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

You might also read

Related Articles

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

Sort by
Same author

MDIntrinsicDimension: Dimensionality-Based Analysis of Collective Motions in Macromolecules from Molecular Dynamics Trajectories.

Journal of chemical information and modeling·2026
Same author

Test-Time Training Scaling Laws for Chemical Exploration in Drug Design.

Journal of chemical information and modeling·2025
Same author

REINFORCE-ING Chemical Language Models for Drug Discovery.

Journal of chemical information and modeling·2025
Same author

Navigating protein landscapes with a machine-learned transferable coarse-grained model.

Nature chemistry·2025
Same author

QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials.

Journal of chemical information and modeling·2025
Same author

PLUMED Tutorials: A collaborative, community-driven learning ecosystem.

The Journal of chemical physics·2025

Related Experiment Video

Updated: Jun 6, 2025

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs
05:00

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

Published on: August 9, 2024

1.1K

mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics.

Antonio Mirarchi1, Toni Giorgino2, Gianni De Fabritiis3,4,5

  • 1Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Carrer Dr. Aiguader 88, Barcelona, 08003, Spain.

Scientific Data
|November 28, 2024
PubMed
Summary

We introduce mdCATH, a large dataset of protein dynamics from molecular dynamics simulations. This resource aids in understanding protein function, folding, and interactions across the proteome.

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

3.1K
Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.2K

Related Experiment Videos

Last Updated: Jun 6, 2025

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs
05:00

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

Published on: August 9, 2024

1.1K
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

3.1K
Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.2K

Area of Science:

  • Structural Biology
  • Computational Biology
  • Biophysics

Background:

  • Protein structure determination has advanced, yet data on protein dynamics is scarce.
  • Protein dynamics are essential for understanding protein function, folding, and interactions.
  • Existing datasets lack comprehensive dynamic information crucial for proteome-wide analysis.

Purpose of the Study:

  • To introduce mdCATH, a novel dataset of protein dynamics.
  • To address the gap in comprehensive protein dynamics data.
  • To provide a resource for statistical analyses of protein unfolding thermodynamics and kinetics.

Main Methods:

  • Generated mdCATH using extensive all-atom molecular dynamics simulations.
  • Simulated 5,398 protein domains across five temperatures (320–450 K) with five replicates each.
  • Recorded coordinates and forces every 1 ns, accumulating over 62 ms of simulation time.

Main Results:

  • mdCATH captures the dynamics of diverse protein domain classes.
  • The dataset provides over 62 ms of accumulated simulation data.
  • Four reproducible case studies demonstrate the dataset's utility.

Conclusions:

  • mdCATH is a unique resource for proteome-wide statistical analyses.
  • The dataset advances the understanding of protein unfolding thermodynamics and kinetics.
  • mdCATH facilitates breakthroughs in protein science through comprehensive dynamic data.