Exact Protein Structure Classification Using the Maximum Contact Map Overlap Metric

Inken Wohlers 1 Mathilde Le Boudic-Jamin 2 Hristo Djidjev 3 Gunnar W. Klau 4 Rumen Andonov 2
1 Genominformatik Universität Duisburg-Essen
2 GenScale - Scalable, Optimized and Parallel Algorithms for Genomics
IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE, Inria Rennes – Bretagne Atlantique
3 LANL - Los Alamos National Laboratory
4 MAC4 - Life Sciences [Amsterdam]
Abstract : In this work we propose a new distance measure for compar-ing two protein structures based on their contact map representations. We show that our novel measure, which we refer to as the maximum con-tact 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 clas-sification of protein structures based on contact map overlap.
Type de document :
Communication dans un congrès
1st International Conference on Algorithms for Computational Biology, AlCoB 2014, Jul 2014, Tarragona, Spain. pp.262 - 273, 2014, 〈10.1007/978-3-319-07953-0_21〉
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Inken Wohlers, Mathilde Le Boudic-Jamin, Hristo Djidjev, Gunnar W. Klau, Rumen Andonov. Exact Protein Structure Classification Using the Maximum Contact Map Overlap Metric. 1st International Conference on Algorithms for Computational Biology, AlCoB 2014, Jul 2014, Tarragona, Spain. pp.262 - 273, 2014, 〈10.1007/978-3-319-07953-0_21〉. 〈hal-01093803〉

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