MBE Advance Access published online on November 3, 2004
Molecular Biology and Evolution, doi:10.1093/molbev/msi034
Molecular Biology and Evolution © Society for Molecular Biology and Evolution 2004; all rights reserved
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1 School of Biosciences, University of Birmingham, Birmingham B15 2TT, United Kingdom
* To whom correspondence should be addressed. The impact of the biological network structures on the divergence between the two copies of one duplicate gene pair involved in the networks has not been documented on a genome scale. Having analyzed the most recently updated Database of Interacting Proteins (DIP) by incorporating the information for duplicate genes of the same age in yeast, we find that there was a highly significantly positive correlation between the level of connectivity of ancient genes and the number of shared partners of their duplicates in the protein-protein interaction networks. This suggests that duplicate genes with a low ancestral connectivity tend to provide raw materials for functional novelty while those duplicate genes with a high ancestral connectivity tend to create functional redundancy for a genome during the same evolutionary period. Moreover, the difference in the number of partners between two copies of a duplicate pair was found to follow a power-law distribution. This suggests that loss and gain of interacting partners for most duplicate genes with a lower level of ancestral connectivity is largely symmetrical whilst the "hub duplicate genes" with a higher level of ancient connectivity display an asymmetrical divergence pattern in protein-protein interactions. Thus, it is clear that the protein-protein interaction network structures affect the divergence pattern of duplicate genes. Our findings also provide insights into the origin and development of biological networks.
Research Article
Divergence Pattern of Duplicate Genes in Protein-Protein Interactions Follows the Power Law
2 School of Biosciences, University of Birmingham, Birmingham B15 2TT, United Kingdom; Laboratory of Population & Quantitative Genetics, The State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, 200433, China
3 Graduate School of Agriculture and Life Sciences, University of Tokyo, 1-1-1 Yayoi, Bunkyoku, Tokyo 113-8657, Japan
Ze Zhang, E-mail: z.zhang.2{at}bham.ac.uk
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