RNA structure is central to the function of every RNA class yet the gap between annotated sequences and experimentally determined structures remains large. Computational methods to fill this gap have evolved from thermodynamic free energy minimization through supervised deep learning to self-supervised RNA language models trained on millions of sequences, progressively improving structure prediction. Here we review the state of the art in RNA structure prediction, covering key training datasets, community benchmarks, and the performance of current models. We further discuss perspectives on integrating other data modalities, such as chemical probing signals and RNA modifications, as well as the emerging role of generative models. Challenges in generalization, handling of noncanonical interactions, and contextual structure prediction remain open frontiers for the field.
Lambert Moyon, Annalisa Marsico· Current Opinion in Structura...· 0 citations
RNA-binding proteins (RBPs) orchestrate a complex combinatorial regulatory “code” that governs RNA splicing, stability, localization, and translation. Learning the relationship between RNA sequences and these processes is a central challenge in genomics. Foundation models, notably RNA language models, have emerged as the dominant approach, learning general-purpose representations from unlabeled sequence at scale. While RNA language models have demonstrated impressive performance across a broad range of downstream tasks, they generally learn from sequence reconstruction objectives alone, lacking direct connections to the regulatory principles that govern RNA function. Here we introduce Parnet, an RNA foundation model trained directly and exclusively on experimental CLIP-seq data. Parnet is a multi-task foundation model trained end-to-end on 223 eCLIP-seq experiments spanning 150 RBPs to predict base-resolution RBP binding profiles directly from RNA sequence. This CLIP-seq pretraining strategy departs fundamentally from the masked-language-modeling paradigm, anchoring learned RNA representations directly in measured protein–RNA interactions rather than sequence statistics. Parnet substantially outperforms its single-task predecessor RBPNet in binding profile and motif recovery, generalizes to unseen cell types and iCLIP data, and recapitulates position-dependent splicing regulation. Frozen Parnet embeddings, without task-specific fine-tuning, match or exceed the performance of both task-specific tools, as well as larger self-supervised RNA and genomic language models across diverse downstream tasks, including RNA biotype classification, lncRNA chromatin localization, translational efficiency, splice-site recognition, intron retention, and non-coding variant effect prediction. Importantly, Parnet remains mechanistically interpretable, tracing predictions back to the specific RBPs and motifs that drive them. These results establish the RBP interactome as a compact, functionally sufficient, and interpretable basis for foundation model pretraining in RNA biology.