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Protein Dynamics in Living Cells01:19

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Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
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JED: a Java Essential Dynamics Program for comparative analysis of protein trajectories.

Charles C David1,2, Ettayapuram Ramaprasad Azhagiya Singam3, Donald J Jacobs4,5

  • 1Department of Bioinformatics and Genomics, University of North Carolina, Charlotte, USA. charles.david@plantandfood.co.nz.

BMC Bioinformatics
|May 27, 2017
PubMed
Summary
This summary is machine-generated.

We developed JED, a Java toolkit for comparing protein dynamics using Essential Dynamics (ED) and principal component analysis (PCA). JED facilitates comparative analysis of multiple protein trajectories, aiding in understanding conserved protein mechanisms.

Keywords:
Distance pairsEssential dynamicsPartial correlationsPrincipal anglesPrincipal component analysisVector space comparison

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Area of Science:

  • Biophysics
  • Computational Biology
  • Structural Biology

Background:

  • Essential Dynamics (ED) applies principal component analysis (PCA) to protein trajectories for biologically relevant motion extraction.
  • Covariance and correlation-based PCA are standard methods for determining protein dynamics modes.
  • Comparative analysis of protein dynamics aids in identifying conserved mechanisms.

Purpose of the Study:

  • To develop a comprehensive software toolkit for comparative analysis of Essential Dynamics across multiple protein trajectories.
  • To facilitate user-friendly comparative studies rooted in multivariate statistics best practices.

Main Methods:

  • Developed JED, a Java-based toolkit for comparing Essential Dynamics from multiple protein trajectories.
  • Implemented Cartesian-based (cPCA) and internal distance pair (dpPCA) coordinates for covariance (Q) or correlation (R) matrix construction.
  • Integrated statistical methods for outlier treatment, sampling adequacy, matrix precision, and partial correlations.

Main Results:

  • JED enables pooling of trajectories from different simulations and proteins for comparative studies.
  • Outputs include transformed coordinates, mobility metrics, PCA modes/eigenvalues, and displacement vector projections.
  • Visualization tools (Pymol scripts) allow movie generation of PCA modes and essential dynamics over selected time scales.
  • Statistical metrics quantify the similarity/overlap of high-dimensional vector spaces defined by top eigenvectors.
  • Free energy landscapes can be generated for both cPCA and dpPCA.

Conclusions:

  • JED provides a convenient toolkit for comparative Essential Dynamics studies using multivariate statistics.
  • Allows quantification of mobility and dynamic correlations using Cartesian or internal distance pair coordinates.
  • Facilitates insights into protein structure/function relationships by comparing dynamics across proteins.