Biology subjects

Tokuriki, N.

Publications and source records attributed to Tokuriki, N..

At least 19 recordsLinked to original sources

bioRxiv2026

Substrate-dependent epistasis probes active site intramolecular wiring

Enzyme adaptation toward novel substrates involves the rewiring of intramolecular residue networks, yet how this rewiring differs across multiple substrates, and how it underpins functional trade-offs and promiscuity, remains poorly understood. Here, we profile all 64 combinations of six key mutations in a phosphotriesterase across nine structurally diverse substrates spanning three chemical classes (organophosphates, esters, and lactones), thus generating a multi-dimensional map of epistasis and promiscuity within the phosphotriesterases active site. We developed a statistically robust reference-based analysis pipeline incorporating error propagation and significance testing to move beyond global epistatic trends and resolve idiosyncratic, substrate-dependent intramolecular wiring in specific genetic backgrounds. Simulations confirm that this pipeline reliably identifies genuine higher-order epistatic interactions while minimizing false positives. We reveal that intramolecular network wiring varies substantially between substrates - even within the same chemical class - with notable divergences between the adaptive target substrate 2-naphthyl hexanoate and its shorter-chain ester analogs. Key higher-order networks, including d233E/h254R/l271F and l271F/f306I/i313F, exhibit substrate-specific epistatic signatures that discriminate between subtle structural features such as acyl chain length, leaving group identity, and heteroatom substitution. These substrate-dependent rewiring events account for observed functional trade-offs, particularly the strong anti-correlation between the adaptive and native substrates. Collectively, these findings demonstrate that comprehensive cross-substrate epistatic profiling, paired with rigorous statistical analysis, provides a powerful framework for dissecting the molecular basis of enzyme promiscuity and the trade-offs that define adaptive evolution. Significance of the studyEnzymes are biological catalysts whose function depends on precisely coordinated networks of interacting amino acids. Understanding how these networks change as enzymes evolve to recognize new molecules is fundamental to understanding and predicting evolution. Here, we developed a statistical framework to map these networks across multiple molecules simultaneously, revealing how subtle differences in molecular shape drive functional trade-offs during evolution. These insights could inform the rational design of enzymes with tailored specificities for industrial and therapeutic applications.

biochemistry↗

bioRxiv2025

Evolution tunes functional sub-state interconversion to boost enzyme function

Enzymes do not operate as static structures, but continuously fluctuate between different conformations. Enzymes therefore dynamically sample conformations with varying catalytic activity. However, it remains largely unexplored whether evolution can exploit the conformational dynamics between sub-states to improve activity. Here, we dissect the evolutionary trajectory of the {beta}-lactamase OXA-48 toward improved ceftazidime hydrolysis. Evolution relieved conformational bottlenecks by promoting alternate functional sub-states, gradually shifting the rate-limiting step from substrate binding to sub-state interconversion, and finally to the chemical step. Reorganization of the conformational landscape enhanced OXA-48s ability to hydrolyze ceftazidime and introduced a trade-off in its native activity against meropenem. This trade-off stemmed from catalytic incompatibility between the native and the evolved sub-state populations. Our findings highlight the transitions between functional sub-states as a mechanism of natural selection, shaping functional divergence and offering new strategies for enzyme and antibiotic engineering.

molecular biology↗

bioRxiv2025

Free energy perturbations in enzyme kinetic models reveal cryptic epistasis

Epistasis--the context-dependence of mutational effects--is a key driver of protein evolution, influencing adaptive pathways and functional diversity. While specific epistasis arises from direct physical interactions between mutations, non-specific epistasis emerges when a non-linear mapping links a proteins biophysical properties to its function. Enzyme kinetic parameters are assumed to be devoid of non-specific epistasis when measured in vitro, enabling direct connection of epistasis to the enzymes structural features. Here, we show that this assumption is incorrect: enzyme catalytic parameters like kcat and KM inherently exhibit non-specific epistasis due to the multi-state nature of the catalytic cycle. Using in silico enzyme models, parameterized by free energies of ground and transition states, we simulated 1000 "mutations" or perturbations to the sub-state free energies within the kinetic ensemble. We then combined these mutations, creating one million double mutants with strictly additive free energy effects. Despite the absence of explicit mutational interactions, we observed substantial epistasis in catalytic parameters; its prevalence and complexity increasing with the number of kinetic states in the mechanism. We derived analytical conditions for the emergence of this form of epistasis in a simple kinetic model, demonstrating that non-specific epistasis depends on the relative values of key microscopic rate constants. Finally, we validated our framework by reanalyzing kinetic data for double mutants in Bacillus cereus {beta}-lactamase I and found that reported specific epistasis in catalytic efficiency was substantially stronger than previously inferred, altering mechanistic interpretations. Our results identify an intrinsic, previously unknown source of epistasis that can distort both the magnitude and sign of mutational effects in enzyme kinetics. We provide theoretical and computational tools for recognizing and correcting for this form of non-specific epistasis, enabling accurate mechanistic inference from kinetic data and improving our understanding of the links between epistasis, structure-function relationships, enzyme evolution, and protein design. Author SummaryEnzymes help organisms convert reactants to products through a series of steps; each associated with an energy that dictates how well the enzyme catalyzes a reaction. Enzymes evolve to become more efficient, or catalyze new reactions, through mutations that change the free energies of these steps. Sometimes, the effect of one mutation depends on the presence of another, a phenomenon called epistasis. Epistasis is typically studied by measuring the effect of mutations on standard enzyme parameters under the assumption that changes in these values reflect structural interactions between mutations in the protein. Our study shows that this assumption is misleading. Even when mutations act independently on the energies of steps in an enzymes reaction, when combined they can create epistasis. This phenomenon arises from the complex, non-linear relationships between the parameters that define each step in the enzyme reaction and the measurements we obtain during experiments probing enzyme functions. Using computational simulations, we mathematically derive the necessary conditions for this form of epistasis, demonstrate that epistasis increases in prevalence as the enzyme reaction becomes more complex, and apply our model to published experimental data. Our findings urge that researchers should account for these effects before drawing structural conclusions from epistasis.

evolutionary biology↗

bioRxiv2025

Widespread promiscuous alkaline phosphatases underscore early microbial phosphite utilization

Phosphate is often a limiting resource, directly affecting the availability of key biomolecules such as nucleotides. To cope with phosphate scarcity, bacteria have evolved enzymes that utilize alternative phosphorus compounds, including phosphite (Pt). Although a few enzymes oxidize Pt to produce phosphate, the enzymes responsible for Pt oxidation in many environmental bacteria remain unidentified, and the role of microbial Pt oxidation in the global phosphorus cycle is not yet fully understood. In this study, we performed bioinformatic analyses of three Pt-oxidizing enzymes: the native Pt oxidase phosphite dehydrogenase (PtxD), and two promiscuous Pt oxidases, alkaline phosphatase (AP) and carbon-phosphorus (CP) lyase. Among these, AP was found to be widely distributed across bacteria since the early stages of their evolution. In contrast, PtxD emerged later in a limited number of bacterial lineages that had lost AP. Our biochemical characterizations revealed that most extant and reconstructed ancestral APs tested exhibited Pt oxidation activity. Moreover, disruption of active-site residues diminished Pt oxidase activity in AP, while only partially affecting its native function. This promiscuous function of AP reveals an overlooked mechanism in bacterial phosphate metabolism and underscores the role of Pt in the cycling of bioavailable phosphorus in ecosystems.

biochemistry↗

bioRxiv2025

Single-Molecule Kinetic Exploration of Functional Substates in an Evolving Phosphotriesterase

Enzymes achieve catalysis by dynamically sampling diverse conformational states. Beyond this plasticity, individual enzyme molecules occupy metastable substates, forming an ensemble of functional substates within a population. Since shifts in functional substate dynamics drive phenotypic variation, their evolutionary trajectories are central to the emergence of new functions. However, the challenge of measuring functional substates has hindered our understanding of their role in enzyme evolution and the optimization of conformational substates. Here, we address this gap by investigating how functional and conformational substates were modulated during enzyme functional transitions, using single-molecule kinetic assays and molecular dynamics simulations. We analyzed wild-type phosphotriesterase (PTE) and 18 evolved variants that transitioned from the native PTE to promiscuous arylesterase (AE) activity. Our findings reveal that evolutionary transitions reshape functional and conformational substate landscapes: PTE-specialized variants exhibit broader substate distributions, whereas AE-specialized variants display more uniform substates. These results provide the first direct evidence that enzyme evolution is accompanied by coordinated shifts in functional and conformational substate equilibria, optimizing both for the enzymes catalytic efficiency. This work highlights the power of single-molecule techniques in uncovering how heterogeneous enzyme populations navigate substate transitions and, ultimately, how these transitions shape enzyme evolvability.

biochemistry↗

bioRxiv2025

Enthalpy-entropy trade-offs in the evolution of ligand specificity in a superfamily of transcription factors

Proteins operate through ligand and solvent interactions governed by thermodynamics, yet the enthalpy-entropy trade-offs that guide their functional evolution remain poorly understood. The LacI/GalR family (LGF) of transcription factors provides a system for examining how these trade-offs evolve over billions of years but has seldom been studied from a full thermodynamic perspective. While the evolution of ligand specificity has been well-studied, the thermodynamic determinants underlying the changes in specificity is not well understood - especially how proteins alter thermodynamic strategies to optimize affinity. By reconstructing LGF ancestors, we reveal a shift from entropy-driven binding in the most distant ancestor, to enthalpy-driven binding in the most recent ancestor and extant LacI. The most distant ancestor is characterized by the ability to bind its ligand in an open and dynamic conformation, and we propose that entropically-driven binding is driven by the presence of entropic reservoirs. This thermodynamic binding trade-off between the most distant and most recent ancestor is in accordance with the concept of ancient life that existed in a hot Earth environment, where higher temperatures enhanced entropically-driven binding. This suggests that molecular binding mechanisms evolved not just for ligand specificity, but to adapt to environmental pressures such as cooling Earth temperatures where enthalpic binding modes are favored.

biochemistry↗

bioRxiv2025

Stability of multi-species consortia during microbial metabolic evolution

Explaining multi-genic adaptations is a major objective of evolutionary theory. Metabolic pathways require multiple functional enzymes to generate a phenotype, and their evolution in microbes remains underexplored. In particular, sites polluted with manmade chemicals or "xenobiotics", like plastic or pesticides, provide evidence for the rapid adaptation of novel metabolic pathways in microbes, which degrade these xenobiotics into utilizable nutrients. Decades of microbiological studies revealed that these pathways often are not consolidated within a single microbial species, but are rather distributed across several different ones, which cooperatively degrade xenobiotics. These species form remarkably stable consortia in the laboratory, but the determinants of this stability have not been hereto addressed. In this study, we show that trade-offs in microbial life history explain stable co-existence in a mathematical model of a three-species consortium, growing on a xenobiotic as the sole source of a limiting nutrient. Stability is predicted to hinge on a specific "ecological matching" between a species metabolic role in the novel metabolic pathway and its nutrient utilization strategy.

evolutionary biology↗

bioRxiv2024

Exploring large protein sequence space through homology- and representation-based hierarchical clustering

Exploration of protein sequence space can offer insight into protein sequence-function relationships, benefitting both basic science and industrial applications. The use of sequence similarity networks (SSNs) is a standard method for exploring large sequence datasets, but is currently limited when scaling to very large datasets and when viewing more than one level (hierarchy) of homology. Here, we present a sequence analysis pipeline with a number of innovations that address some limitations of traditional SSNs. First, we develop a hierarchical visualization approach that captures the full range of homologies across protein superfamilies. Second, we leverage representations embedded by protein language models as an alternative homology metric to the basic local alignment search tool (BLAST), showing that they produce comparable results when identifying isofunctional protein families. Finally, we demonstrate that unbiased representative sampling of sequences from genetic neighborhoods can be achieved through the use of hidden Markov models (HMMs) or vector representations. The utility of these methods is exemplified by updating the sequence-function analysis of the FMN/F420-binding split barrel superfamily and improving phylogenetic analyses. We provide our sequence exploration pipeline as publicly available code (ProteinClusterTools) and show it to be scalable to large datasets ([~]300k sequences) using desktop computers.

bioinformatics↗

bioRxiv2024

The global β-lactam resistome revealed by comprehensive sequence analysis

Most antibiotic-resistance genes (ARGs) evolved in environmental microbes long before humanitys antibiotic breakthrough, and widespread antibiotic use expedited the dissemination of ARGs among clinical pathogens. While widely discussed, the investigation of environmental ARG distributions lacks the scalability and taxonomic information necessary for a comprehensive analysis. Here, we present a global distribution of all five classes of {beta}-lactamases among microbes and environments. We generated a {beta}-lactamase taxonomy-environment map by identifying >113,000 {beta}-lactamases across diverse bacterial phyla and environmental ecosystems. Remarkably abundant, their occurrence is only [~]2.6-fold lower than the essential recA gene in various environmental ecosystems, with particularly strong enrichment in wastewater and plant samples. The enrichment in plant samples implies an environment where the arms race of {beta}-lactam producers and resistant bacteria occurred over millions of years. We uncover the origins of clinically relevant {beta}-lactamases (mainly in {gamma}-Proteobacteria) and expand beyond the previously suggested wastewater samples in plant, terrestrial, and other aquatic settings.

bioinformatics↗

bioRxiv2023

Leveraging ancestral sequence reconstruction for protein representation learning

Protein language models (PLMs) convert amino acid sequences into the numerical representations required to train machine learning (ML) models. Many PLMs are large (>600 M parameters) and trained on a broad span of protein sequence space. However, these models have limitations in terms of predictive accuracy and computational cost. Here, we use multiplexed Ancestral Sequence Reconstruction (mASR) to generate small but focused functional protein sequence datasets for PLM training. Compared to large PLMs, this local ancestral sequence embedding (LASE) produces representations 10-fold faster and with higher predictive accuracy. We show that due to the evolutionary nature of the ASR data, LASE produces smoother fitness landscapes in which protein variants that are closer in fitness value become numerically closer in representation space. This work contributes to the implementation of ML-based protein design in real-world settings, where data is sparse and computational resources are limited.

bioinformatics↗

bioRxiv2023

Understanding epistatic networks in the B1 -lactamases through coevolutionary statistical modeling and deep mutational scanning

Over the course of evolution, proteins families undergo sequence diversification via mutation accumulation, with extant homologs often sharing less than 25% sequence identity. The resulting diversity presents a complex view of sequence-structure-function relationships, as epistasis is prevalent, and deleterious mutations in one protein can be tolerated in homologous sequences through networks of intramolecular, compensatory interactions. Understanding these epistatic networks is crucial for understanding and predicting protein function, yet comprehensive analysis of such networks across protein families is limited. In this study, we combine computational and experimental approaches to examine epistatic networks in the class B1 metallo-{beta}-lactamases, a diverse family of antibiotic-degrading enzymes. Using Direct Coupling Analysis, we assess global coevolutionary signatures across the B1 family. We also obtain detailed experimental data from deep mutational scanning on two distant B1 homologs, NDM-1 and VIM-2. There is good agreement between the two approaches, revealing both family-wide and homolog specific patterns that can be associated with 3D structure. However, specific interactions remain complex, and strong epistasis in evolutionarily entrenched residues are not easily compensated for by changes in nearby interactions.

biochemistry↗

bioRxiv2023

Origin Of Evolutionary Bifurcation In An Enzyme

Evolution can lead to significantly distinct outcomes depending on the mutational path taken. Evolutionary bifurcation, in which two mutational trajectories segregate, becoming non-interchangeable over time, is the basis of diversification in all kingdoms of life. Here, we present a detailed molecular description of a bifurcation event that rapidly led to the emergence of two distinct enzymes from a common ancestor. When initiated from two starting points that differed by a single amino acid, the laboratory evolution of a phosphotriesterase (PTE) toward arylester hydrolysis resulted in different genetic and phenotypic outcomes. One trajectory led to a >35,000-fold increase in activity via the reorganization of its active site to achieve exquisite enzyme-substrate complementarity. The second trajectory gave rise to an evolved variant with a [~]500-fold increase in activity, but exhibiting an alternative substrate binding mode resulting from the destabilization of an active site loop. While initial mutations tend to dictate mutational accessibility, we rather observed the gradual divergence and specialisation of each trajectory, following the emergence of distinct molecular interaction networks. Intramolecular epistasis underlay pathway bifurcation by promoting unique synergistic interactions within each trajectory, while restricting the fixation of mutation across pathways. Our results illustrate how distinct molecular outcomes can radiate from a common protein ancestor and give rise to phenotypic diversity.

evolutionary biology↗

bioRxiv2023

Epistasis Arises from Shifting the Rate-Limiting Step during Enzyme Evolution

The molecular mechanisms by which epistasis boosts enzyme activity remain elusive, undermining our ability to predict the evolution of pathogens and engineer novel biocatalysts. Here, we reveal how directed evolution of a {beta}-lactamase yielded highly epistatic activity enhancements. Evolution selected four mutations that increase antibiotic resistance 40-fold, despite their marginal individual effects ([≤] 2-fold). Synergistic improvements coincided with the introduction of super-stochiometric burst kinetics, indicating that epistasis is rooted in the enzymes conformational dynamics. Kinetic, structural, and dynamical analyses reveal that epistasis was driven by distinct effects of each mutation on the catalytic cycle. The first mutation acquired during evolution increases protein flexibility and accelerates substrate binding, which is rate-limiting in the wild-type enzyme. The ensuing mutations predominantly boosted the chemical steps by fine-tuning substrate interactions. Our work identifies an overlooked cause for epistasis: changing the rate-limiting step can result in substantial positive synergy boosting enzyme activity.

evolutionary biology↗

bioRxiv2023

Neutral Drift and Threshold Selection Promote Phenotypic Variation

Phenotypic variations within a population exist on different scales of biological organization and play a central role in evolution by providing adaptive capacity at the population-level. Thus, the question of how evolution generates phenotypic variation within an evolving population is fundamental in evolutionary biology. Here we address this question by performing experimental evolution of an antibiotic resistance gene, VIM-2 {beta}-lactamase, combined with diverse biochemical assays and population genetics. We found that neutral drift, i.e., evolution under a static environment, with a low antibiotic concentration can promote and maintain significant phenotypic variation within the population with >100-fold differences in resistance strength. We developed a model based on the phenotype-environment-fitness landscape generated with >5,000 VIM-2 variants, and demonstrated that the combination of "mutation-selection balance" and "threshold-like fitness-phenotype relationship" is sufficient to explain the generation of large phenotypic variation within the evolving population. Importantly, high-resistance conferring variants can emerge during neutral drift, without being a product of adaptation. Our findings provide a novel and simple mechanistic explanation for why most genes in nature, and by extension, systems and organisms, inherently exhibit phenotypic variation, and thus, population-level evolvability.

evolutionary biology↗

bioRxiv2022

Higher-order epistasis creates idiosyncrasy, confounding predictions in protein evolution

Epistasis shapes evolutionary outcomes during protein adaptation. In particular, when the effects of single mutations or mutational interactions are idiosyncratic, that is, unique to a genetic background, the predictability of protein evolution becomes greatly impaired. Here, we unveil a quantitative picture of the prevalence and role of idiosyncrasy in protein evolution by analysing 45 protein fitness landscapes, generated from seven enzymes. We found that mutational effects and epistasis are highly idiosyncratic across the landscapes. Idiosyncrasy obscured functional predictions of mutated proteins when using limited mutational data, and often continued to impair prediction upon incorporation of epistatic information. We show that idiosyncrasy stems from higher-order epistasis, and highlight examples where it permits, or restricts, evolutionary accessibility of certain genotypes. Our work suggests that idiosyncrasy deeply confounds predictions in protein evolution necessitating its incorporation into predictive models and in-depth exploration of its underlying molecular mechanisms.

evolutionary biology↗

bioRxiv2022

Evolution of β-lactamase mediated cefiderocol resistance

Cefiderocol is a novel siderophore {beta}-lactam with improved hydrolytic stability toward {beta}-lactamases, including carbapenemases, achieved by combining structural moieties of two clinically efficient cephalosporins, ceftazidime and cefepime. Consequently, cefiderocol represents a treatment alternative for infections caused by multi-drug resistant Gram-negatives. Using directed evolution on a wide variety of different {beta}-lactamases, such as KPC-2 and CTX-M-15 (Ambler class A), NDM-1 (class B), CMY-2 (class C) and OXA-48 (class D), we studied the role of cefiderocol during {beta}-lactamase-mediated resistance development. First, we investigated how the expression of different {beta}-lactamases causes changes in cefiderocol susceptibility. In a low-copy number vector, we found that OXA-48 and KPC-2 conferred non or marginal decreases in cefiderocol susceptibility, respectively. On the contrary, CMY-2, CTX-M-15 and NDM-1 substantially decreased cefiderocol susceptibility by 16-, 8- and 32-fold, respectively. Second, we determined the evolutionary potential of these enzymes to adapt to increasing concentrations of cefiderocol. Our data show that with the acquisition of only 1 to 2 mutations, all {beta}-lactamases were evolvable to further cefiderocol resistance by 2- (NDM-1, CTX-M-15), 4- (CMY-2), 8- (OXA-48) and 16-fold (KPC-2). Cefiderocol resistance development was often associated with collateral susceptibility changes including increased resistance to ceftazidime and ceftazidime-avibactam as well as functional trade-offs against different {beta}-lactam drugs. Taken together, contemporary {beta}-lactamases of all Ambler classes can potentially contribute to cefiderocol resistance development and can acquire mutations allowing them to adapt to increasing cefiderocol concentration. At the same time, resistance development caused clinically important cross-resistance, especially against ceftazidime combinations. SummaryDespite the reported higher stability of cefiderocol against {beta}-lactamase hydrolysis, we show that the expression of {beta}-lactamases from different Ambler classes significantly contributes to cefiderocol resistance and that these enzymes have the evolutionary potential to evolve towards increasing cefiderocol concentrations.

molecular biology↗

bioRxiv2021

Genetic perturbation enhances functional heterogeneity in alkaline phosphatase

Enzymes inherently exhibit molecule-to-molecule heterogeneity in catalytic activity or function, which underlies the acquisition of new functions in evolutionary processes. However, correlations between the functional heterogeneity of an enzyme and its multi-functionality or promiscuity remain elusive. In addition, the modulation of functional heterogeneity upon genetic perturbation is currently unexplored. Here, we quantitatively analyzed functional heterogeneity in the wild-type and 69 single-point mutants of Escherichia coli alkaline phosphatase (AP) by employing single-molecule assay with a femtoliter reactor array device. Most mutant enzymes exhibited higher functional heterogeneity than the wild-type enzyme, irrespective of catalytic activity. These results indicated that the wild-type AP minimizes functional heterogeneity, and single-point mutations can significantly expand the span of functional heterogeneity in AP. Moreover, we identified a clear correlation between functional heterogeneity and promiscuous activities. These findings suggest that enzymes can acquire greater functional heterogeneity following marginal genetic perturbations that concomitantly promote catalytic promiscuity.

biochemistry↗

bioRxiv2021

Environmental changes dictate selection and nonspecific epistasis in an empirical phenotype-environment-fitness landscape

Fitness landscapes, mappings of genotype/phenotype to their effects on fitness, are invaluable concepts in evolutionary biochemistry. Though widely discussed, measurements of phenotype-fitness landscapes in proteins remain scarce. Here, we quantify all single mutational effects on fitness and phenotype of VIM-2 {beta}-lactamase across a 64-fold range of ampicillin concentrations. We then construct a phenotype-fitness landscape that takes variations in environmental selection pressure into account. We found that a simple, empirical landscape accurately models the ~39,000 mutational data points, suggesting the evolution of VIM-2 can be predicted based on the selection environment. Our landscape provides new quantitative knowledge on the evolution of the {beta}-lactamases and proteins in general, particularly their evolutionary dynamics under sub-inhibitory antibiotic concentrations, as well as the mechanisms and environmental dependence of nonspecific epistasis. One Sentence SummaryAn empirical fitness landscape discloses the environmental dependence of mutational effects in VIM-2 {beta}-lactamase.

evolutionary biology↗