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Structure prediction using sparse simulated NOE restraints with Rosetta in CASP11.

Sergey Ovchinnikov1,2, Hahnbeom Park1,2, David E Kim2,3

  • 1Department of Biochemistry, University of Washington, Seattle, Washington, 98195.

Proteins
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PubMed
Summary

Protein structure modeling improved significantly using simulated nuclear Overhauser effect (NOE) restraints in a two-stage protocol. This method achieved atomic accuracy with sparse NOE data, outperforming previous predictions.

Keywords:
CASP11NMRRosettacontact assistedprotein structure prediction

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Area of Science:

  • Computational biology
  • Structural biology
  • Biophysics

Background:

  • Protein structure determination is crucial for understanding biological function.
  • Nuclear Overhauser effect (NOE) restraints are key experimental data for structure modeling.
  • Ambiguous NOE restraints present a challenge in traditional structure prediction.

Purpose of the Study:

  • To develop and evaluate a two-stage protocol for protein structure modeling using simulated ambiguous and unambiguous NOE restraints.
  • To assess the impact of NOE data quality and quantity on model accuracy.
  • To improve protein structure prediction accuracy in the CASP11 competition.

Main Methods:

  • Generated low-resolution models using continuous chain folding (alpha/alpha-beta proteins) and iterative annealing (all-beta proteins).
  • Employed the Rosetta fragment/model hybridization protocol for recombination and regularization.
  • Refined models using the Rosetta full atom energy function with both ambiguous and unambiguous NOE restraints.

Main Results:

  • Successfully modeled 15 out of 19 targets with GDT-TS quality scores > 60 for the top model.
  • Demonstrated significant improvement over non-assisted prediction methods.
  • Achieved high accuracy with sparse NOE data, requiring at least one assigned NOE per residue.

Conclusions:

  • The developed two-stage protocol effectively utilizes both ambiguous and unambiguous NOE restraints for accurate protein structure modeling.
  • Sparse NOE data, when appropriately assigned, can lead to atomic-level accuracy in protein structure prediction.
  • This approach offers a robust strategy for structure modeling, particularly when experimental data is limited.