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Proc Natl Acad Sci U S A. 2012 Sep 11;109(37):14888-93. doi: 10.1073/pnas.1115620109. Epub 2012 Aug 27.

Escape from Adaptive Conflict follows from weak functional trade-offs and mutational robustness.

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  • 1Evolutionary Bioinformatics Group, Institute for Evolution and Biodiversity, University of Münster, Hüfferstraße 1, 48149 Münster, Germany.

Abstract

A fundamental question in molecular evolution is how proteins can adapt to new functions while being conserved for an existing function at the same time. Several theoretical models have been put forward to explain this apparent paradox. The most popular models include neofunctionalization, subfunctionalization (SUBF) by degenerative mutations, and dosage models. All of these models focus on adaptation after gene duplication. A newly proposed model named "Escape from Adaptive Conflict" (EAC) includes adaptive processes before and after gene duplication that lead to multifunctional proteins, and divergence (SUBF). Support for the importance of multifunctionality for the evolution of new protein functions comes from two experimental observations. First, many enzymes have highly evolvable promiscuous side activities. Second, different structural states of the same protein can be associated with different functions. How these observations may be related to the EAC model, under which conditions EAC is possible, and how the different models relate to each other is still unclear. Here, we present a theoretical framework that uses biophysical principles to infer the roles of functional promiscuity, gene dosage, gene duplication, point mutations, and selection pressures in the evolution of proteins. We find that selection pressures can determine whether neofunctionalization or SUBF is the more likely evolutionary process. Multifunctional proteins, arising during EAC evolution, allow rapid adaptation independent of gene duplication. This becomes a crucial advantage when gene duplications are rare. Finally, we propose that an increase in mutational robustness, not necessarily functional optimization, can be the sole driving force behind SUBF.

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