Diminishing returns and tradeoffs constrain the laboratory optimization of an enzyme

Enzyme Enhancement Through Computational Stability Design Targeting NMR-Determined Catalytic Hotspots

Enzymes are the quintessential green catalysts, but realizing their full potential for biotechnology typically requires improvement of their biomolecular properties. Catalysis enhancement, however, is often accompanied by impaired stability. Here, we show how the interplay between activity and stability in enzyme optimization can be efficiently addressed by coupling two recently proposed methodologies for guiding directed evolution. We first identify catalytic hotspots from chemical shift perturbations induced by transition-state-analogue binding and then use computational/phylogenetic design (FuncLib) to predict stabilizing combinations of mutations at sets of such hotspots. We test this approach on a previously designed de novo Kemp eliminase, which is already highly optimized in terms of both activity and stability. Most tested variants displayed substantially increased denaturation temperatures and purification yields. Notably, our most efficient engineered variant shows a ∼3-fold enhancement in activity (kcat ∼ 1700 s-1, kcat/KM ∼ 4.3 × 105 M-1 s-1) from an already heavily optimized starting variant, resulting in the most proficient proton-abstraction Kemp eliminase designed to date, with a catalytic efficiency on a par with naturally occurring enzymes. Molecular simulations pinpoint the origin of this catalytic enhancement as being due to the progressive elimination of a catalytically inefficient substrate conformation that is present in the original design. Remarkably, interaction network analysis identifies a significant fraction of catalytic hotspots, thus providing a computational tool which we show to be useful even for natural-enzyme engineering. Overall, our work showcases the power of dynamically guided enzyme engineering as a design principle for obtaining novel biocatalysts with tailored physicochemical properties, toward even anthropogenic reactions.

Computational and Experimental Exploration of Protein Fitness Landscapes: Navigating Smooth and Rugged Terrains

Proteins evolve through complex sequence spaces, with fitness landscapes serving as a conceptual framework that links sequence to function. Fitness landscapes can be smooth, where multiple similarly accessible evolutionary paths are available, or rugged, where the presence of multiple local fitness optima complicate evolution and prediction. Indeed, many proteins, especially those with complex functions or under multiple selection pressures, exist on rugged fitness landscapes. Here we discuss the theoretical framework that underpins our understanding of fitness landscapes, alongside recent work that has advanced our understanding─particularly the biophysical basis for smoothness versus ruggedness. Finally, we address the rapid advances that have been made in computational and experimental exploration and exploitation of fitness landscapes, and how these can identify efficient routes to protein optimization.

How enzyme functions evolve: genetic, structural, and kinetic perspectives

Understanding the emergence or loss of enzyme functions comprises several approaches, such as genetic, structural, and kinetic studies. Promiscuous enzyme activities have been proposed as starting points for the emergence of novel enzyme functions, for example, through genetic models such as neofunctionalization and subfunctionalization. In both cases, neutral evolution would fix gene redundancy, critical in relaxing functional constraints and allowing specific mutations to drive innovation. The evolution of enzyme activities has a structural basis, with genetic mutations modifying the active site architecture, conformational dynamics, or interaction networks, which leads to the creation, enhancement, or restriction of enzyme functions where epistatic interactions are crucial. These structural changes impact the described kinetic mechanisms like ground-state stabilization (affinity), transition-state stabilization (catalysis), or a combination of both. Case studies across diverse enzyme families illustrate these principles, emphasizing the interplay between genetic, structural, and kinetic approaches. Finally, we discuss the importance of understanding evolutionary mechanisms and their impact on protein engineering and drug design for biomedical and industrial applications. However, these studies highlight that further experimental evolutionary data collection is necessary to enable the training of advanced machine learning models for use in biotechnological applications.

From duplication to fusion: Expanding Dayhoff's model of protein evolution

Dayhoff's hypothesis suggests that complex proteins emerged from simpler peptides or domains, which duplicated and fused to create more complex proteins and novel functions. These processes expanded and diversified the protein repertoire within organisms. Extensive studies and reviews over the past two decades have highlighted the impact of gene duplication on protein evolution. However, the role of fusion in this evolutionary narrative remains less understood. This perspective seeks to address this gap by emphasizing the role of fusion in evolution. Fusion is critical in determining the evolutionary fate of duplicated protomers, either preserving their ancestral function or evolving entirely new functions. It complements mutations, insertions, and deletions as evolutionary steps to enhance protein evolvability by expanding the capacity of the protein to explore new structural and functional space.

Computational Stabilization of a Non-Heme Iron Enzyme Enables Efficient Evolution of New Function

Deep learning tools for enzyme design are rapidly emerging, and there is a critical need to evaluate their effectiveness in engineering workflows. Here we show that the deep learning-based tool ProteinMPNN can be used to redesign Fe(II)/αKG superfamily enzymes for greater stability, solubility, and expression while retaining both native activity and industrially relevant non-native functions. This superfamily has diverse catalytic functions and could provide a rich new source of biocatalysts for synthesis and industrial processes. Through systematic comparisons of directed evolution trajectories for a non-native, remote C(sp3)-H hydroxylation reaction, we demonstrate that the stabilized redesign can be evolved more efficiently than the wild-type enzyme. After three rounds of directed evolution, we obtained a 6-fold activity increase from the wild-type parent and an 80-fold increase from the stabilized variant. To generate the initial stabilized variant, we identified multiple structural and sequence constraints to preserve catalytic function. We applied these criteria to produce stabilized, catalytically active variants of a second Fe(II)/αKG enzyme, suggesting that the approach is generalizable to additional members of the Fe(II)/αKG superfamily. ProteinMPNN is user-friendly and widely accessible, and our results provide a framework for the routine implementation of deep learning-based protein stabilization tools in directed evolution workflows for novel biocatalysts.

Protein representations: Encoding biological information for machine learning in biocatalysis

Enzymes offer a more environmentally friendly and low-impact solution to conventional chemistry, but they often require additional engineering for their application in industrial settings, an endeavour that is challenging and laborious. To address this issue, the power of machine learning can be harnessed to produce predictive models that enable the in silico study and engineering of improved enzymatic properties. Such machine learning models, however, require the conversion of the complex biological information to a numerical input, also called protein representations. These inputs demand special attention to ensure the training of accurate and precise models, and, in this review, we therefore examine the critical step of encoding protein information to numeric representations for use in machine learning. We selected the most important approaches for encoding the three distinct biological protein representations - primary sequence, 3D structure, and dynamics - to explore their requirements for employment and inductive biases. Combined representations of proteins and substrates are also introduced as emergent tools in biocatalysis. We propose the division of fixed representations, a collection of rule-based encoding strategies, and learned representations extracted from the latent spaces of large neural networks. To select the most suitable protein representation, we propose two main factors to consider. The first one is the model setup, which is influenced by the size of the training dataset and the choice of architecture. The second factor is the model objectives such as consideration about the assayed property, the difference between wild-type models and mutant predictors, and requirements for explainability. This review is aimed at serving as a source of information and guidance for properly representing enzymes in future machine learning models for biocatalysis.

Evolutionary insights into the stereoselectivity of imine reductases based on ancestral sequence reconstruction

The stereoselectivity of enzymes plays a central role in asymmetric biocatalytic reactions, but there remains a dearth of evolution-driven biochemistry studies investigating the evolutionary trajectory of this vital property. Imine reductases (IREDs) are one such enzyme that possesses excellent stereoselectivity, and stereocomplementary members are pervasive in the family. However, the regulatory mechanism behind stereocomplementarity remains cryptic. Herein, we reconstruct a panel of active ancestral IREDs and trace the evolution of stereoselectivity from ancestors to extant IREDs. Combined with coevolution analysis, we reveal six historical mutations capable of recapitulating stereoselectivity evolution. An investigation of the mechanism with X-ray crystallography shows that they collectively reshape the substrate-binding pocket to regulate stereoselectivity inversion. In addition, we construct an empirical fitness landscape and discover that epistasis is prevalent in stereoselectivity evolution. Our findings emphasize the power of ASR in circumventing the time-consuming large-scale mutagenesis library screening for identifying mutations that change functions and support a Darwinian premise from a molecular perspective that the evolution of biological functions is a stepwise process.

Mutations Selectively Evolving Peroxidase Activity Among Alternative Catalytic Functions of Human Glutathione Transferase P1-1

Glutathione transferases are detoxication enzymes with broad catalytic diversity, and small alterations to the protein's primary structure can have considerable effects on the enzyme's substrate selectivity profile. We demonstrate that two point mutations in glutathione transferase P1-1 suffice to generate 20-fold enhanced non-selenium-dependent peroxidase activity indicating a facile evolutionary trajectory. Designed mutant libraries of the enzyme were screened for catalytic activities with alternative substrates representing four divergent chemistries. The chemical reactions comprised aromatic substitution, Michael addition, thiocarbamoylation, and hydroperoxide reduction. Two mutants, R1 (Y109H) and an R1-based mutant V2 (Q40M-E41Q-A46S-Y109H-V200L), were discovered with 16.3- and 30-foldincreased peroxidase activity with cumene hydroperoxide (CuOOH) compared to the wildtype enzyme, respectively. The basis of the improved peroxidase activity of the mutant V2 was elucidated by constructing double-point mutants. The mutants V501 (Q40M-Y109H) and V503 (E41Q-Y109H) were found to have 20- and 21-fold improvements in peroxidase activity relative to the wildtype enzyme, respectively. The steady-state kinetic profiles of mutants R1 and V2 in the reduction of CuOOH were compared to the wildtype parameters. The kcat values for R1 and V2 were 34- and 57-fold higher, respectively, than that of the wildtype enzyme, whereas the mutant Km values were increased approximately 3-fold. A 10-fold increased catalytic efficiency (kcat/Km) in CuOOH reduction is accomplished by the Tyr109His point mutation in R1. The 23-fold increase of the efficiency obtained in V2 was caused by adding further mutations primarily enhancing kcat. In all mutants with elevated peroxidase activity, His109 played a pivotal role.

Effects of selection stringency on the outcomes of directed evolution

Directed evolution makes mutant lineages compete in climbing complicated sequence-function landscapes. Given this underlying complexity it is unclear how selection stringency, a ubiquitous parameter of directed evolution, impacts the outcome. Here we approach this question in terms of the fitnesses of the candidate variants at each round and the heterogeneity of their distributions of fitness effects. We show that even if the fittest mutant is most likely to yield the fittest mutants in the next round of selection, diversification can improve outcomes by sampling a larger variety of fitness effects. We find that heterogeneity in fitness effects between variants, larger population sizes, and evolution over a greater number of rounds all encourage diversification.

Formalizing the law of diminishing returns in metabolic networks using an electrical analogy

The way biological systems respond to changes in parameter values caused by mutations is a key issue in evolution and quantitative genetics, as it affects fundamental aspects such as adaptation, selective neutrality, robustness, optimality, evolutionary equilibria, etc. We address this question using the enzyme-flux relationship in a metabolic network as a model of the genotype-phenotype relationship. The lack of a suitable mathematical tool from biochemical theory to investigate this relationship led us to use an analogy between electrical circuits and metabolic networks with uni-uni reactions. We show that a behaviour of diminishing returns, which is commonly observed at various phenotypic levels, is inevitable, irrespective of the complexity of the system. We also present a possible generalization to metabolic networks with both uni-uni and bi-bi reactions.

Effects of Clinical Mutations in the Second Coordination Sphere and Remote Regions on the Catalytic Mechanism of Non-Heme Fe(II)/2-Oxoglutarate-Dependent Aspartyl Hydroxylase AspH

Aspartyl/asparaginyl hydroxylase (AspH) catalyzes the post-translational hydroxylations of vital human proteins, playing an essential role in maintaining their biological functions. Single-point mutations in the Second Coordination Sphere (SCS) and long-range (LR) residues of AspH have been linked to pathological conditions such as the ophthalmologic condition Traboulsi syndrome and chronic kidney disease (CKD). Although the clinical impacts of these mutations are established, there is a critical knowledge gap regarding their specific atomistic effects on the catalytic mechanism of AspH. In this study, we report integrated computational investigations on the potential mechanistic implications of four mutant forms of human AspH with clinical importance: R735W, R735Q, R688Q, and G434V. All the mutant forms exhibited altered binding interactions with the co-substrate 2-oxoglutarate (2OG) and the main substrate in the ferric-superoxo and ferryl complexes, which are critical for catalysis, compared to the wild-type (WT). Importantly, the mutations strongly influence the energetics of the frontier molecular orbitals (FMOs) and, thereby, the activation energies for the hydrogen atom transfer (HAT) step compared to the WT AspH. Insights from our study can contribute to enzyme engineering and the development of selective modulators for WT and mutants of AspH, ultimately aiding in treating cancers, Traboulsi syndrome and, CKD.

Molecular Basis of the Substrate Specificity of Phosphotriesterase from Pseudomonas diminuta: A Combined QM/MM MD and Electron Density Study

The occurrence of organophosphorus compounds, pesticides, and flame-retardants in wastes is an emerging ecological problem. Bacterial phosphotriesterases are capable of hydrolyzing some of them. We utilize modern molecular modeling tools to study the hydrolysis mechanism of organophosphorus compounds with good and poor leaving groups by phosphotriesterase from Pseudomonas diminuta (Pd-PTE). We compute Gibbs energy profiles for enzymes with different cations in the active site: native Zn2+cations and Co2+cations, which increase the steady-state rate constant. Hydrolysis occurs in two elementary steps via an associative mechanism and formation of the pentacoordinated intermediate. The first step, a nucleophilic attack, occurs with a low energy barrier independently of the substrate. The second step has a low energy barrier and considerable stabilization of products for substrates with good leaving groups. For substrates with poor leaving groups, the reaction products are destabilized relative to the ES complex that suppresses the reaction. The reaction proceeds with low energy barriers for substrates with good leaving groups with both Zn2+and Co2+cations in the active site; thus, the product release is likely to be a limiting step. Electron density and geometry analysis of the QM/MM MD trajectories of the intermediate states with all considered compounds allow us to discriminate substrates by their ability to be hydrolyzed by the Pd-PTE. For hydrolyzable substrates, the cleaving bond between a phosphorus atom and a leaving group is elongated, and electron density depletion is observed on the Laplacian of electron density maps.

Learning from Protein Engineering by Deconvolution of Multi-Mutational Variants

This review analyzes a development in biochemistry, enzymology and biotechnology that originally came as a surprise. Following the establishment of directed evolution of stereoselective enzymes in organic chemistry, the concept of partial or complete deconvolution of selective multi-mutational variants was introduced. Early deconvolution experiments of stereoselective variants led to the finding that mutations can interact cooperatively or antagonistically with one another, not just additively. During the past decade, this phenomenon was shown to be general. In some studies, molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) computations were performed in order to shed light on the origin of non-additivity at all stages of an evolutionary upward climb. Data of complete deconvolution can be used to construct unique multi-dimensional rugged fitness pathway landscapes, which provide mechanistic insights different from traditional fitness landscapes. Along a related line, biochemists have long tested the result of introducing two point mutations in an enzyme for mechanistic reasons, followed by a comparison of the respective double mutant in so-called double mutant cycles, which originally showed only additive effects, but more recently also uncovered cooperative and antagonistic non-additive effects. We conclude with suggestions for future work, and call for a unified overall picture of non-additivity and epistasis.

Evolution of Pyrrolysyl-tRNA Synthetase: From Methanogenesis to Genetic Code Expansion

Over 20 years ago, the pyrrolysine encoding translation system was discovered in specific archaea. Our Review provides an overview of how the once obscure pyrrolysyl-tRNA synthetase (PylRS) tRNA pair, originally responsible for accurately translating enzymes crucial in methanogenic metabolic pathways, laid the foundation for the burgeoning field of genetic code expansion. Our primary focus is the discussion of how to successfully engineer the PylRS to recognize new substrates and exhibit higher in vivo activity. We have compiled a comprehensive list of ncAAs incorporable with the PylRS system. Additionally, we also summarize recent successful applications of the PylRS system in creating innovative therapeutic solutions, such as new antibody-drug conjugates, advancements in vaccine modalities, and the potential production of new antimicrobials.

Addressing epistasis in the design of protein function

Mutations in protein active sites can dramatically improve function. The active site, however, is densely packed and extremely sensitive to mutations. Therefore, some mutations may only be tolerated in combination with others in a phenomenon known as epistasis. Epistasis reduces the likelihood of obtaining improved functional variants and dramatically slows natural and lab evolutionary processes. Research has shed light on the molecular origins of epistasis and its role in shaping evolutionary trajectories and outcomes. In addition, sequence- and AI-based strategies that infer epistatic relationships from mutational patterns in natural or experimental evolution data have been used to design functional protein variants. In recent years, combinations of such approaches and atomistic design calculations have successfully predicted highly functional combinatorial mutations in active sites. These were used to design thousands of functional active-site variants, demonstrating that, while our understanding of epistasis remains incomplete, some of the determinants that are critical for accurate design are now sufficiently understood. We conclude that the space of active-site variants that has been explored by evolution may be expanded dramatically to enhance natural activities or discover new ones. Furthermore, design opens the way to systematically exploring sequence and structure space and mutational impacts on function, deepening our understanding and control over protein activity.

Opportunities and challenges in design and optimization of protein function

The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities.

Computational stabilization of a non-heme iron enzyme enables efficient evolution of new function

Directed evolution has emerged as a powerful tool for engineering new biocatalysts. However, introducing new catalytic residues can be destabilizing, and it is generally beneficial to start with a stable enzyme parent. Here we show that the deep learning-based tool ProteinMPNN can be used to redesign Fe(II)/αKG superfamily enzymes for greater stability, solubility, and expression while retaining both native activity and industrially-relevant non-native functions. For the Fe(II)/αKG enzyme tP4H, we performed site-saturation mutagenesis with both the wild-type and stabilized design variant and screened for activity increases in a non-native C-H hydroxylation reaction. We observed substantially larger increases in non-native activity for variants obtained from the stabilized scaffold compared to those from the wild-type enzyme. ProteinMPNN is user-friendly and widely-accessible, and straightforward structural criteria were sufficient to obtain stabilized, catalytically-functional variants of the Fe(II)/αKG enzymes tP4H and GriE. Our work suggests that stabilization by computational sequence redesign could be routinely implemented as a first step in directed evolution campaigns for novel biocatalysts.

Effects of selection stringency on the outcomes of directed evolution

Directed evolution makes mutant lineages compete in climbing complicated sequence-function landscapes. Given this underlying complexity it is unclear how selection stringency, a ubiquitous parameter of directed evolution, impacts the outcome. Here we approach this question in terms of the fitnesses of the candidate variants at each round and the heterogeneity of their distributions of fitness effects. We show that even if the fittest mutant is most likely to yield the fittest mutants in the next round of selection, diversification can improve outcomes by sampling a larger variety of fitness effects. We find that heterogeneity in fitness effects between variants, larger population sizes, and evolution over a greater number of rounds all encourage diversification.

Changes in Active Site Loop Conformation Relate to the Transition toward a Novel Enzymatic Activity

Enzymatic promiscuity, the ability of enzymes to catalyze multiple, distinct chemical reactions, has been well documented and is hypothesized to be a major driver of the emergence of new enzymatic functions. Yet, the molecular mechanisms involved in the transition from one activity to another remain debated and elusive. Here, we evaluated the redesign of the active site binding cleft of lactonase SsoPox using structure-based design and combinatorial libraries. We created variants with largely improved catalytic abilities against phosphotriesters, the best ones being >1000-fold better compared to the wild-type enzyme. The observed shifts in activity specificity are large, and some variants completely lost their initial activity. The selected combinations of mutations have considerably reshaped the active site cavity via side chain changes but mostly through large rearrangements of the active site loops and changes to their conformations, as revealed by a suite of crystal structures. This suggests that a specific active site loop configuration is critical to the lactonase activity. Interestingly, analysis of high-resolution structures hints at the potential role of conformational sampling and its directionality in defining the enzyme activity profile.

Epistasis facilitates functional evolution in an ancient transcription factor

A protein's genetic architecture - the set of causal rules by which its sequence produces its functions - also determines its possible evolutionary trajectories. Prior research has proposed that the genetic architecture of proteins is very complex, with pervasive epistatic interactions that constrain evolution and make function difficult to predict from sequence. Most of this work has analyzed only the direct paths between two proteins of interest - excluding the vast majority of possible genotypes and evolutionary trajectories - and has considered only a single protein function, leaving unaddressed the genetic architecture of functional specificity and its impact on the evolution of new functions. Here, we develop a new method based on ordinal logistic regression to directly characterize the global genetic determinants of multiple protein functions from 20-state combinatorial deep mutational scanning (DMS) experiments. We use it to dissect the genetic architecture and evolution of a transcription factor's specificity for DNA, using data from a combinatorial DMS of an ancient steroid hormone receptor's capacity to activate transcription from two biologically relevant DNA elements. We show that the genetic architecture of DNA recognition consists of a dense set of main and pairwise effects that involve virtually every possible amino acid state in the protein-DNA interface, but higher-order epistasis plays only a tiny role. Pairwise interactions enlarge the set of functional sequences and are the primary determinants of specificity for different DNA elements. They also massively expand the number of opportunities for single-residue mutations to switch specificity from one DNA target to another. By bringing variants with different functions close together in sequence space, pairwise epistasis therefore facilitates rather than constrains the evolution of new functions.

Automated in vivo enzyme engineering accelerates biocatalyst optimization

Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.

Remote loop evolution reveals a complex biological function for chitinase enzymes beyond the active site

Loops are small secondary structural elements that play a crucial role in the emergence of new enzyme functions. However, the evolutionary molecular mechanisms how proteins acquire these loop elements and obtain new function is poorly understood. To address this question, we study glycoside hydrolase family 19 (GH19) chitinase-an essential enzyme family for pathogen degradation in plants. By revealing the evolutionary history and loops appearance of GH19 chitinase, we discover that one loop which is remote from the catalytic site, is necessary to acquire the new antifungal activity. We demonstrate that this remote loop directly accesses the fungal cell wall, and surprisingly, it needs to adopt a defined structure supported by long-range intramolecular interactions to perform its function. Our findings prove that nature applies this strategy at the molecular level to achieve a complex biological function while maintaining the original activity in the catalytic pocket, suggesting an alternative way to design new enzyme function.

Mononuclear binding and catalytic activity of europium(III) and gadolinium(III) at the active site of the model metalloenzyme phosphotriesterase

Lanthanide ions have ideal chemical properties for catalysis, such as hard Lewis acidity, fast ligand-exchange kinetics, high coordination-number preferences and low geometric requirements for coordination. As a result, many small-molecule lanthanide catalysts have been described in the literature. Yet, despite the ability of enzymes to catalyse highly stereoselective reactions under gentle conditions, very few lanthanoenzymes have been investigated. In this work, the mononuclear binding of europium(III) and gadolinium(III) to the active site of a mutant of the model enzyme phosphotriesterase are described using X-ray crystallography at 1.78 and 1.61 Å resolution, respectively. It is also shown that despite coordinating a single non-natural metal cation, the PTE-R18 mutant is still able to maintain esterase activity.

Frustration can Limit the Adaptation of Promiscuous Enzymes Through Gene Duplication and Specialisation

Virtually all enzymes catalyse more than one reaction, a phenomenon known as enzyme promiscuity. It is unclear whether promiscuous enzymes are more often generalists that catalyse multiple reactions at similar rates or specialists that catalyse one reaction much more efficiently than other reactions. In addition, the factors that shape whether an enzyme evolves to be a generalist or a specialist are poorly understood. To address these questions, we follow a three-pronged approach. First, we examine the distribution of promiscuity in empirical enzymes reported in the BRENDA database. We find that the promiscuity distribution of empirical enzymes is bimodal. In other words, a large fraction of promiscuous enzymes are either generalists or specialists, with few intermediates. Second, we demonstrate that enzyme biophysics is not sufficient to explain this bimodal distribution. Third, we devise a constraint-based model of promiscuous enzymes undergoing duplication and facing selection pressures favouring subfunctionalization. The model posits the existence of constraints between the catalytic efficiencies of an enzyme for different reactions and is inspired by empirical case studies. The promiscuity distribution predicted by our constraint-based model is consistent with the empirical bimodal distribution. Our results suggest that subfunctionalization is possible and beneficial only in certain enzymes. Furthermore, the model predicts that conflicting constraints and selection pressures can cause promiscuous enzymes to enter a 'frustrated' state, in which competing interactions limit the specialisation of enzymes. We find that frustration can be both a driver and an inhibitor of enzyme evolution by duplication and subfunctionalization. In addition, our model predicts that frustration becomes more likely as enzymes catalyse more reactions, implying that natural selection may prefer catalytically simple enzymes. In sum, our results suggest that frustration may play an important role in enzyme evolution.

Open-endedness in synthetic biology: A route to continual innovation for biological design

Design in synthetic biology is typically goal oriented, aiming to repurpose or optimize existing biological functions, augmenting biology with new-to-nature capabilities, or creating life-like systems from scratch. While the field has seen many advances, bottlenecks in the complexity of the systems built are emerging and designs that function in the lab often fail when used in real-world contexts. Here, we propose an open-ended approach to biological design, with the novelty of designed biology being at least as important as how well it fulfils its goal. Rather than solely focusing on optimization toward a single best design, designing with novelty in mind may allow us to move beyond the diminishing returns we see in performance for most engineered biology. Research from the artificial life community has demonstrated that embracing novelty can automatically generate innovative and unexpected solutions to challenging problems beyond local optima. Synthetic biology offers the ideal playground to explore more creative approaches to biological design.

Pervasive epistasis exposes intramolecular networks in adaptive enzyme evolution

Enzyme evolution is characterized by constant alterations of the intramolecular residue networks supporting their functions. The rewiring of these network interactions can give rise to epistasis. As mutations accumulate, the epistasis observed across diverse genotypes may appear idiosyncratic, that is, exhibit unique effects in different genetic backgrounds. Here, we unveil a quantitative picture of the prevalence and patterns of epistasis in enzyme evolution by analyzing 41 fitness landscapes generated from seven enzymes. We show that >94% of all mutational and epistatic effects appear highly idiosyncratic, which greatly distorted the functional prediction of the evolved enzymes. By examining seemingly idiosyncratic changes in epistasis along adaptive trajectories, we expose several instances of higher-order, intramolecular rewiring. Using complementary structural data, we outline putative molecular mechanisms explaining higher-order epistasis along two enzyme trajectories. Our work emphasizes the prevalence of epistasis and provides an approach to exploring this phenomenon through a molecular lens.

The impact of molecular variants, crystallization conditions and the space group on ligand-protein complexes: a case study on bacterial phosphotriesterase

A bacterial phosphotriesterase was employed as an experimental paradigm to examine the effects of multiple factors, such as the molecular constructs, the ligands used during protein expression and purification, the crystallization conditions and the space group, on the visualization of molecular complexes of ligands with a target enzyme. In this case, the ligands used were organophosphates that are fragments of the nerve agents and insecticides on which the enzyme acts as a bioscavenger. 12 crystal structures of various phosphotriesterase constructs obtained by directed evolution were analyzed, with resolutions of up to 1.38 Å. Both apo forms and holo forms, complexed with the organophosphate ligands, were studied. Crystals obtained from three different crystallization conditions, crystallized in four space groups, with and without N-terminal tags, were utilized to investigate the impact of these factors on visualizing the organophosphate complexes of the enzyme. The study revealed that the tags used for protein expression can lodge in the active site and hinder ligand binding. Furthermore, the space group in which the protein crystallizes can significantly impact the visualization of bound ligands. It was also observed that the crystallization precipitants can compete with, and even preclude, ligand binding, leading to false positives or to the incorrect identification of lead drug candidates. One of the co-crystallization conditions enabled the definition of the spaces that accommodate the substituents attached to the P atom of several products of organophosphate substrates after detachment of the leaving group. The crystal structures of the complexes of phosphotriesterase with the organophosphate products reveal similar short interaction distances of the two partially charged O atoms of the P-O bonds with the exposed β-Zn2+ ion and the buried α-Zn2+ ion. This suggests that both Zn2+ ions have a role in stabilizing the transition state for substrate hydrolysis. Overall, this study provides valuable insights into the challenges and considerations involved in studying the crystal structures of ligand-protein complexes, highlighting the importance of careful experimental design and rigorous data analysis in ensuring the accuracy and reliability of the resulting phosphotriesterase-organophosphate structures.

Adapting enzymes to improve their functionality in plants: why and how

Synthetic biology creates new metabolic processes and improves existing ones using engineered or natural enzymes. These enzymes are often sourced from cells that differ from those in the target plant organ with respect to, e.g. redox potential, effector levels, or proteostasis machinery. Non-native enzymes may thus need to be adapted to work well in their new plant context ('plantized') even if their specificity and kinetics in vitro are adequate. Hence there are two distinct ways in which an enzyme destined for use in plants can require improvement: In catalytic properties such as substrate and product specificity, kcat, and KM; and in general compatibility with the milieu of cells that express the enzyme. Continuous directed evolution systems can deliver both types of improvement and are so far the most broadly effective way to deliver the second type. Accordingly, in this review we provide a short account of continuous evolution methods, emphasizing the yeast OrthoRep system because of its suitability for plant applications. We then cover the down-to-earth and increasingly urgent issues of which enzymes and enzyme properties can - or cannot - be improved in theory, and which in practice are the best to target for crop improvement, i.e. those that are realistically improvable and important enough to warrant deploying continuous directed evolution. We take horticultural crops as examples because of the opportunities they present and to sharpen the focus.

Protein engineering using mutability landscapes: Controlling site-selectivity of P450-catalyzed steroid hydroxylation

Directed evolution and rational design have been used widely in engineering enzymes for their application in synthetic organic chemistry and biotechnology. With stereoselectivity playing a crucial role in catalysis for the synthesis of valuable chemical and pharmaceutical compounds, rational design has not achieved such wide success in this specific area compared to directed evolution. Nevertheless, one bottleneck of directed evolution is the laborious screening efforts and the observed trade-offs in catalytic profiles. This has motivated researchers to develop more efficient protein engineering methods. As a prime approach, mutability landscaping avoids such trade-offs by providing more information of sequence-function relationships. Here, we describe an application of this efficient protein engineering method to improve the regio-/stereoselectivity and activity of P450BM3 for steroid hydroxylation, while keeping the mutagenesis libraries small so that they will require only minimal screening.

Changes in Active Site Loop Conformation Relate to the Transition toward a Novel Enzymatic Activity

Enzymatic promiscuity, the ability of enzymes to catalyze multiple, distinct chemical reactions, has been well documented and is hypothesized to be a major driver for the emergence of new enzymatic functions. Yet, the molecular mechanisms involved in the transition from one activity to another remain debated and elusive. Here, we evaluated the redesign of the active site binding cleft of the lactonase SsoPox using structure-based design and combinatorial libraries. We created variants with largely improved catalytic abilities against phosphotriesters, the best ones being > 1,000-fold better compared to the wild-type enzyme. The observed shifts in activity specificity are large, ~1,000,000-fold and beyond, since some variants completely lost their initial activity. The selected combinations of mutations have considerably reshaped the active site cavity via side chain changes but mostly through large rearrangements of the active site loops, as revealed by a suite of crystal structures. This suggests that specific active site loop configuration is critical to the lactonase activity. Interestingly, analysis of high-resolution structures hints at the potential role of conformational sampling and its directionality in defining an enzyme activity profile.

Global epistasis on fitness landscapes

Epistatic interactions between mutations add substantial complexity to adaptive landscapes and are often thought of as detrimental to our ability to predict evolution. Yet, patterns of global epistasis, in which the fitness effect of a mutation is well-predicted by the fitness of its genetic background, may actually be of help in our efforts to reconstruct fitness landscapes and infer adaptive trajectories. Microscopic interactions between mutations, or inherent nonlinearities in the fitness landscape, may cause global epistasis patterns to emerge. In this brief review, we provide a succinct overview of recent work about global epistasis, with an emphasis on building intuition about why it is often observed. To this end, we reconcile simple geometric reasoning with recent mathematical analyses, using these to explain why different mutations in an empirical landscape may exhibit different global epistasis patterns-ranging from diminishing to increasing returns. Finally, we highlight open questions and research directions. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

Ultrahigh-Throughput Enzyme Engineering and Discovery in In Vitro Compartments

Novel and improved biocatalysts are increasingly sourced from libraries via experimental screening. The success of such campaigns is crucially dependent on the number of candidates tested. Water-in-oil emulsion droplets can replace the classical test tube, to provide in vitro compartments as an alternative screening format, containing genotype and phenotype and enabling a readout of function. The scale-down to micrometer droplet diameters and picoliter volumes brings about a >107-fold volume reduction compared to 96-well-plate screening. Droplets made in automated microfluidic devices can be integrated into modular workflows to set up multistep screening protocols involving various detection modes to sort >107 variants a day with kHz frequencies. The repertoire of assays available for droplet screening covers all seven enzyme commission (EC) number classes, setting the stage for widespread use of droplet microfluidics in everyday biochemical experiments. We review the practicalities of adapting droplet screening for enzyme discovery and for detailed kinetic characterization. These new ways of working will not just accelerate discovery experiments currently limited by screening capacity but profoundly change the paradigms we can probe. By interfacing the results of ultrahigh-throughput droplet screening with next-generation sequencing and deep learning, strategies for directed evolution can be implemented, examined, and evaluated.

Comparative genomics reveals the diversification of triterpenoid biosynthesis and origin of ocotillol-type triterpenes in Panax

Gene duplication is assumed to be the major force driving the evolution of metabolite biosynthesis in plants. Freed from functional burdens, duplicated genes can mutate toward novelties until fixed due to selective fitness. However, the extent to which this mechanism has driven the diversification of metabolite biosynthesis remains to be tested. Here we performed comparative genomics analysis and functional characterization to evaluate the impact of gene duplication on the evolution of triterpenoid biosynthesis using Panax species as models. We found that whole-genome duplications (WGDs) occurred independently in Araliaceae and Apiaceae lineages. Comparative genomics revealed the evolutionary trajectories of triterpenoid biosynthesis in plants, which was mainly promoted by WGDs and tandem duplication. Lanosterol synthase (LAS) was likely derived from a tandem duplicate of cycloartenol synthase that predated the emergence of Nymphaeales. Under episodic diversifying selection, the LAS gene duplicates produced by γ whole-genome triplication have given rise to triterpene biosynthesis in core eudicots through neofunctionalization. Moreover, functional characterization revealed that oxidosqualene cyclases (OSCs) responsible for synthesizing dammarane-type triterpenes in Panax species were also capable of producing ocotillol-type triterpenes. Genomic and biochemical evidence suggested that Panax genes encoding the above OSCs originated from the specialization of one OSC gene duplicate produced from a recent WGD shared by Araliaceae (Pg-β). Our results reveal the crucial role of gene duplication in diversification of triterpenoid biosynthesis in plants and provide insight into the origin of ocotillol-type triterpenes in Panax species.

Conformational exchange divergence along the evolutionary pathway of eosinophil-associated ribonucleases

The evolutionary role of conformational exchange in the emergence and preservation of function within structural homologs remains elusive. While protein engineering has revealed the importance of flexibility in function, productive modulation of atomic-scale dynamics has only been achieved on a finite number of distinct folds. Allosteric control of unique members within dynamically diverse structural families requires a better appreciation of exchange phenomena. Here, we examined the functional and structural role of conformational exchange within eosinophil-associated ribonucleases. Biological and catalytic activity of various EARs was performed in parallel to mapping their conformational behavior on multiple timescales using NMR and computational analyses. Despite functional conservation and conformational seclusion to a specific domain, we show that EARs can display similar or distinct motional profiles, implying divergence rather than conservation of flexibility. Comparing progressively more distant enzymes should unravel how this subfamily has evolved new functions and/or altered their behavior at the molecular level.

Role of distal sites in enzyme engineering

The limitations associated with natural enzyme catalysis have triggered the rise of the field of protein engineering. Traditional rational design was based on the analysis of protein structural information and catalytic mechanisms to identify key active sites or ligand binding sites to reshape the substrate pocket. The role and significance of functional sites in the active center have been studied extensively. With a deeper understanding of the structure-catalysis relationship map, the entire protein molecule can be filled with residues that play a substantial role in its structure and function. However, the catalytic mechanism underlying distal mutations remains unclear. The aim of this review was to highlight the criticality of the distal site in enzyme engineering based on the following three aspects: What can distal mutations exert on function from mutability landscape? How do distal sites influence enzyme function? How to predict and design distal mutations? This review provides insights into the catalytic mechanism of enzymes from the global interaction network, knowledge from sequence-structure-dynamics-function relationships, and strategies for distal mutation-based protein engineering.

Evo-devo beyond development: Generalizing evo-devo to all levels of the phenotypic evolution

A foundational idea of evo-devo is that morphological variation is not isotropic, that is, it does not occur in all directions. Instead, some directions of morphological variation are more likely than others from DNA-level variation and these largely depend on development. We argue that this evo-devo perspective should apply not only to morphology but to evolution at all phenotypic levels. At other phenotypic levels there is no development, but there are processes that can be seen, in analogy to development, as constructing the phenotype (e.g., protein folding, learning for behavior, etc.). We argue that to explain the direction of evolution two types of arguments need to be combined: generative arguments about which phenotypic variation arises in each generation and selective arguments about which of it passes to the next generation. We explain how a full consideration of the two types of arguments improves the explanatory power of evolutionary theory. Also see the video abstract here: https://youtu.be/Egbvma_uaKc.

Nature-inspired engineering of an artificial ligase enzyme by domain fusion

The function of most proteins is accomplished through the interplay of two or more protein domains and fine-tuned by natural evolution. In contrast, artificial enzymes have often been engineered from a single domain scaffold and frequently have lower catalytic activity than natural enzymes. We previously generated an artificial enzyme that catalyzed an RNA ligation by >2 million-fold but was likely limited in its activity by low substrate affinity. Inspired by nature's concept of domain fusion, we fused the artificial enzyme to a series of protein domains known to bind nucleic acids with the goal of improving its catalytic activity. The effect of the fused domains on catalytic activity varied greatly, yielding severalfold increases but also reductions caused by domains that previously enhanced nucleic acid binding in other protein engineering projects. The combination of the two better performing binding domains improved the activity of the parental ligase by more than an order of magnitude. These results demonstrate for the first time that nature's successful evolutionary mechanism of domain fusion can also improve an unevolved primordial-like protein whose structure and function had just been created in the test tube. The generation of multi-domain proteins might therefore be an ancient evolutionary process.

Rational Engineering of 3α-Hydroxysteroid Dehydrogenase/Carbonyl Reductase for a Biomimetic Nicotinamide Mononucleotide Cofactor

Enzymes are powerful biological catalysts for natural substrates but they have low catalytic efficiency for non-natural substrates. Protein engineering can be used to optimize enzymes for catalysis and stability. 3α-Hydroxysteroid dehydrogenase/carbonyl reductase (3α-HSD/CR) catalyzes the oxidoreduction reaction of NAD+ with androsterone. Based on the structure and catalytic mechanism, we mutated the residues of T11, I13, D41, A70, and I112 and they interacted with different portions of NAD+ to switch cofactor specificity to biomimetic cofactor nicotinamide mononucleotide (NMN+). Compared to wild-type 3α-HSD/CR, the catalytic efficiency of these mutants for NAD+ decreased significantly except for the T11 mutants but changed slightly for NMN+ except for the A70K mutant. The A70K mutant increased the catalytic efficiency for NMN+ by 8.7-fold, concomitant with a significant decrease in NAD+ by 1.4 × 104-fold, resulting in 9.6 × 104-fold cofactor specificity switch toward NMN+ over NAD+. Meanwhile, the I112K variant increased the thermal stability and changed to a three-state transition from a two-state transition of thermal unfolding of wild-type 3α-HSD/CR by differential scanning fluorimetry. Molecular docking analysis indicated that mutations on these residues affect the position and conformation of the docked NAD+ and NMN+, thereby affecting their activity. A70K variant sterically blocks the binding with NAD+, restores the H-bonding interactions of catalytic residues of Y155 and K159 with NMN+, and enhances the catalytic efficiency for NMN+.

Mechanisms of protein evolution

How do proteins evolve? How do changes in sequence mediate changes in protein structure, and in turn in function? This question has multiple angles, ranging from biochemistry and biophysics to evolutionary biology. This review provides a brief integrated view of some key mechanistic aspects of protein evolution. First, we explain how protein evolution is primarily driven by randomly acquired genetic mutations and selection for function, and how these mutations can even give rise to completely new folds. Then, we also comment on how phenotypic protein variability, including promiscuity, transcriptional and translational errors, may also accelerate this process, possibly via "plasticity-first" mechanisms. Finally, we highlight open questions in the field of protein evolution, with respect to the emergence of more sophisticated protein systems such as protein complexes, pathways, and the emergence of pre-LUCA enzymes.

Catalytic efficiency of soil enzymes explains temperature sensitivity: Insights from physiological theory

Soil enzymes are crucial for carbon and nutrient cycling and are highly sensitive to warming. Biochemical reaction rates increase with temperature according to the Arrhenius law, but changes in microbial physiology may partially counteract this warming-induced acceleration that leads enzymatic rates to deviate from Arrhenius law. Here, we attempt to reconcile disparate views on the enzyme responses to warming based on the Arrhenius law and physiological theory by enzyme catalytic efficiency. In this study, we tested the kinetic parameters of five key enzymes of C, N, and P cycling to warming (from 0 to 40 °C) in cropland soils originating from 5 different temperate zones. The soils were incubated for one month at 0, 10, 20, 30, and 40 °C (±0.5 °C) with 60% water holding capacity (WHC). The kinetic parameters were calculated and measured at a range of 4-methyumbelliferone (MUB)-substrate concentrations. We found that catalytic efficiency (V_max/K_m) of individual enzymes ranged from 0.05 to 27 s^-1 between 0 and 40 °C. Maximum reaction rate (V_max) increased with warming, while V_max/K_m of most enzymes remained stable by warming at low temperatures (up to 10 °C), and it raised from 20 to 40 °C. Most enzymes had lower substrate affinities (K_m) and increased their efficiency with warming. Consistent with studies considering Arrhenius law solely, the temperature sensitivity (Q₁₀) of V_max decreased with warming. However, the Q₁₀ of V_max/K_m displayed a lower value in the cold but a higher value in warmer temperature, which confirmed microbial adaptation based on physiological theory, consequently encouraging its linking with the Arrhenius law. Therefore, Arrhenius linked with physiological theory could correct explanation of enzyme activities by warming. Considering the microbial adaptation to temperature, the present predicted warming-induced acceleration of soil organic matter decomposition might be overestimated in cold and underestimated in warm environments.

Reducing substrate inhibition of malate dehydrogenase from Geobacillus stearothermophilus by C-terminal truncation

Malate dehydrogenase (MDH) catalyzes the reduction of oxaloacetate to L-malate. Geobacillus stearothermophilus MDH (gs-MDH) is used as a diagnostic reagent; however, gs-MDH is robustly inhibited at high substrate concentrations, which limits its reaction rate. Here, we reduced substrate inhibition of gs-MDH by deleting its C-terminal residues. Computational analysis showed that C-terminal residues regulate the position of the active site loop. C-terminal deletions of gs-MDH successfully increased Ki values by 5- to 8-fold with maintained thermal stability (>90% of the wild-type enzyme), although kcat/Km values were decreased by <2-fold. The structure of the mutant showed a shift in the location of the active site loop and a decrease in its volume, suggesting that substrate inhibition was reduced by eliminating the putative substrate binding site causing inhibition. Our results provide an effective method to reduce substrate inhibition of the enzyme without loss of other parameters, including binding and stability constants.

Learning Strategies in Protein Directed Evolution

Synthetic biology is a fast-evolving research field that combines biology and engineering principles to develop new biological systems for medical, pharmacological, and industrial applications. Synthetic biologists use iterative "design, build, test, and learn" cycles to efficiently engineer genetic systems that are reliable, reproducible, and predictable. Protein engineering by directed evolution can benefit from such a systematic engineering approach for various reasons. Learning can be carried out before starting, throughout or after finalizing a directed evolution project. Computational tools, bioinformatics, and scanning mutagenesis methods can be excellent starting points, while molecular dynamics simulations and other strategies can guide engineering efforts. Similarly, studying protein intermediates along evolutionary pathways offers fascinating insights into the molecular mechanisms shaped by evolution. The learning step of the cycle is not only crucial for proteins or enzymes that are not suitable for high-throughput screening or selection systems, but it is also valuable for any platform that can generate a large amount of data that can be aided by machine learning algorithms. The main challenge in protein engineering is to predict the effect of a single mutation on one functional parameter-to say nothing of several mutations on multiple parameters. This is largely due to nonadditive mutational interactions, known as epistatic effects-beneficial mutations present in a genetic background may not be beneficial in another genetic background. In this work, we provide an overview of experimental and computational strategies that can guide the user to learn protein function at different stages in a directed evolution project. We also discuss how epistatic effects can influence the success of directed evolution projects. Since machine learning is gaining momentum in protein engineering and the field is becoming more interdisciplinary thanks to collaboration between mathematicians, computational scientists, engineers, molecular biologists, and chemists, we provide a general workflow that familiarizes nonexperts with the basic concepts, dataset requirements, learning approaches, model capabilities and performance metrics of this intriguing area. Finally, we also provide some practical recommendations on how machine learning can harness epistatic effects for engineering proteins in an "outside-the-box" way.

Design of novel cyanovirin-N variants by modulation of binding dynamics through distal mutations

We develop integrated co-evolution and dynamic coupling (ICDC) approach to identify, mutate, and assess distal sites to modulate function. We validate the approach first by analyzing the existing mutational fitness data of TEM-1 β-lactamase and show that allosteric positions co-evolved and dynamically coupled with the active site significantly modulate function. We further apply ICDC approach to identify positions and their mutations that can modulate binding affinity in a lectin, cyanovirin-N (CV-N), that selectively binds to dimannose, and predict binding energies of its variants through Adaptive BP-Dock. Computational and experimental analyses reveal that binding enhancing mutants identified by ICDC impact the dynamics of the binding pocket, and show that rigidification of the binding residues compensates for the entropic cost of binding. This work suggests a mechanism by which distal mutations modulate function through dynamic allostery and provides a blueprint to identify candidates for mutagenesis in order to optimize protein function.

Exploiting enzyme evolution for computational protein design

Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to generate new starting points for enzyme design efforts; (ii) learning from how nature uses conformational dynamics in enzyme evolution to mimic this process in silico; and (iii) modular design of enzymes from smaller fragments, again mimicking the process by which nature appears to create new protein folds. Using showcase examples, we highlight the importance of incorporating evolutionary information to continue to push forward the boundaries of enzyme design studies.

Epistasis and intramolecular networks in protein evolution

Proteins are molecular machines composed of complex, highly connected amino acid networks. Their functional optimization requires the reorganization of these intramolecular networks by evolution. In this review, we discuss the mechanisms by which epistasis, that is, the dependence of the effect of a mutation on the genetic background, rewires intramolecular interactions to alter protein function. Deciphering the biophysical basis of epistasis is crucial to our understanding of evolutionary dynamics and the elucidation of sequence-structure-function relationships. We featured recent studies that provide insights into the molecular mechanisms giving rise to epistasis, particularly at the structural level. These studies illustrate the convoluted and fascinating nature of the intramolecular networks co-opted by epistasis during the evolution of protein function.

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.

Genetic Context Significantly Influences the Maintenance and Evolution of Degenerate Pathways

Understanding the evolution of novel physiological traits is highly relevant for expanding the characterization and manipulation of biological systems. Acquisition of new traits can be achieved through horizontal gene transfer (HGT). Here, we investigate drivers that promote or deter the maintenance of HGT-driven degeneracy, occurring when processes accomplish identical functions through nonidentical components. Subsequent evolution can optimize newly acquired functions; for example, beneficial alleles identified in an engineered Methylorubrum extorquens strain allowed it to utilize a “Foreign” formaldehyde oxidation pathway substituted for its Native pathway for methylotrophic growth. We examined the fitness consequences of interactions between these alleles when they were combined with the Native pathway or both (Dual) pathways. Unlike the Foreign pathway context where they evolved, these alleles were often neutral or deleterious when moved into these alternative genetic backgrounds. However, there were instances where combinations of multiple alleles resulted in higher fitness outcomes than individual allelic substitutions could provide. Importantly, the genetic context accompanying these allelic substitutions significantly altered the fitness landscape, shifting local fitness peaks and restricting the set of accessible evolutionary trajectories. These findings highlight how genetic context can negatively impact the probability of maintaining native and HGT-introduced functions together, making it difficult for degeneracy to evolve. However, in cases where the cost of maintaining degeneracy was mitigated by adding evolved alleles impacting the function of these pathways, we observed rare opportunities for pathway coevolution to occur. Together, our results highlight the importance of genetic context and resulting epistasis in retaining or losing HGT-acquired degenerate functions.