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Related Concept Videos

RNA Structure01:23

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 Structure01:19

RNA Structure

The basic structure of RNA consists of a string of ribonucleotides attached by phosphodiester bonds. 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) involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three...
RNA Structure01:23

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...
Nucleic Acid Structure01:25

Nucleic Acid Structure

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.
DNA Structure
DNA has a double-helix structure. The...
RNA Stability01:53

RNA Stability

Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
RNA Stability01:53

RNA Stability

Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...

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Related Experiment Videos

Coarse-grained prediction of RNA loop structures.

Liang Liu1, Shi-Jie Chen

  • 1Department of Physics and Department of Biochemistry, University of Missouri, Columbia, MO, USA.

Plos One
|November 13, 2012
PubMed
Summary
This summary is machine-generated.

Predicting RNA folding is challenging. This study develops sequence-dependent potentials to accurately model RNA loop structures, improving predictions of native RNA conformations.

Related Experiment Videos

Area of Science:

  • Computational Biology
  • Biophysics
  • Structural Biology

Background:

  • Predicting RNA folding, particularly loop structures, is crucial for understanding RNA function.
  • Current models often overlook sequence-dependent free energy contributions in RNA loops.
  • The Vfold model generates RNA loop and junction conformations but lacks sequence-specific scoring.

Purpose of the Study:

  • To derive sequence-dependent statistical potentials for RNA loops and junctions.
  • To enhance the Vfold model's ability to predict native RNA structures.
  • To account for the full free energy landscape, including nonnative folds.

Main Methods:

  • Utilized an iterative method to extract knowledge-based potential parameters from known RNA structures.
  • Developed dinucleotide-based statistical potentials for RNA loops and junctions.
  • Applied these potentials to the Vfold model's conformational ensemble.

Main Results:

  • Successfully derived sequence-dependent statistical potentials for RNA loops and junctions.
  • Demonstrated accurate prediction of coarse-grained 3D structures from the Vfold ensemble for specific sequences.
  • Validated the ability to identify native and near-native RNA structures.

Conclusions:

  • Sequence-dependent statistical potentials significantly improve RNA loop structure prediction.
  • The enhanced Vfold model provides accurate initial folds for further structural refinement.
  • This approach offers a more comprehensive understanding of RNA folding free energy landscapes.