Jul 2026· Current Opinion in Structural Biology· Vol 100, pp.
103339
· 0 citations· 63 references
Medicine
TL;DR
The state of the art in RNA structure prediction is reviewed, covering key training datasets, community benchmarks, and the performance of current models, as well as perspectives on integrating other data modalities, such as chemical probing signals and RNA modifications, as well as the emerging role of generative models.
Abstract
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.
These approaches improve generalisability, reduce reliance on deep evolutionary information, and enable proteome-scale prediction of RNA-binding residues, providing a route to map and interpret the molecular logic of protein-RNA interactions.
Rozeena Arif, Alfredo Castello· Current Opinion in Structura...· 0 citations
A large and diverse dataset comprising paired RNA sequences and their corresponding secondary structures is developed, and SincFold exhibited improved performance on a set of previously unseen RNA families, enhancing its capability to predict accurate de novo RNA secondary structures.
Conner J. Langeberg, Taehan Kim, Roma Nagle et al.· RNA: A publication of the RN...· 0 citations
NucleicBERT is developed, a self-supervised masked-language model that learns contextual representations from single sequences without evolutionary information that advances RNA structure prediction and informs how large language models encode biological information.
Utkarsh Upadhyay, Julian Herold, Markus Götz et al.· Nature Machine Intelligence· 1 citation
Shifu, a framework of three coupled parts, scores a model on three axes (correctness, breadth across diverse RNAs, and whether its confidence can be trusted) rather than one number, and releases the dataset, code, and model backbones.
Gabriel Galvez, Quentin Vicens· bioRxiv· 0 citations
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, and establishes the RBP interactome as a compact, functionally sufficient, and interpretable basis for foundation model pretraining in RNA...
Crucially, the analysis reveals that the backbone encoder becomes self-contained post-training: the memory module facilitates the internalization of motif semantics into the model parameters, allowing the backbone to retain performance advantages even when the memory is detached during inference.
Xiang-Yu Ji, Xin Wang, Yang Zhang et al.· Proceedings of the 32nd ACM...· 0 citations
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