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 Structure01:23

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...
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
DNA Base Pairing02:27

DNA Base Pairing

Erwin Chargaff’s rules on DNA equivalence paved the way for the discovery of base pairing in DNA. Chargaff’s rules state that in a double-stranded DNA molecule,

You might also read

Related Articles

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

Sort by
Same author

ORIGAMI: Orientation-Aware Graph Neural Network for Assessing Multimeric Interfaces of Protein Complex Structures.

bioRxiv : the preprint server for biology·2026
Same author

xBind: an integrated webserver for large language model-enabled cross-molecular protein binding site prediction.

Nucleic acids research·2026
Same author

PARSEbp: pairwise agreement-based RNA scoring with emphasis on base pairings.

Bioinformatics advances·2026
Same author

A protocol for single-sequence protein-RNA complex structure prediction using ProRNA3D-single.

STAR protocols·2026
Same author

PARSEbp: Pairwise Agreement-based RNA Scoring with Emphasis on Base Pairings.

bioRxiv : the preprint server for biology·2025
Same author

Protein-Protein Interaction Site Prediction via EquiPPIS and Its Application in Studying Viral Replication.

Methods in molecular biology (Clifton, N.J.)·2025
Same journal

A comprehensive benchmark of sequence-based subcellular localization predictors for human proteins.

Nature methods·2026
Same journal

Efficient evidence-based genome annotation with EviAnn.

Nature methods·2026
Same journal

ClairS: a deep-learning method for long-read tumor-normal pair somatic small variant calling.

Nature methods·2026
Same journal

Spatio-DARLIN enables robust and efficient in situ lineage tracing in mice at single-cell resolution.

Nature methods·2026
Same journal

EasyGrid: a versatile platform for automated cryo-EM sample preparation and quality control.

Nature methods·2026
Same journal

Cloud-based microscope enables live neuroimaging for 24 h and beyond with worldwide access.

Nature methods·2026
See all related articles

Related Experiment Video

Updated: Jul 2, 2026

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

RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation.

Sumit Tarafder1, Debswapna Bhattacharya2

  • 1Department of Computer Science, Virginia Tech, Blacksburg, VA, USA.

Nature Methods
|July 1, 2026
PubMed
Summary
This summary is machine-generated.

RNAbpFlow generates accurate three-dimensional (3D) RNA structures using a novel deep learning approach. This method improves RNA modeling by leveraging sequence and base-pairing information without relying on evolutionary data.

More Related Videos

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

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Analyzing and Building Nucleic Acid Structures with 3DNA
16:24

Analyzing and Building Nucleic Acid Structures with 3DNA

Published on: April 26, 2013

Related Experiment Videos

Last Updated: Jul 2, 2026

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

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

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Analyzing and Building Nucleic Acid Structures with 3DNA
16:24

Analyzing and Building Nucleic Acid Structures with 3DNA

Published on: April 26, 2013

Area of Science:

  • Computational Biology
  • Structural Biology
  • Deep Learning

Background:

  • Predicting RNA 3D structures is difficult due to RNA's flexibility and limited data.
  • Current deep learning methods face challenges with RNA structure prediction.

Purpose of the Study:

  • Introduce RNAbpFlow, a new model for generating RNA 3D structural ensembles.
  • Enable end-to-end generation of all-atom RNA structures.

Main Methods:

  • Developed RNAbpFlow, a sequence- and base pair-conditioned SE(3)-equivariant flow-matching model.
  • Utilized a nucleobase center representation for RNA structure generation.
  • Avoided explicit or implicit use of evolutionary information or homologous templates.

Main Results:

  • RNAbpFlow successfully generates RNA 3D structural ensembles.
  • Base-pairing conditioning significantly improved performance in RNA topology sampling.
  • Demonstrated broadly generalizable performance improvements over existing methods in large-scale benchmarking.

Conclusions:

  • RNAbpFlow offers a powerful new approach for RNA 3D structure prediction.
  • The model advances biomolecular modeling by overcoming data limitations.
  • This method enhances the accuracy and generalizability of RNA structural modeling.