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Homology modelling by distance geometry

A Aszódi1, W R Taylor

  • 1Division of Mathematical Biology, National Institute for Medical Research, London, UK. a-aszodi@nimr.mrc.ac.uk

Folding & Design
|January 1, 1996
PubMed
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This study introduces a novel homology modeling algorithm using distance geometry to predict unknown protein structures from known scaffolds. The method efficiently identifies correct protein folds, serving as a fast tool for generating accurate starting conformations.

Area of Science:

  • Structural bioinformatics
  • Computational biology
  • Protein structure prediction

Background:

  • Protein structure prediction relies on homology modeling, where similar sequences adopt similar folds.
  • Distance geometry offers a framework for satisfying structural restraints and exploring conformation space.

Purpose of the Study:

  • To develop and present a homology modeling algorithm based on distance geometry.
  • To predict unknown protein structures using known protein scaffolds.

Main Methods:

  • Utilizes distance geometry for homology modeling.
  • Employs gradual projection of model chain coordinates into decreasing dimensional Euclidean spaces.
  • Maps secondary structure assignments and C-alpha atom distance restraints from scaffold proteins via multiple alignment.

Related Experiment Videos

  • Incorporates stereochemical restraints and general protein structure principles (hydrophobic core, mainchain untangling).

    • Main Results:

    • Presents a homology modeling algorithm grounded in distance geometry.
    • The algorithm successfully predicts protein folds even with modest sequence conservation.
    • Demonstrates efficient exploration of conformation space through coordinate projection.

    Conclusions:

    • The developed method rapidly identifies correct protein folds.
    • It serves as a fast tool for obtaining low-resolution starting conformations for further refinement.
    • Highlights the utility of distance geometry in protein structure prediction.