Automatic classification of protein structure using the maximum contact map overlap metric

Rumen Andonov*, Hristo Djidjev, Gunnar W. Klau, Mathilde Le Boudic-Jamin, Inken Wohlers

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

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, satisfiesall properties of a metric on the space of protein representations. Having a metric in that space allows one to avoid pairwise comparisons on the entire database and, thus, to significantly accelerate exploring the protein space compared to no-metric spaces. We show on a gold standard superfamily classification benchmark set of 6759 proteins that our exact k-nearest neighbor (k-NN) scheme classifies up to 224 out of 236 queries correctly and on a larger, extended version of the benchmarkwith 60; 850 additional structures, up to 1361 out of 1369 queries. Our k-NN classification thus provides a promising approach for the automatic classification of protein structures based on flexible contact map overlap alignments.

Original languageEnglish
Pages (from-to)850-869
Number of pages20
JournalAlgorithms
Volume8
Issue number4
DOIs
Publication statusPublished - 2015

Keywords

  • K-nearest neighbor classification
  • Maximum contact map overlap
  • Protein space metric
  • SCOP
  • Superfamily classification

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