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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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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
09:51

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

Published on: July 16, 2017

Superimposition of protein structures with dynamically weighted RMSD.

Di Wu1, Zhijun Wu

  • 1Department of Mathematics and Computer Science, Bioinformatics and Information Sciences Center, Western Kentucky University, Bowling Green, KY 42101, USA. di.wu@wku.edu

Journal of Molecular Modeling
|July 2, 2009
PubMed
Summary

This study introduces dynamically weighted RMSD (dRMSD) for protein structure superposition, assigning weights based on atomic thermal motion. dRMSD effectively identifies protein domains and motions, improving structural alignment.

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Area of Science:

  • Structural Biology
  • Computational Biology
  • Biophysics

Background:

  • Protein structure superposition is crucial for analysis, commonly using root-mean-square deviation (RMSD).
  • Standard RMSD treats all atoms equally, neglecting their differing properties and motions.
  • Weighted RMSD (wRMSD) addresses this by applying differential weights to atomic coordinate differences.

Purpose of the Study:

  • To introduce and investigate dynamically weighted RMSD (dRMSD) for protein structure superposition.
  • To utilize atomic thermal motions for assigning weights in RMSD calculations.
  • To demonstrate the utility of dRMSD in identifying protein domains, motions, and improving structural alignments.

Main Methods:

  • Developed dRMSD by incorporating atomic thermal motion-derived weights into RMSD calculations.
  • Estimated atomic thermal motions using methods like Gaussian network model analysis to obtain mean-square fluctuations.
  • Applied dRMSD to superimpose groups of protein structures.

Main Results:

  • dRMSD successfully identified distinct protein domains.
  • dRMSD effectively captured characteristic protein motions.
  • The method showed practical implications, particularly in aligning nuclear magnetic resonance (NMR) ensembles.

Conclusions:

  • Dynamically weighted RMSD (dRMSD) offers a more nuanced approach to protein structure superposition than standard RMSD.
  • Incorporating atomic thermal motion provides biologically relevant weighting for structural comparisons.
  • dRMSD has significant potential for advancing protein modeling and structural analysis, especially for dynamic structures like NMR ensembles.