Visual Molecular Dynamics
Visual Molecular Dynamics - Related Videos
05:00
New Features in Visual Dynamics 3.0
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2024
Visual Dynamics is an open-source tool that accelerates implementations and learning in molecular dynamics simulation using Gromacs. The presented protocol will guide you through the steps to perform a protein-ligand simulation prepared in ACPYPE with ease and general steps to other simulation models.
14:14
Visualization of Cortex Organization and Dynamics in Microorganisms, using Total Internal Reflection Fluorescence Microscopy
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Cited by 12 •
2012
Total Internal Reflection Fluorescence (TIRF) microscopy is a powerful approach to observe structures close to the cell surface at high contrast and temporal resolution. We demonstrate how TIRF can be employed to study protein dynamics at the cortex of cell wall-enclosed bacterial and fungal cells.
11:26
Visualizing Single Molecular Complexes In Vivo Using Advanced Fluorescence Microscopy
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Cited by 7 •
2009
Here we demonstrate the protocols for performing single-molecule fluorescence microscopy on living bacterial cells to enable functional molecular complexes to be detected, tracked and quantified.
10:29
Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Cited by 3 •
2025
This study used in-silico strategies to identify Enumerated Etravirine as a promising therapeutic agent for HIV. Our findings on molecular interactions and dynamics support the rational design of novel NNRTIs as possible HIV treatment alternatives.
10:59
Visualizing the Conformational Dynamics of Membrane Receptors Using Single-Molecule FRET
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Cited by 2 •
2022
This study presents a detailed procedure to perform single-molecule fluorescence resonance energy transfer (smFRET) experiments on G protein-coupled receptors (GPCRs) using site-specific labeling via unnatural amino acid (UAA) incorporation. The protocol provides a step-by-step guide for smFRET sample preparation, experiments, and data analysis.
FAQs
Visual Molecular Dynamics maps atom coordinates from structural data into an interactive three-dimensional display while representing relationships such as bonds. This coordinate-based view lets users inspect spatial arrangement rather than relying only on textual or numerical records. Because the graphics can be examined interactively, researchers can connect molecular geometry with the visible organization of a protein, nucleic acid, membrane, or complex.
A trajectory adds a time dimension to the coordinate display, allowing Visual Molecular Dynamics users to compare molecular arrangements across successive points in a simulation. Examining those changes can reveal conformational shifts and help researchers evaluate whether the simulated system behaves consistently over time. This temporal perspective is especially useful when a static structure cannot show how biomolecular shape changes.
Geometry and distance analyses translate visual inspection into more explicit structural observations. Researchers can examine molecular geometry, measure distances between relevant atoms or regions, and relate those results to observed dynamics. In biochemistry, this combination helps connect a changing three-dimensional arrangement with questions about protein structure, nucleic-acid organization, membranes, or protein-ligand interactions.
A practical workflow begins with structural data, followed by interactive inspection of atom and bond representations. Researchers can then examine coordinates, apply analyses of geometry, distances, or dynamics, and compare what they observe with the behavior of the molecular system. This progression moves from structural visualization to targeted interpretation without separating the graphical view from the measurements.
It is particularly useful for studying proteins, nucleic acids, membranes, and protein-ligand interactions, where spatial relationships and movement influence interpretation. Researchers can investigate conformational changes and simulation behavior through structural observations. The software is valuable when biochemical conclusions depend on molecular features that are difficult to infer from a single static structure, linking computational results with molecular mechanisms.
The results may include recognition of conformational changes, evaluation of simulation behavior, and measurements of molecular geometry or distances. Researchers can also produce publication-quality images that present these observations clearly. Together, these outcomes support interpretation of computational experiments and help communicate molecular mechanisms that are difficult to convey through static structural information alone.