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Predicting the beta-helix fold from protein sequence data.

Lenore Cowen1, Phil Bradley, Matthew Menke

  • 1Department of EECS, Tufts University, Medford, MA 02155, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|May 23, 2002
PubMed
Summary
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A new computational method predicts protein beta-helix structures using beta-strand interactions. This tool, BetaWrap, identifies these motifs in bacterial proteins, including those involved in pathogenesis.

Area of Science:

  • Structural bioinformatics
  • Computational biology
  • Protein structure prediction

Background:

  • The parallel right-handed beta-helix is a common protein super-secondary structure.
  • Predicting protein structural motifs is crucial for understanding protein function and evolution.

Purpose of the Study:

  • To develop and validate a computational method for predicting beta-helix structures in protein sequences.
  • To identify novel beta-helix proteins, particularly in bacterial proteomes.

Main Methods:

  • A method utilizing beta-strand interactions to predict beta-helix motifs.
  • Implementation of the BetaWrap program for sequence analysis.
  • Cross-validation against the Protein Data Bank (PDB) and analysis of SCOP families.

Related Experiment Videos

Main Results:

  • BetaWrap successfully distinguishes known beta-helices from non-beta-helices in the PDB.
  • The program learns distinct beta-helix families and generalizes to new structural features.
  • BetaWrap predicts numerous bacterial proteins, including adhesins and toxins from Chlamydia and Helicobacter pylori, to possess beta-helix structures.

Conclusions:

  • The BetaWrap method effectively predicts parallel right-handed beta-helices based on sequence data.
  • This approach has implications for understanding bacterial pathogenesis by identifying potential virulence factors.
  • The computational strategy may be adaptable to predicting other conserved beta-structures.