4.7 Article

Assessing protein co-evolution in the context of the tree of life assists in the prediction of the interactome

Journal

JOURNAL OF MOLECULAR BIOLOGY
Volume 352, Issue 4, Pages 1002-1015

Publisher

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jmb.2005.07.005

Keywords

protein interactions; phylogenetic tree; co-evolution; interactome; horizontal gene transfer

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The identification of the whole set of protein interactions taking place in an organism is one of the main tasks in genomics, proteomic and systems biology. One of the computational techniques used by many investigators for studying and predicting protein interactions is the comparison of evolutionary histories (phylogenetic trees), under the hypothesis that interacting proteins would be subject to a similar evolutionary pressure resulting in a similar topology of the corresponding trees. Here, we present a new approach to predict protein interactions from phylogenetic trees, which incorporates information on the overall evolutionary histories of the species (i.e. the canonical '' tree of life '') in order to correct by the expected background similarity due to the underlying speciation events. We test the new approach in the largest set of annotated interacting proteins for Escherichia coli. This assessment of co-evolution in the context of the tree of life leads to a highly significant improvement (P(N) by sign test similar to 10E-6) in predicting interaction partners with respect to the previous technique, which does not incorporate, information on the overall speciation tree. For half of the proteins we found a real interactor among the 6.4% top scores, compared with the 16.5% by the previous method. We applied the new method to the whole E. coli proteome and propose functions for some hypothetical proteins based on their predicted interactors. The new approach allows us also to detect non-canonical evolutionary events, in particular horizontal gene transfers. We also show that taking into account these non-canonical evolutionary events when assessing the similarity between evolutionary trees improves the performance of the method predicting interactions. (c) 2005 Elsevier Ltd. All rights reserved.

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