Functional Trade-Offs in Promiscuous Enzymes Cannot Be Explained by Intrinsic Mutational Robustness of the Native Activity.

The extent to which an emerging new function trades off with the original function is a key characteristic of the dynamics of enzyme evolution. Various cases of laboratory evolution have unveiled a characteristic trend; a large increase in a new, promiscuous activity is often accompanied by only a mild reduction of the native, original activity. A model that associates weak trade-offs with "evolvability" was put forward, which proposed that enzymes possess mutational robustness in the native activity and plasticity in promiscuous activities. This would enable the acquisition of a new function without compromising the original one, reducing the benefit of early gene duplication and therefore the selection pressure thereon. Yet, to date, no experimental study has examined this hypothesis directly. Here, we investigate the causes of weak trade-offs by systematically characterizing adaptive mutations that occurred in two cases of evolutionary transitions in enzyme function: (1) from phosphotriesterase to arylesterase, and (2) from atrazine chlorohydrolase to melamine deaminase. Mutational analyses in various genetic backgrounds revealed that, in contrast to the prevailing model, the native activity is less robust to mutations than the promiscuous activity. For example, in phosphotriesterase, the deleterious effect of individual mutations on the native phosphotriesterase activity is much larger than their positive effect on the promiscuous arylesterase activity. Our observations suggest a revision of the established model: weak trade-offs are not caused by an intrinsic robustness of the native activity and plasticity of the promiscuous activity. We propose that upon strong adaptive pressure for the new activity without selection against the original one, selected mutations will lead to the largest possible increases in the new function, but whether and to what extent they decrease the old function is irrelevant, creating a bias towards initially weak trade-offs and the emergence of generalist enzymes.This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC, Discovery Grant RGPIN 418262- 12, http://www.nserc-crsng.gc.ca/), the Biotechnology and Biological Sciences Research Council (BBSRC, Grant BB/L002469/1, http://www. bbsrc.ac.uk/), the European Research Council (ERC, Advanced Investigator Grant 695669, https:// erc.europa.eu/), and the Human Frontiers Science Program (Grant RGP0006/2013, http://www.hfsp. org/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscrip

Diminishing returns and tradeoffs constrain the laboratory optimization of an enzyme

Optimization processes, such as evolution, are constrained by diminishing returns - the closer the optimum, the smaller the benefit per mutation, and by tradeoffs - improvement of one property at the cost of others. However, the magnitude and molecular basis of these parameters, and their effect on evolutionary transitions, remain unknown. Here we pursue a complete functional transition of an enzyme with a >109-fold change in the enzyme's selectivity using laboratory evolution. We observed strong diminishing returns, with the initial mutations conferring >25-fold higher improvements than later ones, and asymmetric tradeoffs whereby the gain/loss ratio of the new/old activity decreased 400-fold from the beginning of the trajectory to its end. We describe the molecular basis for these phenomena and suggest they have an important role in shaping natural proteins. These findings also suggest that the catalytic efficiency and specificity of many natural enzymes may be far from their optimum

A biophysical protein folding model accounts for most mutational fitness effects in viruses

Fitness effects of mutations fall on a continuum ranging from lethal to deleterious to beneficial. The distribution of fitness effects (DFE) among random mutations is an essential component of every evolutionary model and a mathematical portrait of robustness. Recent experiments on five viral species all revealed a characteristic bimodal shaped DFE, featuring peaks at neutrality and lethality. However, the phenotypic causes underlying observed fitness effects are still unknown, and presumably thought to vary unpredictably from one mutation to another. By combining population genetics simulations with a simple biophysical protein folding model, we show that protein thermodynamic stability accounts for a large fraction of observed mutational effects. We assume that moderately destabilizing mutations inflict a fitness penalty proportional to the reduction in folded protein, which depends continuously on folding free energy (\Delta G). Most mutations in our model affect fitness by altering \Delta G, while, based on simple estimates, \approx10% abolish activity and are unconditionally lethal. Mutations pushing \Delta G>0 are also considered lethal. Contrary to neutral network theory, we find that, in mutation/selection/drift steady-state, high mutation rates (m) lead to less stable proteins and a more dispersed DFE, i.e. less mutational robustness. Small population size (N) also decreases stability and robustness. In our model, a continuum of non-lethal mutations reduces fitness by \approx2% on average, while \approx10-35% of mutations are lethal, depending on N and m. Compensatory mutations are common in small populations with high mutation rates. More broadly, we conclude that interplay between biophysical and population genetic forces shapes the DFE.Comment: Main text: 12 pages, 5 figures Supplementary Information: 10 pages, 5 figure

PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality

ABSTRACT:Journal ArticleSCOPUS: ar.jinfo:eu-repo/semantics/publishe

Mechanisms for the Evolution of a Derived Function in the Ancestral Glucocorticoid Receptor

Understanding the genetic, structural, and biophysical mechanisms that caused protein functions to evolve is a central goal of molecular evolutionary studies. Ancestral sequence reconstruction (ASR) offers an experimental approach to these questions. Here we use ASR to shed light on the earliest functions and evolution of the glucocorticoid receptor (GR), a steroid-activated transcription factor that plays a key role in the regulation of vertebrate physiology. Prior work showed that GR and its paralog, the mineralocorticoid receptor (MR), duplicated from a common ancestor roughly 450 million years ago; the ancestral functions were largely conserved in the MR lineage, but the functions of GRs—reduced sensitivity to all hormones and increased selectivity for glucocorticoids—are derived. Although the mechanisms for the evolution of glucocorticoid specificity have been identified, how reduced sensitivity evolved has not yet been studied. Here we report on the reconstruction of the deepest ancestor in the GR lineage (AncGR1) and demonstrate that GR's reduced sensitivity evolved before the acquisition of restricted hormone specificity, shortly after the GR–MR split. Using site-directed mutagenesis, X-ray crystallography, and computational analyses of protein stability to recapitulate and determine the effects of historical mutations, we show that AncGR1's reduced ligand sensitivity evolved primarily due to three key substitutions. Two large-effect mutations weakened hydrogen bonds and van der Waals interactions within the ancestral protein, reducing its stability. The degenerative effect of these two mutations is extremely strong, but a third permissive substitution, which has no apparent effect on function in the ancestral background and is likely to have occurred first, buffered the effects of the destabilizing mutations. Taken together, our results highlight the potentially creative role of substitutions that partially degrade protein structure and function and reinforce the importance of permissive mutations in protein evolution

SNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants

Single nucleotide variants (SNVs) are, together with copy number variation, the primary source of variation in the human genome and are associated with phenotypic variation such as altered response to drug treatment and susceptibility to disease. Linking structural effects of non-synonymous SNVs to functional outcomes is a major issue in structural bioinformatics. The SNPeffect database (http://snpeffect.switchlab.org) uses sequence- and structure-based bioinformatics tools to predict the effect of protein-coding SNVs on the structural phenotype of proteins. It integrates aggregation prediction (TANGO), amyloid prediction (WALTZ), chaperone-binding prediction (LIMBO) and protein stability analysis (FoldX) for structural phenotyping. Additionally, SNPeffect holds information on affected catalytic sites and a number of post-translational modifications. The database contains all known human protein variants from UniProt, but users can now also submit custom protein variants for a SNPeffect analysis, including automated structure modeling. The new meta-analysis application allows plotting correlations between phenotypic features for a user-selected set of variants

How Protein Stability and New Functions Trade Off

Numerous studies have noted that the evolution of new enzymatic specificities is accompanied by loss of the protein's thermodynamic stability (ΔΔG), thus suggesting a tradeoff between the acquisition of new enzymatic functions and stability. However, since most mutations are destabilizing (ΔΔG>0), one should ask how destabilizing mutations that confer new or altered enzymatic functions relative to all other mutations are. We applied ΔΔG computations by FoldX to analyze the effects of 548 mutations that arose from the directed evolution of 22 different enzymes. The stability effects, location, and type of function-altering mutations were compared to ΔΔG changes arising from all possible point mutations in the same enzymes. We found that mutations that modulate enzymatic functions are mostly destabilizing (average ΔΔG = +0.9 kcal/mol), and are almost as destabilizing as the “average” mutation in these enzymes (+1.3 kcal/mol). Although their stability effects are not as dramatic as in key catalytic residues, mutations that modify the substrate binding pockets, and thus mediate new enzymatic specificities, place a larger stability burden than surface mutations that underline neutral, non-adaptive evolutionary changes. How are the destabilizing effects of functional mutations balanced to enable adaptation? Our analysis also indicated that many mutations that appear in directed evolution variants with no obvious role in the new function exert stabilizing effects that may compensate for the destabilizing effects of the crucial function-altering mutations. Thus, the evolution of new enzymatic activities, both in nature and in the laboratory, is dependent on the compensatory, stabilizing effect of apparently “silent” mutations in regions of the protein that are irrelevant to its function

Unified regression model of binding equilibria in crowded environments

Molecular crowding is a critical feature distinguishing intracellular environments from idealized solution-based environments and is essential to understanding numerous biochemical reactions, from protein folding to signal transduction. Many biochemical reactions are dramatically altered by crowding, yet it is extremely difficult to predict how crowding will quantitatively affect any particular reaction systems. We previously developed a novel stochastic off-lattice model to efficiently simulate binding reactions across wide parameter ranges in various crowded conditions. We now show that a polynomial regression model can incorporate several interrelated parameters influencing chemistry under crowded conditions. The unified model of binding equilibria accurately reproduces the results of particle simulations over a broad range of variation of six physical parameters that collectively yield a complicated, non-linear crowding effect. The work represents an important step toward the long-term goal of computationally tractable predictive models of reaction chemistry in the cellular environment