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Related Experiment Videos

Protein structure alignment using environmental profiles.

J Jung1, B Lee

  • 1Laboratory of Molecular Biology, Division of Basic Sciences, National Cancer Institute, National Institutes of Health, 37 Convent Drive, MSC 4255, Bethesda, MD 20892-4255, USA.

Protein Engineering
|August 31, 2000
PubMed
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A novel protein structure alignment method combines primary, secondary, and tertiary features with 3D geometry for faster, more comprehensive comparisons. This approach aids in identifying evolutionary relationships across thousands of protein structures.

Area of Science:

  • Structural bioinformatics
  • Computational biology
  • Protein science

Background:

  • Protein structure alignment is crucial for understanding function and evolution.
  • Existing methods often rely solely on 3D geometry, potentially missing evolutionary links.
  • A need exists for efficient and comprehensive protein structure comparison techniques.

Purpose of the Study:

  • To introduce a new protein structure alignment procedure.
  • To enable routine all-against-all structural comparisons of known protein structures.
  • To identify evolutionary relationships not apparent from geometry-based methods alone.

Main Methods:

  • An initial alignment is generated by comparing 1D profiles of primary, secondary, and tertiary structural features.

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  • The alignment is iteratively refined using 3D superposition based on the current alignment.
  • All pair-wise comparisons were performed on 3539 protein structural domains.

    • Main Results:

    • The procedure is efficient enough for routine all-against-all comparisons.
    • It successfully identified potential evolutionary relationships missed by geometry-only methods.
    • Clustering 3539 protein domains revealed patterns in protein evolution, with a maximum of multiple-member clusters at z-cutoff values of 5.0 and 5.5.

    Conclusions:

    • The new alignment procedure offers a powerful tool for protein structure analysis.
    • Integrating feature profiles with 3D geometry enhances the detection of evolutionary relationships.
    • The method facilitates large-scale structural comparisons and protein clustering for evolutionary insights.