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Comparative modelling by restraint-based conformational sampling.

Nicholas Furnham1, Paul Iw de Bakker, Swanand Gore

  • 1Department of Biochemistry, Sanger Building, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK. nick@cryst.bioc.cam.ac.uk

BMC Structural Biology
|February 2, 2008
PubMed
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This study introduces RAPPER, a new tool for protein comparative modeling that uses experimental data and homologous structures to generate accurate models. It represents protein structures as ensembles, improving accuracy and reflecting experimental data better.

Area of Science:

  • Structural bioinformatics
  • Computational biology
  • Protein modeling

Background:

  • Comparative modeling is standard for protein structure prediction.
  • Existing automated methods struggle to integrate experimental restraints and homologous structures.
  • Proteins are often modeled as single conformers, ignoring structural heterogeneity.

Purpose of the Study:

  • To explore the application of the restraint-based conformational search engine, RAPPER, for comparative protein modeling.
  • To incorporate experimental data and homologous structure information into automated protein modeling.
  • To represent protein structures as ensembles rather than single models.

Main Methods:

  • Utilized RAPPER, a restraint-based conformational space search engine.

Related Experiment Videos

  • Employed positional restraints derived from experimental data and homologous templates.
  • Incorporated knowledge-based sampling based on geometrical features and conformational rules.

    • Main Results:

    • Achieved comparative modeling accuracy comparable to leading tools.
    • Demonstrated the ability to estimate accuracy limits by varying template-derived restraints.
    • Generated models using geometrical features of homologous templates and conformational rules.

    Conclusions:

    • RAPPER effectively explores conformational space for target sequences in comparative modeling.
    • Enhanced positional restraints significantly improve protein structure prediction accuracy.
    • Ensemble generation offers a more accurate representation of structural predictions than single models.