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Related Experiment Videos

Progress and challenges in high-resolution refinement of protein structure models.

Kira M S Misura1, David Baker

  • 1Department of Biochemistry, University of Washington Health Sciences, Seattle, Washington 98195-7350, USA.

Proteins
|February 4, 2005
PubMed
Summary

Accurate de novo protein structure prediction remains challenging. Refinement protocols can identify correct models, but sampling local backbone and side-chain conformations is a key obstacle.

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Biophysics

Background:

  • Accurate de novo protein structure prediction is crucial but challenging, even with correct topologies.
  • High-resolution refinement is a key test for force field accuracy and sampling methods.
  • Current refinement approaches have limitations in both comparative modeling and de novo prediction.

Purpose of the Study:

  • To identify bottlenecks in current de novo protein structure prediction approaches.
  • To guide progress in achieving atomic-level accuracy in structure prediction.
  • To test the efficacy of refinement protocols on de novo models.

Main Methods:

  • Developed four tests to evaluate protein structure refinement protocols.
  • Simulated idealized native structures to assess stability under refinement conditions.

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  • Applied refinement protocols to de novo predicted models to identify accurate structures.

    • Main Results:

    • Idealized native structures were stable; refinement reduced RMSD for perturbed native structures.
    • Accurate de novo models were identifiable by energy, with native-like buried side chains observed.
    • Conformational differences between refined de novo and native models were localized to loop regions and unusual native structural features.

    Conclusions:

    • Refinement protocols can identify accurate de novo protein models.
    • Local backbone sampling and side-chain packing in condensed states are primary obstacles.
    • Further development is needed to overcome limitations in de novo structure prediction refinement.