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Bioinformatics Vol. 19 no. 16 2003
pages 2039-2045
© 2003 Oxford University Press

Inferring protein interactions from phylogenetic distance matrices

Jason Gertz 1, Georgiy Elfond 2, Anna Shustrova 2, Matt Weisinger 3, Matteo Pellegrini 4,*, Shawn Cokus 5 and Bruce Rothschild 5

1 Department of Mathematics, 310 Malott Hall, Cornell University, Ithaca, NY 14853-4201, USA, 2 Department of Mathematics, 970 Evans Hall #3840, University of California, Berkeley, CA 94720-3840, USA, 3 Division of Engineering and Applied Sciences, Pierce Hall, 29 Oxford Street, Cambridge, MA 02138, USA, 4 Protein Pathways, Woodland Hills, CA, USA and 5 Department of Mathematics, University of California, Los Angeles, CA, USA

Received on September 30, 2002 ; revised on March 3, 2003 ; accepted on April 25, 2003

Finding the interacting pairs of proteins between two different protein families whose members are known to interact is an important problem in molecular biology. We developed and tested an algorithm that finds optimal matches between two families of proteins by comparing their distance matrices. A distance matrix provides a measure of the sequence similarity of proteins within a family. Since the protein sets of interest may have dozens of proteins each, the use of an efficient approximate solution is necessary. Therefore the approach we have developed consists of a Metropolis Monte Carlo optimization algorithm which explores the search space of possible matches between two distance matrices. We demonstrate that by using this algorithm we are able to accurately match chemokines and chemokine-receptors as well as the tgfß family of ligands and their receptors.

Contact: matteope{at}proteinpathways.com

* To whom correspondence should be addressed at 21111 Oxnard Blvd, Woodland Hills CA 91367, USA.


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