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EvDTree: structure-dependent substitution profiles based on decision tree classification of 3D environments.

Jean-Christophe Gelly1, Laurent Chiche, Jérôme Gracy

  • 1Centre de Biochimie Structurale, Faculté de Pharmacie, Université Montpellier I, 15 avenue Charles Flahault, 34093 Montpellier Cedex 5, France. gelly@cbs.cnrs.fr <gelly@cbs.cnrs.fr>

BMC Bioinformatics
|January 11, 2005
PubMed
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EvDTree is a new automated method that derives amino acid substitution probabilities using decision trees. This structure-dependent approach improves sequence alignment accuracy compared to conventional methods.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Bioinformatics

Background:

  • Structure-dependent substitution matrices enhance sequence alignment accuracy when 3D structures are known.
  • Existing methods often require empirical determination of structural descriptors.
  • Automated derivation of substitution probabilities is crucial for improving alignment accuracy.

Purpose of the Study:

  • To introduce EvDTree, a novel automated method for deriving structure-dependent amino acid substitution probabilities.
  • To enable unbiased selection of informative structural descriptors and thresholds.
  • To facilitate the creation of custom substitution scores for specific protein sets.

Main Methods:

  • EvDTree utilizes a decision tree algorithm to process sequence-structure alignments.

Related Experiment Videos

  • Amino acid substitution probabilities are derived from decision tree clusters.
  • Structural environments are classified using selected descriptors and thresholds.

    • Main Results:

    • EvDTree automatically derives environment-dependent substitution profiles.
    • The resulting structure-dependent scores outperform conventional matrices.
    • EvDTree scores show comparable or superior performance to other structure-dependent matrices.

    Conclusions:

    • EvDTree offers a fully automated method for classifying structural environments and inferring substitution profiles.
    • The approach demonstrates superior accuracy over existing methods.
    • EvDTree facilitates the development of class-specific substitution scores for applications like remote homology searches.