RNAStructFold: MSA-free RNA 3D Structure Prediction via RNA Language-Model Attention Priors

Accurate RNA three-dimensional structure prediction remains an open problem, and the strongest current methods (e.g., RhoFold+, RoseTTAFold2NA) depend heavily on multiple sequence alignments (MSAs) to reach their best accuracy. MSA construction is computationally expensive and fundamentally limited for RNAs with few or no homologs, such as orphan viral RNAs, orphan non-coding RNAs, and many regulatory riboswitches. RhoFold+'s own ablations report up to a 10 Å RMSD improvement with increasing MSA depth, underscoring this dependence on homolog availability. We introduce RNAStructFold, which replaces the alignment-dependent input branch of RhoFold+ with structural priors derived from a pretrained RNA language model (ERNIE-RNA), treating single-sequence inference as a first-class objective rather than a degraded fallback. Residue embeddings are obtained via learnable layer fusion, and attention maps are projected into pair representations that initialize the Evoformer; a lightweight confidence head additionally predicts global RMSD and TM-score without requiring reference structures at inference. To guard against coevolutionary information re-entering through the pretrained encoder, we filter ERNIE-RNA's pretraining corpus to exclude sequences homologous to our evaluation sets. RNAStructFold is trained across an extended sequence range (16–960 nt), broadening applicability to longer RNAs while eliminating MSA search overhead. On RNA-Puzzles and CASP15, RNAStructFold achieves TM-scores and RMSDs close to RhoFold+ on average, with clear improvements on several individual targets (PZ14, PZ17, PZ18, R1116, R1107), while trailing on a small number of outliers, and yields roughly 2–3× faster inference. On a newly curated OrphanRNA benchmark of low-homology viral RNAs, riboswitches, and long RNA structures, performance is more mixed, with the largest gains on medium- and long-length (>200 nt) targets. These results indicate that language-model-derived structural priors can substitute for a substantial fraction of MSA-derived evolutionary signal, offering a scalable, faster alternative for RNA tertiary structure prediction in low-homology and long-sequence regimes.