Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

¹H NMR of Conformationally Flexible Molecules: Temporal Resolution

¹H NMR of Conformationally Flexible Molecules: Temporal Resolution

At room temperature, the chair conformer of cyclohexane undergoes rapid ring flipping between two equivalent chair conformers at a rate of approximately 105 times per second. These two chair conformers are in equilibrium. The rapid ring flipping results in the interconversion of the axial proton to an equatorial proton and an equatorial to the axial proton. Such interconversions are too rapid and cannot be detected on the NMR timescale. Hence, the NMR spectrometer cannot distinguish between the...

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

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

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.

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Molecular Models

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.

Conservation of Protein Domains Over Different Proteins

Conservation of Protein Domains Over Different Proteins

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...

Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

You might also read

Related Articles

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

Sort by

Same author

Collective variable design for biomolecular conformational dynamics.

Current opinion in structural biology·2026

Same author

Toward Lab-Ready AI Synthesis Plans with Protection Strategies and Route Scoring.

Journal of chemical information and modeling·2026

Same author

Molecular mechanisms of native ligand selectivity in catecholamine G protein-coupled receptors.

Nature communications·2026

Same author

A two-step mechanism for sugar translocation.

Nature structural & molecular biology·2026

Same author

Nanoscale Spatial Organization of ARC High- and Low-Order Assemblies at Excitatory Synapses.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same author

Unveiling the Activation Mechanism of Glucagon-Like Peptide-1 Receptor by an Ago-Allosteric Modulator via Molecular Dynamics Simulations.

Journal of chemical information and modeling·2026

Search research articles

Related Experiment Video

Updated: Dec 30, 2025

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

Molecular Insights from Conformational Ensembles via Machine Learning.

Oliver Fleetwood¹, Marina A Kasimova¹, Annie M Westerlund¹

¹Science for Life Laboratory, Department of Applied Physics, KTH Royal Institute of Technology, Solna, Sweden.

Biophysical Journal

|January 19, 2020

Summary

Machine learning (ML) methods can interpret complex biomolecular simulation data. This study develops interpretable ML models to reveal key features in protein dynamics, ligand binding, and channel activation.

More Related Videos

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

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

Published on: July 16, 2017

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

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

Related Experiment Videos

Last Updated: Dec 30, 2025

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

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

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

Published on: July 16, 2017

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

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

Area of Science:

Computational Biology
Biophysics
Machine Learning

Background:

Biomolecular simulations generate large, high-dimensional, and noisy datasets.
Extracting meaningful biophysical insights from simulation data is challenging.
Existing machine learning (ML) methods often lack human interpretability.

Purpose of the Study:

To develop and benchmark interpretable ML methods for analyzing biomolecular simulation data.
To create human-understandable feature maps from complex molecular dynamics.
To apply these methods to diverse biological systems for uncovering critical functional features.

Main Methods:

Utilized supervised and unsupervised machine learning techniques.
Benchmarked methods including neural networks, random forests, and principal component analysis (PCA).
Applied methods to a toy model and three biological systems: calmodulin, GPCRs, and ion channel voltage-sensor domains.

Main Results:

Successfully generated interpretable maps of important features from molecular simulations.
Demonstrated the effectiveness of ML in identifying critical features for protein conformational changes, ligand binding, and voltage sensing.
Validated ML approach on diverse biological processes.

Conclusions:

Machine learning offers powerful, interpretable tools for understanding complex biomolecular simulations.
This approach demystifies simulation data, providing crucial insights into biological mechanisms.
The developed methods enhance the utility of ML in biophysics and computational biology.