PT  - JOURNAL ARTICLE
AU  - Brian K. Mannakee
AU  - Ryan N. Gutenkunst
TI  - Selection on network dynamics drives differential rates of protein domain evolution
AID  - 10.1101/026658
DP  - 2015 Jan 01
TA  - bioRxiv
PG  - 026658
4099  - http://biorxiv.org/content/early/2015/09/11/026658.short
4100  - http://biorxiv.org/content/early/2015/09/11/026658.full
AB  - The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein’s rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical per-turbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces.