Journal Article

Relative Contributions of Intrinsic Structural–Functional Constraints and Translation Rate to the Evolution of Protein-Coding Genes

Yuri I. Wolf, Irina V. Gopich, David J. Lipman and Eugene V. Koonin

in Genome Biology and Evolution

Published on behalf of Society for Molecular Biology and Evolution

Volume 2, issue , pages 190-199
Published in print January 2010 |
Published online March 2010 | e-ISSN: 1759-6653 | DOI:

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A long-standing assumption in evolutionary biology is that the evolution rate of protein-coding genes depends, largely, on specific constraints that affect the function of the given protein. However, recent research in evolutionary systems biology revealed unexpected, significant correlations between evolution rate and characteristics of genes or proteins that are not directly related to specific protein functions, such as expression level and protein–protein interactions. The strongest connections were consistently detected between protein sequence evolution rate and the expression level of the respective gene. A recent genome-wide proteomic study revealed an extremely strong correlation between the abundances of orthologous proteins in distantly related animals, the nematode Caenorhabditis elegans and the fruit fly Drosophila melanogaster. We used the extensive protein abundance data from this study along with short-term evolutionary rates (ERs) of orthologous genes in nematodes and flies to estimate the relative contributions of structural–functional constraints and the translation rate to the evolution rate of protein-coding genes. Together the intrinsic constraints and translation rate account for approximately 50% of the variance of the ERs. The contribution of constraints is estimated to be 3- to 5-fold greater than the contribution of translation rate.

Keywords: protein evolution; structural–functional constraints; misfolding; protein abundance

Journal Article.  5664 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology ; Evolutionary Biology ; Genetics and Genomics

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