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Related Experiment Videos

Statistical potentials for fold assessment.

Francisco Melo1, Roberto Sánchez, Andrej Sali

  • 1Laboratories of Molecular Biophysics, Pels Family Center for Biochemistry and Structural Biology, The Rockefeller University, New York, New York 10021, USA.

Protein Science : a Publication of the Protein Society
|January 16, 2002
PubMed
Summary
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Optimizing statistical potentials improves protein fold assessment. The best method combines distance-dependent and accessible surface potentials for accurate model evaluation, especially for larger protein structures.

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein modeling

Background:

  • Protein structure models require evaluation for fold accuracy.
  • Automated comparative modeling generates numerous protein models.

Purpose of the Study:

  • To optimize residue-level statistical potentials for improved protein fold assessment.
  • To identify the most effective combination of potentials for discriminating correct from incorrect protein models.

Main Methods:

  • Optimized four types of residue-level statistical potentials: distance-dependent, contact, Phi/Psi dihedral angle, and accessible surface.
  • Built approximately 10,000 test models using automated comparative modeling.
  • Assessed model performance using the Z-score of model energy.

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Main Results:

  • The combination of normalized distance-dependent and accessible surface potentials showed the highest discrimination.
  • Optimal distance-dependent potential uses C(alpha) and C(beta) atoms, considers all residue types, has a 30 Å range, and accounts for sequence separation.
  • Optimal accessible surface potential uses C(beta) atoms and considers all residue types.

Conclusions:

  • Statistical potentials offer a robust method for protein fold assessment.
  • The performance of statistical potentials is unlikely to significantly improve with more data.
  • Fold assessment is most challenging for small protein models, posing a challenge for large-scale modeling.