PubMed HealthSearch

PubMed · 1406607

[Four-stranded complexes of oligonucleotides--quadruplexes].

Abstract

The review presents analysis of the experimental, model and calculation studies concerned with the formation of the four-stranded helices of the natural and synthetic oligonucleotides. Guanine-rich oligonucleotides form stable four-stranded helices. Structures of such complexes were investigated by means of X-rays and spectrographic methods. These works have been reviewed in the first part. There are three possible variants of noncanonical structures formed by oligoguanylic acids. Two of them--four-stranded helices differed by the mutual direction of the sugar-phosphate chains. The third one is the two-stranded hairpin. Regulation of the number of cellular processes by means of the structural conversions between these three forms of guanine-rich motifs are investigated in articles reviewed in the second part. These works are concerned with the structural organization and functions of telomers, and on the other hand with the possible role of quadruplexes in self-recognition processes of the four homologous chromatids during meiosis and the following recombination. The third part of the review considers quadruplexes with an arbitrary sequence. In general there are model works inspired by investigations of recombination and replication processes. Experimental data concerned with the formation of quadruplex structures from two decamer Watson-Crick base paired duplexes oligo(dA).oligo(dT) are also presented.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

I A Il'icheva, V L Florent'ev. [Four-stranded complexes of oligonucleotides--quadruplexes].. https://pubmed.ncbi.nlm.nih.gov/1406607/

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

mRNA degradation in procaryotes.

The fast turnover of mRNA permits rapid changes in the pattern of gene expression. In procaryotes, many enzymes involved in mRNA degradation have been identified and some of these endo- and exo-ribonucleases are now being intensively studied. Some of the structural features of mRNA that influence decay rates have also recently been defined. Although important components of the decay pathway are still elusive, a coherent and simple model for mRNA decay has emerged in the last few years.

Nucleic Acid Conformation