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

Conserved Binding Sites01:49

Conserved Binding Sites

4.9K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
4.9K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.7K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
6.7K
Nucleic Acid Structure01:25

Nucleic Acid Structure

8.0K
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...
8.0K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.8K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
7.8K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

3.3K
3.3K
Protein Organization01:13

Protein Organization

154.0K
Overview
154.0K

You might also read

Related Articles

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

Sort by
Same author

High-throughput functional profiling and evolutionary covariation analysis of entire riboswitch sequences.

Nucleic acids research·2026
Same author

What does it take to learn the rules of RNA base pairing? A lot less than you may think.

Communications biology·2026
Same author

High-throughput functional profiling and evolutionary covariation analysis of entire riboswitch sequences.

bioRxiv : the preprint server for biology·2025
Same author

All-at-once RNA folding with 3D motif prediction framed by evolutionary information.

Nature methods·2025
Same author

What does it take to learn the rules of RNA base pairing? A lot less than you may think.

bioRxiv : the preprint server for biology·2025
Same author

RNAhub-an automated pipeline to search and align RNA homologs with secondary structure assessment.

Nucleic acids research·2025

Related Experiment Video

Updated: Dec 3, 2025

The ITS2 Database
16:17

The ITS2 Database

Published on: March 12, 2012

31.7K

RNA structure prediction using positive and negative evolutionary information.

Elena Rivas1

  • 1Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA.

Plos Computational Biology
|October 30, 2020
PubMed
Summary

Predicting conserved RNA structures is crucial for understanding function. CaCoFold (Cascade variation/covariation Constrained Folding algorithm) improves accuracy by integrating phylogenetic correction, negative evolutionary information, and probabilistic folding.

More Related Videos

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

894
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

69.4K

Related Experiment Videos

Last Updated: Dec 3, 2025

The ITS2 Database
16:17

The ITS2 Database

Published on: March 12, 2012

31.7K
Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

894
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

69.4K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Conserved structural RNAs are vital for biological functions, but their structure prediction remains challenging.
  • Existing methods combining thermodynamic stability and evolutionary covariation show limitations in accuracy.

Purpose of the Study:

  • To develop an improved computational method for predicting conserved RNA structures.
  • To enhance RNA structure prediction by incorporating refined evolutionary information and probabilistic folding.

Main Methods:

  • Developed CaCoFold (Cascade variation/covariation Constrained Folding algorithm).
  • CaCoFold filters significant covariation, removes phylogenetic noise, and utilizes negative evolutionary information.
  • Employs probabilistic folding algorithms to integrate positive covariation and predict nested structures with alternative helices (e.g., pseudoknots).

Main Results:

  • CaCoFold successfully predicts conserved RNA structures by leveraging corrected evolutionary data.
  • The algorithm accounts for complex structural features like pseudoknots and competing base pairings.
  • Predicted structures demonstrate consistency with experimentally determined structures from crystallography.

Conclusions:

  • CaCoFold offers a more reliable approach to predicting conserved RNA structures.
  • The method's ability to integrate diverse evolutionary signals and complex interactions advances RNA structure prediction.
  • This tool has significant implications for elucidating RNA function and mechanisms of action.