Toward Accurate RNA Non-Canonical Structure Prediction: The NC-Bench Benchmark and the NCfold Framework

Abstract

AO_SCPLOWBSTRACTC_SCPLOWAccurate prediction of non-canonical (NC) base pairs is a pivotal step toward un-raveling the full functional landscape of RNA. This goal requires both a standardized dataset for evaluation and powerful models that overcome data limitations. Here, we make a dual contribution to this endeavor: the NC-Bench benchmark and the NCfold framework. NC-Bench offers a trusted ground for comparison with 925 sequences and 6,708 annotated NC pairs, defining tasks for edge and orientation prediction. NCfold provides a powerful analytical engine, a closed-loop dual-branch network that synergizes sequence features with structure-aware embeddings from RNA foundation models via our proposed REFweighted self-attention. This iterative design enables robust learning from scarce NC data. On the NC-Bench benchmark, NCfold establishes new state-of-the-art results, significantly outperforming existing methods. Comprehensive analyses validate its design and affirm the critical role of NC-Bench in driving future progress. NC-Bench and NCfold together form a cornerstone for the next generation of RNA structure prediction. The datasets and codes are publicly available at https://github.com/heqin-zhu/NCBench