Exact Protein Structure Classification Using the Maximum Contact Map Overlap Metric

  • Inken Wohlers
  • Mathilde Le Boudic-Jamin
  • Hristo Djidjev
  • Gunnar W. Klau
  • Rumen Andonov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8542)


In this work we propose a new distance measure for comparing two protein structures based on their contact map representations. We show that our novel measure, which we refer to as the maximum contact map overlap (max-CMO) metric, satisfies all properties of a metric on the space of protein representations. Having a metric in that space allows to avoid pairwise comparisons on the entire database and thus to significantly accelerate exploring the protein space compared to non-metric spaces. We show on a gold-standard classification benchmark set of 6,759 and 67,609 proteins, resp., that our exact k-nearest neighbor scheme classifies up to 95% and 99% of queries correctly. Our k-NN classification thus provides a promising approach for the automatic classification of protein structures based on contact map overlap.


k-nearest neighbours metric spaces maximum contact map overlap automatic classification of proteins 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Inken Wohlers
    • 1
  • Mathilde Le Boudic-Jamin
    • 2
  • Hristo Djidjev
    • 3
  • Gunnar W. Klau
    • 4
  • Rumen Andonov
    • 2
  1. 1.Genome InformaticsUniversity of DuisburgEssenGermany
  2. 2.INRIA RennesBretagne Atlantique and University of Rennes 1France
  3. 3.Los Alamos National LaboratoryLos AlamosUSA
  4. 4.Life Sciences, CWIAmsterdamThe Netherlands

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