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A local alignment method for protein structure motifs

C A Orengo1, W R Taylor

  • 1Laboratory of Mathematical Biology, National Institute for Medical Research, Mill Hill, London, U.K.

Journal of Molecular Biology
|October 5, 1993
PubMed
Summary
This summary is machine-generated.

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A new computational method identifies local structural similarities in proteins, revealing novel globin folds in unrelated proteins like colicin A and diphtheria toxin.

Area of Science:

  • Structural bioinformatics
  • Computational biology
  • Protein structure analysis

Background:

  • Comparing protein three-dimensional (3D) substructures is crucial for understanding protein function and evolution.
  • Existing methods often focus on global alignments, potentially missing localized similarities.

Purpose of the Study:

  • To develop a novel computational method for comparing protein 3D substructures.
  • To identify multiple local structural alignments, irrespective of length and composition.
  • To enable efficient searching for specific structural motifs within protein databases.

Main Methods:

  • The method utilizes a modified double dynamic programming approach, inspired by Taylor & Orengo.
  • Incorporation of the Smith-Waterman algorithm facilitates automatic identification and growth of local alignments.

Related Experiment Videos

  • The algorithm is optimized for speed, identifying Greek-key motifs in a 100-residue protein within approximately five minutes.

    • Main Results:

    • The developed method successfully identifies multiple, structurally similar local alignments.
    • It can be employed in a search mode to match specific substructures within proteins.
    • Two novel globin folds were discovered in the antibiotic protein colicin A and diphtheria toxin.

    Conclusions:

    • The new method offers a powerful tool for detecting local structural similarities in proteins.
    • It can uncover unexpected evolutionary relationships by identifying shared folds in proteins with no apparent functional connection.
    • This approach advances the field of structural bioinformatics and protein motif discovery.