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Related Experiment Videos

Predicting transmembrane helix pair configurations with knowledge-based distance-dependent pair potentials.

Christina Wendel1, Holger Gohlke

  • 1Department of Biological Sciences, Molecular Bioinformatics Group, J. W. Goethe-University, Frankfurt, Germany.

Proteins
|September 12, 2007
PubMed
Summary
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We developed knowledge-based potentials to predict transmembrane (TM) helix configurations in membrane proteins. This method accurately predicts helix arrangements, aiding novel de novo structure prediction for alpha-helical membrane proteins.

Area of Science:

  • Structural Biology
  • Computational Biology
  • Biophysics

Background:

  • Predicting the structure of alpha-helical membrane proteins is crucial for understanding their function.
  • De novo structure prediction methods are needed for membrane proteins, which are challenging targets.
  • Coarse-grained potentials offer a computationally efficient approach for structure prediction.

Purpose of the Study:

  • To develop and evaluate coarse-grained knowledge-based potentials for scoring transmembrane (TM) helix configurations.
  • To establish a foundation for a novel de novo structure prediction approach for alpha-helical membrane proteins.
  • To assess the accuracy and generalizability of the developed potentials.

Main Methods:

  • Derived pairwise potentials based on amino acid types and C(alpha)-atom distances from 71 known membrane protein structures.

Related Experiment Videos

  • Utilized potentials as an objective function for rigid docking of 442 TM helix pairs, the largest dataset to date.
  • Employed cluster analysis of docking runs, using cluster size as a significance measure for solutions.

    • Main Results:

    • Achieved root mean squared (RMS) deviation <2 Å for ~30% of TM helix pairs using the largest cluster.
    • Obtained a success rate >71% (RMSD <2 Å) when considering clusters comprising at least 20% of decoys.
    • Demonstrated predictive power in cross-validation, with >2/3 of pairs predicted within 2.5 Å RMSD (using the 20% decoy threshold).

    Conclusions:

    • The developed coarse-grained potentials effectively score TM helix configurations, serving as a significant step towards de novo structure prediction.
    • Cluster size is a reliable indicator of docking solution significance.
    • The approach shows promise for identifying 'anchor helix pairs' to facilitate the prediction of larger TM helix bundles.