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

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.

Noncovalent Attractions in Biomolecules

Noncovalent Attractions in Biomolecules

Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...

Noncovalent Attractions in Biomolecules

Noncovalent Attractions in Biomolecules

Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...

You might also read

Related Articles

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

Sort by

Same author

Multiscale Neural Network Potential with Anisotropic Message Passing for the Fast and Accurate Simulation of Protein Dynamics and Enzymatic Reactions.

Journal of the American Chemical Society·2026

Same author

Balancing Data Quantity and Quality: Evaluating Curation Strategies for Bioactivity Prediction in Lead Optimization.

Journal of chemical information and modeling·2026

Same author

Leveraging the Potential of Machine-Learning Interatomic Potentials for QM/MM Simulations.

Chimia·2026

Same author

MD-Phylogeny: Constructing Statistically Supported Phylogenetic Trees from Protein Structures Using Molecular Dynamics.

Methods in molecular biology (Clifton, N.J.)·2026

Same author

Membrane lipid composition: an important but neglected aspect of membrane protein simulation.

Physical chemistry chemical physics : PCCP·2026

Same author

Torsion angular bin strings: algorithmic update and additional validation.

Journal of cheminformatics·2026

Same journal

Lower bound of the capacitance of constant phase elements based on electrochemical impedance spectra.

Physical chemistry chemical physics : PCCP·2026

Same journal

Stability constants of lanthanide-nitrate complexes in aqueous solutions: a theoretical study.

Physical chemistry chemical physics : PCCP·2026

Same journal

Lead-free Cs<sub>3</sub>MnCl<sub>5</sub> and CsMnCl<sub>3</sub> crystals: rapid on-chip crystallization, phase transition and fluorescence sensing applications.

Physical chemistry chemical physics : PCCP·2026

Same journal

F-Interstitial passivation preserves host-like optoelectronic properties in <sup>229</sup>Th:YLF nuclear-clock platforms.

Physical chemistry chemical physics : PCCP·2026

Same journal

Structural trends of tryptophan dimer: hydrogen bonding <i>versus</i> π-stacking from an energy decomposition analysis perspective.

Physical chemistry chemical physics : PCCP·2026

Same journal

Achieving high thermoelectric performance in Sb<sub>2</sub>Se<sub>3</sub>-alloyed GeTe through synergistic optimization of electrical and thermal transport.

Physical chemistry chemical physics : PCCP·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 21, 2026

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

On developing coarse-grained models for biomolecular simulation: a review.

Sereina Riniker¹, Jane R Allison, Wilfred F van Gunsteren

¹Laboratory of Physical Chemistry, Swiss Federal Institute of Technology, ETH, 8093 Zürich, Switzerland.

Physical Chemistry Chemical Physics : PCCP

|June 9, 2012

Summary

This summary is machine-generated.

Coarse-grained models simplify biomolecular simulations for longer timescales. This review outlines developing realistic coarse-grained models by preserving essential physical mechanisms from fine-grained data.

More Related Videos

Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies

Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies

Published on: September 1, 2023

Related Experiment Videos

Last Updated: May 21, 2026

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

Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies

Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies

Published on: September 1, 2023

Area of Science:

Biomolecular simulations
Computational biophysics
Statistical mechanics

Background:

Coarse-grained models are widely used to simulate biomolecular processes over extended time scales.
These models simplify atomic-level complexity by reducing degrees of freedom and interactions.
All modeling inherently involves coarse-graining, abstracting essential features from finer details.

Purpose of the Study:

To review the fundamental principles for developing coarse-grained models.
To outline conditions necessary for preserving physical mechanisms during coarse-graining.
To guide the assessment of coarse-grained model realism in biomolecular systems.

Main Methods:

Review of established coarse-graining methodologies.
Analysis of the relationship between fine-grained and coarse-grained model properties.
Identification of criteria for maintaining physical fidelity.

Main Results:

Coarse-graining requires careful selection of eliminated degrees of freedom and interactions.
Preservation of underlying physical mechanisms is crucial for model validity.
Guidelines are provided for evaluating the accuracy of coarse-grained models.

Conclusions:

Realistic coarse-grained models accurately reflect biomolecular physical mechanisms.
Understanding coarse-graining principles ensures models are more than just visual representations.
This work aids researchers in developing and validating robust biomolecular simulations.