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Related Experiment Videos

Modeling three-dimensional protein structures for CASP5 using the 3D-SHOTGUN meta-predictors.

Iris Sasson1, Daniel Fischer

  • 1Bioinformatics, Department of Computer Science, Ben Gurion University, Beer-Sheva, Israel.

Proteins
|October 28, 2003
PubMed
Summary

This study shows that refining 3D-SHOTGUN models improves accuracy. Human intervention further enhanced protein model quality, especially for domain identification.

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Modeling

Background:

  • Automated protein structure prediction servers, like 3D-SHOTGUN, generate models for the Critical Assessment of protein Structure Prediction (CASP) experiments.
  • These servers often produce C(alpha)-only models that require refinement to achieve full-atom accuracy.

Purpose of the Study:

  • To evaluate the utility of 3D-SHOTGUN models as input for a refinement procedure.
  • To determine if human intervention can outperform automated servers in protein model generation.
  • To identify human-guided procedures for future automation within 3D-SHOTGUN.

Main Methods:

  • Generation of full-atom models using the 3D-SHOTGUN meta-predictor.
  • Application of a refinement procedure using the Modeller program with 3D-SHOTGUN models and sequence-template alignments.

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  • Manual analysis of models, incorporating data from CAFASP servers for select targets.

    • Main Results:

    • Refined models demonstrated consistently higher accuracy than the original 3D-SHOTGUN models.
    • Human intervention, particularly using CAFASP data, yielded improved models for specific targets.
    • Manual refinement proved especially beneficial for accurate domain identification, a challenge for automated methods.

    Conclusions:

    • 3D-SHOTGUN hybrid models serve as a valuable starting point for full-atom refinement.
    • Refined models generated through this approach are, on average, more accurate than server-generated models.
    • The study successfully achieved its goals, leading to the development of a preliminary automated refinement tool, SHGUM.