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Predicting
Protein-Protein Interaction by Searching Evolutioinary Tree Automorphism
Space
Raja Jothi,
Maricel Kann,and
Teresa M. Przytycka
National Center for Biotechnology
Information, National Library of Medicine,
National Institutes of Health,
Bethesda,
MD 20894,
USA |
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Data Files
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Abstract |
Methodology
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Data Files
Abstract
Motivation :
Uncovering the protein-protein interaction
network is a fundamental step in the quest to understand the molecular
machinery of a cell. This motivates the search for efficient computational
methods for predicting such interactions. Among the available predictors
are those that are based on the co-evolution hypothesis "evolutionary
trees of protein families (that are known to interact) are expected to
have similar topologies". Many of these methods are limited by the fact
that they can handle only a small number of protein sequences. Also,
details on evolutionary tree topology are missing as they use similarity
matrices in lieu of the trees.
Results: We introduce MORPH, a
new algorithm for predicting protein interaction partners between members
of two protein families that are known to interact. Our approach can also
be seen as a new method for searching the best superposition of the
corresponding evolutionary trees based on tree
automorphism group. We discuss relevant facts related to the
predictability of protein-protein interaction based on their co-evolution.
When compared with related computational approaches, our method reduces
the search space by approximately 3 x 10(5)-fold and at the same time
increases the accuracy of predicting correct binding partners.
Methodology
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