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 Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...

You might also read

Related Articles

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

Sort by
Same author

AlphaFold-SFA: Accelerated sampling of cryptic pocket opening, protein-ligand binding and allostery by AlphaFold, slow feature analysis and metadynamics.

PloS one·2024
Same author

Systematic computer-aided disulfide design as a general strategy to stabilize prefusion class I fusion proteins.

bioRxiv : the preprint server for biology·2024
Same author

A general computational design strategy for stabilizing viral class I fusion proteins.

Nature communications·2024
Same author

Accelerating Cryptic Pocket Discovery Using AlphaFold.

Journal of chemical theory and computation·2023
Same author

Epitopes in the Glycosylphosphatidylinositol Attachment Signal Peptide of Trypanosoma cruzi Mucin Proteins Generate Robust but Delayed and Nonprotective CD8+ T Cell Responses.

Journal of immunology (Baltimore, Md. : 1950)·2023
Same author

Sampling of structure and sequence space of small protein folds.

Nature communications·2022

Related Experiment Video

Updated: Jun 12, 2026

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy
08:03

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy

Published on: April 13, 2022

Accelerated Sampling of Protein Dynamics Using BioEmu-Augmented Molecular Simulation.

Soumendranath Bhakat1, Eva-Maria Strauch2

  • 1AlloTec Bio Inc., 4059 Utah St., St. Louis, Missouri 63116, United States.

Journal of Chemical Information and Modeling
|June 10, 2026
PubMed
Summary
This summary is machine-generated.

Generative models like BioEmu predict protein structures, but identifying functional states and their populations is challenging. This study introduces a workflow combining AI predictions with simulations to estimate these populations, improving understanding of protein dynamics.

More Related Videos

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

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

Related Experiment Videos

Last Updated: Jun 12, 2026

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy
08:03

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy

Published on: April 13, 2022

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

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

Area of Science:

  • Structural Biology
  • Computational Biology
  • Biophysics

Background:

  • Generative models like BioEmu produce diverse protein conformational ensembles, but determining physiological relevance and population distributions is difficult.
  • Identifying functional protein states and transitions between them from predicted ensembles remains a significant challenge.

Purpose of the Study:

  • To investigate the physiological relevance and population of protein conformational states predicted by generative models.
  • To develop and validate a computational framework for quantifying Boltzmann-weighted conformational ensembles.
  • To assess the impact of disease-associated mutations on protein conformational landscapes.

Main Methods:

  • Utilized BioEmu for generating protein conformational ensembles.
  • Employed short molecular dynamics simulations and Markov State Models to estimate Boltzmann-weighted state populations.
  • Integrated BioEmu ensembles with cryo-electron microscopy data for all-atom ensemble construction.
  • Compared BioEmu-seeded simulations with AlphaFold2's rMSA-AF2 approach.

Main Results:

  • The developed workflow effectively samples functionally important metastable states and captures mutation-induced population shifts in serine-threonine kinases (BRAF, CDK2).
  • BioEmu-seeded simulations show improved performance over rMSA-AF2 in capturing functionally relevant dynamics.
  • The approach failed to reproduce experimentally observed conformational heterogeneity in systems like GlyT1 and PlmII, particularly when side-chain dynamics are critical.

Conclusions:

  • A framework integrating generative models with statistical reweighting can recover Boltzmann-weighted conformational ensembles at scale.
  • System-specific evaluation is crucial for interpreting AI-generated protein conformational landscapes, especially for complex dynamics.
  • Limitations exist in capturing allosteric effects and side-chain mediated conformational heterogeneity with current generative models and simulation workflows.