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

Causality in Epidemiology01:21

Causality in Epidemiology

1.5K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.5K
Kinetic Energy00:23

Kinetic Energy

43.4K
Kinetic energy is the ability of an object in motion to do work or enact change. It can take on many forms. For instance, water flowing down a waterfall has kinetic energy. In biological systems, particles of light travel and are absorbed by plants to create chemical energy. Animals consume the chemical energy and give off molecules that carry their scent through the air. They also generate kinetic energy when they run away from predators. Entire systems also possess kinetic energy, like the...
43.4K
Protein-Drug Binding: Mechanism and Kinetics01:16

Protein-Drug Binding: Mechanism and Kinetics

1.8K
Protein-drug binding refers to the interaction between drugs and proteins within the body. This binding process can occur intracellularly, involving drug interactions with enzymes or receptors within cells, or extracellularly, involving plasma proteins in the blood.
Various forces drive these interactions, including hydrogen bonds, hydrophobic interactions, ionic bonds, electrostatic interactions, and van der Waals forces. These bonds enable drugs to bind to specific sites on proteins,...
1.8K
Enzyme Kinetics01:19

Enzyme Kinetics

104.0K
Enzymes speed up reactions by lowering the activation energy of the reactants. The speed at which the enzyme turns reactants into products is called the rate of reaction. Several factors impact the rate of reaction, including the number of available reactants. Enzyme kinetics is the study of how an enzyme changes the rate of a reaction.
Scientists typically study enzyme kinetics with a fixed amount of enzyme in the controlled environment of a test tube. When more reactant, or substrate, is...
104.0K
Kinetic Molecular Theory: Molecular Velocities, Temperature, and Kinetic Energy03:07

Kinetic Molecular Theory: Molecular Velocities, Temperature, and Kinetic Energy

29.8K
The kinetic molecular theory qualitatively explains the behaviors described by the various gas laws. The postulates of this theory may be applied in a more quantitative fashion to derive these individual laws.
29.8K
Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

2.7K
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...
2.7K

You might also read

Related Articles

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

Sort by
Same author

Detecting and quantifying overparametrization in RNA language models with REDIAL.

bioRxiv : the preprint server for biology·2026
Same author

Hierarchical AF2RAVE for Multiconformation Virtual Screening Targeting S100 Ca<sup>2+</sup>-Binding Proteins.

Journal of chemical theory and computation·2025
Same author

af2rave: protein ensemble generation with physics-based sampling.

Digital discovery·2025
Same author

Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE.

eLife·2024
Same author

Information Bottleneck Approach for Markov Model Construction.

Journal of chemical theory and computation·2024
Same author

Thermodynamically Optimized Machine-Learned Reaction Coordinates for Hydrophobic Ligand Dissociation.

The journal of physical chemistry. B·2024
Same journal

QSAR in the Browser: An Interactive Cheminformatics Web Application.

Journal of chemical information and modeling·2026
Same journal

FoldDoF: Utilizing the Primary Degrees of Freedom of Protein Backbone for Geometric Modeling and Generation.

Journal of chemical information and modeling·2026
Same journal

Derisking Affinity Optimization for Macrocycles and Cyclic Peptides: High-Precision Free Energy Simulations across Five Diverse Targets.

Journal of chemical information and modeling·2026
Same journal

An End-User Audit of Reproducibility, Data Leakage, and Overfitting of the Top-Ranked ADMET Prediction Models in TDC Leaderboards.

Journal of chemical information and modeling·2026
Same journal

PFASGroups: An Open-Source Framework for Automated Identification, Structural Classification, and Prioritization of Per- and Polyfluoroalkyl Substances.

Journal of chemical information and modeling·2026
Same journal

DeepKbhb: Context-Aware Prediction of Human Lysine β-Hydroxybutyrylation Sites.

Journal of chemical information and modeling·2026
See all related articles

Related Experiment Video

Updated: Jan 29, 2026

Kinetic Analysis of Vasculogenesis Quantifies Dynamics of Vasculogenesis and Angiogenesis In Vitro
11:03

Kinetic Analysis of Vasculogenesis Quantifies Dynamics of Vasculogenesis and Angiogenesis In Vitro

Published on: January 31, 2018

10.1K

Applied Causality to Infer Protein Dynamics and Kinetics.

Akashnathan Aranganathan1, Eric R Beyerle2

  • 1Biophysics Program, University of Maryland, College Park, Maryland 20742, United States.

Journal of Chemical Information and Modeling
|January 27, 2026
PubMed
Summary
This summary is machine-generated.

Generative models like AlphaFold2 predict protein structures but lack time scales. This study links AlphaFold2 ensembles to a causal model, revealing how multiple sequence alignment depth affects protein dynamics time scales.

More Related Videos

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.6K
Transcranial Magnetic Stimulation for Investigating Causal Brain-behavioral Relationships and their Time Course
11:33

Transcranial Magnetic Stimulation for Investigating Causal Brain-behavioral Relationships and their Time Course

Published on: July 18, 2014

43.9K

Related Experiment Videos

Last Updated: Jan 29, 2026

Kinetic Analysis of Vasculogenesis Quantifies Dynamics of Vasculogenesis and Angiogenesis In Vitro
11:03

Kinetic Analysis of Vasculogenesis Quantifies Dynamics of Vasculogenesis and Angiogenesis In Vitro

Published on: January 31, 2018

10.1K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.6K
Transcranial Magnetic Stimulation for Investigating Causal Brain-behavioral Relationships and their Time Course
11:33

Transcranial Magnetic Stimulation for Investigating Causal Brain-behavioral Relationships and their Time Course

Published on: July 18, 2014

43.9K

Area of Science:

  • Computational biology
  • Structural biology
  • Machine learning in structural biology

Background:

  • Generative machine learning models, trained on Protein Data Bank structures, offer a promising method for protein conformational ensemble sampling.
  • These models currently lack crucial time scale and causal information essential for understanding protein dynamics.
  • AlphaFold2 is a prominent generative model used for predicting protein structures.

Purpose of the Study:

  • To integrate AlphaFold2-generated structural ensembles with a causal model to estimate protein conformational dynamics time scales.
  • To investigate the relationship between multiple sequence alignment (MSA) depth and the time scales of conformational fluctuations in protein ensembles.
  • To apply this approach to HIV-1 protease variants and its functional dimer.

Main Methods:

  • Utilized structural ensembles generated by AlphaFold2 at varying MSA depths.
  • Parametrized the potential of mean force for a coarse-grained Langevin equation using AlphaFold2 ensembles.
  • Coupled AlphaFold2 ensembles to a causal model to estimate time scales for each MSA depth.
  • Analyzed six variants of HIV-1 protease and the HIV-1 protease dimer.

Main Results:

  • Confirmed an inverse relationship between MSA depth and the time scale of conformational fluctuations.
  • Demonstrated that higher MSA depth acts as a conformational restraint, leading to shorter time scales.
  • Showed that AlphaFold2 can probe time scales comparable to or faster than unbiased molecular dynamics simulations.
  • Successfully applied the method to predict dynamics for the biologically functional HIV-1 protease dimer.

Conclusions:

  • The developed approach successfully estimates time scales for generative model-predicted protein ensembles.
  • MSA depth is a critical factor influencing the dynamics time scales predicted by AlphaFold2.
  • This method provides a framework for incorporating dynamics into predictions from other generative structural ensemble methods and cofolding models.
  • The findings offer insights into the conformational dynamics of HIV-1 protease and its dimer.