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Related Concept Videos

Protein Diffusion in the Membrane01:24

Protein Diffusion in the Membrane

Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
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Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Diffusion

Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

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...

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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Configurational subdiffusion of peptides: a network study.

Thomas Neusius1, Isabella Daidone, Igor M Sokolov

  • 1Computational Molecular Biophysics, Universität Heidelberg, Im Neuenheimer Feld 368, D-69120 Heidelberg, Germany.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|March 17, 2011
PubMed
Summary
This summary is machine-generated.

Molecular dynamics simulations reveal subdiffusion in linear peptides over 10⁻¹² to 10⁻⁸ seconds. Network analysis links this to fractal configuration space geometry, offering insights into peptide dynamics.

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Area of Science:

  • Computational chemistry and biophysics.
  • Statistical mechanics of polymers.

Background:

  • Molecular dynamics (MD) simulations are crucial for understanding peptide dynamics.
  • Subdiffusion is a key phenomenon observed in complex systems, including biomolecules.
  • Existing models like Rouse chain and continuous-time random walk have limitations in describing peptide subdiffusion.

Purpose of the Study:

  • To investigate the nature of configurational subdiffusion in linear peptides using MD simulations.
  • To critically evaluate existing theoretical models for subdiffusion.
  • To explore novel network approaches for analyzing MD data and understanding the origins of subdiffusion.

Main Methods:

  • Performing molecular dynamics simulations of linear peptides.
  • Applying Rouse chain and continuous-time random walk models.
  • Utilizing network analysis techniques to analyze simulation trajectories.
  • Comparing explicit and implicit solvent models.

Main Results:

  • Configurational subdiffusion observed in linear peptides from 10⁻¹² to 10⁻⁸ seconds.
  • Network approaches successfully reproduce the time dependence of the subdiffusive mean squared displacement.
  • Subdiffusion is attributed to the fractal-like geometry of the accessible configuration space.
  • Simulation convergence properties and solvent representation effects were characterized.

Conclusions:

  • Network analysis provides a powerful tool for understanding subdiffusion in peptide systems.
  • The fractal nature of configuration space is a key determinant of subdiffusion.
  • Further examination of non-Markovianity is needed to define the limits of network model applicability.