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

Stabilizing Mutations Enhance Evolvability of BlaC β-lactamase by Widening the Mutational Landscape

Antimicrobial resistance is fueled by the rapid evolution of β-lactamases. However, a gain of new enzyme activity often comes at the expense of reduced protein stability. This evolutionary constraint is often overcome by the acquisition of stabilizing mutations that compensate for the loss of stability invoked by new function mutations. Here, we report three stabilizing mutations (I105F, H184R, and V263I) in BlaC, a serine β-lactamase from Mycobacterium tuberculosis. Using a severely destabilized variant as a template for random mutagenesis and selection, these three mutations emerged together and were able to fully restore resistance toward the antibiotic carbenicillin. In vitro characterization shows that all three mutations increase chemical and thermal stability, which leads to elevated protein levels in the periplasm of Escherichia coli. We demonstrate that the introduction of stabilizing mutations substantially enhances the evolvability of the enzyme. These findings illustrate the important role of stabilizing mutations in enzyme evolution by alleviating function-stability trade-offs and broadening the accessible evolutionary landscape.

Thermal Adaptation of Cytosolic Malate Dehydrogenase Revealed by Deep Learning and Coevolutionary Analysis

Protein evolution has shaped enzymes that maintain stability and function across diverse thermal environments. While sequence variation, thermal stability and conformational dynamics are known to influence an enzyme's thermal adaptation, how these factors collectively govern stability and function across diverse temperatures remains unresolved. Cytosolic malate dehydrogenase (cMDH), a citric acid cycle enzyme, is an ideal model for studying these mechanisms due to its temperature-sensitive flexibility and broad presence in species from diverse thermal environments. In this study, we employ techniques inspired by deep learning and statistical mechanics to uncover how sequence variation and conformational dynamics shape patterns of cMDH's thermal adaptation. By integrating coevolutionary models with variational autoencoders (VAE), we generate a latent generative landscape (LGL) of the cMDH sequence space, enabling us to explore mutational pathways and predict fitness using direct coupling analysis (DCA). Structure predictions via AlphaFold and molecular dynamics simulations further illuminate how variations in hydrophobic interactions and conformational flexibility contribute to the thermal stability of warm- and cold-adapted cMDH orthologs. Notably, we identify the ratio of hydrophobic contacts between two regions as a predictive order parameter for thermal stability features, providing a quantitative metric for understanding cMDH dynamics across temperatures. The integrative computational framework employed in this study provides mechanistic insights into protein adaptation at both sequence and structural levels, offering unique perspectives on the evolution of thermal stability and creating avenues for the rational design of proteins with optimized thermal properties.

DEKP: a deep learning model for enzyme kinetic parameter prediction based on pretrained models and graph neural networks

The prediction of enzyme kinetic parameters is crucial for screening enzymes with high catalytic efficiency and desired characteristics to catalyze natural or non-natural reactions. Data-driven machine learning models have been explored to reduce experimental cost and speed up the enzyme design process. However, the prediction performance is still subject to significant limitations due to the variance in sequence similarity between training and testing datasets. In this work, we introduce DEKP, an integrated deep learning approach enzyme kinetic parameter prediction. It leverages pretrained models of protein sequences and incorporates enhanced graph neural networks that provide comprehensive representation of protein structural features. This novel approach can effectively alleviate the performance degradation caused by sequence similarity variation. Moreover, it provides sensitive detection of changes in catalytic efficiency due to enzyme mutations. Experiments validate that DEKP outperforms existing models in predicting enzyme kinetic parameters. This work is expected to significantly improve the performance of the enzyme screening process and provide a robust tool for enzyme-directed evolution research.

Artificial Intelligence-Based Approaches for AAV Vector Engineering

Adeno-associated virus (AAV) has emerged as a leading vector for gene therapy due to its broad host range, low pathogenicity, and ability to facilitate long-term gene expression. However, AAV vectors face limitations, including immunogenicity and insufficient targeting specificity. To enhance the efficacy of gene therapy, researchers have been modifying the AAV vector using various methods. Traditional experimental approaches for optimizing AAV vector are often time-consuming, resource-intensive, and difficult to replicate. The advancement of artificial intelligence (AI), particularly machine learning, offers significant potential to accelerate capsid optimization while reducing development time and manufacturing costs. This review compares traditional and AI-based methods of AAV vector engineering and highlights recent research in AAV engineering using AI algorithms.

Integrating protein language models and automatic biofoundry for enhanced protein evolution

Traditional protein engineering methods, such as directed evolution, while effective, are often slow and labor-intensive. Advances in machine learning and automated biofoundry present new opportunities for optimizing these processes. This study devises a protein language model-enabled automatic evolution platform, a closed-loop system for automated protein engineering within the Design-Build-Test-Learn cycle. The protein language model ESM-2 makes zero-shot prediction of 96 variants to initiate the cycle. The biofoundry constructs and evaluates these variants, and feeds the results back to a multi-layer perceptron to train a fitness predictor, which then makes prediction of second round of 96 variants with improved fitness. With the tRNA synthetase as a model enzyme, four-rounds of evolution carried out within 10 days lead to mutants with enzyme activity improved by up to 2.4-fold. Our system significantly enhances the speed and accuracy of protein evolution, driving faster advancements in protein engineering for industrial applications.

Protein Quality Control is a Master Modulator of Molecular Evolution in Bacteria

The bacterial protein quality control (PQC) network comprises a set of genes that promote proteostasis (proteome homeostasis) through proper protein folding and function via chaperones, proteases, and protein translational machinery. It participates in vital cellular processes and influences organismal development and evolution. In this review, we examine the mechanistic bases for how the bacterial PQC network influences molecular evolution. We discuss the relevance of PQC components to contemporary issues in evolutionary biology including epistasis, evolvability, and the navigability of protein space. We examine other areas where proteostasis affects aspects of evolution and physiology, including host-parasite interactions. More generally, we demonstrate that the study of bacterial systems can aid in broader efforts to understand the relationship between genotype and phenotype across the biosphere.

Confronting PFAS persistence: enzymes catalyzing C-F bond cleavage

Studies of enzymes catalyzing carbon-fluorine (C-F) bond cleavage have focused largely on a limited number of native microbial hydrolases that are reactive with the natural product fluoroacetate. Driven by widespread interest in biodegrading commercial fluorinated compounds, many of which are known as per- and polyfluorinated alkyl substances (PFAS), it is necessary to identify and engineer new enzymes. For example, some hydrolases react with -CF2- moieties, a common functionality in PFAS. Additional enzymatic C-F cleaving mechanisms catalyzed by reductases, lyases, and oxygenases have been identified via screening. Screening and evolving PFAS defluorination in bacteria is inhibited by the obligate release of toxic fluoride from C-F cleavage. Engineering greater fluoride tolerance in bacteria is a problem that must be solved in tandem with enzyme improvement.

A metalloenzyme platform for catalytic asymmetric radical dearomatization

Catalytic asymmetric dearomatization represents a powerful means to convert flat aromatic compounds into stereochemically well-defined three-dimensional molecular scaffolds. Using new-to-nature metalloredox biocatalysis, we describe an enzymatic strategy for catalytic asymmetric dearomatization via a challenging radical mechanism that has eluded small-molecule catalysts. Enabled by directed evolution, new-to-nature radical dearomatases P450rad1-P450rad5 facilitated asymmetric dearomatization of a broad spectrum of aromatic substrates, including indoles, pyrroles and phenols, allowing both enantioconvergent and enantiodivergent radical dearomatization reactions to be accomplished with excellent enzymatic control. Computational studies revealed the importance of additional hydrogen bonding interactions between the engineered metalloenzyme and the reactive intermediate in enhancing enzymatic activity and enantiocontrol. Furthermore, designer non-ionic surfactants were found to significantly accelerate this biotransformation, providing an alternative means to promote otherwise sluggish new-to-nature biotransformations. Together, this evolvable metalloenzyme platform opens up new avenues to advance challenging catalytic asymmetric dearomatization processes involving free radical intermediates.

An Engineered Imine Reductase for Highly Diastereo- and Enantioselective Synthesis of β-Branched Amines with Contiguous Stereocenters

β-Branched chiral amines with contiguous stereocenters are valuable building blocks for preparing various biologically active molecules. However, their asymmetric synthesis remains challenging. Herein, we report a highly diastereo- and enantioselective biocatalytic approach for preparing a broad range of β-branched chiral amines starting from their corresponding racemic ketones. This involves a dynamic kinetic resolution-asymmetric reductive amination process catalyzed using only an imine reductase. Four rounds of protein engineering endowed wild-type PocIRED with higher reactivity, better stereoselectivity, and a broader substrate scope. Using the engineered enzyme, various chiral amine products were synthesized with up to >99.9 % ee, >99 : 1 dr, and >99 % conversion. The practicability of the developed biocatalytic method was confirmed by producing a key intermediate of tofacitinib in 74 % yield, >99.9 % ee, and 98 : 2 dr at a challenging substrate loading of 110 g L-1. Our study provides a highly capable imine reductase and a protocol for developing an efficient biocatalytic dynamic kinetic resolution-asymmetric reductive amination reaction system.

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.

Determinants of Unfolding Cooperativity and Binding Are Decoupled in a DNA Binding Domain

The relative magnitudes of noncovalent stabilization energies or the coupling free energies in folded proteins are anisotropically distributed, uniquely influencing folding and functional behaviors. In this regard, the fructose repressor (FruR) DBD belonging to the LacR repressor family harbors a three-residue insertion─KQY─between the canonical second and third helices. This sequence insertion promotes a strong Tyr-Tyr stacking interaction that is not observed in related homologues. Combining experiments with simulations, we show that the Tyr-Tyr stacking contributes to a decoupled unfolding due to the localization of a large part of the stabilization energy in this specific structural region. This leads to melting temperatures from different probes spanning nearly 10 K, while concomitantly stabilizing a partially structured intermediate state. Disruption of the aromatic stacking interaction via an alanine mutation promotes a molten-globular state whose native ensemble is replete with non-native interactions while displaying enhanced thermodynamic fluctuations and minimal calorimetric cooperativity. Surprisingly, the molten-globular variant of FruR DBD binds to the operator site on DNA with an affinity similar to that of the wild-type but with altered secondary-structure characteristics in the bound state, underscoring the chaperone-like role of DNA through its large negative electrostatic potential. FruR DBD thus appears to be at the verge of disorder as expected of an entropically destabilizing three-residue insertion but is rescued by the aromatic stacking interaction that distinctly dictates the finer details of stability, cooperativity, and binding.

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.

C-H functionalization reactions catalyzed by artificial metalloenzymes

CH functionalization, a promising frontier in modern organic chemistry, facilitates the direct conversion of inert CH bonds into many valuable functional groups. Despite its merits, traditional homogeneous catalysis, often faces challenges in efficiency, selectivity, and sustainability towards this transformation. In this context, artificial metalloenzymes (ArMs), resulting from the incorporation of a catalytically-competent metal cofactor within an evolvable protein scaffold, bridges the gap between the efficiency of enzymatic transformations and the versatility of transition metal catalysis. Accordingly, ArMs have emerged as attractive tools for various challenging catalytic transformations. Additionally, the coming of age of directed evolution has unlocked unprecedented avenues for optimizing enzymatic catalysis. Taking advantage of their genetically-encoded protein scaffold, ArMs have been evolved to catalyze various CH functionalization reactions. This review delves into the recent developments of ArM-catalyzed CH functionalization reactions, highlighting the benefits of engineering the second coordination sphere around a metal cofactor within a host protein.

Analysis of proteins in the light of mutations

Proteins have evolved through mutations-amino acid substitutions-since life appeared on Earth, some 109 years ago. The study of these phenomena has been of particular significance because of their impact on protein stability, function, and structure. This study offers a new viewpoint on how the most recent findings in these areas can be used to explore the impact of mutations on protein sequence, stability, and evolvability. Preliminary results indicate that: (1) mutations can be viewed as sensitive probes to identify 'typos' in the amino-acid sequence, and also to assess the resistance of naturally occurring proteins to unwanted sequence alterations; (2) the presence of 'typos' in the amino acid sequence, rather than being an evolutionary obstacle, could promote faster evolvability and, in turn, increase the likelihood of higher protein stability; (3) the mutation site is far more important than the substituted amino acid in terms of the marginal stability changes of the protein, and (4) the unpredictability of protein evolution at the molecular level-by mutations-exists even in the absence of epistasis effects. Finally, the Darwinian concept of evolution "descent with modification" and experimental evidence endorse one of the results of this study, which suggests that some regions of any protein sequence are susceptible to mutations while others are not. This work contributes to our general understanding of protein responses to mutations and may spur significant progress in our efforts to develop methods to accurately forecast changes in protein stability, their propensity for metamorphism, and their ability to evolve.

Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering

The effective design of combinatorial libraries to balance fitness and diversity facilitates the engineering of useful enzyme functions, particularly those that are poorly characterized or unknown in biology. We introduce MODIFY, a machine learning (ML) algorithm that learns from natural protein sequences to infer evolutionarily plausible mutations and predict enzyme fitness. MODIFY co-optimizes predicted fitness and sequence diversity of starting libraries, prioritizing high-fitness variants while ensuring broad sequence coverage. In silico evaluation shows that MODIFY outperforms state-of-the-art unsupervised methods in zero-shot fitness prediction and enables ML-guided directed evolution with enhanced efficiency. Using MODIFY, we engineer generalist biocatalysts derived from a thermostable cytochrome c to achieve enantioselective C-B and C-Si bond formation via a new-to-nature carbene transfer mechanism, leading to biocatalysts six mutations away from previously developed enzymes while exhibiting superior or comparable activities. These results demonstrate MODIFY's potential in solving challenging enzyme engineering problems beyond the reach of classic directed evolution.

Coupling Peptide-Based Encapsulation of Enzymes with Bacteria for Paraoxon Bioremediation

The catalytic efficiency of enzymes can be harnessed as an environmentally friendly solution for decontaminating various xenobiotics and toxins. However, for some xenobiotics, several enzymatic steps are needed to obtain nontoxic products. Another challenge is the low durability and stability of many native enzymes in their purified form. Herein, we coupled peptide-based encapsulation of bacterial phosphotriesterase with soil-originated bacteria, Arthrobacter sp. 4Hβ as an efficient system capable of biodegradation of paraoxon, a neurotoxin pesticide. Specifically, recombinantly expressed and purified methyl parathion hydrolase (MPH), with high hydrolytic activity toward paraoxon, was encapsulated within peptide nanofibrils, resulting in increased shelf life and retaining ∼50% activity after 132 days since purification. Next, the addition of Arthrobacter sp. 4Hβ, capable of degrading para-nitrophenol (PNP), the hydrolysis product of paraoxon, which is still toxic, resulted in nondetectable levels of PNP. These results present an efficient one-pot system that can be further developed as an environmentally friendly solution, coupling purified enzymes and native bacteria, for pesticide bioremediation. We further suggest that this system can be tailored for different xenobiotics by encapsulating the rate-limiting key enzymes followed by their combination with environmental bacteria that can use the enzymatic step products for full degradation without the need to engineer synthetic bacteria.

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.

AlphaFold2 Predictions of Conformational Ensembles and Atomistic Simulations of the SARS-CoV-2 Spike XBB Lineages Reveal Epistatic Couplings between Convergent Mutational Hotspots that Control ACE2 Affinity

In this study, we combined AlphaFold-based atomistic structural modeling, microsecond molecular simulations, mutational profiling, and network analysis to characterize binding mechanisms of the SARS-CoV-2 spike protein with the host receptor ACE2 for a series of Omicron XBB variants including XBB.1.5, XBB.1.5+L455F, XBB.1.5+F456L, and XBB.1.5+L455F+F456L. AlphaFold-based structural and dynamic modeling of SARS-CoV-2 Spike XBB lineages can accurately predict the experimental structures and characterize conformational ensembles of the spike protein complexes with the ACE2. Microsecond molecular dynamics simulations identified important differences in the conformational landscapes and equilibrium ensembles of the XBB variants, suggesting that combining AlphaFold predictions of multiple conformations with molecular dynamics simulations can provide a complementary approach for the characterization of functional protein states and binding mechanisms. Using the ensemble-based mutational profiling of protein residues and physics-based rigorous calculations of binding affinities, we identified binding energy hotspots and characterized the molecular basis underlying epistatic couplings between convergent mutational hotspots. Consistent with the experiments, the results revealed the mediating role of the Q493 hotspot in the synchronization of epistatic couplings between L455F and F456L mutations, providing a quantitative insight into the energetic determinants underlying binding differences between XBB lineages. We also proposed a network-based perturbation approach for mutational profiling of allosteric communications and uncovered the important relationships between allosteric centers mediating long-range communication and binding hotspots of epistatic couplings. The results of this study support a mechanism in which the binding mechanisms of the XBB variants may be determined by epistatic effects between convergent evolutionary hotspots that control ACE2 binding.

Rugged fitness landscapes minimize promiscuity in the evolution of transcriptional repressors

How a protein's function influences the shape of its fitness landscape, smooth or rugged, is a fundamental question in evolutionary biochemistry. Smooth landscapes arise when incremental mutational steps lead to a progressive change in function, as commonly seen in enzymes and binding proteins. On the other hand, rugged landscapes are poorly understood because of the inherent unpredictability of how sequence changes affect function. Here, we experimentally characterize the entire sequence phylogeny, comprising 1,158 extant and ancestral sequences, of the DNA-binding domain (DBD) of the LacI/GalR transcriptional repressor family. Our analysis revealed an extremely rugged landscape with rapid switching of specificity, even between adjacent nodes. Further, the ruggedness arises due to the necessity of the repressor to simultaneously evolve specificity for asymmetric operators and disfavors potentially adverse regulatory crosstalk. Our study provides fundamental insight into evolutionary, molecular, and biophysical rules of genetic regulation through the lens of fitness landscapes.

Ensemble-Based Mutational Profiling and Network Analysis of the SARS-CoV-2 Spike Omicron XBB Lineages for Interactions with the ACE2 Receptor and Antibodies: Cooperation of Binding Hotspots in Mediating Epistatic Couplings Underlies Binding Mechanism and Immune Escape

In this study, we performed a computational study of binding mechanisms for the SARS-CoV-2 spike Omicron XBB lineages with the host cell receptor ACE2 and a panel of diverse class one antibodies. The central objective of this investigation was to examine the molecular factors underlying epistatic couplings among convergent evolution hotspots that enable optimal balancing of ACE2 binding and antibody evasion for Omicron variants BA.1, BA2, BA.3, BA.4/BA.5, BQ.1.1, XBB.1, XBB.1.5, and XBB.1.5 + L455F/F456L. By combining evolutionary analysis, molecular dynamics simulations, and ensemble-based mutational scanning of spike protein residues in complexes with ACE2, we identified structural stability and binding affinity hotspots that are consistent with the results of biochemical studies. In agreement with the results of deep mutational scanning experiments, our quantitative analysis correctly reproduced strong and variant-specific epistatic effects in the XBB.1.5 and BA.2 variants. It was shown that Y453W and F456L mutations can enhance ACE2 binding when coupled with Q493 in XBB.1.5, while these mutations become destabilized when coupled with the R493 position in the BA.2 variant. The results provided a molecular rationale of the epistatic mechanism in Omicron variants, showing a central role of the Q493/R493 hotspot in modulating epistatic couplings between convergent mutational sites L455F and F456L in XBB lineages. The results of mutational scanning and binding analysis of the Omicron XBB spike variants with ACE2 receptors and a panel of class one antibodies provide a quantitative rationale for the experimental evidence that epistatic interactions of the physically proximal binding hotspots Y501, R498, Q493, L455F, and F456L can determine strong ACE2 binding, while convergent mutational sites F456L and F486P are instrumental in mediating broad antibody resistance. The study supports a mechanism in which the impact on ACE2 binding affinity is mediated through a small group of universal binding hotspots, while the effect of immune evasion could be more variant-dependent and modulated by convergent mutational sites in the conformationally adaptable spike regions.

Viral Receptor-Binding Protein Evolves New Function through Mutations That Cause Trimer Instability and Functional Heterogeneity

When proteins evolve new activity, a concomitant decrease in stability is often observed because the mutations that confer new activity can destabilize the native fold. In the conventional model of protein evolution, reduced stability is considered a purely deleterious cost of molecular innovation because unstable proteins are prone to aggregation and are sensitive to environmental stressors. However, recent work has revealed that nonnative, often unstable protein conformations play an important role in mediating evolutionary transitions, raising the question of whether instability can itself potentiate the evolution of new activity. We explored this question in a bacteriophage receptor-binding protein during host-range evolution. We studied the properties of the receptor-binding protein of bacteriophage λ before and after host-range evolution and demonstrated that the evolved protein is relatively unstable and may exist in multiple conformations with unique receptor preferences. Through a combination of structural modeling and in vitro oligomeric state analysis, we found that the instability arises from mutations that interfere with trimer formation. This study raises the intriguing possibility that protein instability might play a previously unrecognized role in mediating host-range expansions in viruses.

Complete Genome Sequences of Four Mycobacteriophages Involved in Directed Evolution against Undisputed Mycobacterium abscessus Clinical Strains

Phage therapy is still in its infancy, but it is increasingly promising as a future alternative for treating antibiotic-resistant bacteria. To investigate the effect of phages on Mycobacterium abscessus complex (MABC), we isolated 113 environmental phages, grown them to high titres, and assayed them on MABC clinical strains through the spot test. Of all the phages, only 16 showed killing activity. Their activity was so temperate to MABC that they could not generate any plaque-forming units (PFUs). The Appelmans method of directed evolution was carried out to evolve these 16 phages into more lytic ones. After only 11 of 30 rounds of evolution, every single clinical strain in our collection, including those that were unsusceptible up to this point, could be lysed by at least one phage. The evolved phages were able to form PFUs on the clinical strains tested. Still, they are temperate at best and require further training. The genomes of one random parental phage and three random evolved phages from Round 13 were sequenced, revealing a diversity of clusters and genes of a variety of evolutionary origins, mostly of unknown function. These complete annotated genomes will be key for future molecular characterisations.

Moderating the neutralist-selectionist debate: exactly which propositions are we debating, and which arguments are valid?

Half a century after its foundation, the neutral theory of molecular evolution continues to attract controversy. The debate has been hampered by the coexistence of different interpretations of the core proposition of the neutral theory, the 'neutral mutation-random drift' hypothesis. In this review, we trace the origins of these ambiguities and suggest potential solutions. We highlight the difference between the original, the revised and the nearly neutral hypothesis, and re-emphasise that none of them equates to the null hypothesis of strict neutrality. We distinguish the neutral hypothesis of protein evolution, the main focus of the ongoing debate, from the neutral hypotheses of genomic and functional DNA evolution, which for many species are generally accepted. We advocate a further distinction between a narrow and an extended neutral hypothesis (of which the latter posits that random non-conservative amino acid substitutions can cause non-ecological phenotypic divergence), and we discuss the implications for evolutionary biology beyond the domain of molecular evolution. We furthermore point out that the debate has widened from its initial focus on point mutations, and also concerns the fitness effects of large-scale mutations, which can alter the dosage of genes and regulatory sequences. We evaluate the validity of neutralist and selectionist arguments and find that the tested predictions, apart from being sensitive to violation of underlying assumptions, are often derived from the null hypothesis of strict neutrality, or equally consistent with the opposing selectionist hypothesis, except when assuming molecular panselectionism. Our review aims to facilitate a constructive neutralist-selectionist debate, and thereby to contribute to answering a key question of evolutionary biology: what proportions of amino acid and nucleotide substitutions and polymorphisms are adaptive?

Evolution shapes interaction patterns for epistasis and specific protein binding in a two-component signaling system

The elegant design of protein sequence/structure/function relationships arises from the interaction patterns between amino acid positions. A central question is how evolutionary forces shape the interaction patterns that encode long-range epistasis and binding specificity. Here, we combined family-wide evolutionary analysis of natural homologous sequences and structure-oriented evolution simulation for two-component signaling (TCS) system. The magnitude-frequency relationship of coupling conservation between positions manifests a power-law-like distribution and the positions with highly coupling conservation are sparse but distributed intensely on the binding surfaces and hydrophobic core. The structure-specific interaction pattern involves further optimization of local frustrations at or near the binding surface to adapt the binding partner. The construction of family-wide conserved interaction patterns and structure-specific ones demonstrates that binding specificity is modulated by both direct intermolecular interactions and long-range epistasis across the binding complex. Evolution sculpts the interaction patterns via sequence variations at both family-wide and structure-specific levels for TCS system.

Unlocking novel therapies: cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning

Existing therapies for neurodegenerative diseases like Parkinson's and Alzheimer's address only their symptoms and do not prevent disease onset. Common therapeutic agents, such as small molecules and antibodies struggle with insufficient selectivity, stability and bioavailability, leading to poor performance in clinical trials. Peptide-based therapeutics are emerging as promising candidates, with successful applications for cardiovascular diseases and cancers due to their high bioavailability, good efficacy and specificity. In particular, cyclic peptides have a long in vivo stability, while maintaining a robust antibody-like binding affinity. However, the de novo design of cyclic peptides is challenging due to the lack of long-lived druggable pockets of the target polypeptide, absence of exhaustive conformational distributions of the target and/or the binder, unknown binding site, methodological limitations, associated constraints (failed trials, time, money) and the vast combinatorial sequence space. Hence, efficient alignment and cooperation between disciplines, and synergies between experiments and simulations complemented by popular techniques like machine-learning can significantly speed up the therapeutic cyclic-peptide development for neurodegenerative diseases. We review the latest advancements in cyclic peptide design against amyloidogenic targets from a computational perspective in light of recent advancements and potential of machine learning to optimize the design process. We discuss the difficulties encountered when designing novel peptide-based inhibitors and we propose new strategies incorporating experiments, simulations and machine learning to design cyclic peptides to inhibit the toxic propagation of amyloidogenic polypeptides. Importantly, these strategies extend beyond the mere design of cyclic peptides and serve as template for the de novo generation of (bio)materials with programmable properties.

Programmable synthetic receptors: the next-generation of cell and gene therapies

Cell and gene therapies hold tremendous promise for treating a range of difficult-to-treat diseases. However, concerns over the safety and efficacy require to be further addressed in order to realize their full potential. Synthetic receptors, a synthetic biology tool that can precisely control the function of therapeutic cells and genetic modules, have been rapidly developed and applied as a powerful solution. Delicately designed and engineered, they can be applied to finetune the therapeutic activities, i.e., to regulate production of dosed, bioactive payloads by sensing and processing user-defined signals or biomarkers. This review provides an overview of diverse synthetic receptor systems being used to reprogram therapeutic cells and their wide applications in biomedical research. With a special focus on four synthetic receptor systems at the forefront, including chimeric antigen receptors (CARs) and synthetic Notch (synNotch) receptors, we address the generalized strategies to design, construct and improve synthetic receptors. Meanwhile, we also highlight the expanding landscape of therapeutic applications of the synthetic receptor systems as well as current challenges in their clinical translation.

AlphaFold2-Enabled Atomistic Modeling of Epistatic Binding Mechanisms for the SARS-CoV-2 Spike Omicron XBB.1.5, EG.5 and FLip Variants: Convergent Evolution Hotspots Cooperate to Control Stability and Conformational Adaptability in Balancing ACE2 Binding and Antibody Resistance

In this study, we combined AI-based atomistic structural modeling and microsecond molecular simulations of the SARS-CoV-2 Spike complexes with the host receptor ACE2 for XBB.1.5+L455F, XBB.1.5+F456L(EG.5) and XBB.1.5+L455F/F456L (FLip) lineages to examine the mechanisms underlying the role of convergent evolution hotspots in balancing ACE2 binding and antibody evasion. Using the ensemble-based mutational scanning of the spike protein residues and physics-based rigorous computations of binding affinities, we identified binding energy hotspots and characterized molecular basis underlying epistatic couplings between convergent mutational hotspots. Consistent with the experiments, the results revealed the mediating role of Q493 hotspot in synchronization of epistatic couplings between L455F and F456L mutations providing a quantitative insight into the mechanism underlying differences between XBB lineages. Mutational profiling is combined with network-based model of epistatic couplings showing that the Q493, L455 and F456 sites mediate stable communities at the binding interface with ACE2 and can serve as stable mediators of non-additive couplings. Structure-based mutational analysis of Spike protein binding with the class 1 antibodies quantified the critical role of F456L and F486P mutations in eliciting strong immune evasion response. The results of this analysis support a mechanism in which the emergence of EG.5 and FLip variants may have been dictated by leveraging strong epistatic effects between several convergent revolutionary hotspots that provide synergy between the improved ACE2 binding and broad neutralization resistance. This interpretation is consistent with the notion that functionally balanced substitutions which simultaneously optimize immune evasion and high ACE2 affinity may continue to emerge through lineages with beneficial pair or triplet combinations of RBD mutations involving mediators of epistatic couplings and sites in highly adaptable RBD regions.

Simulation-guided engineering of split GFPs with efficient β-strand photodissociation

Green fluorescent proteins (GFPs) are ubiquitous for protein tagging and live-cell imaging. Split-GFPs are widely used to study protein-protein interactions by fusing proteins of interest to split GFP fragments that create a fluorophore upon typically irreversible complementation. Thus, controlled dissociation of the fragments is desirable. Although we have found that split strands can be photodissociated, the quantum efficiency of light-induced photodissociation of split GFPs is low. Traditional protein engineering approaches to increase efficiency, including extensive mutagenesis and screening, have proved difficult to implement. To reduce the search space, key states in the dissociation process are modeled by combining classical and enhanced sampling molecular dynamics with QM/MM calculations, enabling the rational design and engineering of split GFPs with up to 20-fold faster photodissociation rates using non-intuitive amino acid changes. This demonstrates the feasibility of modeling complex molecular processes using state-of-the-art computational methods, and the potential of integrating computational methods to increase the success rate in protein engineering projects.

Facile Method for High-throughput Identification of Stabilizing Mutations

Characterizing the effects of mutations on stability is critical for understanding the function and evolution of proteins and improving their biophysical properties. High throughput folding and abundance assays have been successfully used to characterize missense mutations associated with reduced stability. However, screening for increased thermodynamic stability is more challenging since such mutations are rarer and their impact on assay readout is more subtle. Here, a multiplex assay for high throughput screening of protein folding was developed by combining deep mutational scanning, fluorescence-activated cell sorting, and deep sequencing. By analyzing a library of 2000 variants of Adenylate kinase we demonstrate that the readout of the method correlates with stability and that mutants with up to 13 °C increase in thermal melting temperature could be identified with low false positive rate. The discovery of many stabilizing mutations also enabled the analysis of general substitution patterns associated with increased stability in Adenylate kinase. This high throughput method to identify stabilizing mutations can be combined with functional screens to identify mutations that improve both stability and activity.

A biocatalytic platform for asymmetric alkylation of α-keto acids by mining and engineering of methyltransferases

Catalytic asymmetric α-alkylation of carbonyl compounds represents a long-standing challenge in synthetic organic chemistry. Herein, we advance a dual biocatalytic platform for the efficient asymmetric alkylation of α-keto acids. First, guided by our recently obtained crystal structures, we develop SgvMVAV as a general biocatalyst for the enantioselective methylation, ethylation, allylation and propargylation of a range of α-keto acids with total turnover numbers (TTNs) up to 4,600. Second, we mine a family of bacterial HMTs from Pseudomonas species sharing less than 50% sequence identities with known HMTs and evaluated their activities in SAM regeneration. Our best performing HMT from P. aeruginosa, PaHMT, displays the highest SAM regeneration efficiencies (TTN up to 7,700) among HMTs characterized to date. Together, the synergistic use of SgvMVAV and PaHMT affords a fully biocatalytic protocol for asymmetric methylation featuring a record turnover efficiency, providing a solution to the notorious problem of asymmetric alkylation.

DNA Tile and Invading Stacking Primer-Assisted CRISPR-Cas12a Multiple Amplification System for Entropy-Driven Electrochemical Detection of MicroRNA with Tunable Sensitivity

Conventional electrochemical detection of microRNA (miRNA) encounters issues of poor sensitivity and fixed dynamic range. Here, we report a DNA tile and invading stacking primer-assisted CRISPR-Cas12a multiple amplification strategy to construct an entropy-controlled electrochemical biosensor for the detection of miRNA with tunable sensitivity and dynamic range. To amplify the signal, a cascade amplification of the CRISPR-Cas12a system along with invading stacking primer signal amplification (ISPSA) was designed to detect trace amounts of miRNA-31 (miR-31). The target miR-31 could activate ISPSA and produce numerous DNAs, triggering the cleavage of the single-stranded linker probe (LP) that connects a methylene blue-labeled DNA tile with a DNA tetrahedron to form a Y-shaped DNA scaffold on the electrode. Based on the decrease of current, miR-31 can be accurately and efficiently detected. Impressively, by changing the loop length of the LP, it is possible to finely tune the entropic contribution while keeping the enthalpic contribution constant. This strategy has shown a tunable limit of detection for miRNA from 0.31 fM to 0.56 pM, as well as a dynamic range from ∼2200-fold to ∼270,000-fold. Moreover, it demonstrated satisfactory results in identifying cancer cells with a high expression of miR-31. Our strategy broadens the application of conventional electrochemical biosensing and provides a tunable strategy for detecting miRNAs at varying concentrations.

Directed evolution of cholesterol oxidase with improved thermostability using error-prone PCR

Cholesterol oxidase is industrially important as it is frequently used as a biosensor in food and agriculture industries and measurement of cholesterol. Although, most natural enzymes show low thermostability, which limits their application. Here, we obtained an improved variant of Chromobacterium sp. DS1 cholesterol oxidase (ChOS) with enhanced thermostability by random mutant library applying two forms of error-prone PCR (serial dilution and single step). Wild-type ChOS indicated an optimal temperature and pH of 70 ºC and pH 7.5, respectively. The best mutant ChOS-M acquired three amino acid substitutions (S112T, I240V and A500S) and enhanced thermostability (at 50 °C for 5 h) by 30%. The optimum temperature and pH in the mutant were not changed. In comparison to wild type, circular dichroism disclosed no significant secondary structural alterations in mutants. These findings show that error-prone PCR is an effective method for enhancing enzyme characteristics and offers a platform for the practical use of ChOS as a thermal-resistance enzyme in industrial fields and clinical diagnosis.

Analysis of neutral mutational drift in an allosteric enzyme

Neutral mutational drift is an important source of biological diversity that remains underexploited in fundamental studies of protein biophysics. This study uses a synthetic transcriptional circuit to study neutral drift in protein tyrosine phosphatase 1B (PTP1B), a mammalian signaling enzyme for which conformational changes are rate limiting. Kinetic assays of purified mutants indicate that catalytic activity, rather than thermodynamic stability, guides enrichment under neutral drift, where neutral or mildly activating mutations can mitigate the effects of deleterious ones. In general, mutants show a moderate activity-stability tradeoff, an indication that minor improvements in the activity of PTP1B do not require concomitant losses in its stability. Multiplexed sequencing of large mutant pools suggests that substitutions at allosterically influential sites are purged under biological selection, which enriches for mutations located outside of the active site. Findings indicate that the positional dependence of neutral mutations within drifting populations can reveal the presence of allosteric networks and illustrate an approach for using synthetic transcriptional systems to explore these mutations in regulatory enzymes.

Evolvability-enhancing mutations in the fitness landscapes of an RNA and a protein

Can evolvability-the ability to produce adaptive heritable variation-itself evolve through adaptive Darwinian evolution? If so, then Darwinian evolution may help create the conditions that enable Darwinian evolution. Here I propose a framework that is suitable to address this question with available experimental data on adaptive landscapes. I introduce the notion of an evolvability-enhancing mutation, which increases the likelihood that subsequent mutations in an evolving organism, protein, or RNA molecule are adaptive. I search for such mutations in the experimentally characterized and combinatorially complete fitness landscapes of a protein and an RNA molecule. I find that such evolvability-enhancing mutations indeed exist. They constitute a small fraction of all mutations, which shift the distribution of fitness effects of subsequent mutations towards less deleterious mutations, and increase the incidence of beneficial mutations. Evolving populations which experience such mutations can evolve significantly higher fitness. The study of evolvability-enhancing mutations opens many avenues of investigation into the evolution of evolvability.

Electrochemical Biosensors for Whole Blood Analysis: Recent Progress, Challenges, and Future Perspectives

Whole blood, as one of the most significant biological fluids, provides critical information for health management and disease monitoring. Over the past 10 years, advances in nanotechnology, microfluidics, and biomarker research have spurred the development of powerful miniaturized diagnostic systems for whole blood testing toward the goal of disease monitoring and treatment. Among the techniques employed for whole-blood diagnostics, electrochemical biosensors, as known to be rapid, sensitive, capable of miniaturization, reagentless and washing free, become a class of emerging technology to achieve the target detection specifically and directly in complex media, e.g., whole blood or even in the living body. Here we are aiming to provide a comprehensive review to summarize advances over the past decade in the development of electrochemical sensors for whole blood analysis. Further, we address the remaining challenges and opportunities to integrate electrochemical sensing platforms.

Leveraging a Structural Blueprint to Rationally Engineer the Rieske Oxygenase TsaM

Rieske nonheme iron oxygenases use two metallocenters, a Rieske-type [2Fe-2S] cluster and a mononuclear iron center, to catalyze oxidation reactions on a broad range of substrates. These enzymes are widely used by microorganisms to degrade environmental pollutants and to build complexity in a myriad of biosynthetic pathways that are industrially interesting. However, despite the value of this chemistry, there is a dearth of understanding regarding the structure-function relationships in this enzyme class, which limits our ability to rationally redesign, optimize, and ultimately exploit the chemistry of these enzymes. Therefore, in this work, by leveraging a combination of available structural information and state-of-the-art protein modeling tools, we show that three "hotspot" regions can be targeted to alter the site selectivity, substrate preference, and substrate scope of the Rieske oxygenase p-toluenesulfonate methyl monooxygenase (TsaM). Through mutation of six to 10 residues distributed between three protein regions, TsaM was engineered to behave as either vanillate monooxygenase (VanA) or dicamba monooxygenase (DdmC). This engineering feat means that TsaM was rationally engineered to catalyze an oxidation reaction at the meta and ortho positions of an aromatic substrate, rather than its favored native para position, and that TsaM was redesigned to perform chemistry on dicamba, a substrate that is not natively accepted by the enzyme. This work thus contributes to unlocking our understanding of structure-function relationships in the Rieske oxygenase enzyme class and expands foundational principles for future engineering of these metalloenzymes.

Balancing Functional Tradeoffs between Protein Stability and ACE2 Binding in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB Lineages: Dynamics-Based Network Models Reveal Epistatic Effects Modulating Compensatory Dynamic and Energetic Changes

Evolutionary and functional studies suggested that the emergence of the Omicron variants can be determined by multiple fitness trade-offs including the immune escape, binding affinity for ACE2, conformational plasticity, protein stability and allosteric modulation. In this study, we systematically characterize conformational dynamics, structural stability and binding affinities of the SARS-CoV-2 Spike Omicron complexes with the host receptor ACE2 for BA.2, BA.2.75, XBB.1 and XBB.1.5 variants. We combined multiscale molecular simulations and dynamic analysis of allosteric interactions together with the ensemble-based mutational scanning of the protein residues and network modeling of epistatic interactions. This multifaceted computational study characterized molecular mechanisms and identified energetic hotspots that can mediate the predicted increased stability and the enhanced binding affinity of the BA.2.75 and XBB.1.5 complexes. The results suggested a mechanism driven by the stability hotspots and a spatially localized group of the Omicron binding affinity centers, while allowing for functionally beneficial neutral Omicron mutations in other binding interface positions. A network-based community model for the analysis of epistatic contributions in the Omicron complexes is proposed revealing the key role of the binding hotspots R498 and Y501 in mediating community-based epistatic couplings with other Omicron sites and allowing for compensatory dynamics and binding energetic changes. The results also showed that mutations in the convergent evolutionary hotspot F486 can modulate not only local interactions but also rewire the global network of local communities in this region allowing the F486P mutation to restore both the stability and binding affinity of the XBB.1.5 variant which may explain the growth advantages over the XBB.1 variant. The results of this study are consistent with a broad range of functional studies rationalizing functional roles of the Omicron mutation sites that form a coordinated network of hotspots enabling a balance of multiple fitness tradeoffs and shaping up a complex functional landscape of virus transmissibility.

Genotype-phenotype landscapes for immune-pathogen coevolution

Our immune systems constantly coevolve with the pathogens that challenge them, as pathogens adapt to evade our defense responses, with our immune repertoires shifting in turn. These coevolutionary dynamics take place across a vast and high-dimensional landscape of potential pathogen and immune receptor sequence variants. Mapping the relationship between these genotypes and the phenotypes that determine immune-pathogen interactions is crucial for understanding, predicting, and controlling disease. Here, we review recent developments applying high-throughput methods to create large libraries of immune receptor and pathogen protein sequence variants and measure relevant phenotypes. We describe several approaches that probe different regions of the high-dimensional sequence space and comment on how combinations of these methods may offer novel insight into immune-pathogen coevolution.

Water potential governs the effector specificity of the transcriptional regulator XylR of Pseudomonas putida

The biodegradative capacity of bacteria in their natural habitats is affected by water availability. In this work, we have examined the activity and effector specificity of the transcriptional regulator XylR of the TOL plasmid pWW0 of Pseudomonas putida mt-2 for biodegradation of m-xylene when external water potential was manipulated with polyethylene glycol PEG8000. By using non-disruptive luxCDEAB reporter technology, we noticed that the promoter activated by XylR (Pu) restricted its activity and the regulator became more effector-specific towards head TOL substrates when cells were grown under water subsaturation. Such a tight specificity brought about by water limitation was relaxed when intracellular osmotic stress was counteracted by the external addition of the compatible solute glycine betaine. With these facts in hand, XylR variants isolated earlier as effector-specificity responders to the non-substrate 1,2,4-trichlorobenzene under high matric stress were re-examined and found to be unaffected by water potential in vivo. All these phenomena could be ultimately explained as the result of water potential-dependent conformational changes in the A domain of XylR and its effector-binding pocket, as suggested by AlphaFold prediction of protein structures. The consequences of this scenario for the evolution of specificities in regulators and the emergence of catabolic pathways are discussed.

Domain-wise dissection of thermal stability enhancement in multidomain proteins

Stability is critical for the proper functioning of all proteins. Optimization of protein thermostability is a key step in the development of industrial enzymes and biologics. Herein, we demonstrate that multidomain proteins can be stabilized significantly using domain-based engineering followed by the recombination of the optimized domains. Domain-level analysis of designed protein variants with similar structures but different thermal profiles showed that the independent enhancement of the thermostability of a constituent domain improves the overall stability of the whole multidomain protein. The crystal structure and AlphaFold-predicted model of the designed proteins via domain-recombination provided a molecular explanation for domain-based stepwise stabilization. Our study suggests that domain-based modular engineering can minimize the sequence space for calculations in computational design and experimental errors, thereby offering useful guidance for multidomain protein engineering.

The community-function landscape of microbial consortia

Quantitatively linking the composition and function of microbial communities is a major aspiration of microbial ecology. Microbial community functions emerge from a complex web of molecular interactions between cells, which give rise to population-level interactions among strains and species. Incorporating this complexity into predictive models is highly challenging. Inspired by a similar problem in genetics of predicting quantitative phenotypes from genotypes, an ecological community-function (or structure-function) landscape could be defined that maps community composition and function. In this piece, we present an overview of our current understanding of these community landscapes, their uses, limitations, and open questions. We argue that exploiting the parallels between both landscapes could bring powerful predictive methodologies from evolution and genetics into ecology, providing a boost to our ability to engineer and optimize microbial consortia.

Rational design of enzyme activity and enantioselectivity

The strategy of rational design to engineer enzymes is to predict the potential mutants based on the understanding of the relationships between protein structure and function, and subsequently introduce the mutations using the site-directed mutagenesis. Rational design methods are universal, relatively fast and have the potential to be developed into algorithms that can quantitatively predict the performance of the designed sequences. Compared to the protein stability, it was more challenging to design an enzyme with improved activity or selectivity, due to the complexity of enzyme molecular structure and inadequate understanding of the relationships between enzyme structures and functions. However, with the development of computational force, advanced algorithm and a deeper understanding of enzyme catalytic mechanisms, rational design could significantly simplify the process of engineering enzyme functions and the number of studies applying rational design strategy has been increasing. Here, we reviewed the recent advances of applying the rational design strategy to engineer enzyme functions including activity and enantioselectivity. Five strategies including multiple sequence alignment, strategy based on steric hindrance, strategy based on remodeling interaction network, strategy based on dynamics modification and computational protein design are discussed and the successful cases using these strategies are introduced.

Logistic Regression-Guided Identification of Cofactor Specificity-Contributing Residues in Enzyme with Sequence Datasets Partitioned by Catalytic Properties

Changing the substrate/cofactor specificity of an enzyme requires multiple mutations at spatially adjacent positions around the substrate pocket. However, this is challenging when solely based on crystal structure information because enzymes undergo dynamic conformational changes during the reaction process. Herein, we proposed a method for estimating the contribution of each amino acid residue to substrate specificity by deploying a phylogenetic analysis with logistic regression. Since this method can estimate the candidate amino acids for mutation by ranking, it is readable and can be used in protein engineering. We demonstrated our concept using redox cofactor conversion of the Escherichia coli malic enzyme as a model, which still lacks crystal structure elucidation. The use of logistic regression with amino acid sequences classified by cofactor specificity showed that the NADP⁺-dependent malic enzyme completely switched cofactor specificity to NAD⁺ dependence without the need for a practical screening step. The model showed that surrounding residues made a greater contribution to cofactor specificity than those in the interior of the substrate pocket. These residues might be difficult to identify from crystal structure observations. We show that a highly accurate and inferential machine learning model was obtained using amino acid sequences of structurally homologous and functionally distinct enzymes as input data.

Structural and functional analyses of GH51 alpha-L-arabinofuranosidase of Geobacillus vulcani GS90 reveal crucial residues for catalytic activity and thermostability

Alpha-L-arabinofuranosidase (Abf) is of big interest in various industrial areas. Directed evolution is a powerful strategy to identify significant residues underlying Abf properties. Here, six active variants from GH51 Abf of Geobacillus vulcani GS90 (GvAbf) by directed evolution were overproduced, extracted, and analyzed at biochemical and structural levels. According to the activity and thermostability results, the most-active and the least-active variants were found as GvAbf51 and GvAbf52, respectively. GvAbf63 variant was more active than parent GvAbf by 20% and less active than GvAbf51. Also, the highest thermostability belonged to GvAbf52 with 80% residual activity after 1 h. Comparative sequence and structure analyses revealed that GvAbf51 possessed L307S displacement. Thus, this study suggested that L307 residue may be critical for GvAbf activity. GvAbf63 had H30D, Q90H, and L307S displacements, and H30 was covalently bound to E29 catalytic residue. Thus, H30D may decrease the positive effect of L307S on GvAbf63 activity, preventing E29 action. Besides, GvAbf52 possessed S215N, L307S, H473P, and G476C substitutions and S215 was close to E175 (acid-base residue). S215N may partially disrupt E175 action. Overall effect of all substitutions in GvAbf52 may result in the formation of the C-C bond between C171 and C213 by becoming closer to each other.

The rapid evolution of flagellar ion selectivity in experimental populations of E. coli

Determining which cellular processes facilitate adaptation requires a tractable experimental model where an environmental cue can generate variants that rescue function. The bacterial flagellar motor (BFM) is an excellent candidate-an ancient and highly conserved molecular complex for bacterial propulsion toward favorable environments. Motor rotation is often powered by H⁺ or Na⁺ ion transit through the torque-generating stator subunit of the motor complex, and ion selectivity has adapted over evolutionary time scales. Here, we used CRISPR engineering to replace the native Escherichia coli H⁺-powered stator with Na⁺-powered stator genes and report the spontaneous reversion of our edit in a low-sodium environment. We followed the evolution of the stators during their reversion to H⁺-powered motility and used both whole-genome and RNA sequencing to identify genes involved in the cell's adaptation. Our transplant of an unfit protein and the cells' rapid response to this edit demonstrate the adaptability of the stator subunit and highlight the hierarchical modularity of the flagellar motor.

Adaptive evolvability through direct selection instead of indirect, second-order selection

Can evolvability itself be the product of adaptive evolution? To answer this question is challenging, because any DNA mutation that alters only evolvability is subject to indirect, "second order" selection on the future effects of this mutation. Such indirect selection is weaker than "first-order" selection on mutations that alter fitness, in the sense that it can operate only under restrictive conditions. Here I discuss a route to adaptive evolvability that overcomes this challenge. Specifically, a recent evolution experiment showed that some mutations can enhance both fitness and evolvability through a combination of direct and indirect selection. Unrelated evidence from gene duplication and the evolution of gene regulation suggests that mutations with such dual effects may not be rare. Through such mutations, evolvability may increase at least in part because it provides an adaptive advantage. These observations suggest a research program on the adaptive evolution of evolvability, which aims to identify such mutations and to disentangle their direct fitness effects from their indirect effects on evolvability. If evolvability is itself adaptive, Darwinian evolution may have created more than life's diversity. It may also have helped create the very conditions that made the success of Darwinian evolution possible.

Scaffold proteins as dynamic integrators of biological processes

Scaffold proteins act as molecular hubs for the docking of multiple proteins to organize efficient functional units for signaling cascades. Over 300 human proteins have been characterized as scaffolds, acting in a variety of signaling pathways. While the term scaffold implies a static, supportive platform, it is now clear that scaffolds are not simply inert docking stations but can undergo conformational changes that affect their dependent signaling pathways. In this review, we catalog scaffold proteins that have been shown to undergo actionable conformational changes, with a focus on the role that conformational change plays in the activity of the classic yeast scaffold STE5, as well as three human scaffold proteins (KSR, NEMO, SHANK3) that are integral to well-known signaling pathways (RAS, NF-κB, postsynaptic density). We also discuss scaffold protein conformational changes vis-à-vis liquid-liquid phase separation. Changes in scaffold structure have also been implicated in human disease, and we discuss how aberrant conformational changes may be involved in disease-related dysregulation of scaffold and signaling functions. Finally, we discuss how understanding these conformational dynamics will provide insight into the flexibility of signaling cascades and may enhance our ability to treat scaffold-associated diseases.

Bypassing evolutionary dead ends and switching the rate-limiting step of a human immunotherapeutic enzyme

The Trp metabolite kynurenine (KYN) accumulates in numerous solid tumours and mediates potent immunosuppression. Bacterial kynureninases (KYNases), which preferentially degrade kynurenine, can relieve immunosuppression in multiple cancer models, but immunogenicity concerns preclude their clinical use, while the human enzyme (HsKYNase) has very low activity for kynurenine and shows no therapeutic effect. Using fitness selections, we evolved a HsKYNase variant with 27-fold higher activity, beyond which exploration of >30 evolutionary trajectories involving the interrogation of >109 variants led to no further improvements. Introduction of two amino acid substitutions conserved in bacterial KYNases reduced enzyme fitness but potentiated rapid evolution of variants with ~500-fold improved activity and reversed substrate specificity, resulting in an enzyme capable of mediating strong anti-tumour effects in mice. Pre-steady-state kinetics revealed a switch in rate-determining step attributable to changes in both enzyme structure and conformational dynamics. Apart from its clinical significance, our work highlights how rationally designed substitutions can potentiate trajectories that overcome barriers in protein evolution.

A Trait-Based Approach to Predicting Viral Host-Range Evolvability

Predicting the evolution of virus host range has proven to be extremely difficult, in part because of the sheer diversity of viruses, each with unique biology and ecological interactions. We have not solved this problem, but to make the problem more tractable, we narrowed our focus to three traits intrinsic to all viruses that may play a role in host-range evolvability: mutation rate, recombination rate, and phenotypic heterogeneity. Although each trait should increase evolvability, they cannot do so unbounded because fitness trade-offs limit the ability of all three traits to maximize evolvability. By examining these constraints, we can begin to identify groups of viruses with suites of traits that make them especially concerning, as well as ecological and environmental conditions that might push evolution toward accelerating host-range expansion.

Evolution-Guided Biosynthesis of Terpenoid Inhibitors

Terpenoids, the largest and most structurally diverse group of natural products, include a striking variety of biologically active compounds, from flavors to medicines. Despite their well-documented biochemical versatility, the evolutionary processes that generate new functional terpenoids are poorly understood and difficult to recapitulate in engineered systems. This study uses a synthetic biochemical objective─a transcriptional system that links the inhibition of protein tyrosine phosphatase 1B (PTP1B), a human drug target, to the expression of a gene for antibiotic resistance in Escherichia coli (E. coli)─to evolve a terpene synthase to produce enzyme inhibitors. Site saturation mutagenesis of poorly conserved residues on γ-humulene synthase (GHS), a promicuous enzyme, yielded mutants that improved fitness (i.e., the antibiotic resistance of E. coli) by reducing GHS toxicity and/or by increasing inhibitor production. Intriguingly, a combination of two mutations enhanced the titer of a minority product─a terpene alcohol that inhibits PTP1B─by over 50-fold, and a comparison of similar mutants enabled the identification of a site where mutations permit efficient hydroxylation. Findings suggest that the plasticity of terpene synthases enables an efficient sampling of structurally distinct starting points for building new functional molecules and provide an experimental framework for exploiting this plasticity in activity-guided screens.