PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 10872451

Structural motifs in RNA.

P B Moore
View original source ↗Export metadata ↓

Abstract

An RNA motif is a discrete sequence or combination of base juxtapositions found in naturally occurring RNAs in unexpectedly high abundance. Because all the motifs examined so far have three-dimensional structures independent of the context in which they are embedded, they are important components of the "kit" of structural elements from which RNAs are constructed. This review discusses the structures of the motifs that have been identified so far and speculates on the importance of their role in determining RNA conformation and their evolutionary origin.

Explore related subjects

Nucleic Acid ConformationRNAJournal ArticleResearch Support, U.S. Gov't, P.H.S.Review

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

P B Moore. 1999. Structural motifs in RNA.. https://doi.org/10.1146/annurev.biochem.68.1.287

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

UFold-X: an enhanced Dual & Dynamic U-Mamba model for long-range RNA secondary structure prediction.

RNA secondary structure is essential for understanding the functions of non-coding RNAs, ribosomal RNAs, and viral genomes. However, accurate prediction of long RNA structures remains challenging due to complex long-range interactions and the limited availability of long-RNA training data. We present UFold-X, a dual-branch deep learning framework that combines a convolutional encoder for local structure modeling with a Mamba-based Visual State Space Module for capturing long-range dependencies. A dynamic gating mechanism adaptively integrates the two branches according to sequence length. UFold-X was evaluated on multiple benchmark datasets containing RNAs up to 5000 nucleotides. To rigorously assess generalization, we introduced a cross-clan benchmark for long RNAs. Under this stringent setting, UFold-X achieved performance comparable to state-of-the-art classical approaches while achieving the best performance among deep learning-based methods. Additional cross-family and within-family evaluations further demonstrated robust transferability and competitive predictive performance. UFold-X also maintained excellent computational efficiency, requiring only 0.08 s per sequence on average. To assess biological consistency, we developed a SHAPE-based reactivity prediction variant (UFold-X-R) and an integrated metric, the Hybrid Reactivity-Pairing Score (HRPS). UFold-X-R showed strong agreement with experimental icSHAPE data and achieved the highest HRPS among all evaluated methods. A user-friendly web server is available at https://ufold-x.ai4bread.com.

Nucleic Acid Conformation↗
PubMed2026

LinearCapR: linear-time computation of per-nucleotide structural-context probabilities of RNA without base-pair span limits.

MOTIVATION: RNA molecules adopt dynamic ensembles of secondary structures, where the local structural context of each nucleotide-such as whether it resides in a stem or a specific type of loop-strongly shapes molecular interactions and regulatory function. Structural-context probabilities therefore provide a more functionally informative view of RNA folding than the minimum free energy structures or base-pairing probabilities. However, existing tools either require O(N3) time or employ span-restricted approximations that omit long-range base-pairs, limiting their applicability to large and biologically important RNAs. RESULTS: We introduce LinearCapR, enabling linear-time, span-unrestricted computation of structural-context marginalized probabilities, using beam-pruned Stochastic Context Free Grammar-based computation. LinearCapR retains global ensemble features lost by span-limited methods and yields superior predictive power on bpRNA-1m(90) dataset, especially for multiloops and exterior regions, as well as long-distance stems. LinearCapR supports analysis of long RNAs, demonstrated on the full genome of SARS-CoV-2. LinearCapR provides the first base-pair-span-unrestricted, linear-time framework for RNA structural-context analysis, retaining key thermodynamic ensemble features essential for functional interpretation. It enables large-scale studies of viral genomes, long non-coding RNAs, and downstream analyses such as RNA-binding protein site prediction. AVAILABILITY AND IMPLEMENTATION: The source code of LinearCapR is available at https://github.com/hoget157/LinearCapR. The archived software release used in this work is available at Zenodo: https://doi.org/10.5281/zenodo.19450645.

Nucleic Acid Conformation↗
PubMed2007

Structures of RNA switches: insight into molecular recognition and tertiary structure.

RNA switches (riboswitches) have important functions in gene regulation. They comprise an aptamer domain, which is responsible for ligand binding, and an expression platform that transmits the ligand-binding state of the aptamer domain through a conformational change. Riboswitches can regulate gene expression either at the level of transcription or translation, and it has been proposed that riboswitch mechanisms are even used to regulate the processing of mRNA. This Minireview summarizes the current understanding of the structures and mode of action of RNA switches, with particular focus on secondary and tertiary interactions, which stabilize the global RNA structure and thus determine the function of the aptamer domain.

Nucleic Acid Conformation↗