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Molecular Models02:00

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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
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Related Experiment Video

Updated: Mar 30, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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iGNM 2.0: the Gaussian network model database for biomolecular structural dynamics.

Hongchun Li1, Yuan-Yu Chang2, Lee-Wei Yang3

  • 1Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, PA 15213, USA.

Nucleic Acids Research
|November 20, 2015
PubMed
Summary
This summary is machine-generated.

The updated iGNM 2.0 database provides comprehensive protein structural dynamics analysis for over 95% of Protein Data Bank structures. This resource aids in understanding protein function by linking structure and dynamics.

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Area of Science:

  • Biophysics
  • Structural Biology
  • Computational Biology

Background:

  • Gaussian network model (GNM) is a key method for analyzing protein dynamics.
  • The iGNM database (launched in 2005) facilitated GNM analysis for biomolecular structures.
  • Previous versions had limitations in scope and accessibility.

Purpose of the Study:

  • To present iGNM 2.0, an updated and expanded resource for GNM analysis.
  • To enhance the accessibility and utility of protein conformational dynamics data.
  • To bridge the understanding of protein structure, dynamics, and function.

Main Methods:

  • Developed iGNM 2.0, covering over 95% of Protein Data Bank (PDB) structures.
  • Implemented advanced 2D and 3D search and visualization tools.
  • Integrated data on inter-residue/domain correlations, motion modes, hinge sites, and energy localization.

Main Results:

  • iGNM 2.0 provides extensive structural dynamics data for a vast majority of PDB entries.
  • Advanced features allow detailed investigation of protein conformational dynamics.
  • The database effectively analyzes biological assemblies.

Conclusions:

  • iGNM 2.0 is a valuable resource for researchers studying protein dynamics.
  • The platform facilitates the connection between protein structure, dynamics, and biological function.
  • Enhanced data accessibility and analysis capabilities support broader scientific inquiry.