Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution

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 perturbations, 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 within networks 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.Author Summary: Different proteins evolve at dramatically different rates. To understand this variation, it is necessary to determine which characteristics of proteins are visible to natural selection and how the strength of selection depends on those characteristics. One protein characteristic that is evidently visible to natural selection is expression level; more highly expressed proteins are subject to stronger purifying selection and evolve more slowly. Theory and intuition suggest another such characteristic should be some measure of functional importance, but studies of various measures of functional importance, such as knockout essentiality or knockout growth rate, have shown at best weak correlations with evolutionary rate. Here we develop a novel measure of functional importance, dynamical influence, which quantifies the importance of a protein or protein domain to the dynamics of the network of proteins in which it functions. Using 18 biochemically-detailed systems biology models, we compute dynamical influences for each protein domain in each model. We find that dynamical influence is indeed visible to natural selection and that within networks protein domains with higher dynamical influence evolve more slowly.