case study helps define the current limits of RNA structure prediction and highlights priorities for improving predictive accuracy. Graphical Abstract Key Points RNA structure prediction in CASP16 shows a hierarchical decline in accuracy, from relatively reliable local secondary structure to increasingly uncertain global architecture and multimer assembly. Prediction accuracy declines markedly at the level of multi-helix junctions, where correct 2D topology often does not translate into realistic 3D geometry. Non-canonical interactions, stacking geometry, and specialized tertiary motifs remain major sources of error in current RNA modeling pipelines. High relative performance in RNA–RNA multimer prediction can be achieved despite limited atomic accuracy, highlighting the importance of expert-guided assembly and model curation. Many current multimer models are better interpreted as coarse-grained organizational hypotheses than as precise atomic structures.