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Bioinformatics Advance Access originally published online on March 13, 2009
Bioinformatics 2009 25(16):2110-2117; doi:10.1093/bioinformatics/btp144
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Evolutionary construction of multiple graph alignments for the structural analysis of biomolecules

Thomas Fober 1,{dagger}, Marco Mernberger 1,2,{dagger}, Gerhard Klebe 2 and Eyke Hüllermeier 1,*

1Department of Mathematics and Computer Science and 2Department of Pharmaceutical Chemistry, Philipps-Universität Marburg, Marburg, Germany

*To whom correspondence should be addressed.


  Abstract

The concept of multiple graph alignment (MGA) has recently been introduced as a novel method for the structural analysis of biomolecules. Using approximate graph matching techniques, this method enables the robust identification of approximately conserved patterns in biologically related structures. In particular, MGA enables the characterization of functional protein families independent of sequence or fold homology. This article first recalls the concept of MGA and then addresses the problem of computing optimal alignments from an algorithmic point of view. In this regard, a method from the field of evolutionary algorithms is proposed and empirically compared with a hitherto existing heuristic approach. Empirically, it is shown that the former yields significantly better results than the latter, albeit at the cost of an increased runtime.

Contact: eyke{at}mathematik.uni-marburg.de

Supplementary information: Supplementary data are available at Bioinformatics online.

{dagger}The author wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.

Associate Editor: Trey Ideker

Received on October 30, 2008; revised on March 6, 2009; accepted on March 10, 2009

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