Title: Comparing protein contact maps via Universal Similarity Metric: an improvement in the noise-tolerance

Authors: Sara Rahmati, Janice I. Glasgow

Addresses: School of Medical Biophysics, University of Toronto, Ontario Cancer Institute, Toronto Medical Discovery Tower, 9-303, 101 College Street, Toronto, Ontario M5G 1L7, Canada. ' School of Computing, Queen's University, Kingston, Ontario K7L 3N6, Canada

Abstract: Comparing protein structures based on their contact maps similarity is an important problem in molecular biology. One motivation to seek fast algorithms for comparing contact maps is devising systems for reconstructing three-dimensional structure of proteins from their predicted contact maps. In this paper, we propose an algorithm to apply the Universal Similarity Metric (USM) to contact map comparison problem in a two-dimensional space. The major advantage of this algorithm is the highly improved noise-tolerance of the metric in comparison with its previous one-dimensional implementations. This is the first successful attempt to apply the USM to two-dimensional objects, without reducing their dimensionality.

Keywords: protein contact maps; contact map comparison; protein structure comparison; USM; universal similarity metric; mutual information; bioinformatics; molecular biology; noise tolerance.

DOI: 10.1504/IJCBDD.2009.028821

International Journal of Computational Biology and Drug Design, 2009 Vol.2 No.2, pp.149 - 167

Available online: 03 Oct 2009 *

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