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Related Experiment Videos

Protein refolding in silico with atom-based statistical potentials and conformational search using a simple genetic

Qiaojun Fang1, David Shortle

  • 1Department of Biological Chemistry, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.

Journal of Molecular Biology
|May 9, 2006
PubMed
Summary

A new protein structure scoring function effectively distinguishes native protein conformations from decoys. This method aids in accurately predicting protein folding and structure, crucial for understanding biological function.

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Biophysics

Background:

  • Accurate prediction of protein three-dimensional structures is fundamental to understanding biological function.
  • Distinguishing native protein structures from a vast number of possible incorrect conformations (decoys) remains a significant challenge in structural bioinformatics.

Purpose of the Study:

  • To develop and optimize a novel distance-dependent atom-pair potential for enhanced discrimination between native and decoy protein structures.
  • To integrate this potential into a scoring function for protein structure prediction and conformational search.

Main Methods:

  • Development of a statistical, distance-dependent atom-pair potential considering 30 atom types.
  • Optimization of potential parameters including reference state, bin width, cutoff distances, and residue separation.

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  • Integration with existing potentials (ROSETTA hydrogen bonding, Lazaridis & Karplus solvation) for a comprehensive scoring function.
  • Application of a genetic algorithm for conformational search and protein refolding simulations.

    • Main Results:

    • The developed potential successfully identified native structures as having the lowest energy in 39/40 original and 23/25 improved decoy sets.
    • The scoring function, combined with other potentials, enabled efficient refolding of five small proteins, achieving high accuracy (e.g., within 0.5 Å Cα distance matrix error).
    • A strong correlation was observed between the radius of gyration and the distance matrix error for low-energy structures, despite no clear correlation with total energy.

    Conclusions:

    • The novel atom-pair potential significantly improves the ability to discriminate native protein structures from decoys.
    • The integrated scoring function demonstrates efficacy in protein structure prediction and conformational refinement.
    • Radius of gyration serves as a useful indicator of structural accuracy for low-energy protein models.