Evolution favors protein mutational robustness in sufficiently large populations

BACKGROUND: An important question is whether evolution favors properties such as mutational robustness or evolvability that do not directly benefit any individual, but can influence the course of future evolution. Functionally similar proteins can differ substantially in their robustness to mutations and capacity to evolve new functions, but it has remained unclear whether any of these differences might be due to evolutionary selection for these properties. RESULTS: Here we use laboratory experiments to demonstrate that evolution favors protein mutational robustness if the evolving population is sufficiently large. We neutrally evolve cytochrome P450 proteins under identical selection pressures and mutation rates in populations of different sizes, and show that proteins from the larger and thus more polymorphic population tend towards higher mutational robustness. Proteins from the larger population also evolve greater stability, a biophysical property that is known to enhance both mutational robustness and evolvability. The excess mutational robustness and stability is well described by existing mathematical theories, and can be quantitatively related to the way that the proteins occupy their neutral network. CONCLUSIONS: Our work is the first experimental demonstration of the general tendency of evolution to favor mutational robustness and protein stability in highly polymorphic populations. We suggest that this phenomenon may contribute to the mutational robustness and evolvability of viruses and bacteria that exist in large populations

Bringing Molecules Back into Molecular Evolution

Much molecular-evolution research is concerned with sequence analysis. Yet these sequences represent real, three-dimensional molecules with complex structure and function. Here I highlight a growing trend in the field to incorporate molecular structure and function into computational molecular-evolution work. I consider three focus areas: reconstruction and analysis of past evolutionary events, such as phylogenetic inference or methods to infer selection pressures; development of toy models and simulations to identify fundamental principles of molecular evolution; and atom-level, highly realistic computational modeling of molecular structure and function aimed at making predictions about possible future evolutionary events

Does Mutational Robustness Inhibit Extinction by Lethal Mutagenesis in Viral Populations?

Lethal mutagenesis is a promising new antiviral therapy that kills a virus by raising its mutation rate. One potential shortcoming of lethal mutagenesis is that viruses may resist the treatment by evolving genomes with increased robustness to mutations. Here, we investigate to what extent mutational robustness can inhibit extinction by lethal mutagenesis in viruses, using both simple toy models and more biophysically realistic models based on RNA secondary-structure folding. We show that although the evolution of greater robustness may be promoted by increasing the mutation rate of a viral population, such evolution is unlikely to greatly increase the mutation rate required for certain extinction. Using an analytic multi-type branching process model, we investigate whether the evolution of robustness can be relevant on the time scales on which extinction takes place. We find that the evolution of robustness matters only when initial viral population sizes are small and deleterious mutation rates are only slightly above the level at which extinction can occur. The stochastic calculations are in good agreement with simulations of self-replicating RNA sequences that have to fold into a specific secondary structure to reproduce. We conclude that the evolution of mutational robustness is in most cases unlikely to prevent the extinction of viruses by lethal mutagenesis

Directed Evolution of Protein-Based Neurotransmitter Sensors for MRI

The production of contrast agents sensitive to neuronal signaling events is a rate-limiting step in the development of molecular-level functional magnetic resonance imaging (molecular fMRI) approaches for studying the brain. High-throughput generation and evaluation of potential probes are possible using techniques for macromolecular engineering of protein-based contrast agents. In an initial exploration of this strategy, we used the method of directed evolution to identify mutants of a bacterial heme protein that allowed detection of the neurotransmitter dopamine in vitro and in living animals. The directed evolution method involves successive cycles of mutagenesis and screening that could be generalized to produce contrast agents sensitive to a variety of molecular targets in the nervous system

Inferring stabilizing mutations from protein phylogenies : application to influenza hemagglutinin

One selection pressure shaping sequence evolution is the requirement that a protein fold with sufficient stability to perform its biological functions. We present a conceptual framework that explains how this requirement causes the probability that a particular amino acid mutation is fixed during evolution to depend on its effect on protein stability. We mathematically formalize this framework to develop a Bayesian approach for inferring the stability effects of individual mutations from homologous protein sequences of known phylogeny. This approach is able to predict published experimentally measured mutational stability effects (ΔΔG values) with an accuracy that exceeds both a state-of-the-art physicochemical modeling program and the sequence-based consensus approach. As a further test, we use our phylogenetic inference approach to predict stabilizing mutations to influenza hemagglutinin. We introduce these mutations into a temperature-sensitive influenza virus with a defect in its hemagglutinin gene and experimentally demonstrate that some of the mutations allow the virus to grow at higher temperatures. Our work therefore describes a powerful new approach for predicting stabilizing mutations that can be successfully applied even to large, complex proteins such as hemagglutinin. This approach also makes a mathematical link between phylogenetics and experimentally measurable protein properties, potentially paving the way for more accurate analyses of molecular evolution

The host galaxies and explosion sites of long-duration gamma-ray bursts: Hubble Space Telescope near-infrared imaging

We present the results of a Hubble Space Telescope WFC3/F160WSnapshot survey of the host galaxies of 39 long-duration gamma-ray bursts (LGRBs) at z < 3. We have non-detections of hosts at the locations of four bursts. Sufficient accuracy to astrometrically align optical afterglowimages and determine the location of the LGRB within its hostwas possible for 31/35 detected hosts. In agreement with other work, we find the luminosity distribution of LGRB hosts is significantly fainter than that of a star formation rate-weighted field galaxy sample over the same redshift range, indicating LGRBs are not unbiasedly tracing the star formation rate. Morphologically, the sample of LGRB hosts is dominated by spiral-like or irregular galaxies. We find evidence for evolution of the population of LGRB hosts towards lower luminosity, higher concentrated hosts at lower redshifts. Their half-light radii are consistent with other LGRB host samples where measurements were made on rest-frame UV observations. In agreement with recent work, we find their 80 per cent enclosed flux radii distribution to be more extended than previously thought, making them intermediate between core-collapse supernova (CCSN) and superluminous supernova (SLSN) hosts. The galactocentric projectedoffset distribution confirms LGRBs as centrally concentrated, much more so than CCSNe and similar to SLSNe. LGRBs are strongly biased towards the brighter regions in their host light distributions, regardless of their offset. We find a correlation between the luminosity of the LGRB explosion site and the intrinsic column density, NH, towards the burst. © 2017 The Authors

Treatment compliance and effectiveness of a cognitive behavioural intervention for low back pain : a complier average causal effect approach to the BeST data set

Background: Group cognitive behavioural intervention (CBI) is effective in reducing low-back pain and disability in comparison to advice in primary care. The aim of this analysis was to investigate the impact of compliance on estimates of treatment effect and to identify factors associated with compliance. Methods: In this multicentre trial, 701 adults with troublesome sub-acute or chronic low-back pain were recruited from 56 general practices. Participants were randomised to advice (control n = 233) or advice plus CBI (n = 468). Compliance was specified a priori as attending a minimum of three group sessions and the individual assessment. We estimated the complier average causal effect (CACE) of treatment. Results: Comparison of the CACE estimate of the mean treatment difference to the intention-to-treat (ITT) estimate at 12 months showed a greater benefit of CBI amongst participants compliant with treatment on the Roland Morris Questionnaire (CACE: 1.6 points, 95% CI 0.51 to 2.74; ITT: 1.3 points, 95% CI 0.55 to 2.07), the Modified Von Korff disability score (CACE: 12.1 points, 95% CI 6.07 to 18.17; ITT: 8.6 points, 95% CI 4.58 to 12.64) and the Modified von Korff pain score (CACE: 10.4 points, 95% CI 4.64 to 16.10; ITT: 7.0 points, 95% CI 3.26 to 10.74). People who were non-compliant were younger and had higher pain scores at randomisation. Conclusions: Treatment compliance is important in the effectiveness of group CBI. Younger people and those with more pain are at greater risk of non-compliance

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

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