Exact Protein Structure Classification Using the Maximum Contact Map Overlap Metric

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

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, 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.

OriginalspracheEnglisch
TitelAlCoB 2014: Algorithms for Computational Biology
Seitenumfang12
Band8542
Herausgeber (Verlag)Springer, Cham
Erscheinungsdatum2014
Seiten262-273
ISBN (Print)978-3-319-07952-3
ISBN (elektronisch)978-3-319-07953-0
DOIs
PublikationsstatusVeröffentlicht - 2014
Veranstaltung1st International Conference on Algorithms for Computational Biology - Tarragona, Spanien
Dauer: 01.07.201403.07.2014

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  • Forschungsschwerpunkt: Infektion und Entzündung - Zentrum für Infektions- und Entzündungsforschung Lübeck (ZIEL)

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