Evolution favors protein mutational robustness in sufficiently large populations

The effectiveness of selection in a species affects the direction of amino acid frequency evolution

Nearly neutral theory predicts that species with higher effective population size (N e ) are better able to purge slightly deleterious mutations. We compare evolution in high-N e vs. low-N e vertebrates to reveal which amino acid frequencies are subject to subtle selective preferences. We take three complementary approaches, two measuring flux and one measuring outcomes. First, we fit non-stationary substitution models of amino acid flux using maximum likelihood, comparing the high-N e clade of rodents and lagomorphs to its low-N e sister clade of primates and colugos. Second, we compare evolutionary outcomes across a wider range of vertebrates, via correlations between amino acid frequencies and N e . Third, we dissect the details of flux in human, chimpanzee, mouse, and rat, as scored by parsimony - this also enables comparison to a historical paper. All three methods agree on which amino acids are preferred under more effective selection. Preferred amino acids tend to be smaller, less costly to synthesize, and to promote intrinsic structural disorder. Parsimony-induced bias in the historical study produces an apparent reduction in structural disorder, perhaps driven by slightly deleterious substitutions. Within highly exchangeable pairs of amino acids, arginine is strongly preferred over lysine, and valine over isoleucine, consistent with more effective selection preferring a marginally larger free energy of folding. These two preferences match differences between thermophiles and mesophilic relatives. These results reveal the biophysical consequences of mutation-selection-drift balance, and demonstrate the utility of nearly neutral theory for understanding protein evolution.

Mutational robustness and the role of buffer genes in evolvability

Organisms rely on mutations to fuel adaptive evolution. However, many mutations impose a negative effect on fitness. Cells may have therefore evolved mechanisms that affect the phenotypic effects of mutations, thus conferring mutational robustness. Specifically, so-called buffer genes are hypothesized to interact directly or indirectly with genetic variation and reduce its effect on fitness. Environmental or genetic perturbations can change the interaction between buffer genes and genetic variation, thereby unmasking the genetic variation's phenotypic effects and thus providing a source of variation for natural selection to act on. This review provides an overview of our understanding of mutational robustness and buffer genes, with the chaperone gene HSP90 as a key example. It discusses whether buffer genes merely affect standing variation or also interact with de novo mutations, how mutational robustness could influence evolution, and whether mutational robustness might be an evolved trait or rather a mere side-effect of complex genetic interactions.

Changing fitness effects of mutations through long-term bacterial evolution

The distribution of fitness effects of new mutations shapes evolution, but it is challenging to observe how it changes as organisms adapt. Using Escherichia coli lineages spanning 50,000 generations of evolution, we quantify the fitness effects of insertion mutations in every gene. Macroscopically, the fraction of deleterious mutations changed little over time whereas the beneficial tail declined sharply, approaching an exponential distribution. Microscopically, changes in individual gene essentiality and deleterious effects often occurred in parallel; altered essentiality is only partly explained by structural variation. The identity and effect sizes of beneficial mutations changed rapidly over time, but many targets of selection remained predictable because of the importance of loss-of-function mutations. Taken together, these results reveal the dynamic-but statistically predictable-nature of mutational fitness effects.

Surface frustration re-patterning underlies the structural landscape and evolvability of fungal orphan candidate effectors

Pathogens secrete effector proteins to subvert host physiology and cause disease. Effectors are engaged in a molecular arms race with the host resulting in conflicting evolutionary constraints to manipulate host cells without triggering immune responses. The molecular mechanisms allowing effectors to be at the same time robust and evolvable remain largely enigmatic. Here, we show that 62 conserved structure-related families encompass the majority of fungal orphan effector candidates in the Pezizomycotina subphylum. These effectors diversified through changes in patterns of thermodynamic frustration at surface residues. The underlying mutations tended to increase the robustness of the overall effector protein structure while switching potential binding interfaces. This mechanism could explain how conserved effector families maintained biological activity over long evolutionary timespans in different host environments and provides a model for the emergence of sequence-unrelated effector families with conserved structures.

Marginal specificity in protein interactions constrains evolution of a paralogous family

The evolution of novel functions in biology relies heavily on gene duplication and divergence, creating large paralogous protein families. Selective pressure to avoid detrimental cross-talk often results in paralogs that exhibit exquisite specificity for their interaction partners. But how robust or sensitive is this specificity to mutation? Here, using deep mutational scanning, we demonstrate that a paralogous family of bacterial signaling proteins exhibits marginal specificity, such that many individual substitutions give rise to substantial cross-talk between normally insulated pathways. Our results indicate that sequence space is locally crowded despite overall sparseness, and we provide evidence that this crowding has constrained the evolution of bacterial signaling proteins. These findings underscore how evolution selects for "good enough" rather than optimized phenotypes, leading to restrictions on the subsequent evolution of paralogs.

Experimental exploration of a ribozyme neutral network using evolutionary algorithm and deep learning

A neutral network connects all genotypes with equivalent phenotypes in a fitness landscape and plays an important role in the mutational robustness and evolvability of biomolecules. In contrast to earlier theoretical works, evidence of large neutral networks has been lacking in recent experimental studies of fitness landscapes. This suggests that evolution could be constrained globally. Here, we demonstrate that a deep learning-guided evolutionary algorithm can efficiently identify neutral genotypes within the sequence space of an RNA ligase ribozyme. Furthermore, we measure the activities of all 2¹⁶ variants connecting two active ribozymes that differ by 16 mutations and analyze mutational interactions (epistasis) up to the 16th order. We discover an extensive network of neutral paths linking the two genotypes and reveal that these paths might be predicted using only information from lower-order interactions. Our experimental evaluation of over 120,000 ribozyme sequences provides important empirical evidence that neutral networks can increase the accessibility and predictability of the fitness landscape.

Neutral networks, which are sets of genotypes connected via single mutations that share the same phenotype, are important for evolvability. Here, the authors provide experimental evidence of a neutral network in an RNA enzyme using a high-throughput assay and deep learning.

Dan Tawfik's Lessons for Protein Engineers about Enzymes Adapting to New Substrates

Natural evolution has been creating new complex systems for billions of years. The process is spontaneous and requires neither intelligence nor moral purpose but is nevertheless difficult to understand. The late Dan Tawfik spent years studying enzymes as they adapted to recognize new substrates. Much of his work focused on gaining fundamental insights, so the practical utility of his experiments may not be obvious even to accomplished protein engineers. Here we focus on two questions fundamental to any directed evolution experiment. Which proteins are the best starting points for such experiments? Which trait(s) of the chosen parental protein should be evolved to achieve the desired outcome? We summarize Tawfik's contributions to our understanding of these problems, to honor his memory and encourage those unfamiliar with his ideas to read his publications.

Evolutionary Landscapes of Host-Virus Arms Races

Vertebrate immune systems suppress viral infection using both innate restriction factors and adaptive immunity. Viruses mutate to escape these defenses, driving hosts to counterevolve to regain fitness. This cycle recurs repeatedly, resulting in an evolutionary arms race whose outcome depends on the pace and likelihood of adaptation by host and viral genes. Although viruses evolve faster than their vertebrate hosts, their proteins are subject to numerous functional constraints that impact the probability of adaptation. These constraints are globally defined by evolutionary landscapes, which describe the fitness and adaptive potential of all possible mutations. We review deep mutational scanning experiments mapping the evolutionary landscapes of both host and viral proteins engaged in arms races. For restriction factors and some broadly neutralizing antibodies, landscapes favor the host, which may help to level the evolutionary playing field against rapidly evolving viruses. We discuss the biophysical underpinnings of these landscapes and their therapeutic implications.

Evolutionary dynamics, evolutionary forces, and robustness: A nonequilibrium statistical mechanics perspective

Evolution through natural selection is an overwhelmingly complex process, and it is not surprising that theoretical approaches are strongly simplifying it. For instance, population genetics considers mainly dynamics of gene allele frequencies. Here, we develop a complementary approach to evolutionary dynamics based on three elements—organism reproduction, variations, and selection—that are essential for any evolutionary theory. By considering such general dynamics as a stochastic thermodynamic process, we clarify the nature and action of the evolutionary forces. We show that some of the forces cannot be described solely in terms of fitness landscapes. We also find that one force contribution can make organism reproduction insensitive (robust) to variations.

Any realistic evolutionary theory has to consider 1) the dynamics of organisms that reproduce and possess heritable traits, 2) the appearance of stochastic variations in these traits, and 3) the selection of those organisms that better survive and reproduce. These elements shape the “evolutionary forces” that characterize the evolutionary dynamics. Here, we introduce a general model of reproduction–variation–selection dynamics. By treating these dynamics as a nonequilibrium thermodynamic process, we make precise the notion of the forces that characterize evolution. One of these forces, in particular, can be associated with the robustness of reproduction to variations. Some of the detailed predictions of our model can be tested by quantitative laboratory experiments, similar to those performed in the past on evolving populations of proteins or viruses.

Microfluidic deep mutational scanning of the human executioner caspases reveals differences in structure and regulation

The human caspase family comprises 12 cysteine proteases that are centrally involved in cell death and inflammation responses. The members of this family have conserved sequences and structures, highly similar enzymatic activities and substrate preferences, and overlapping physiological roles. In this paper, we present a deep mutational scan of the executioner caspases CASP3 and CASP7 to dissect differences in their structure, function, and regulation. Our approach leverages high-throughput microfluidic screening to analyze hundreds of thousands of caspase variants in tightly controlled in vitro reactions. The resulting data provides a large-scale and unbiased view of the impact of amino acid substitutions on the proteolytic activity of CASP3 and CASP7. We use this data to pinpoint key functional differences between CASP3 and CASP7, including a secondary internal cleavage site, CASP7 Q196 that is not present in CASP3. Our results will open avenues for inquiry in caspase function and regulation that could potentially inform the development of future caspase-specific therapeutics.

Lanza V, Rodríguez-Beltrán J, Galán JC, San Millán A, Cantón R, Coque TM. Evolutionary Pathways and Trajectories in Antibiotic Resistance

Evolution is the hallmark of life. Descriptions of the evolution of microorganisms have provided a wealth of information, but knowledge regarding "what happened" has precluded a deeper understanding of "how" evolution has proceeded, as in the case of antimicrobial resistance. The difficulty in answering the "how" question lies in the multihierarchical dimensions of evolutionary processes, nested in complex networks, encompassing all units of selection, from genes to communities and ecosystems. At the simplest ontological level (as resistance genes), evolution proceeds by random (mutation and drift) and directional (natural selection) processes; however, sequential pathways of adaptive variation can occasionally be observed, and under fixed circumstances (particular fitness landscapes), evolution is predictable. At the highest level (such as that of plasmids, clones, species, microbiotas), the systems' degrees of freedom increase dramatically, related to the variable dispersal, fragmentation, relatedness, or coalescence of bacterial populations, depending on heterogeneous and changing niches and selective gradients in complex environments. Evolutionary trajectories of antibiotic resistance find their way in these changing landscapes subjected to random variations, becoming highly entropic and therefore unpredictable. However, experimental, phylogenetic, and ecogenetic analyses reveal preferential frequented paths (highways) where antibiotic resistance flows and propagates, allowing some understanding of evolutionary dynamics, modeling and designing interventions. Studies on antibiotic resistance have an applied aspect in improving individual health, One Health, and Global Health, as well as an academic value for understanding evolution. Most importantly, they have a heuristic significance as a model to reduce the negative influence of anthropogenic effects on the environment.

The antibiotic efflux protein TolC is a highly evolvable target under colicin E1 or TLS phage selection

Bacteriophages and bacterial toxins are promising antibacterial agents to treat infections caused by multidrug-resistant (MDR) bacteria. In fact, bacteriophages have recently been successfully used to treat life-threatening infections caused by MDR bacteria (Schooley RT, Biswas B, Gill JJ, Hernandez-Morales A, Lancaster J, Lessor L, Barr JJ, Reed SL, Rohwer F, Benler S, et al. 2017. Development and use of personalized bacteriophage-based therapeutic cocktails to treat a patient with a disseminated resistant Acinetobacter baumannii infection. Antimicrob Agents Chemother. 61(10); Chan BK, Turner PE, Kim S, Mojibian HR, Elefteriades JA, Narayan D. 2018. Phage treatment of an aortic graft infected with Pseudomonas aeruginosa. Evol Med Public Health. 2018(1):60–66; Petrovic Fabijan A, Lin RCY, Ho J, Maddocks S, Ben Zakour NL, Iredell JR, Westmead Bacteriophage Therapy Team. 2020. Safety of bacteriophage therapy in severe Staphylococcus aureus infection. Nat Microbiol. 5(3):465–472). One potential problem with using these antibacterial agents is the evolution of resistance against them in the long term. Here, we studied the fitness landscape of the Escherichia coli TolC protein, an outer membrane efflux protein that is exploited by a pore forming toxin called colicin E1 and by TLS phage (Pagie L, Hogeweg P. 1999. Colicin diversity: a result of eco-evolutionary dynamics. J Theor Biol. 196(2):251–261; Andersen C, Hughes C, Koronakis V. 2000. Chunnel vision. Export and efflux through bacterial channel-tunnels. EMBO Rep. 1(4):313–318; Koronakis V, Andersen C, Hughes C. 2001. Channel-tunnels. Curr Opin Struct Biol. 11(4):403–407; Czaran TL, Hoekstra RF, Pagie L. 2002. Chemical warfare between microbes promotes biodiversity. Proc Natl Acad Sci U S A. 99(2):786–790; Cascales E, Buchanan SK, Duché D, Kleanthous C, Lloubès R, Postle K, Riley M, Slatin S, Cavard D. 2007. Colicin biology. Microbiol Mol Biol Rev. 71(1):158–229). By systematically assessing the distribution of fitness effects of ∼9,000 single amino acid replacements in TolC using either positive (antibiotics and bile salts) or negative (colicin E1 and TLS phage) selection pressures, we quantified evolvability of the TolC. We demonstrated that the TolC is highly optimized for the efflux of antibiotics and bile salts. In contrast, under colicin E1 and TLS phage selection, TolC sequence is very sensitive to mutations. Finally, we have identified a large set of mutations in TolC that increase resistance of E. coli against colicin E1 or TLS phage without changing antibiotic susceptibility of bacterial cells. Our findings suggest that TolC is a highly evolvable target under negative selection which may limit the potential clinical use of bacteriophages and bacterial toxins if evolutionary aspects are not taken into account.

Resource uptake and the evolution of moderately efficient enzymes

Enzymes speed up reactions that would otherwise be too slow to sustain the metabolism of selfreplicators. Yet, most enzymes seem only moderately efficient, exhibiting kinetic parameters orders of magnitude lower than their expected physically achievable maxima and spanning over surprisingly large ranges of values. Here, we question how these parameters evolve using a mechanistic model where enzyme efficiency is a key component of individual competition for resources. We show that kinetic parameters are under strong directional selection only up to a point, above which enzymes appear to evolve under near-neutrality, thereby confirming the qualitative observation of other modeling approaches. While the existence of a large fitness plateau could potentially explain the extensive variation in enzyme features reported, we show using a population genetics model that such a widespread distribution is an unlikely outcome of evolution on a common landscape, as mutation–selection–drift balance occupy a narrow area even when very moderate biases towards lower efficiency are considered. Instead, differences in the evolutionary context encountered by each enzyme should be involved, such that each evolves on an individual, unique landscape. Our results point to drift and effective population size playing an important role, along with the kinetics of nutrient transporters, the tolerance to high concentrations of intermediate metabolites, and the reversibility of reactions. Enzyme concentration also shapes selection on kinetic parameters, but we show that the joint evolution of concentration and efficiency does not yield extensive variance in evolutionary outcomes when documented costs to protein expression are applied.

Quantifying the Mutational Robustness of Protein-Coding Genes

We use large-scale mutagenesis data and computer simulations to quantify the mutational robustness of protein-coding genes by taking into account constraints arising from protein function and the genetic code. Analyses of the distribution of amino acid substitutions from 18 mutagenesis studies revealed an average of 45% of neutral variants; while mutagenesis data of 12 proteins artificially designed under no other constraints but stability, reach an average of 60%. Simulations using a lattice protein model allow us to contrast these estimates to the expected mutational robustness of protein families by generating unbiased samples of foldable sequences, which we find to have 30% of neutral variants. In agreement with mutagenesis data of designed proteins, the model shows that maximally robust protein families might access up to twice the amount of neutral variants observed in the unbiased samples (i.e. 60%). A biophysical model of protein-ligand binding suggests that constraints associated to molecular function have only a moderate impact on robustness of approximately 5 to 10% of neutral variants; and that the direction of this effect depends on the relation between functional performance and thermodynamic stability. Although the genetic code constraints the access of a gene's nucleotide sequence to only 30% of the full distribution of amino acid mutations, it provides an extra 15 to 20% of neutral variants to the estimations above, such that the expected, observed, and maximal robustness of protein-coding genes are approximately 50, 65, and 75%, respectively. We discuss our results in the light of three main hypothesis put forward to explain the existence of mutationally robust genes.

Selection enhances protein evolvability by increasing mutational robustness and foldability

Natural selection can promote or hinder a population’s evolvability—the ability to evolve new and adaptive phenotypes—but the underlying mechanisms are poorly understood. To examine how the strength of selection affects evolvability, we subjected populations of yellow fluorescent protein to directed evolution under different selection regimes and then evolved them toward the new phenotype of green fluorescence. Populations under strong selection for the yellow phenotype evolved the green phenotype most rapidly. They did so by accumulating mutations that increase both robustness to mutations and foldability. Under weak selection, neofunctionalizing mutations rose to higher frequency at first, but more frequent deleterious mutations undermined their eventual success. Our experiments show how selection can enhance evolvability by enhancing robustness and create the conditions necessary for evolutionary success.

Combining structure and genomics to understand antimicrobial resistance

Antimicrobials against bacterial, viral and parasitic pathogens have transformed human and animal health. Nevertheless, their widespread use (and misuse) has led to the emergence of antimicrobial resistance (AMR) which poses a potentially catastrophic threat to public health and animal husbandry. There are several routes, both intrinsic and acquired, by which AMR can develop. One major route is through non-synonymous single nucleotide polymorphisms (nsSNPs) in coding regions. Large scale genomic studies using high-throughput sequencing data have provided powerful new ways to rapidly detect and respond to such genetic mutations linked to AMR. However, these studies are limited in their mechanistic insight. Computational tools can rapidly and inexpensively evaluate the effect of mutations on protein function and evolution. Subsequent insights can then inform experimental studies, and direct existing or new computational methods. Here we review a range of sequence and structure-based computational tools, focussing on tools successfully used to investigate mutational effect on drug targets in clinically important pathogens, particularly Mycobacterium tuberculosis. Combining genomic results with the biophysical effects of mutations can help reveal the molecular basis and consequences of resistance development. Furthermore, we summarise how the application of such a mechanistic understanding of drug resistance can be applied to limit the impact of AMR.

Large-scale in silico mutagenesis experiments reveal optimization of genetic code and codon usage for protein mutational robustness

Background

How, and the extent to which, evolution acts on DNA and protein sequences to ensure mutational robustness and evolvability is a long-standing open question in the field of molecular evolution. We addressed this issue through the first structurome-scale computational investigation, in which we estimated the change in folding free energy upon all possible single-site mutations introduced in more than 20,000 protein structures, as well as through available experimental stability and fitness data.

Results

At the amino acid level, we found the protein surface to be more robust against random mutations than the core, this difference being stronger for small proteins. The destabilizing and neutral mutations are more numerous in the core and on the surface, respectively, whereas the stabilizing mutations are about 4% in both regions. At the genetic code level, we observed smallest destabilization for mutations that are due to substitutions of base III in the codon, followed by base I, bases I+III, base II, and other multiple base substitutions. This ranking highly anticorrelates with the codon-anticodon mispairing frequency in the translation process. This suggests that the standard genetic code is optimized to limit the impact of random mutations, but even more so to limit translation errors. At the codon level, both the codon usage and the usage bias appear to optimize mutational robustness and translation accuracy, especially for surface residues.

Conclusion

Our results highlight the non-universality of mutational robustness and its multiscale dependence on protein features, the structure of the genetic code, and the codon usage. Our analyses and approach are strongly supported by available experimental mutagenesis data.

Small-Molecule Antiviral β-d-N4-Hydroxycytidine Inhibits a Proofreading-Intact Coronavirus with a High Genetic Barrier to Resistance

Coronaviruses (CoVs) have emerged from animal reservoirs to cause severe and lethal disease in humans, but there are currently no FDA-approved antivirals to treat the infections. One class of antiviral compounds, nucleoside analogues, mimics naturally occurring nucleosides to inhibit viral replication. While these compounds have been successful therapeutics for several viral infections, mutagenic nucleoside analogues, such as ribavirin and 5-fluorouracil, have been ineffective at inhibiting CoVs. This has been attributed to the proofreading activity of the viral 3'-5' exoribonuclease (ExoN). β-d-N4-Hydroxycytidine (NHC) (EIDD-1931; Emory Institute for Drug Development) has recently been reported to inhibit multiple viruses. Here, we demonstrate that NHC inhibits both murine hepatitis virus (MHV) (50% effective concentration [EC50] = 0.17 μM) and Middle East respiratory syndrome CoV (MERS-CoV) (EC50 = 0.56 μM) with minimal cytotoxicity. NHC inhibited MHV lacking ExoN proofreading activity similarly to wild-type (WT) MHV, suggesting an ability to evade or overcome ExoN activity. NHC inhibited MHV only when added early during infection, decreased viral specific infectivity, and increased the number and proportion of G:A and C:U transition mutations present after a single infection. Low-level NHC resistance was difficult to achieve and was associated with multiple transition mutations across the genome in both MHV and MERS-CoV. These results point to a virus-mutagenic mechanism of NHC inhibition in CoVs and indicate a high genetic barrier to NHC resistance. Together, the data support further development of NHC for treatment of CoVs and suggest a novel mechanism of NHC interaction with the CoV replication complex that may shed light on critical aspects of replication.IMPORTANCE The emergence of coronaviruses (CoVs) into human populations from animal reservoirs has demonstrated their epidemic capability, pandemic potential, and ability to cause severe disease. However, no antivirals have been approved to treat these infections. Here, we demonstrate the potent antiviral activity of a broad-spectrum ribonucleoside analogue, β-d-N4-hydroxycytidine (NHC), against two divergent CoVs. Viral proofreading activity does not markedly impact sensitivity to NHC inhibition, suggesting a novel interaction between a nucleoside analogue inhibitor and the CoV replicase. Further, passage in the presence of NHC generates only low-level resistance, likely due to the accumulation of multiple potentially deleterious transition mutations. Together, these data support a mutagenic mechanism of inhibition by NHC and further support the development of NHC for treatment of CoV infections.

Structural and biochemical insights into an engineered high-redox potential laccase overproduced in Aspergillus

Fungal laccases have great potential as biocatalysts oxidizing a variety of aromatic compounds using oxygen as co-substrate. Here, the crystal structure of 7D5 laccase (PDB 6H5Y), developed in Saccharomyces cerevisiae and overproduced in Aspergillus oryzae, is compared with that of the wild type produced by basidiomycete PM1 (Coriolopsis sp.), PDB 5ANH. SAXS showed both enzymes form monomers in solution, 7D5 laccase with a more oblate geometric structure due to heavier and more heterogeneous glycosylation. The enzyme presents superior catalytic constants towards all tested substrates, with no significant change in optimal pH or redox potential. It shows noticeable high catalytic efficiency with ABTS and dimethyl-4-phenylenediamine, 7 and 32 times better than the wild type, respectively. Computational simulations demonstrated a more favorable binding and electron transfer from the substrate to the T1 copper due to the introduced mutations. PM1 laccase is exceptionally stable to thermal inactivation (t_1/2 70 °C = 1.2 h). Yet, both enzymes display outstanding structural robustness at high temperature. They keep folded during 2 h at 100 °C though, thereafter, 7D5 laccase unfolds faster. Rigidification of certain loops due to the mutations added on the protein surface would diminish the capability to absorb temperature fluctuations leading to earlier protein unfolding.

The causes of evolvability and their evolution

Evolvability is the ability of a biological system to produce phenotypic variation that is both heritable and adaptive. It has long been the subject of anecdotal observations and theoretical work. In recent years, however, the molecular causes of evolvability have been an increasing focus of experimental work. Here, we review recent experimental progress in areas as different as the evolution of drug resistance in cancer cells and the rewiring of transcriptional regulation circuits in vertebrates. This research reveals the importance of three major themes: multiple genetic and non-genetic mechanisms to generate phenotypic diversity, robustness in genetic systems, and adaptive landscape topography. We also discuss the mounting evidence that evolvability can evolve and the question of whether it evolves adaptively.

Effect of Correlated Pair Mutations in Protein Misfolding

A Monte Carlo simulation based sequence design method is proposed to explore the effect of correlated pair mutations in proteins. In the designed sequences, the most correlated residue pairs are identified and mutated with all possible amino acid pairs except those already present. The cumulative correlated pair mutations generated an array of mutated sequences. Results show a significant increase in the probability of misfolding for correlated pair mutations as compared to that of the random pair mutations. The pair mutations of correlated residues that are in contact record a higher probability of misfolding as compared to the correlated residues that are not in contact. The probability of misfolding increases on pair mutation of nonlocally correlated residue pairs as compared to that of the locally correlated residue pairs. The choice of a compact or expanded conformation does not depend on the type of correlated pair mutations. Pair mutation of the most correlated residue pairs at the surface with hydrophobic amino acids results in higher misfolding probability as compared to that in the core. An exactly opposite behavior is observed on pair mutation with hydrophilic and charged amino acid pairs. The neutral amino acid pairs do not differentiate between core and surface sites. This study may be used for targeted mutation experiments to predict complex mutation patterns, reengineer the existing proteins, and design new proteins with reduced misfolding propensity.

Shuffling the Neutral Drift of Unspecific Peroxygenase in Saccharomyces cerevisiae

Fungal peroxygenases resemble the peroxide shunt pathway of cytochrome P450 monoxygenases, performing selective oxyfunctionalizations of unactivated C-H bonds in a broad range of organic compounds. In this study, we combined neutral genetic drift and in vivo DNA shuffling to generate highly functional peroxygenase mutant libraries. The panel of neutrally evolved peroxygenases showed different activity profiles for peroxygenative substrates and improved stability with respect to temperature and the presence of organic cosolvents, making the enzymes valuable blueprints for emerging evolution campaigns. This association of DNA recombination and neutral drift is paving the way for future work in peroxygenase engineering and, from a more general perspective, to any other enzyme system heterologously expressed in S. cerevisiae.

Unspecific peroxygenase (UPO) is a highly promiscuous biocatalyst, and its selective mono(per)oxygenase activity makes it useful for many synthetic chemistry applications. Among the broad repertory of library creation methods for directed enzyme evolution, genetic drift allows neutral mutations to be accumulated gradually within a polymorphic network of variants. In this study, we conducted a campaign of genetic drift with UPO in Saccharomyces cerevisiae, so that neutral mutations were simply added and recombined in vivo. With low mutational loading and an activity threshold of 45% of the parent's native function, mutant libraries enriched in folded active UPO variants were generated. After only eight rounds of genetic drift and DNA shuffling, we identified an ensemble of 25 neutrally evolved variants with changes in peroxidative and peroxygenative activities, kinetic thermostability, and enhanced tolerance to organic solvents. With an average of 4.6 substitutions introduced per clone, neutral mutations covered approximately 10% of the protein sequence. Accordingly, this study opens new avenues for UPO design by bringing together neutral genetic drift and DNA recombination in vivo.

IMPORTANCE Fungal peroxygenases resemble the peroxide shunt pathway of cytochrome P450 monoxygenases, performing selective oxyfunctionalizations of unactivated C-H bonds in a broad range of organic compounds. In this study, we combined neutral genetic drift and in vivo DNA shuffling to generate highly functional peroxygenase mutant libraries. The panel of neutrally evolved peroxygenases showed different activity profiles for peroxygenative substrates and improved stability with respect to temperature and the presence of organic cosolvents, making the enzymes valuable blueprints for emerging evolution campaigns. This association of DNA recombination and neutral drift is paving the way for future work in peroxygenase engineering and, from a more general perspective, to any other enzyme system heterologously expressed in S. cerevisiae.

Decanalizing thinking on genetic canalization

The concept of genetic canalization has had an abiding influence on views of complex-trait evolution. A genetically canalized system has evolved to become less sensitive to the effects of mutation. When a gene product that supports canalization is compromised, the phenotypic impacts of a mutation should be more pronounced. This expected increase in mutational effects not only has important consequences for evolution, but has also motivated strategies to treat disease. However, recent studies demonstrate that, when putative agents of genetic canalization are impaired, systems do not behave as expected. Here, we review the evidence that is used to infer whether particular gene products are agents of genetic canalization. Then we explain how such inferences often succumb to a converse error. We go on to show that several candidate agents of genetic canalization increase the phenotypic impacts of some mutations while decreasing the phenotypic impacts of others. These observations suggest that whether a gene product acts as a 'buffer' (lessening mutational effects) or a 'potentiator' (increasing mutational effects) is not a fixed property of the gene product but instead differs for the different mutations with which it interacts. To investigate features of genetic interactions that might predispose them toward buffering versus potentiation, we explore simulated gene-regulatory networks. Similarly to putative agents of genetic canalization, the gene products in simulated networks also modify the phenotypic effects of mutations in other genes without a strong overall tendency towards lessening or increasing these effects. In sum, these observations call into question whether complex traits have evolved to become less sensitive (i.e., are canalized) to genetic change, and the degree to which trends exist that predict how one genetic change might alter another's impact. We conclude by discussing approaches to address these and other open questions that are brought into focus by re-thinking genetic canalization.

Directed -in vitro- evolution of Precambrian and extant Rubiscos

Rubisco is an ancient, catalytically conserved yet slow enzyme, which plays a central role in the biosphere's carbon cycle. The design of Rubiscos to increase agricultural productivity has hitherto relied on the use of in vivo selection systems, precluding the exploration of biochemical traits that are not wired to cell survival. We present a directed -in vitro- evolution platform that extracts the enzyme from its biological context to provide a new avenue for Rubisco engineering. Precambrian and extant form II Rubiscos were subjected to an ensemble of directed evolution strategies aimed at improving thermostability. The most recent ancestor of proteobacteria -dating back 2.4 billion years- was uniquely tolerant to mutagenic loading. Adaptive evolution, focused evolution and genetic drift revealed a panel of thermostable mutants, some deviating from the characteristic trade-offs in CO2-fixing speed and specificity. Our findings provide a novel approach for identifying Rubisco variants with improved catalytic evolution potential.

Determining the factors driving selective effects of new nonsynonymous mutations

The distribution of fitness effects (DFE) of new mutations plays a fundamental role in evolutionary genetics. However, the extent to which the DFE differs across species has yet to be systematically investigated. Furthermore, the biological mechanisms determining the DFE in natural populations remain unclear. Here, we show that theoretical models emphasizing different biological factors at determining the DFE, such as protein stability, back-mutations, species complexity, and mutational robustness make distinct predictions about how the DFE will differ between species. Analyzing amino acid-changing variants from natural populations in a comparative population genomic framework, we find that humans have a higher proportion of strongly deleterious mutations than Drosophila melanogaster. Furthermore, when comparing the DFE across yeast, Drosophila, mice, and humans, the average selection coefficient becomes more deleterious with increasing species complexity. Last, pleiotropic genes have a DFE that is less variable than that of nonpleiotropic genes. Comparing four categories of theoretical models, only Fisher's geometrical model (FGM) is consistent with our findings. FGM assumes that multiple phenotypes are under stabilizing selection, with the number of phenotypes defining the complexity of the organism. Our results suggest that long-term population size and cost of complexity drive the evolution of the DFE, with many implications for evolutionary and medical genomics.

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.

Understanding how enzymes evolve is a fundamental question that can help us decipher not only the mechanisms of evolution on a higher level, i.e., whole organisms, but also advances our knowledge of sequence-structure-function relationships as a guide to artificial evolution in the test tube. An important yet unexplained phenomenon occurs during the evolution of a new enzymatic function; it has been observed that new and ancestral functions often trade-off only weakly, meaning the original native activity is initially maintained at a high level despite drastic improvement of the new promiscuous activity. It has previously been proposed that weak trade-offs occur because the native activity is robust to mutations while the promiscuous activity is not. However, the present work contradicts this hypothesis, based on the detailed characterization of mutational effects on both activities in two examples of enzyme evolution. We propose an alternative explanation: the weak activity trade-off is consistent with being a by-product of strong selection for the new activity rather than an intrinsic property of the native activity.

Antigenic diversification is correlated with increased thermostability in a mammalian virus

The theory of plastogenetic congruence posits that ultimately, the pressure to maintain function in the face of biomolecular destabilization produces robustness. As temperature goes up so does destabilization. Thus, genetic robustness, defined as phenotypic constancy despite mutation, should correlate with survival during thermal challenge. We tested this hypothesis using vesicular stomatitis virus (VSV). We produced two sets of evolved strains after selection for higher thermostability by either preincubation at 37°C or by incubation at 40°C during infection. These VSV populations became more thermostable and also more fit in the absence of thermal selection, demonstrating an absence of tradeoffs. Eleven out of 12 evolved populations had a fixed, nonsynonymous substitution in the nucleocapsid (N) open reading frame. There was a partial correlation between thermostability and mutational robustness that was observed when the former was measured at 42°C, but not at 37°C. These results are consistent with our earlier work and suggest that the relationship between robustness and thermostability is complex. Surprisingly, many of the thermostable strains also showed increased resistance to monoclonal antibody and polyclonal sera, including sera from natural hosts. These data suggest that evolved thermostability may lead to antigenic diversification and an increased ability to escape immune surveillance in febrile hosts, and potentially to an improved robustness. These relationships have important implications not only in terms of viral pathogenesis, but also for the development of vaccine vectors and oncolytic agents.

Dissecting enzyme function with microfluidic-based deep mutational scanning

Natural enzymes are incredibly proficient catalysts, but engineering them to have new or improved functions is challenging due to the complexity of how an enzyme's sequence relates to its biochemical properties. Here, we present an ultrahigh-throughput method for mapping enzyme sequence-function relationships that combines droplet microfluidic screening with next-generation DNA sequencing. We apply our method to map the activity of millions of glycosidase sequence variants. Microfluidic-based deep mutational scanning provides a comprehensive and unbiased view of the enzyme function landscape. The mapping displays expected patterns of mutational tolerance and a strong correspondence to sequence variation within the enzyme family, but also reveals previously unreported sites that are crucial for glycosidase function. We modified the screening protocol to include a high-temperature incubation step, and the resulting thermotolerance landscape allowed the discovery of mutations that enhance enzyme thermostability. Droplet microfluidics provides a general platform for enzyme screening that, when combined with DNA-sequencing technologies, enables high-throughput mapping of enzyme sequence space.

Comparative laboratory evolution of ordered and disordered enzymes

Intrinsically disordered proteins are ubiquitous in nature. To assess potential evolutionary advantages and disadvantages of structural disorder under controlled laboratory conditions, we directly compared the evolvability of weakly active ordered and disordered variants of dihydrofolate reductase by genetic selection. The circularly permuted Escherichia coli enzyme, which exists as a molten globule in the absence of ligands, and a well folded deletion mutant of the Bacillus stearothermophilus enzyme served as starting points. Both scaffolds evolved at similar rates and to similar extents, reaching near-native activity after three rounds of mutagenesis and selection. Surprisingly, however, the starting structural properties of the two scaffolds changed only marginally during optimization. Although the ordered and disordered proteins accumulated distinct sets of mutations, the changes introduced likely improved catalytic efficiency indirectly in both cases by bolstering the network of dynamic conformational fluctuations that productively couple into the reaction coordinate.

On the Nature and Evolutionary Impact of Phenotypic Robustness Mechanisms

Biologists have long observed that physiological and developmental processes are insensitive, or robust, to many genetic and environmental perturbations. A complete understanding of the evolutionary causes and consequences of this robustness is lacking. Recent progress has been made in uncovering the regulatory mechanisms that underlie environmental robustness in particular. Less is known about robustness to the effects of mutations, and indeed the evolution of mutational robustness remains a controversial topic. The controversy has spread to related topics, in particular the evolutionary relevance of cryptic genetic variation. This review aims to synthesize current understanding of robustness mechanisms and to cut through the controversy by shedding light on what is and is not known about mutational robustness. Some studies have confused mutational robustness with non-additive interactions between mutations (epistasis). We conclude that a profitable way forward is to focus investigations (and rhetoric) less on mutational robustness and more on epistasis.

DNA polymerases engineered by directed evolution to incorporate non-standard nucleotides

DNA polymerases have evolved for billions of years to accept natural nucleoside triphosphate substrates with high fidelity and to exclude closely related structures, such as the analogous ribonucleoside triphosphates. However, polymerases that can accept unnatural nucleoside triphosphates are desired for many applications in biotechnology. The focus of this review is on non-standard nucleotides that expand the genetic "alphabet." This review focuses on experiments that, by directed evolution, have created variants of DNA polymerases that are better able to accept unnatural nucleotides. In many cases, an analysis of past evolution of these polymerases (as inferred by examining multiple sequence alignments) can help explain some of the mutations delivered by directed evolution.

Limits of neutral drift: lessons from the in vitro evolution of two ribozymes

The relative contributions of adaptive selection and neutral drift to genetic change are unknown but likely depend on the inherent abundance of functional genotypes in sequence space and how accessible those genotypes are to one another. To better understand the relative roles of selection and drift in evolution, local fitness landscapes for two different RNA ligase ribozymes were examined using a continuous in vitro evolution system under conditions that foster the capacity for neutral drift to mediate genetic change. The exploration of sequence space was accelerated by increasing the mutation rate using mutagenic nucleotide analogs. Drift was encouraged by carrying out evolution within millions of separate compartments to exploit the founder effect. Deep sequencing of individuals from the evolved populations revealed that the distribution of genotypes did not escape the starting local fitness peak, remaining clustered around the sequence used to initiate evolution. This is consistent with a fitness landscape where high-fitness genotypes are sparse and well isolated, and suggests, at least in this context, that neutral drift alone is not a primary driver of genetic change. Neutral drift does, however, provide a repository of genetic variation upon which adaptive selection can act.

Engineering the product profile of a polysialyltransferase

Oligo- and polysaccharides have myriad applications as therapeutic reagents from glycoconjugate vaccines to matrices for tissue engineering. Polysaccharide length may vary over several orders of magnitude and is a critical determinant of both their physical properties and biological activities. Therefore, the tailored synthesis of oligo- and polysaccharides of defined size is a major goal for glycoengineering. By mutagenesis and screening of a bacterial polysialyltransferase (polyST), we identified a single-residue switch that controls the size distribution of polymeric products. Specific substitutions at this site yielded distributive enzymes that synthesize polysaccharides with narrow size distribution ideal for glycoengineering applications. Mechanistic investigation revealed that the wild-type enzyme has an extended binding site that accommodates at least 20 residues of the growing polymer; changes in affinity along this binding site allow fine-tuning of the enzyme's product distribution.

A tractable genotype-phenotype map modelling the self-assembly of protein quaternary structure

The mapping between biological genotypes and phenotypes is central to the study of biological evolution. Here, we introduce a rich, intuitive and biologically realistic genotype–phenotype (GP) map that serves as a model of self-assembling biological structures, such as protein complexes, and remains computationally and analytically tractable. Our GP map arises naturally from the self-assembly of polyomino structures on a two-dimensional lattice and exhibits a number of properties: redundancy (genotypes vastly outnumber phenotypes), phenotype bias (genotypic redundancy varies greatly between phenotypes), genotype component disconnectivity (phenotypes consist of disconnected mutational networks) and shape space covering (most phenotypes can be reached in a small number of mutations). We also show that the mutational robustness of phenotypes scales very roughly logarithmically with phenotype redundancy and is positively correlated with phenotypic evolvability. Although our GP map describes the assembly of disconnected objects, it shares many properties with other popular GP maps for connected units, such as models for RNA secondary structure or the hydrophobic-polar (HP) lattice model for protein tertiary structure. The remarkable fact that these important properties similarly emerge from such different models suggests the possibility that universal features underlie a much wider class of biologically realistic GP maps.

Mutagenic Organized Recombination Process by Homologous IN vivo Grouping (MORPHING) for directed enzyme evolution

Approaches that depend on directed evolution require reliable methods to generate DNA diversity so that mutant libraries can focus on specific target regions. We took advantage of the high frequency of homologous DNA recombination in Saccharomyces cerevisiae to develop a strategy for domain mutagenesis aimed at introducing and in vivo recombining random mutations in defined segments of DNA. Mutagenic Organized Recombination Process by Homologous IN vivo Grouping (MORPHING) is a one-pot random mutagenic method for short protein regions that harnesses the in vivo recombination apparatus of yeast. Using this approach, libraries can be prepared with different mutational loads in DNA segments of less than 30 amino acids so that they can be assembled into the remaining unaltered DNA regions in vivo with high fidelity. As a proof of concept, we present two eukaryotic-ligninolytic enzyme case studies: i) the enhancement of the oxidative stability of a H₂O₂-sensitive versatile peroxidase by independent evolution of three distinct protein segments (Leu28-Gly57, Leu149-Ala174 and Ile199-Leu268); and ii) the heterologous functional expression of an unspecific peroxygenase by exclusive evolution of its native 43-residue signal sequence.

Generation of effective libraries by neutral drift

Neutral drift is a recently developed experimental technique used to identify superior starting points for protein engineering. Neutral drift explores accessible sequence space by repeated rounds of mutagenesis and selection to maintain wild-type function. Mutations that are largely neutral for the native function accumulate, and those that are highly detrimental are purged, yielding a library of high diversity and quality. This technique is useful in situations where laboratory evolution is at a dead end, i.e., when the enzyme activity intended for evolution proves recalcitrant to improvements or is too low to be detected.

Critical mutation rate has an exponential dependence on population size in haploid and diploid populations

Understanding the effect of population size on the key parameters of evolution is particularly important for populations nearing extinction. There are evolutionary pressures to evolve sequences that are both fit and robust. At high mutation rates, individuals with greater mutational robustness can outcompete those with higher fitness. This is survival-of-the-flattest, and has been observed in digital organisms, theoretically, in simulated RNA evolution, and in RNA viruses. We introduce an algorithmic method capable of determining the relationship between population size, the critical mutation rate at which individuals with greater robustness to mutation are favoured over individuals with greater fitness, and the error threshold. Verification for this method is provided against analytical models for the error threshold. We show that the critical mutation rate for increasing haploid population sizes can be approximated by an exponential function, with much lower mutation rates tolerated by small populations. This is in contrast to previous studies which identified that critical mutation rate was independent of population size. The algorithm is extended to diploid populations in a system modelled on the biological process of meiosis. The results confirm that the relationship remains exponential, but show that both the critical mutation rate and error threshold are lower for diploids, rather than higher as might have been expected. Analyzing the transition from critical mutation rate to error threshold provides an improved definition of critical mutation rate. Natural populations with their numbers in decline can be expected to lose genetic material in line with the exponential model, accelerating and potentially irreversibly advancing their decline, and this could potentially affect extinction, recovery and population management strategy. The effect of population size is particularly strong in small populations with 100 individuals or less; the exponential model has significant potential in aiding population management to prevent local (and global) extinction events.

A nonadaptive origin of a beneficial trait: in silico selection for free energy of folding leads to the neutral emergence of mutational robustness in single domain proteins

Proteins are regarded as being robust to the deleterious effects of mutations. Here, the neutral emergence of mutational robustness in a population of single domain proteins is explored using computer simulations. A pairwise contact model was used to calculate the ΔG of folding (ΔG folding) using the three dimensional protein structure of leech eglin C. A random amino acid sequence with low mutational robustness, defined as the average ΔΔG resulting from a point mutation (ΔΔG average), was threaded onto the structure. A population of 1,000 threaded sequences was evolved under selection for stability, using an upper and lower energy threshold. Under these conditions, mutational robustness increased over time in the most common sequence in the population. In contrast, when the wild type sequence was used it did not show an increase in robustness. This implies that the emergence of mutational robustness is sequence specific and that wild type sequences may be close to maximal robustness. In addition, an inverse relationship between ∆∆G average and protein stability is shown, resulting partly from a larger average effect of point mutations in more stable proteins. The emergence of mutational robustness was also observed in the Escherichia coli colE1 Rop and human CD59 proteins, implying that the property may be common in single domain proteins under certain simulation conditions. The results indicate that at least a portion of mutational robustness in small globular proteins might have arisen by a process of neutral emergence, and could be an example of a beneficial trait that has not been directly selected for, termed a "pseudaptation."

Integration of new genes into cellular networks, and their structural maturation

It has been recently discovered that new genes can originate de novo from noncoding DNA, and several biological traits including expression or sequence composition form a continuum from noncoding sequences to conserved genes. In this article, using yeast genes I test whether the integration of new genes into cellular networks and their structural maturation shows such a continuum by analyzing their changes with gene age. I show that 1) The number of regulatory, protein-protein, and genetic interactions increases continuously with gene age, although with very different rates. New regulatory interactions emerge rapidly within a few million years, while the number of protein-protein and genetic interactions increases slowly, with a rate of 2-2.25 × 10(-8)/year and 4.8 × 10(-8)/year, respectively. 2) Gene essentiality evolves relatively quickly: the youngest essential genes appear in proto-genes ∼14 MY old. 3) In contrast to interactions, the secondary structure of proteins and their robustness to mutations indicate that new genes face a bottleneck in their evolution: proto-genes are characterized by high β-strand content, high aggregation propensity, and low robustness against mutations, while conserved genes are characterized by lower strand content and higher stability, most likely due to the higher probability of gene loss among young genes and accumulation of neutral mutations.

Adaptive laboratory evolution -- principles and applications for biotechnology

Adaptive laboratory evolution is a frequent method in biological studies to gain insights into the basic mechanisms of molecular evolution and adaptive changes that accumulate in microbial populations during long term selection under specified growth conditions. Although regularly performed for more than 25 years, the advent of transcript and cheap next-generation sequencing technologies has resulted in many recent studies, which successfully applied this technique in order to engineer microbial cells for biotechnological applications. Adaptive laboratory evolution has some major benefits as compared with classical genetic engineering but also some inherent limitations. However, recent studies show how some of the limitations may be overcome in order to successfully incorporate adaptive laboratory evolution in microbial cell factory design. Over the last two decades important insights into nutrient and stress metabolism of relevant model species were acquired, whereas some other aspects such as niche-specific differences of non-conventional cell factories are not completely understood. Altogether the current status and its future perspectives highlight the importance and potential of adaptive laboratory evolution as approach in biotechnological engineering.

Visualizing time-related data in biology, a review

Time is of the essence in biology as in so much else. For example, monitoring disease progression or the timing of developmental defects is important for the processes of drug discovery and therapy trials. Furthermore, an understanding of the basic dynamics of biological phenomena that are often strictly time regulated (e.g. circadian rhythms) is needed to make accurate inferences about the evolution of biological processes. Recent advances in technologies have enabled us to measure timing effects more accurately and in more detail. This has driven related advances in visualization and analysis tools that try to effectively exploit this data. Beyond timeline plots, notable attempts at more involved temporal interpretation have been made in recent years, but awareness of the available resources is still limited within the scientific community. Here, we review some advances in biological visualization of time-driven processes and consider how they aid data analysis and interpretation.

Random field model reveals structure of the protein recombinational landscape

We are interested in how intragenic recombination contributes to the evolution of proteins and how this mechanism complements and enhances the diversity generated by random mutation. Experiments have revealed that proteins are highly tolerant to recombination with homologous sequences (mutation by recombination is conservative); more surprisingly, they have also shown that homologous sequence fragments make largely additive contributions to biophysical properties such as stability. Here, we develop a random field model to describe the statistical features of the subset of protein space accessible by recombination, which we refer to as the recombinational landscape. This model shows quantitative agreement with experimental results compiled from eight libraries of proteins that were generated by recombining gene fragments from homologous proteins. The model reveals a recombinational landscape that is highly enriched in functional sequences, with properties dominated by a large-scale additive structure. It also quantifies the relative contributions of parent sequence identity, crossover locations, and protein fold to the tolerance of proteins to recombination. Intragenic recombination explores a unique subset of sequence space that promotes rapid molecular diversification and functional adaptation.

Mutation and recombination are the primary sources of genetic variation in evolving populations. The relative benefit of these two diversification mechanisms and how they complement each other has been a long-standing question in evolutionary biology. While it is clear what types of genetic diversity these two mechanisms can create, a significant challenge is relating these sequence changes to changes in fitness. The fitness landscape, which describes this mapping from genotype to phenotype, is extraordinarily complex and defined over an incomprehensibly large space of sequences. Here, we develop a model of the landscape that relies not on the details of this mapping, but rather on the statistical relationships between sequences. By studying the expected values of landscape properties, we can gain insights into the structure of the landscape that are independent of the details of how genotype dictates phenotype. We use this random field model to understand how recombination explores a functionally enriched and diverse subset of protein sequence space.

Weak selection and protein evolution

The "nearly neutral" theory of molecular evolution proposes that many features of genomes arise from the interaction of three weak evolutionary forces: mutation, genetic drift, and natural selection acting at its limit of efficacy. Such forces generally have little impact on allele frequencies within populations from generation to generation but can have substantial effects on long-term evolution. The evolutionary dynamics of weakly selected mutations are highly sensitive to population size, and near neutrality was initially proposed as an adjustment to the neutral theory to account for general patterns in available protein and DNA variation data. Here, we review the motivation for the nearly neutral theory, discuss the structure of the model and its predictions, and evaluate current empirical support for interactions among weak evolutionary forces in protein evolution. Near neutrality may be a prevalent mode of evolution across a range of functional categories of mutations and taxa. However, multiple evolutionary mechanisms (including adaptive evolution, linked selection, changes in fitness-effect distributions, and weak selection) can often explain the same patterns of genome variation. Strong parameter sensitivity remains a limitation of the nearly neutral model, and we discuss concave fitness functions as a plausible underlying basis for weak selection.

Quantifying protein modularity and evolvability: a comparison of different techniques

Modularity increases evolvability by reducing constraints on adaptation and by allowing preexisting parts to function in new contexts for novel uses. Protein evolution provides an excellent context to study the causes and consequences of biological modularity. In order to address such questions, however, an index for protein modularity is necessary. This paper proposes a simple index for protein modularity-"module density"-which is the number of evolutionarily independent modules that compose a protein divided by the number of amino acids in the protein. The decomposition of proteins into constituent modules can be accomplished by either of two classes of methods. The first class of methods relies on "suppositional" criteria to assign amino acids to modules, whereas the second class of methods relies on "coevolutionary" criteria for this task. One simple and practical method from the first class consists of approximating the number of modules in a protein as the number of regular secondary structure elements (i.e., helices and sheets). Methods based on coevolutionary criteria require more elaborate data, but they have the advantage of being able to specify modules without prior assumptions about why they exist. Given the increasing availability of datasets sampling protein mutational spectra (e.g., from comparative genomics, experimental evolution, and computational prediction), methods based on coevolutionary criteria will likely become more promising in the near future. The ability to meaningfully quantify protein modularity via simple indices has the potential to aid future efforts to understand protein evolutionary rate determinants, improve molecular evolution models and engineer novel proteins.

Level of gene expression is a major determinant of protein evolution in the viral order Mononegavirales

Although the rate at which proteins change is a key parameter in molecular evolution, its determinants are poorly understood in viruses. A variety of factors, including gene length, codon usage bias, protein abundance, protein function, and gene expression level, have been shown to affect the rate of protein evolution in a diverse array of organisms. However, the role of these factors in viral evolution has yet to be addressed. The polar 3'-5' stepwise attenuation of transcription in the Mononegavirales, a group of single-strand negative-sense RNA viruses, provides a unique system to explore the determinants of protein evolution in viruses. We analyzed the relative importance of a variety of factors in shaping patterns of sequence variation in full-length genomes from 13 Mononegavirales species. Our analysis suggests that the level of gene expression, and by extension the relative genomic position of each gene, is a key determinant of the protein evolution in these viruses. This appears to be the consequence of selection for translational robustness, but not for translational accuracy, in highly expressed genes. The small genome size and number of proteins encoded by these viruses allowed us to identify other protein-specific factors that may also play a role in virus evolution, such as host-virus interactions and functional constraints. Finally, we explored the evolutionary pressures acting on noncoding regions in Mononegavirales genomes and observed that, despite being less constrained than coding regions, their evolutionary rates are also associated with genomic position.

P450(BM3) (CYP102A1): connecting the dots

P450(BM3) (CYP102A1), a fatty acid hydroxylase from Bacillus megaterium, has been extensively studied over a period of almost forty years. The enzyme has been redesigned to catalyse the oxidation of non-natural substrates as diverse as pharmaceuticals, terpenes and gaseous alkanes using a variety of engineering strategies. Crystal structures have provided a basis for several of the catalytic effects brought about by mutagenesis, while changes to reduction potentials, inter-domain electron transfer rates and catalytic parameters have yielded functional insights. Areas of active research interest include drug metabolite production, the development of process-scale techniques, unravelling general mechanistic aspects of P450 chemistry, methane oxidation, and improving selectivity control to allow the synthesis of fine chemicals. This review draws together the disparate research themes and places them in a historical context with the aim of creating a resource that can be used as a gateway to the field.