PubMed Health⌕ Search

PubMed · 15807532

A uniform mechanism correlating dangling-end stabilization and stacking geometry.

Abstract

The geometry of the dangling base in 105 published structures (from X-ray/NMR) containing single-stranded overhangs has been analyzed and correlated to the thermodynamic stabilization found (UV) for the corresponding dangling base/closing basepair combination in short oligonucleotides. The study considers most combinations of closing basepairs, sequence and dangling base residue type, attached in both the 3'- and 5'-ends of both DNA and RNA. Linear regression analysis showed a straightforward correlation (R = 0.873) between the degree of screening for the hydrogen bonds of the closing basepair provided by the dangling base and the resulting thermodynamic stabilization in both DNA and RNA series with dangling ends either at the 3'- or at the 5'-terminus. Regression analysis of only the datasets from RNA gives an improved correlation, R = 0.934, showing that dangling ends on RNA are more ordered than the dangling ends on DNA, R = 0.376. This study highlights the gain in the free energy of stabilization owing to the favorable stacking between the dangling nucleobase and the neighboring basepair and the resulting strengthening of the hydrogen bond of the closing basepair. By acting as a hydrophobic cap on the terminal of the DNA or RNA duplex, the dangling-end residue restricts the bulk water access to the terminal basepair, thereby providing it with a microenvironment devoid of water, which consequently enhances its thermodynamic stability, making it energetically comparable to the corresponding internal basepair. Thus, one single structural model consisting of the interplay of the above electrostatic interactions can be used to explain the molecular basis of the observed thermodynamic effects for dangling-end attachment to the 3'- and 5'-ends of both DNA and RNA duplexes, which is a key step toward accurate dangling-end effect prediction.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

J Isaksson, J Chattopadhyaya. 2005-04-12. A uniform mechanism correlating dangling-end stabilization and stacking geometry.. https://doi.org/10.1021/bi047414f

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

KEEP EXPLORING

Related citations

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↗

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↗

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↗