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EigenTHREADER: analogous protein fold recognition by efficient contact map threading.

Daniel W A Buchan1, David T Jones1

  • 1Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK.

Bioinformatics (Oxford, England)
|April 19, 2017
PubMed
Summary
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EigenTHREADER is a new protein fold recognition method that identifies protein structures without relying on sequence homology. It outperforms existing methods in recognizing analogous folds, expanding modeling capabilities for new protein families.

Area of Science:

  • Structural bioinformatics
  • Computational biology
  • Protein structure prediction

Background:

  • Protein fold recognition is challenging when homologous structural templates are undetectable.
  • Existing methods struggle with identifying analogous folds lacking sequence homology.

Purpose of the Study:

  • Introduce EigenTHREADER, a novel method for protein fold recognition.
  • Enable identification of analogous folds even with minimal sequence homology.

Main Methods:

  • Generate intra-residue contact maps from query amino acid sequences.
  • Utilize eigenvector decomposition to compare contact maps.
  • Employ dynamic programming for aligning eigenvectors and searching a library of known structures.

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Main Results:

  • EigenTHREADER demonstrates superior performance in analogous fold recognition compared to pGenTHREADER and HHSearch.
  • Achieves a higher True Positive Rate in identifying analogous folds.

Conclusions:

  • EigenTHREADER expands the scope of template-based protein modeling to previously intractable protein families.
  • The method offers improved speed and accuracy over similar approaches like Al-Eigen.