Exact Protein Structure Classification Using the Maximum Contact Map Overlap Metric

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


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.

Original languageEnglish
Title of host publicationAlCoB 2014: Algorithms for Computational Biology
Number of pages12
PublisherSpringer, Cham
Publication date2014
ISBN (Print)978-3-319-07952-3
ISBN (Electronic)978-3-319-07953-0
Publication statusPublished - 2014
Event1st International Conference on Algorithms for Computational Biology - Tarragona, Spain
Duration: 01.07.201403.07.2014

Research Areas and Centers

  • Academic Focus: Center for Infection and Inflammation Research (ZIEL)


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