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Related Experiment Videos

Structure from function: screening structural models with functional data

L Jin1, F E Cohen, J A Wells

  • 1Department of Protein Engineering, Genentech, Inc., South San Francisco, CA 94080.

Proceedings of the National Academy of Sciences of the United States of America
|January 4, 1994
PubMed
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Antibody epitope mapping data effectively screens computer-generated protein models. This method, using structural constraints, helps create accurate, low-resolution models of proteins like human growth hormone (hGH).

Area of Science:

  • * Computational biology
  • * Structural biology
  • * Immunology

Background:

  • * Antibody epitopes provide structural constraints for protein modeling.
  • * Alanine-scanning mutagenesis identified residues critical for monoclonal antibody binding to human growth hormone (hGH).
  • * Functional epitopes comprise 4-14 side chains with clustered alpha-carbons.

Purpose of the Study:

  • * To screen three-dimensional models of hGH using structural constraints from antibody epitopes.
  • * To evaluate the effectiveness of epitope mapping data in refining protein structural models.
  • * To assess the accuracy of computer-generated models compared to experimental data.

Main Methods:

  • * Generating multiple hGH structural models using computer algorithms.

Related Experiment Videos

  • * Applying distance and topographic constraints derived from 21 antibody epitopes.
  • * Evaluating approximately 1400 computer-generated hGH models.
  • * Comparing model accuracy using root-mean-square deviation (RMSD) of alpha-carbon coordinates against the known x-ray structure.

    • Main Results:

    • * Each epitope constraint reduced the pool of potential models by half.
    • * Eight antibody constraints narrowed down possible models to fewer than 10.
    • * Final models showed an average RMSD of 4-7 A, significantly improving upon initial models (13-16 A).
    • * The best model achieved an RMSD of 3.8 A compared to the x-ray structure.

    Conclusions:

    • * Epitope mapping data is a powerful tool for screening and refining protein structural models.
    • * Integrating epitope constraints with predictive algorithms can generate accurate, low-resolution protein models.
    • * This approach offers a viable method for structural modeling when experimental data is limited.