Jesse D. Bloom

Why highly expressed proteins evolve slowly

Much recent work has explored molecular and population-genetic constraints on the rate of protein sequence evolution. The best predictor of evolutionary rate is expression level, for reasons that have remained unexplained. Here, we hypothesize that selection to reduce the burden of protein misfolding will favor protein sequences with increased robustness to translational missense errors. Pressure for translational robustness increases with expression level and constrains sequence evolution. Using several sequenced yeast genomes, global expression and protein abundance data, and sets of paralogs traceable to an ancient whole-genome duplication in yeast, we rule out several confounding effects and show that expression level explains roughly half the variation in Saccharomyces cerevisiae protein evolutionary rates. We examine causes for expressions dominant role and find that genome-wide tests favor the translational robustness explanation over existing hypotheses that invoke constraints on function or translational efficiency. Our results suggest that proteins evolve at rates largely unrelated to their functions and can explain why highly expressed proteins evolve slowly across the tree of life.

Permissive secondary mutations enable the evolution of influenza oseltamivir resistance.

Influenza Escape Tricks Tamiflu, or oseltamivir, has been extensively stockpiled by several governments in anticipation of a dangerous influenza pandemic. So far, its large-scale use has not been required, but, despite this, resistance has emerged in seasonal strains mediated by a single point mutation of histidine to tyrosine in the 274 residue (H274Y) of neuraminidase. When the resistant virus was first discovered in 1998, it grew poorly, but by 2008 the virus was reinvigorated and the mutation had spread worldwide in seasonal influenza. So what happened that improved viral fitness so radically? Bloom et al. (p. 1272; see the Perspective by Holmes) show that the H274Y mutation hinders the folding of the neuraminidase enzyme. In the more vigorous recent oseltamivir-resistant isolates, other mutations compensate for the deleterious effect of H274Y and restore fitness to the virus. Compensatory mutations offset the structural costs of acquiring a drug-resistance mutation in a virus. The His274→Tyr274 (H274Y) mutation confers oseltamivir resistance on N1 influenza neuraminidase but had long been thought to compromise viral fitness. However, beginning in 2007–2008, viruses containing H274Y rapidly became predominant among human seasonal H1N1 isolates. We show that H274Y decreases the amount of neuraminidase that reaches the cell surface and that this defect can be counteracted by secondary mutations that also restore viral fitness. Two such mutations occurred in seasonal H1N1 shortly before the widespread appearance of H274Y. The evolution of oseltamivir resistance was therefore enabled by “permissive” mutations that allowed the virus to tolerate subsequent occurrences of H274Y. An understanding of this process may provide a basis for predicting the evolution of oseltamivir resistance in other influenza strains.

In the light of directed evolution: Pathways of adaptive protein evolution

Directed evolution is a widely-used engineering strategy for improving the stabilities or biochemical functions of proteins by repeated rounds of mutation and selection. These experiments offer empirical lessons about how proteins evolve in the face of clearly-defined laboratory selection pressures. Directed evolution has revealed that single amino acid mutations can enhance properties such as catalytic activity or stability and that adaptation can often occur through pathways consisting of sequential beneficial mutations. When there are no single mutations that improve a particular protein property experiments always find a wealth of mutations that are neutral with respect to the laboratory-defined measure of fitness. These neutral mutations can open new adaptive pathways by at least 2 different mechanisms. Functionally-neutral mutations can enhance a proteins stability, thereby increasing its tolerance for subsequent functionally beneficial but destabilizing mutations. They can also lead to changes in “promiscuous” functions that are not currently under selective pressure, but can subsequently become the starting points for the adaptive evolution of new functions. These lessons about the coupling between adaptive and neutral protein evolution in the laboratory offer insight into the evolution of proteins in nature.

Thermodynamic prediction of protein neutrality

We present a simple theory that uses thermodynamic parameters to predict the probability that a protein retains the wild-type structure after one or more random amino acid substitutions. Our theory predicts that for large numbers of substitutions the probability that a protein retains its structure will decline exponentially with the number of substitutions, with the severity of this decline determined by properties of the structure. Our theory also predicts that a protein can gain extra robustness to the first few substitutions by increasing its thermodynamic stability. We validate our theory with simulations on lattice protein models and by showing that it quantitatively predicts previously published experimental measurements on subtilisin and our own measurements on variants of TEM1 beta-lactamase. Our work unifies observations about the clustering of functional proteins in sequence space, and provides a basis for interpreting the response of proteins to substitutions in protein engineering applications.

Stability-mediated epistasis constrains the evolution of an influenza protein

John Maynard Smith compared protein evolution to the game where one word is converted into another a single letter at a time, with the constraint that all intermediates are words: WORD→WORE→GORE→GONE→GENE. In this analogy, epistasis constrains evolution, with some mutations tolerated only after the occurrence of others. To test whether epistasis similarly constrains actual protein evolution, we created all intermediates along a 39-mutation evolutionary trajectory of influenza nucleoprotein, and also introduced each mutation individually into the parent. Several mutations were deleterious to the parent despite becoming fixed during evolution without negative impact. These mutations were destabilizing, and were preceded or accompanied by stabilizing mutations that alleviated their adverse effects. The constrained mutations occurred at sites enriched in T-cell epitopes, suggesting they promote viral immune escape. Our results paint a coherent portrait of epistasis during nucleoprotein evolution, with stabilizing mutations permitting otherwise inaccessible destabilizing mutations which are sometimes of adaptive value. DOI: http://dx.doi.org/10.7554/eLife.00631.001

Neutral genetic drift can alter promiscuous protein functions, potentially aiding functional evolution

BackgroundMany of the mutations accumulated by naturally evolving proteins are neutral in the sense that they do not significantly alter a proteins ability to perform its primary biological function. However, new protein functions evolve when selection begins to favor other, promiscuous functions that are incidental to a proteins original biological role. If mutations that are neutral with respect to a proteins primary biological function cause substantial changes in promiscuous functions, these mutations could enable future functional evolution.ResultsHere we investigate this possibility experimentally by examining how cytochrome P450 enzymes that have evolved neutrally with respect to activity on a single substrate have changed in their abilities to catalyze reactions on five other substrates. We find that the enzymes have sometimes changed as much as four-fold in the promiscuous activities. The changes in promiscuous activities tend to increase with the number of mutations, and can be largely rationalized in terms of the chemical structures of the substrates. The activities on chemically similar substrates tend to change in a coordinated fashion, potentially providing a route for systematically predicting the change in one activity based on the measurement of several others.ConclusionOur work suggests that initially neutral genetic drift can lead to substantial changes in protein functions that are not currently under selection, in effect poising the proteins to more readily undergo functional evolution should selection favor new functions in the future.ReviewersThis article was reviewed by Martijn Huynen, Fyodor Kondrashov, and Dan Tawfik (nominated by Christoph Adami).

Apparent dependence of protein evolutionary rate on number of interactions is linked to biases in protein-protein interactions data sets

BackgroundSeveral studies have suggested that proteins that interact with more partners evolve more slowly. The strength and validity of this association has been called into question. Here we investigate how biases in high-throughput protein–protein interaction studies could lead to a spurious correlation.ResultsWe examined the correlation between evolutionary rate and the number of protein–protein interactions for sets of interactions determined by seven different high-throughput methods in Saccharomyces cerevisiae. Some methods have been shown to be biased towards counting more interactions for abundant proteins, a fact that could be important since abundant proteins are known to evolve more slowly. We show that the apparent tendency for interactive proteins to evolve more slowly varies directly with the bias towards counting more interactions for abundant proteins. Interactions studies with no bias show no correlation between evolutionary rate and the number of interactions, and the one study biased towards counting fewer interactions for abundant proteins actually suggests that interactive proteins evolve more rapidly. In all cases, controlling for protein abundance significantly decreases the observed correlation between interactions and evolutionary rate. Finally, we disprove the hypothesis that small data set size accounts for the failure of some interactions studies to show a correlation between evolutionary rate and the number of interactions.ConclusionsThe only correlation supported by a careful analysis of the data is between evolutionary rate and protein abundance. The reported correlation between evolutionary rate and protein–protein interactions cannot be separated from the biases of some protein–protein interactions studies to count more interactions for abundant proteins.

Thermodynamics of Neutral Protein Evolution

Naturally evolving proteins gradually accumulate mutations while continuing to fold to stable structures. This process of neutral evolution is an important mode of genetic change and forms the basis for the molecular clock. We present a mathematical theory that predicts the number of accumulated mutations, the index of dispersion, and the distribution of stabilities in an evolving protein population from knowledge of the stability effects (ΔΔG values) for single mutations. Our theory quantitatively describes how neutral evolution leads to marginally stable proteins and provides formulas for calculating how fluctuations in stability can overdisperse the molecular clock. It also shows that the structural influences on the rate of sequence evolution observed in earlier simulations can be calculated using just the single-mutation ΔΔG values. We consider both the case when the product of the population size and mutation rate is small and the case when this product is large, and show that in the latter case the proteins evolve excess mutational robustness that is manifested by extra stability and an increase in the rate of sequence evolution. All our theoretical predictions are confirmed by simulations with lattice proteins. Our work provides a mathematical foundation for understanding how protein biophysics shapes the process of evolution.

The interface of protein structure, protein biophysics, and molecular evolution

Abstract The interface of protein structural biology, protein biophysics, molecular evolution, and molecular population genetics forms the foundations for a mechanistic understanding of many aspects of protein biochemistry. Current efforts in interdisciplinary protein modeling are in their infancy and the state‐of‐the art of such models is described. Beyond the relationship between amino acid substitution and static protein structure, protein function, and corresponding organismal fitness, other considerations are also discussed. More complex mutational processes such as insertion and deletion and domain rearrangements and even circular permutations should be evaluated. The role of intrinsically disordered proteins is still controversial, but may be increasingly important to consider. Protein geometry and protein dynamics as a deviation from static considerations of protein structure are also important. Protein expression level is known to be a major determinant of evolutionary rate and several considerations including selection at the mRNA level and the role of interaction specificity are discussed. Lastly, the relationship between modeling and needed high‐throughput experimental data as well as experimental examination of protein evolution using ancestral sequence resurrection and in vitro biochemistry are presented, towards an aim of ultimately generating better models for biological inference and prediction.

A diverse family of thermostable cytochrome P450s created by recombination of stabilizing fragments

Thermostable enzymes combine catalytic specificity with the toughness required to withstand industrial reaction conditions. Stabilized enzymes also provide robust starting points for evolutionary improvement of other protein properties. We recently created a library of at least 2,300 new active chimeras of the biotechnologically important cytochrome P450 enzymes. Here we show that a chimeras thermostability can be predicted from the additive contributions of its sequence fragments. Based on these predictions, we constructed a family of 44 novel thermostable P450s with half-lives of inactivation at 57 °C up to 108 times that of the most stable parent. Although they differ by as many as 99 amino acids from any known P450, the stable sequences are catalytically active. Among the novel functions they exhibit is the ability to produce drug metabolites. This chimeric P450 family provides a unique ensemble for biotechnological applications and for studying sequence-stability-function relationships.