Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

RNA Structure01:19

RNA Structure

6.0K
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...
6.0K
RNA Structure01:23

RNA Structure

77.0K
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...
77.0K
Improving Translational Accuracy02:07

Improving Translational Accuracy

12.3K
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...
12.3K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.3K
3.3K
Transfer RNA Synthesis02:36

Transfer RNA Synthesis

12.6K
One of the unique features of tRNA is the presence of modified bases. In some tRNAs, modified bases account for nearly 20% of the total bases in the molecule. Altogether, these unusual bases protect the tRNA from enzymatic degradation by RNases.
Each of these chemical modifications is carried by a specific enzyme, post-transcription. All of these enzymes have unique base and site-specificity. Methylation, the most common chemical modification, is carried by at least nine different enzymes, with...
12.6K
Transfer RNA Synthesis02:35

Transfer RNA Synthesis

3.2K
3.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A framework for building a synthetic cell from the SynCell Asia Initiative.

Nature biotechnology·2026
Same author

Interplay of SLC33A1-dependent and -independent Golgi sialic acid O-acetylation in CASD1 catalysis.

Nature communications·2026
Same author

Matrix stiffness induces midnolin-dependent lamin B1 degradation to control myoblast differentiation.

EMBO reports·2026
Same author

Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning.

Briefings in bioinformatics·2026
Same author

Molecular characterisation of the Bacillus subtilis SpbK antiphage defence system.

Nature communications·2025
Same author

Assessing the validity of leucine zipper constructs predicted by AlphaFold.

Protein science : a publication of the Protein Society·2025

Related Experiment Video

Updated: Nov 14, 2025

RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

31.9K

Improved RNA secondary structure and tertiary base-pairing prediction using evolutionary profile, mutational coupling

Jaswinder Singh1, Kuldip Paliwal1, Tongchuan Zhang2

  • 1Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia.

Bioinformatics (Oxford, England)
|March 11, 2021
PubMed
Summary

This study introduces a new computational method to accurately predict RNA secondary and tertiary structures using evolutionary profiles and mutational coupling. The advanced deep learning approach improves base-pairing predictions for complex RNA structures.

More Related Videos

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
10:34

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Published on: December 9, 2022

4.8K
The ITS2 Database
16:17

The ITS2 Database

Published on: March 12, 2012

31.5K

Related Experiment Videos

Last Updated: Nov 14, 2025

RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

31.9K
Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
10:34

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Published on: December 9, 2022

4.8K
The ITS2 Database
16:17

The ITS2 Database

Published on: March 12, 2012

31.5K

Area of Science:

  • Computational Biology
  • Molecular Biology
  • Bioinformatics

Background:

  • The discovery of non-coding RNAs (ncRNAs), especially long non-coding RNAs (lncRNAs), has expanded our understanding of RNA functions.
  • Experimental determination of high-resolution RNA secondary and tertiary structures is challenging and inefficient.
  • Advancements in computational methods, including deep learning, have improved RNA secondary structure prediction.

Purpose of the Study:

  • To develop an improved computational method for predicting RNA secondary and tertiary structures.
  • To leverage evolutionary profiles and mutational coupling data for enhanced prediction accuracy.
  • To provide a powerful tool for structural biologists and researchers studying RNA function.

Main Methods:

  • Expanded deep learning models from single-sequence to evolutionary profiles and mutational coupling.
  • Utilized large datasets of approximate RNA structures and gold-standard base-pairing data.
  • Incorporated artificial homologous sequences generated from deep mutational scanning.

Main Results:

  • Achieved significant improvements in predicting canonical base-pairs (secondary structure).
  • Demonstrated enhanced accuracy for tertiary interactions, including pseudoknots and non-canonical base-pairs.
  • Attained >0.8 F1-score for 14/16 RNAs with over 1000 homologous sequences.
  • Showcased the utility of artificial homologous sequences for improving predictions.

Conclusions:

  • The new method offers a powerful tool for predicting both secondary and tertiary RNA base-pairing information.
  • Accurate RNA structure prediction can be achieved using a large number of natural and artificial homologous sequences.
  • The method facilitates the construction of three-dimensional RNA models, advancing RNA research.