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Using Kendall-τ Meta-Bagging to Improve Protein-Protein Docking Predictions

  • Jérôme Azé
  • Thomas Bourquard
  • Sylvie Hamel
  • Anne Poupon
  • David W. Ritchie
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7036)

Abstract

Predicting the three-dimensional (3D) structures of macromolecular protein-protein complexes from the structures of individual partners (docking), is a major challenge for computational biology. Most docking algorithms use two largely independent stages. First, a fast sampling stage generates a large number (millions or even billions) of candidate conformations, then a scoring stage evaluates these conformations and extracts a small ensemble amongst which a good solution is assumed to exist. Several strategies have been proposed for this stage. However, correctly distinguishing and discarding false positives from the native biological interfaces remains a difficult task. Here, we introduce a new scoring algorithm based on learnt bootstrap aggregation (“bagging”) models of protein shape complementarity. 3D Voronoi diagrams are used to describe and encode the surface shapes and physico-chemical properties of proteins. A bagging method based on Kendall-τ distances is then used to minimise the pairwise disagreements between the ranks of the elements obtained from several different bagging approaches. We apply this method to the protein docking problem using 51 protein complexes from the standard Protein Docking Benchmark. Overall, our approach improves in the ranks of near-native conformation and results in more biologically relevant predictions.

Keywords

Root Mean Square Deviation Pairwise Disagreement Protein Docking Protein Data Bank Code Docking Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jérôme Azé
    • 1
  • Thomas Bourquard
    • 2
  • Sylvie Hamel
    • 3
  • Anne Poupon
    • 4
    • 5
    • 6
  • David W. Ritchie
    • 2
  1. 1.Laboratoire de Recherche en Informatique, Bâtiment 650, Équipe Bioinformatique – INRIA AMIB groupUniversité de Paris-SudOrsayFrance
  2. 2.INRIA Nancy-Grand Est, LORIAVandoeuvre-lès-NancyFrance
  3. 3.Université de MontréalMontréalCanada
  4. 4.BIOS group, INRA, UMR85, Unité Physiologie de la Reproduction et des ComportementsNouzillyFrance
  5. 5.CNRS, UMR6175NouzillyFrance
  6. 6.Université François RabelaisToursFrance

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