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RNA Structure

RNA Structure

Overview
The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA): messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three RNA types consist of a...

RNA Structure

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Overview
The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA): messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three RNA types consist of a...

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

RNA Structure

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Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Related Experiment Video

Updated: May 12, 2026

Analyzing and Building Nucleic Acid Structures with 3DNA

Analyzing and Building Nucleic Acid Structures with 3DNA

Published on: April 26, 2013

gRNAde: Geometric Deep Learning for 3D RNA inverse design.

Chaitanya K Joshi, Arian R Jamasb, Ramon Viñas

|June 3, 2024

Summary

gRNAde designs RNA sequences considering 3D structure and dynamics, outperforming Rosetta in native sequence recovery and pseudoknotted RNA design. This geometric RNA design pipeline offers faster and more accurate sequence generation for diverse RNA structures.

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Area of Science:

Computational biology
Structural biology
Bioinformatics

Background:

RNA sequence design traditionally focuses on single structures, neglecting 3D dynamics and conformational diversity.
Existing computational methods often struggle with complex RNA structures and multi-state designs.

Purpose of the Study:

Introduce gRNAde, a novel geometric RNA design pipeline.
Enable RNA sequence design that explicitly accounts for 3D structure and dynamics.

Main Methods:

Utilizes a multi-state Graph Neural Network and autoregressive decoding.
Operates on 3D RNA backbones with unknown base identities.
Evaluated on single-state and multi-state RNA design benchmarks.

Main Results:

Achieved higher native sequence recovery (56%) than Rosetta (45%) on a fixed backbone benchmark.
Demonstrated success in multi-state design for flexible RNAs and fitness landscape analysis.
Experimental validation showed a 50% success rate for pseudoknotted RNA design, surpassing Rosetta's 35%.

Conclusions:

gRNAde offers a significant advancement in computational RNA design by incorporating 3D structural and dynamic information.
The pipeline provides faster and more accurate RNA sequence generation, particularly for complex and flexible structures.
gRNAde shows promise for designing functional RNAs, including challenging pseudoknotted structures.