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

Protein Organization01:24

Protein Organization

9.0K
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.
The primary structure of a protein is its amino acid sequence....
9.0K
Protein-protein Interfaces02:04

Protein-protein Interfaces

14.4K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
14.4K
Ribosome Profiling02:24

Ribosome Profiling

4.0K
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.
The technique...
4.0K

You might also read

Related Articles

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

Sort by
Same author

HIV-1 budding requires cortical actin disassembly by the oxidoreductase MICAL1.

Proceedings of the National Academy of Sciences of the United States of America·2024
Same author

MMTV RNA packaging requires an extended long-range interaction for productive Gag binding to packaging signals.

PLoS biology·2024
Same author

Identification of a putative Gag binding site critical for feline immunodeficiency virus genomic RNA packaging.

RNA (New York, N.Y.)·2023
Same author

Identification of 2-(4-N,N-Dimethylaminophenyl)-5-methyl-1-phenethyl-1H-benzimidazole targeting HIV-1 CA capsid protein and inhibiting HIV-1 replication in cellulo.

BMC pharmacology & toxicology·2022
Same author

Visualization of Retroviral Gag-Genomic RNA Cellular Interactions Leading to Genome Encapsidation and Viral Assembly: An Overview.

Viruses·2022
Same author

Review and Perspectives on the Structure-Function Relationships of the Gag Subunits of Feline Immunodeficiency Virus.

Pathogens (Basel, Switzerland)·2021

Related Experiment Video

Updated: Jan 7, 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

5.1K

Deep Learning in Modeling Tools for Structural Insights into Protein-RNA Complexes, Bridging Computational and

Mathieu Long1, Serena Bernacchi2

  • 1RNA packaging and viral assembly, UPR 9002 - ARN, IBMC - CNRS - Université de Strasbourg, Strasbourg Cedex, France.

Methods in Molecular Biology (Clifton, N.J.)
|January 1, 2026
PubMed
Summary

Deep learning, particularly AlphaFold3, is revolutionizing protein-RNA structural biology by accurately modeling complexes. This AI approach synergizes with spectroscopy, enhancing experimental design and data interpretation for structural insights.

Keywords:
AlphaFold3Computational modelingDeep learningProteinRNA complexesSpectroscopy methodsStructure prediction

More Related Videos

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

Analyzing and Building Nucleic Acid Structures with 3DNA

Published on: April 26, 2013

21.2K
Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry
07:33

Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

Published on: October 15, 2018

14.8K

Related Experiment Videos

Last Updated: Jan 7, 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

5.1K
Analyzing and Building Nucleic Acid Structures with 3DNA
16:24

Analyzing and Building Nucleic Acid Structures with 3DNA

Published on: April 26, 2013

21.2K
Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry
07:33

Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

Published on: October 15, 2018

14.8K

Area of Science:

  • Structural biology
  • Computational biology
  • Biophysics

Background:

  • Characterizing protein-RNA complexes is challenging due to limited experimental structures compared to vast sequence data.
  • Existing structural databases like the Protein Data Bank have a significant gap in protein-RNA assembly data.
  • Deep learning models offer a promising solution to bridge this structural information gap.

Purpose of the Study:

  • To describe the principles and workflow of AlphaFold3 for modeling protein-RNA complexes.
  • To illustrate the integration of AlphaFold3 with spectroscopic techniques in structural biology.
  • To highlight the synergistic relationship between AI-based modeling and experimental spectroscopy.

Main Methods:

  • Utilizing AlphaFold3 for accurate in silico modeling of proteins, nucleic acids, and their complexes.
  • Integrating computational predictions from AlphaFold3 with experimental spectroscopic data.
  • Guiding experimental design and data interpretation using AI-generated structural models.

Main Results:

  • AlphaFold3 achieves unprecedented accuracy in modeling protein-RNA complexes.
  • The integration of AlphaFold3 with spectroscopy enhances structural biology research.
  • Spectroscopic data can validate and refine computational predictions, creating a feedback loop.

Conclusions:

  • AlphaFold3 and spectroscopy combination accelerates structural biology insights.
  • Limitations include accuracy for flexible RNAs and static nature of predictions.
  • Future advancements in AI and spectroscopy promise further breakthroughs in understanding molecular structures.