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Related Concept Videos

RNA Structure01:23

RNA Structure

71.9K
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...
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Nucleic Acid Structure01:25

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The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
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The DNA Helix01:07

The DNA Helix

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Deoxyribonucleic acid, or DNA, is the genetic material responsible for passing traits from generation to generation in all organisms and most viruses. DNA is composed of two strands of nucleotides that wind around each other to form a spring-like structure called a double helix. However, the double helix is not perfectly symmetrical. Instead, there are regularly occurring grooves in the structure. The major groove occurs where the sugar-phosphate backbones are relatively far apart. This space...
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Ribosome Profiling02:24

Ribosome Profiling

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
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RNA Editing02:23

RNA Editing

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RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...
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Protein Organization01:24

Protein Organization

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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
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Related Experiment Video

Updated: Aug 19, 2025

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
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Advances in RNA 3D Structure Prediction.

Xiujuan Ou1, Yi Zhang1, Yiduo Xiong1

  • 1Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.

Journal of Chemical Information and Modeling
|November 30, 2022
PubMed
Summary

Computational methods predict RNA 3D structures for understanding cellular functions. Machine learning approaches show improved performance over traditional methods for RNA 3D structure prediction.

Keywords:
RNA 3D structureab initio approachmachine-learning modelsstructure predictiontemplate-based approach

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Area of Science:

  • Molecular Biology
  • Computational Biology
  • Biophysics

Background:

  • Ribonucleic acid (RNA) molecules perform diverse cellular roles, necessitating an understanding of their three-dimensional (3D) structures.
  • Knowledge of RNA 3D structures is crucial for elucidating their functional mechanisms within the cell.
  • Computational modeling has become indispensable for predicting these complex structures.

Purpose of the Study:

  • To review traditional computational methods for RNA 3D structure prediction.
  • To highlight recent advancements and the growing application of machine learning techniques in this field.
  • To provide an overview of the current landscape of RNA 3D structure prediction strategies.

Main Methods:

  • Review of established computational algorithms for RNA 3D structure modeling.
  • Analysis of the integration and impact of machine learning algorithms on RNA structure prediction.
  • Comparative assessment of traditional versus machine learning-based approaches.

Main Results:

  • Traditional computational methods have been foundational but require performance enhancements.
  • Machine learning techniques demonstrate significant improvements in RNA 3D structure prediction accuracy.
  • The synergy between traditional and machine learning methods offers promising future directions.

Conclusions:

  • Accurate RNA 3D structure prediction is vital for understanding RNA function.
  • Machine learning represents a paradigm shift, enhancing the capabilities of RNA structure modeling.
  • Continued research integrating diverse computational approaches will advance the field.