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

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

13.1K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
13.1K
Conservation of Protein Domains02:26

Conservation of Protein Domains

3.3K
3.3K
Protein and Protein Structure02:15

Protein and Protein Structure

82.8K
Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme...
82.8K
Protein and Protein Structures02:15

Protein and Protein Structures

12.2K
12.2K
¹H NMR of Conformationally Flexible Molecules: Variable-Temperature NMR01:15

¹H NMR of Conformationally Flexible Molecules: Variable-Temperature NMR

1.2K
The axial and equatorial protons in cyclohexane can be distinguished by performing a variable-temperature NMR experiment. In this process, except for one proton, the remaining eleven protons are replaced by deuterium. The deuterium substitution avoids the possible peak splitting caused by the spin-spin coupling between the adjacent protons. The remaining proton flips between the axial and equatorial positions.
1.2K
Conformations of Ethane and Propane02:18

Conformations of Ethane and Propane

15.5K
In an organic molecule, free rotation about the carbon-carbon single bond results in energetically different conformers of the molecule. Due to this rotation, called the internal rotation, ethane has two major conformations — staggered and eclipsed.
Staggered conformation is a low energy and more stable conformation with the C-H bonds on the front carbon placed at 60°dihedral angles relative to the C-H bonds on the back carbon, leading to a reduced torsional strain. In staggered...
15.5K

You might also read

Related Articles

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

Sort by
Same author

Tensor Hypercontraction Error Correction Using Regression.

Journal of computational chemistry·2026
Same author

Taxane combined with lobaplatin or anthracycline for neoadjuvant chemotherapy of triple-negative breast cancer: a randomized, controlled, phase II study.

BMC medicine·2024
Same author

Smart Microneedle Arrays Integrating Cell-Free Therapy and Nanocatalysis to Treat Liver Fibrosis.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2024
Same author

Integration of a Randomized Sequence Scanning Approach in AlphaFold2 and Local Frustration Profiling of Conformational States Enable Interpretable Atomistic Characterization of Conformational Ensembles and Detection of Hidden Allosteric States in the ABL1 Protein Kinase.

Journal of chemical theory and computation·2024
Same author

Selective C-H Bond Activation in Propane with Molecular Oxygen over Cu(I)-ZSM-5 at Ambient Conditions.

Journal of the American Chemical Society·2024
Same author

Single-cell sequencing depicts tumor architecture and empowers clinical decision in metastatic conjunctival melanoma.

Cell discovery·2024
Same journal

Adenosine metabolism as an endogenous protective mechanism in response to upstream ischemic injury.

Frontiers in molecular biosciences·2026
Same journal

Bound or unbound: mapping and monitoring receptor oligomerization by time-resolved fluorescence live-cell imaging.

Frontiers in molecular biosciences·2026
Same journal

Interaction of diosmetin, diosmin and diosmetin-7-O-glucoside with human erythrocytes, their model membrane, hemoglobin and redox-active metal ions.

Frontiers in molecular biosciences·2026
Same journal

Commentary: A comprehensive review of diagnostic approaches for hepatitis D.

Frontiers in molecular biosciences·2026
Same journal

MBNL1-mediated alternative splicing in cancer: underlying mechanism, isoform regulation, and translational perspectives.

Frontiers in molecular biosciences·2026
Same journal

Molecular insights into nagashima-type palmoplantar keratoderma: SERPINB7 mutation spectrum and mechanistic perspectives.

Frontiers in molecular biosciences·2026
See all related articles

Related Experiment Video

Updated: Oct 11, 2025

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
09:51

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

15.6K

Explore Protein Conformational Space With Variational Autoencoder.

Hao Tian1, Xi Jiang2, Francesco Trozzi1

  • 1Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Department of Chemistry, Southern Methodist University, Dallas, TX, United States.

Frontiers in Molecular Biosciences
|December 6, 2021
PubMed
Summary
This summary is machine-generated.

Deep learning models called variational autoencoders (VAEs) accelerate protein conformational space exploration in molecular dynamics (MD) simulations. VAEs generate novel protein conformations, enabling faster sampling and discovery of hidden states.

Keywords:
conformational spacedeep learningmolecular dynamicsprotein systemvariational autoencoder

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.4K

Related Experiment Videos

Last Updated: Oct 11, 2025

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
09:51

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

15.6K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.4K

Area of Science:

  • Computational biology
  • Biophysics
  • Deep learning

Background:

  • Molecular dynamics (MD) simulations are crucial for studying protein structure and function.
  • Extensive conformational sampling in MD requires significant computational resources and time.

Purpose of the Study:

  • To investigate the utility of variational autoencoders (VAEs) for exploring protein conformational space in MD simulations.
  • To compare the performance of VAEs against traditional autoencoders (AEs) for this task.

Main Methods:

  • Utilized VAEs, a type of deep learning model, to analyze protein conformational space.
  • Performed benchmark studies comparing VAEs and AEs, assessing deviation between training and decoded conformations.
  • Leveraged the learned latent space of VAEs to generate new, unsampled protein conformations.

Main Results:

  • VAEs demonstrated superior performance compared to AEs, exhibiting low deviation in conformational decoding.
  • The latent space learned by VAEs successfully generated previously unsampled protein conformations.
  • Simulations initiated from VAE-generated conformations significantly accelerated the overall sampling process.

Conclusions:

  • VAEs offer a powerful and efficient approach to explore protein conformational landscapes using MD simulations.
  • This deep learning strategy enhances sampling efficiency and facilitates the discovery of hidden protein states.
  • VAEs represent a promising tool for advancing computational structural biology and drug discovery.