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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Dynameomics: data-driven methods and models for utilizing large-scale protein structure repositories for improving

Steven J Rysavy1, David A C Beck, Valerie Daggett

  • 1Division of Biomedical and Health Informatics, University of Washington, Seattle, Washington.

Protein Science : a Publication of the Protein Society
|August 22, 2014
PubMed
Summary

Atomistic molecular dynamics simulations from the Dynameomics database enhance protein structure prediction. These simulations provide diverse peptide structures, improving loop modeling accuracy without sequence homology dependence.

Keywords:
backbone dynamicsdynamic fragmentsloop ensembleloop predictionmodel buildingstructure prediction

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Area of Science:

  • Structural Biology
  • Computational Biology
  • Biophysics

Background:

  • Protein structure determination often yields incomplete models due to dynamic regions.
  • Understanding protein dynamics is crucial for accurate structure prediction and functional analysis.

Purpose of the Study:

  • To leverage atomistic molecular dynamics simulations for improved protein structural modeling.
  • To develop methods for efficiently retrieving and utilizing dynamic protein fragments for structure prediction.

Main Methods:

  • Utilized the Dynameomics data warehouse containing extensive protein fold simulations.
  • Developed novel computational methods for identifying, ranking, and retrieving peptide fragments.
  • Created a new data model for analyzing large structural data repositories like the Protein Data Bank and Dynameomics.
  • Evaluated methods using a standard set of 510 loop structures of varying lengths.

Main Results:

  • Inclusion of Dynameomics structures significantly improved loop prediction quality.
  • Improvements were observed without reliance on sequence homology.
  • Dynameomics fragments contributed 25-75% of the best predictions, reducing root-mean-square deviations.
  • Demonstrated superior performance of Dynameomics fragments over crystal structure fragments for NMR loop structures.

Conclusions:

  • Atomistic molecular dynamics simulations offer a valuable resource for enhancing protein structural modeling.
  • Novel computational tools and data models facilitate the use of dynamic structural data.
  • The Dynameomics fragment library improves the accuracy of protein loop predictions, particularly for NMR structures.