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Related Experiment Videos

Assembly of protein structure from sparse experimental data: an efficient Monte Carlo model

A Kolinski1, J Skolnick

  • 1Department of Molecular Biology, The Scripps Research Institute, La Jolla, California 92037, USA.

Proteins
|September 3, 1998
PubMed
Summary

A novel, efficient protein structure assembly method uses reduced modeling for faster computation. This approach requires fewer restraints and achieves accurate tertiary structure prediction, aiding in protein modeling.

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Folding

Background:

  • Assembling protein tertiary structure from restraints is computationally challenging.
  • Existing methods often require numerous restraints and significant computational resources.
  • Accurate protein structure prediction is crucial for understanding biological function.

Purpose of the Study:

  • To develop and evaluate a new, efficient method for protein tertiary structure assembly.
  • To reduce the number of required experimental restraints for successful fold assembly.
  • To improve the speed and robustness of protein structure prediction algorithms.

Main Methods:

  • A simplified protein model representing the chain by side chain centers of mass on a lattice.

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  • Incorporation of implicit multibody correlations for packing, hydrogen bonding, and hydrophobic interactions.
  • A fast Monte Carlo algorithm for structure assembly, significantly outperforming previous methods.

    • Main Results:

    • The new method is at least an order of magnitude faster than existing Monte Carlo algorithms.
    • It requires fewer tertiary restraints (1 per 7 residues) compared to traditional methods (1 per 4 residues).
    • Achieved coordinate root mean square deviation (cRMSD) values of ~3 Å for small proteins, ~4.3 Å for myoglobin, and ~6 Å for a large TIM barrel.

    Conclusions:

    • The proposed method offers an efficient and reliable approach for protein tertiary structure assembly.
    • Its reduced computational cost and lower restraint requirement enable routine application in model building.
    • The method is robust for various experimentally derived sparse structural restraints.