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Confidence-Guided Local Structure Prediction with HHfrag

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  • Ivan Kalev
  • Michael Habeck

Abstract

We present a method to assess the reliability of local structure prediction from sequence. We introduce a greedy algorithm for filtering and enrichment of dynamic fragment libraries, compiled with remote-homology detection methods such as HHfrag. After filtering false hits at each target position, we reduce the fragment library to a minimal set of representative fragments, which are guaranteed to have correct local structure in regions of detectable conservation. We demonstrate that the location of conserved motifs in a protein sequence can be predicted by examining the recurrence and structural homogeneity of detected fragments. The resulting confidence score correlates with the local RMSD of the representative fragments and allows us to predict torsion angles from sequence with better accuracy compared to existing machine learning methods.

Suggested Citation

  • Ivan Kalev & Michael Habeck, 2013. "Confidence-Guided Local Structure Prediction with HHfrag," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-7, October.
    • Handle: RePEc:plo:pone00:0076512
    DOI: 10.1371/journal.pone.0076512
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    References listed on IDEAS

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    1. Jiangning Song & Hao Tan & Mingjun Wang & Geoffrey I Webb & Tatsuya Akutsu, 2012. "TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-16, February.
    2. Sitao Wu & Yang Zhang, 2008. "ANGLOR: A Composite Machine-Learning Algorithm for Protein Backbone Torsion Angle Prediction," PLOS ONE, Public Library of Science, vol. 3(10), pages 1-8, October.
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