A Bird’s-Eye View of Enzyme Evolution: Chemical, Physicochemical, and Physiological Considerations

Target-Navigated CBT-Cys "Stapling" Coupled with CRISPR/Cas12a Amplification for the Photoelectrochemical Nucleic Acid Assay

Generally, rolling circle amplification (RCA) is based on an enzyme-linked padlock extension reaction. Herein, rapid linking that utilizes click chemistry for joining sticky ends of DNA molecules was developed. The ends of nucleic acid were modified with 2-cyano-6-aminobenzothiazole (CBT) and cystine (Cys-Cys), while glutathione was introduced to break the disulfide bond under target navigation and promote the linkage between CBT and Cys at the terminus of the nucleic acid at pH 7.4. Subsequently, RCA was performed using phi29 polymerase. CRISPR/Cas12a cleavage was triggered by the product of RCA amplification. Assisted by alkaline phosphatase, the electron exchange process between the photoelectroactive Sb@Co(OH)F nanorod and p-aminophenol (p-AP) was collected in the form of photoelectrochemical (PEC) signals. Mass spectrometry, gel electrophoresis, and PEC signals were employed to verify the linking process and the RCA coupled with CRISPR/Cas12a cleavage amplification. CBT-Cys connection exhibited a high reaction rate (23.79 M-1·s-1). This enzyme-free linking process was superior to traditional enzyme catalysis in terms of the reaction environment and linking rate. This efficient nonenzymatic joining system holds great potential for constructing nonhomologous end joining, modifying DNA with molecules, and facilitating nucleic acid-protein modification processes.

qProtein: Exploring Physical Features of Protein Thermostability Based on Structural Proteomics

Thermostability, which is essential for the functional performance of enzymes, is largely determined by intramolecular physical interactions. Although many tools have been developed, existing computational methods have struggled to find the universal principles of protein thermostability. Recent advancements in structural proteomics have been driven by the introduction of deep neural networks such as AlphaFold2 and ESMFold. These innovations have enabled the characterization of protein structures with unprecedented speed and accuracy. Here, we introduce qProtein, a Python-implemented workflow designed for the quantitative analysis of physical interactions on the scale of structural proteomics. This platform accepts protein sequences as input and produces four structural features, including hydrophobic clusters, hydrogen bonds, electrostatic interactions, and disulfide bonds. To demonstrate the use of qProtein, we investigate the structural features related to protein thermostability in six glycoside hydrolase (GH) families, comprising a total of 3,811 protein structures. Our results indicate that in five enzyme families (GH11, GH12, GH5_2, GH10, and GH48), the thermophilic enzymes have a larger average area of hydrophobic clusters compared to the nonthermophilic enzymes within each family. Furthermore, our analysis of the local-structure regions reveals that the hydrophobic clusters are predominantly distributed in the distal regions of the GH11 enzymes. In addition, the average hydrophobic cluster area of the thermophilic enzymes is significantly higher than that of the nonthermophilic enzymes in the distal regions of the GH11 enzymes. Therefore, qProtein is a well-suited platform for analyzing the structural features of thermal stability at the level of structural proteomics. We provide the source code for qProtein at https://github.com/bj600800/qProtein, and the web server is available at http://qProtein.sdu.edu.cn:8888.

Adaptive laboratory evolution recruits the promiscuity of succinate semialdehyde dehydrogenase to repair different metabolic deficiencies

Promiscuous enzymes often serve as the starting point for the evolution of novel functions. Yet, the extent to which the promiscuity of an individual enzyme can be harnessed several times independently for different purposes during evolution is poorly reported. Here, we present a case study illustrating how NAD(P)+-dependent succinate semialdehyde dehydrogenase of Escherichia coli (Sad) is independently recruited through various evolutionary mechanisms for distinct metabolic demands, in particular vitamin biosynthesis and central carbon metabolism. Using adaptive laboratory evolution (ALE), we show that Sad can substitute for the roles of erythrose 4-phosphate dehydrogenase in pyridoxal 5'-phosphate (PLP) biosynthesis and glyceraldehyde 3-phosphate dehydrogenase in glycolysis. To recruit Sad for PLP biosynthesis and glycolysis, ALE employs various mechanisms, including active site mutation, copy number amplification, and (de)regulation of gene expression. Our study traces down these different evolutionary trajectories, reports on the surprising active site plasticity of Sad, identifies regulatory links in amino acid metabolism, and highlights the potential of an ordinary enzyme as innovation reservoir for evolution.

Functional Optimization in Distinct Tissues and Conditions Constrains the Rate of Protein Evolution

Understanding the main determinants of protein evolution is a fundamental challenge in biology. Despite many decades of active research, the molecular and cellular mechanisms underlying the substantial variability of evolutionary rates across cellular proteins are not currently well understood. It also remains unclear how protein molecular function is optimized in the context of multicellular species and why many proteins, such as enzymes, are only moderately efficient on average. Our analysis of genomics and functional datasets reveals in multiple organisms a strong inverse relationship between the optimality of protein molecular function and the rate of protein evolution. Furthermore, we find that highly expressed proteins tend to be substantially more functionally optimized. These results suggest that cellular expression costs lead to more pronounced functional optimization of abundant proteins and that the purifying selection to maintain high levels of functional optimality significantly slows protein evolution. We observe that in multicellular species both the rate of protein evolution and the degree of protein functional efficiency are primarily affected by expression in several distinct cell types and tissues, specifically, in developed neurons with upregulated synaptic processes in animals and in young and fast-growing tissues in plants. Overall, our analysis reveals how various constraints from the molecular, cellular, and species' levels of biological organization jointly affect the rate of protein evolution and the level of protein functional adaptation.

Innovative Strategies in X-ray Crystallography for Exploring Structural Dynamics and Reaction Mechanisms in Metabolic Disorders

Enzymes are crucial in metabolic processes, and their dysfunction can lead to severe metabolic disorders. Structural biology, particularly X-ray crystallography, has advanced our understanding of these diseases by providing 3D structures of pathological enzymes. However, traditional X-ray crystallography faces limitations, such as difficulties in obtaining suitable protein crystals and studying protein dynamics. X-ray free-electron lasers (XFELs) have revolutionized this field with their bright and brief X-ray pulses, providing high-resolution structures of radiation-sensitive and hard-to-crystallize proteins. XFELs also enable the study of protein dynamics through room temperature structures and time-resolved serial femtosecond crystallography, offering comprehensive insights into the molecular mechanisms of metabolic diseases. Understanding these dynamics is vital for developing effective therapies. This review highlights the contributions of protein dynamics studies using XFELs and synchrotrons to metabolic disorder research and their application in designing better therapies. It also discusses G protein-coupled receptors (GPCRs), which, though not enzymes, play key roles in regulating physiological systems and are implicated in many metabolic disorders.

Identifying and Engineering Flavin Dependent Halogenases for Selective Biocatalysis

ConspectusOrganohalogen compounds are extensively used as building blocks, intermediates, pharmaceuticals, and agrochemicals due to their unique chemical and biological properties. Installing halogen substituents, however, frequently requires functionalized starting materials and multistep functional group interconversion. Several classes of halogenases evolved in nature to enable halogenation of a different classes of substrates; for example, site-selective halogenation of electron rich aromatic compounds is catalyzed by flavin-dependent halogenases (FDHs). Mechanistic studies have shown that these enzymes use FADH2 to reduce O2 to water with concomitant oxidation of X- to HOX (X = Cl, Br, I). This species travels through a tunnel within the enzyme to access the FDH active site. Here, it is believed to interact with an active site lysine proximal to bound substrate, enabling electrophilic halogenation with selectivity imparted via molecular recognition, rather than directing groups or strong electronic activation.The unique selectivity of FDHs led to several early biocatalysis efforts, preparative halogenation was rare, and the hallmark catalyst-controlled selectivity of FDHs did not translate to non-native substrates. FDH engineering was limited to site-directed mutagenesis, which resulted in modest changes in site-selectivity or substrate preference. To address these limitations, we optimized expression conditions for the FDH RebH and its cognate flavin reductase (FRed), RebF. We then showed that RebH could be used for preparative halogenation of non-native substrates with catalyst-controlled selectivity. We reported the first examples in which the stability, substrate scope, and site selectivity of a FDH were improved to synthetically useful levels via directed evolution. X-ray crystal structures of evolved FDHs and reversion mutations showed that random mutations throughout the RebH structure were critical to achieving high levels of activity and selectivity on diverse aromatic substrates, and these data were used in combination with molecular dynamics simulations to develop predictive model for FDH selectivity. Finally, we used family wide genome mining to identify a diverse set of FDHs with novel substrate scope and complementary regioselectivity on large, three-dimensionally complex compounds.The diversity of our evolved and mined FDHs allowed us to pursue synthetic applications beyond simple aromatic halogenation. For example, we established that FDHs catalyze enantioselective reactions involving desymmetrization, atroposelective halogenation, and halocyclization. These results highlight the ability of FDH active sites to tolerate different substrate topologies. This utility was further expanded by our recent studies on the single component FDH/FRed, AetF. While we were initially drawn to AetF because it does not require a separate FRed, we found that it halogenates substrates that are not halogenated efficiently or at all by other FDHs and provides high enantioselectivity for reactions that could only be achieved using RebH variants after extensive mutagenesis. Perhaps most notably, AetF catalyzes site-selective aromatic iodination and enantioselective iodoetherification. Together, these studies highlight the origins of FDH engineering, the utility and limitations of the enzymes developed to date, and the promise of FDHs for an ever-expanding range of biocatalytic halogenation reactions.

Elucidating the Enzymatic Mechanism of Dihydrocoumarin Degradation: Insight into the Functional Evolution of Methyl-Parathion Hydrolase from QM/MM and MM MD Simulations

Methyl-parathion hydrolase (MPH), which evolved from dihydrocoumarin hydrolase, offers one of the most efficient enzymes for the hydrolysis of methyl-parathion. Interestingly, the substrate preference of MPH shifts from the methyl-parathion to the lactone dihydrocoumarin (DHC) after its mutation of five specific residues (R72L, L273F, L258H, T271I, and S193Δ, m5-MPH). Here, extensive QM/MM calculations and MM MD simulations have been used to delve into the structure-function relationship of MPH enzymes and plausible mechanisms for the chemical and nonchemical steps, including the transportation and binding of the substrate DHC to the active site, the hydrolysis reaction, and the product release. The results reveal that the five mutations remodel the active pocket and reposition DHC within the active site, leading to stronger enzyme-substrate interactions. The MM/GBSA-estimated binding free energies are about -20.7 kcal/mol for m5-MPH and -17.1 kcal/mol for wild-type MPH. Furthermore, this conformational adjustment of the protein may facilitate the chemical step of DHC hydrolysis and the product release, although there is a certain influence on the substrate transport. The hydrolytic reaction begins with the nucleophilic attack of the bridging OH- with the energy barriers of 22.0 and 18.0 kcal/mol for the wild-type and m5-MPH enzymes, respectively, which is rate-determining for the entire process. Unraveling these mechanistic intricacies may help in the understanding of the natural evolution of enzymes for diverse substrates and establish the enzyme structure-function relationship.

Co-occurrence, toxicity, and biotransformation pathways of metformin and its intermediate product guanylurea: Current state and future prospects for enhanced biodegradation strategy

Accumulation of metformin and its biotransformation product "guanylurea" are posing an increasing concern due to their low biodegradability under natural attenuated conditions. Therefore, in this study, we reviewed the unavoidable function of metformin in human body and the route of its release in different water ecosystems. In addition, metformin and its biotransformation product guanylurea in aquatic environments caused certain toxic effects on aquatic organisms which include neurotoxicity, endocrine disruption, production of ROS, and acetylcholinesterase disturbance in aquatic organisms. Moreover, microorganisms are the first to expose and deal with the release of these contaminants, therefore, the mechanisms of biodegradation pathways of metformin and guanylurea under aerobic and anaerobic environments were studied. It has been reported that certain microbes, such as Aminobacter sp. and Pseudomonas putida can carry potential enzymatic pathways to degrade the dead-end product "guanylurea", and hence guanylurea is no longer the dead-end product of metformin. However, these microbes can easily be affected by certain geochemical cycles, therefore, we proposed certain strategies that can be helpful in the enhanced biodegradation of metformin and its biotransformation product guanylurea. A better understanding of the biodegradation potential is imperative to improve the use of these approaches for the sustainable and cost-effective remediation of the emerging contaminants of concern, metformin and guanylurea in the near future.

Back in time to the Gly-rich prototype of the phosphate binding elementary function

Binding of nucleotides and their derivatives is one of the most ancient elementary functions dating back to the Origin of Life. We review here the works considering one of the key elements in binding of (di)nucleotide-containing ligands - phosphate binding. We start from a brief discussion of major participants, conditions, and events in prebiotic evolution that resulted in the Origin of Life. Tracing back to the basic functions, including metal and phosphate binding, and, potentially, formation of primitive protein-protein interactions, we focus here on the phosphate binding. Critically assessing works on the structural, functional, and evolutionary aspects of phosphate binding, we perform a simple computational experiment reconstructing its most ancient and generic sequence prototype. The profiles of the phosphate binding signatures have been derived in form of position-specific scoring matrices (PSSMs), their peculiarities depending on the type of the ligands have been analyzed, and evolutionary connections between them have been delineated. Then, the apparent prototype that gave rise to all relevant phosphate-binding signatures had also been reconstructed. We show that two major signatures of the phosphate binding that discriminate between the binding of dinucleotide- and nucleotide-containing ligands are GxGxxG and GxxGxG, respectively. It appears that the signature archetypal for dinucleotide-containing ligands is more generic, and it can frequently bind phosphate groups in nucleotide-containing ligands as well. The reconstructed prototype's key signature GxGGxG underlies the role of glycine residues in providing flexibility and interactions necessary for binding the phosphate groups. The prototype also contains other ancient amino acids, valine, and alanine, showing versatility towards evolutionary design and functional diversification.

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

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

Theoretical Improvements in Enzyme Efficiency Associated with Noisy Rate Constants and Increased Dissipation

Previous studies have revealed the extraordinarily large catalytic efficiency of some enzymes. High catalytic proficiency is an essential accomplishment of biological evolution. Natural selection led to the increased turnover number, kcat, and enzyme efficiency, kcat/KM, of uni-uni enzymes, which convert a single substrate into a single product. We added or multiplied random noise with chosen rate constants to explore the correlation between dissipation and catalytic efficiency for ten enzymes: beta-galactosidase, glucose isomerase, β-lactamases from three bacterial strains, ketosteroid isomerase, triosephosphate isomerase, and carbonic anhydrase I, II, and T200H. Our results highlight the role of biological evolution in accelerating thermodynamic evolution. The catalytic performance of these enzymes is proportional to overall entropy production-the main parameter from irreversible thermodynamics. That parameter is also proportional to the evolutionary distance of β-lactamases PC1, RTEM, and Lac-1 when natural or artificial evolution produces the optimal or maximal possible catalytic efficiency. De novo enzyme design and attempts to speed up the rate-limiting catalytic steps may profit from the described connection between kinetics and thermodynamics.

Artificial carbon assimilation: From unnatural reactions and pathways to synthetic autotrophic systems

Synthetic biology is being increasingly used to establish novel carbon assimilation pathways and artificial autotrophic strains that can be used in low-carbon biomanufacturing. Currently, artificial pathway design has made significant progress from advocacy to practice within a relatively short span of just over ten years. However, there is still huge scope for exploration of pathway diversity, operational efficiency, and host suitability. The accelerated research process will bring greater opportunities and challenges. In this paper, we provide a comprehensive summary and interpretation of representative one-carbon assimilation pathway designs and artificial autotrophic strain construction work. In addition, we propose some feasible design solutions based on existing research results and patterns to promote the development and application of artificial autotrophy.

Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering

Enzymes, as paramount protein catalysts, occupy a central role in fostering remarkable progress across numerous fields. However, the intricacy of sequence-function relationships continues to obscure our grasp of enzyme behaviors and curtails our capabilities in rational enzyme engineering. Generative artificial intelligence (AI), known for its proficiency in handling intricate data distributions, holds the potential to offer novel perspectives in enzyme research. Generative models could discern elusive patterns within the vast sequence space and uncover new functional enzyme sequences. This review highlights the recent advancements in employing generative AI for enzyme sequence analysis. We delve into the impact of generative AI in predicting mutation effects on enzyme fitness, catalytic activity and stability, rationalizing the laboratory evolution of de novo enzymes, and decoding protein sequence semantics and their application in enzyme engineering. Notably, the prediction of catalytic activity and stability of enzymes using natural protein sequences serves as a vital link, indicating how enzyme catalysis shapes enzyme evolution. Overall, we foresee that the integration of generative AI into enzyme studies will remarkably enhance our knowledge of enzymes and expedite the creation of superior biocatalysts.

Dendronization of chitosan to afford unprecedent thermoresponsiveness and tunable microconfinement

Convenient chemical modification of biomacromolecules to create novel biocompatible functional materials satisfies the current requirements of sustainable chemistry. Dendronization of chitosan with dendritic oligoethylene glycols (OEGs) paves a strategy for the preparation of functional dendronized chitosans (DCSs) with unprecedent thermoresponsive behavior, which inherit biological features from polysaccharides and the topological features from dendritic OEGs. In addition, densely packed dendritic OEG chains around the backbone provide efficient cooperative interactions and form an intriguing confined microenvironment based on the degradable biopolymers. In this perspective, we describe the principle for the preparation of the thermoresponsive DCSs, and focus on the molecular envelop effect from the hydrophobic microconfinement to the encapsulated guest molecules or moieties. Particular attention is put on their capacity to regulate behavior and the functions of the encapsulated guests through thermally-mediated dehydration and collapse of the densely packed dendritic OEGs. We believe that the methodology described here may provide prospects for the fabrication of functional materials from biomacromolecules, especially when used as environmentally friendly nanomaterials or in accurate diagnosis and therapy.

Enhancing luciferase activity and stability through generative modeling of natural enzyme sequences

The availability of natural protein sequences synergized with generative AI provides new paradigms to engineer enzymes. Although active enzyme variants with numerous mutations have been designed using generative models, their performance often falls short of their wild type counterparts. Additionally, in practical applications, choosing fewer mutations that can rival the efficacy of extensive sequence alterations is usually more advantageous. Pinpointing beneficial single mutations continues to be a formidable task. In this study, using the generative maximum entropy model to analyze Renilla luciferase (RLuc) homologs, and in conjunction with biochemistry experiments, we demonstrated that natural evolutionary information could be used to predictively improve enzyme activity and stability by engineering the active center and protein scaffold, respectively. The success rate to improve either luciferase activity or stability of designed single mutants is ~50%. This finding highlights nature's ingenious approach to evolving proficient enzymes, wherein diverse evolutionary pressures are preferentially applied to distinct regions of the enzyme, ultimately culminating in an overall high performance. We also reveal an evolutionary preference in RLuc toward emitting blue light that holds advantages in terms of water penetration compared to other light spectra. Taken together, our approach facilitates navigation through enzyme sequence space and offers effective strategies for computer-aided rational enzyme engineering.

Design and Application of Peptide-Mimic Phosphonium Salt Catalysts in Asymmetric Synthesis

Chiral phosphonium salt catalysis, traditionally classified as a type of phase transfer catalysis, has proven to be a powerful strategy for the stereoselective preparation of diverse optically active molecules. However, there still remain numerous forbidding issues of reactivity and selectivity in such well-known organocatalysis system. Accordingly, the development of new and high-performance phosphonium salt catalysts with unique chiral backbones is highly desirable, yet challenging. This Minireview describes the prominent endeavours in the development of a new family of chiral peptide-mimic phosphonium salt catalysts with multiple hydrogen-bonding donors and their applications in a plethora of enantioselective synthesis during the past few years. Hopefully, this minireview will pave a way for further developing much more efficient and privileged chiral ligands/catalysts featuring exclusively catalytic ability in asymmetric synthesis.

Harnessing Generative AI to Decode Enzyme Catalysis and Evolution for Enhanced Engineering

Enzymes, as paramount protein catalysts, occupy a central role in fostering remarkable progress across numerous fields. However, the intricacy of sequence-function relationships continues to obscure our grasp of enzyme behaviors and curtails our capabilities in rational enzyme engineering. Generative artificial intelligence (AI), known for its proficiency in handling intricate data distributions, holds the potential to offer novel perspectives in enzyme research. By applying generative models, we could discern elusive patterns within the vast sequence space and uncover new functional enzyme sequences. This review highlights the recent advancements in employing generative AI for enzyme sequence analysis. We delve into the impact of generative AI in predicting mutation effects on enzyme fitness, activity, and stability, rationalizing the laboratory evolution of de novo enzymes, decoding protein sequence semantics, and its applications in enzyme engineering. Notably, the prediction of enzyme activity and stability using natural enzyme sequences serves as a vital link, indicating how enzyme catalysis shapes enzyme evolution. Overall, we foresee that the integration of generative AI into enzyme studies will remarkably enhance our knowledge of enzymes and expedite the creation of superior biocatalysts.

Opine dehydrogenases, an underexplored enzyme family for the enzymatic synthesis of chiral amines

Opines and opine-type chemicals are valuable natural products with diverse biochemical roles, and potential synthetic building blocks of bioactive compounds. Their synthesis involves reductive amination of ketoacids with amino acids. This transformation has high synthetic potential in producing enantiopure secondary amines. Nature has evolved opine dehydrogenases for this chemistry. To date, only one enzyme has been used as biocatalyst, however, analysis of the available sequence space suggests more enzymes to be exploited in synthetic organic chemistry. This review summarizes the current knowledge of this underexplored enzyme class, highlights key molecular, structural, and catalytic features with the aim to provide a comprehensive general description of opine dehydrogenases, thereby supporting future enzyme discovery and protein engineering studies.

Optimal enzyme utilization suggests that concentrations and thermodynamics determine binding mechanisms and enzyme saturations

Deciphering the metabolic functions of organisms requires understanding the dynamic responses of living cells upon genetic and environmental perturbations, which in turn can be inferred from enzymatic activity. In this work, we investigate the optimal modes of operation for enzymes in terms of the evolutionary pressure driving them toward increased catalytic efficiency. We develop a framework using a mixed-integer formulation to assess the distribution of thermodynamic forces and enzyme states, providing detailed insights into the enzymatic mode of operation. We use this framework to explore Michaelis-Menten and random-ordered multi-substrate mechanisms. We show that optimal enzyme utilization is achieved by unique or alternative operating modes dependent on reactant concentrations. We find that in a bimolecular enzyme reaction, the random mechanism is optimal over any other ordered mechanism under physiological conditions. Our framework can investigate the optimal catalytic properties of complex enzyme mechanisms. It can further guide the directed evolution of enzymes and fill in the knowledge gaps in enzyme kinetics.

Data integration across conditions improves turnover number estimates and metabolic predictions

Turnover numbers characterize a key property of enzymes, and their usage in constraint-based metabolic modeling is expected to increase the prediction accuracy of diverse cellular phenotypes. In vivo turnover numbers can be obtained by integrating reaction rate and enzyme abundance measurements from individual experiments. Yet, their contribution to improving predictions of condition-specific cellular phenotypes remains elusive. Here, we show that available in vitro and in vivo turnover numbers lead to poor prediction of condition-specific growth rates with protein-constrained models of Escherichia coli and Saccharomyces cerevisiae, particularly when protein abundances are considered. We demonstrate that correction of turnover numbers by simultaneous consideration of proteomics and physiological data leads to improved predictions of condition-specific growth rates. Moreover, the obtained estimates are more precise than corresponding in vitro turnover numbers. Therefore, our approach provides the means to correct turnover numbers and paves the way towards cataloguing kcatomes of other organisms.

Thermo-Gelling Dendronized Chitosans for Modulating Protein Activity

Regulation of protein activity is important in their applications for biomedicine and therapeutics. Here, an approach for the regulation of protein bioactivity through molecular confinement provided by oligoethylene glycol (OEG)-based dendronized chitosan (DCS) hydrogels is reported. Structural effects on their thermoresponsiveness are investigated. The highly transparent hydrogels are formed from thermoresponsive DCSs through their thermal dehydration and exhibit an intriguing reversible sol-gel transition property when triggered at physiological temperatures. The thermo-gelling behavior and mechanical strength of these hydrogels are investigated, and possible effects from hydrophobicity of the OEG dendrons, grafting rates of the dendrons on the chitosan main chain, and solid content of polymers are examined. These DCS hydrogels are found to have lamellar morphologies and can provide characteristic hydrophobicity microenvironments formed through the crowded OEG dendrons, which show a higher level of confinement to guest proteins. This allows the DCS hydrogels remarkable activity protection capability to proteins. Furthermore, these DCS hydrogels inherit the degradability from chitosan, allowing protein release from these hydrogels through the controllable ways without impairing their activities.

Design and directed evolution of noncanonical β-stereoselective metalloglycosidases

Metallohydrolases are ubiquitous in nearly all subclasses of hydrolases, utilizing metal elements to activate a water molecule and facilitate its subsequent dissociation of diverse chemical bonds. However, such a catalytic role of metal ions is rarely found with glycosidases that hydrolyze the glycosidic bonds in sugars. Herein, we design metalloglycosidases by constructing a hydrolytically active Zn-binding site within a barrel-shaped outer membrane protein OmpF. Structure- and mechanism-based redesign and directed evolution have led to the emergence of Zn-dependent glycosidases with catalytic proficiency of 2.8 × 10⁹ and high β-stereoselectivity. Biochemical characterizations suggest that the Zn-binding site constitutes a key catalytic motif along with at least one adjacent acidic residue. This work demonstrates that unprecedented metalloenzymes can be tailor-made, expanding the scope of inorganic reactivities in proteinaceous environments, resetting the structural and functional diversity of metalloenzymes, and providing the potential molecular basis of unidentified metallohydrolases and novel whole-cell biocatalysts.

Revving an Engine of Human Metabolism: Activity Enhancement of Triosephosphate Isomerase via Hemi-Phosphorylation

Triosephosphate isomerase (TPI) performs the 5th step in glycolysis, operates near the limit of diffusion, and is involved in "moonlighting" functions. Its dimer was found singly phosphorylated at Ser20 (pSer20) in human cells, with this post-translational modification (PTM) showing context-dependent stoichiometry and loss under oxidative stress. We generated synthetic pSer20 proteoforms using cell-free protein synthesis that showed enhanced TPI activity by 4-fold relative to unmodified TPI. Molecular dynamics simulations show that the phosphorylation enables a channel to form that shuttles substrate into the active site. Refolding, kinetic, and crystallographic analyses of point mutants including S20E/G/Q indicate that hetero-dimerization and subunit asymmetry are key features of TPI. Moreover, characterization of an endogenous human TPI tetramer also implicates tetramerization in enzymatic regulation. S20 is highly conserved across eukaryotic TPI, yet most prokaryotes contain E/D at this site, suggesting that phosphorylation of human TPI evolved a new switch to optionally boost an already fast enzyme. Overall, complete characterization of TPI shows how endogenous proteoform discovery can prioritize functional versus bystander PTMs.

Strategies for the Biodegradation of Polyfluorinated Compounds

Many cite the strength of C-F bonds for the poor microbial biodegradability of polyfluorinated organic compounds (PFCs). However, commercial PFCs almost invariably contain more functionality than fluorine. The additional functionality provides a weak entry point for reactions that activate C-F bonds and lead to their eventual cleavage. This metabolic activation strategy is common in microbial biodegradation pathways and is observed with aromatic hydrocarbons, chlorinated compounds, phosphonates and many other compounds. Initial metabolic activation precedes critical bond breakage and assimilation of nutrients. A similar strategy with commercial PFCs proceeds via initial attack at the non-fluorinated functionalities: sulfonates, carboxylates, chlorines, phenyl rings, or phosphonates. Metabolic transformation of these non-fluorinated groups can activate the C-F bonds, allowing more facile cleavage than a direct attack on the C-F bonds. Given that virtually all compounds denoted as "PFAS" are not perfluorinated and are not alkanes, it is posited here that considering their individual chemical classes is more useful for both chemical and microbiological considerations of their fate.

Utilization of diverse organophosphorus pollutants by marine bacteria

Anthropogenic organophosphorus compounds (AOPCs), such as phosphotriesters, are used extensively as plasticizers, flame retardants, nerve agents, and pesticides. To date, only a handful of soil bacteria bearing a phosphotriesterase (PTE), the key enzyme in the AOPC degradation pathway, have been identified. Therefore, the extent to which bacteria are capable of utilizing AOPCs as a phosphorus source, and how widespread this adaptation may be, remains unclear. Marine environments with phosphorus limitation and increasing levels of pollution by AOPCs may drive the emergence of PTE activity. Here, we report the utilization of diverse AOPCs by four model marine bacteria and 17 bacterial isolates from the Mediterranean Sea and the Red Sea. To unravel the details of AOPC utilization, two PTEs from marine bacteria were isolated and characterized, with one of the enzymes belonging to a protein family that, to our knowledge, has never before been associated with PTE activity. When expressed in Escherichia coli with a phosphodiesterase, a PTE isolated from a marine bacterium enabled growth on a pesticide analog as the sole phosphorus source. Utilization of AOPCs may provide bacteria a source of phosphorus in depleted environments and offers a prospect for the bioremediation of a pervasive class of anthropogenic pollutants.

Natural Evolution Provides Strong Hints about Laboratory Evolution of Designer Enzymes

Laboratory evolution combined with computational enzyme design provides the opportunity to generate novel biocatalysts. Nevertheless, it has been challenging to understand how laboratory evolution optimizes designer enzymes by introducing seemingly random mutations. A typical enzyme optimized with laboratory evolution is the abiological Kemp eliminase, initially designed by grafting active site residues into a natural protein scaffold. Here, we relate the catalytic power of laboratory-evolved Kemp eliminases to the statistical energy ([Formula: see text]) inferred from their natural homologous sequences using the maximum entropy model. The [Formula: see text] of designs generated by directed evolution is correlated with enhanced activity and reduced stability, thus displaying a stability-activity trade-off. In contrast, the [Formula: see text] for mutants in catalytic-active remote regions (in which remote residues are important for catalysis) is strongly anticorrelated with the activity. These findings provide an insight into the role of protein scaffolds in the adaption to new enzymatic functions. It also indicates that the valley in the [Formula: see text] landscape can guide enzyme design for abiological catalysis. Overall, the connection between laboratory and natural evolution contributes to understanding what is optimized in the laboratory and how new enzymatic function emerges in nature, and provides guidance for computational enzyme design.

Uniform binding and negative catalysis at the origin of enzymes

Enzymes are well known for their catalytic abilities, some even reaching "catalytic perfection" in the sense that the reaction they catalyze has reached the physical bound of the diffusion rate. However, our growing understanding of enzyme superfamilies has revealed that only some share a catalytic chemistry while others share a substrate-handle binding motif, for example, for a particular phosphate group. This suggests that some families emerged through a "substrate-handle-binding-first" mechanism ("binding-first" for brevity) instead of "chemistry-first" and we are, therefore, left to wonder what the role of non-catalytic binders might have been during enzyme evolution. In the last of their eight seminal, back-to-back articles from 1976, John Albery and Jeremy Knowles addressed the question of enzyme evolution by arguing that the simplest mode of enzyme evolution is what they defined as "uniform binding" (parallel stabilization of all enzyme-bound states to the same degree). Indeed, we show that a uniform-binding proto-catalyst can accelerate a reaction, but only when catalysis is already present, that is, when the transition state is already stabilized to some degree. Thus, we sought an alternative explanation for the cases where substrate-handle-binding preceded any involvement of a catalyst. We find that evolutionary starting points that exhibit negative catalysis can redirect the reaction's course to a preferred product without need for rate acceleration or product release; that is, if they do not stabilize, or even destabilize, the transition state corresponding to an undesired product. Such a mechanism might explain the emergence of "binding-first" enzyme families like the aldolase superfamily.

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

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

Innovation and tinkering in the evolution of oxidases

Although molecular oxygen is a relative newcomer to the biosphere, it has had a profound impact on metabolism. About 700 oxygen‐dependent enzymatic reactions are known, the vast majority of which emerged only after the appearance of oxygen in the biosphere, circa 3 billion years ago. Oxygen was a major driving force for evolutionary innovation—~60% of all known oxygen‐dependent enzyme families emerged as such; that is, the founding ancestor was an O₂‐dependent enzyme. The other 40% seem to have diverged by tinkering from pre‐existing proteins whose function was not related to oxygen. Here, we focus on the latter. We describe transitions from various enzyme classes, as well as from non‐enzymatic proteins, and we explore these transitions in terms of catalytic chemistry, metabolism, and protein structure. These transitions vary from subtle ones, such as simply repurposing oxidoreductases by replacing an electron acceptor such as NAD by O₂, to drastic changes in reaction mechanism, such as turning carboxylases and hydrolases into oxidases. The latter is more common and can occur with strikingly minor changes, for example, only one mutation in the active site. We further suggest that engineering enzymes to harness the extraordinary reactivity of oxygen may yield higher catabolic power and versatility.

Metal-responsive regulation of enzyme catalysis using genetically encoded chemical switches

Dynamic control over protein function is a central challenge in synthetic biology. To address this challenge, we describe the development of an integrated computational and experimental workflow to incorporate a metal-responsive chemical switch into proteins. Pairs of bipyridinylalanine (BpyAla) residues are genetically encoded into two structurally distinct enzymes, a serine protease and firefly luciferase, so that metal coordination biases the conformations of these enzymes, leading to reversible control of activity. Computational analysis and molecular dynamics simulations are used to rationally guide BpyAla placement, significantly reducing experimental workload, and cell-free protein synthesis coupled with high-throughput experimentation enable rapid prototyping of variants. Ultimately, this strategy yields enzymes with a robust 20-fold dynamic range in response to divalent metal salts over 24 on/off switches, demonstrating the potential of this approach. We envision that this strategy of genetically encoding chemical switches into enzymes will complement other protein engineering and synthetic biology efforts, enabling new opportunities for applications where precise regulation of protein function is critical.

What Have We Learned from Design of Function in Large Proteins?

The overarching goal of computational protein design is to gain complete control over protein structure and function. The majority of sophisticated binders and enzymes, however, are large and exhibit diverse and complex folds that defy atomistic design calculations. Encouragingly, recent strategies that combine evolutionary constraints from natural homologs with atomistic calculations have significantly improved design accuracy. In these approaches, evolutionary constraints mitigate the risk from misfolding and aggregation, focusing atomistic design calculations on a small but highly enriched sequence subspace. Such methods have dramatically optimized diverse proteins, including vaccine immunogens, enzymes for sustainable chemistry, and proteins with therapeutic potential. The new generation of deep learning-based ab initio structure predictors can be combined with these methods to extend the scope of protein design, in principle, to any natural protein of known sequence. We envision that protein engineering will come to rely on completely computational methods to efficiently discover and optimize biomolecular activities.

Diffusion control in biochemical specificity

Biochemical specificity is critical in enzyme function, evolution, and engineering. Here we employ an established kinetic model to dissect the effects of reactant geometry and diffusion on product formation speed and accuracy in the presence of cognate (correct) and near-cognate (incorrect) substrates. Using this steady-state model for spherical geometries, we find that, for distinct kinetic regimes, the speed and accuracy of the reactions are optimized on different regions of the geometric landscape. From this model we deduce that accuracy can be strongly dependent on reactant geometric properties even for chemically limited reactions. Notably, substrates with a specific geometry and reactivity can be discriminated by the enzyme with higher efficacy than others through purely diffusive effects. For similar cognate and near-cognate substrate geometries (as is the case for polymerases or the ribosome), we observe that speed and accuracy are maximized in opposing regions of the geometric landscape. We also show that, in relevant environments, diffusive effects on accuracy can be substantial even far from extreme kinetic conditions. Finally, we find how reactant chemical discrimination and diffusion can be related to simultaneously optimize steady-state flux and accuracy. These results highlight how diffusion and geometry can be employed to enhance reaction speed and discrimination, and similarly how they impose fundamental restraints on these quantities.

Enhancing computational enzyme design by a maximum entropy strategy

Although computational enzyme design is of great importance, the advances utilizing physics-based approaches have been slow, and further progress is urgently needed. One promising direction is using machine learning, but such strategies have not been established as effective tools for predicting the catalytic power of enzymes. Here, we show that the statistical energy inferred from homologous sequences with the maximum entropy (MaxEnt) principle significantly correlates with enzyme catalysis and stability at the active site region and the more distant region, respectively. This finding decodes enzyme architecture and offers a connection between enzyme evolution and the physical chemistry of enzyme catalysis, and it deepens our understanding of the stability-activity trade-off hypothesis for enzymes. Overall, the strong correlations found here provide a powerful way of guiding enzyme design.

A robust transition to homochirality in complex chemical reaction networks

Homochirality, i.e. the dominance across all living matter of one enantiomer over the other among chiral molecules, is thought to be a key step in the emergence of life. Building on ideas put forward by Frank and many others, we proposed recently one such mechanism in Laurent et al. (Laurent, 2021 Proc. Natl Acad. Sci. USA 118 , e2012741118. ( doi:10.1073/pnas.2012741118 )) based on the properties of large out of equilibrium chemical networks. We showed that in such networks, a phase transition towards a homochiral state is likely to occur as the number of chiral species in the system becomes large or as the amount of free energy injected into the system increases. This paper aims at clarifying some important points in that scenario, not covered by our previous work. We first analyse the various conventions used to measure chirality, introduce the notion of chiral symmetry of a network and study its implications regarding the relative chiral signs adopted by different groups of molecules. We then propose a generalization of Frank’s model for large chemical networks, which we characterize completely using methods of random matrices. This analysis is extended to sparse networks, which shows that the emergence of homochirality is a robust transition.

De Novo Approach to Encapsulating Biocatalysts into Synthetic Matrixes: From Enzymes to Microbial Electrocatalysts

Biocatalysts hold great promise in chemical and electrochemical reactions. However, biocatalysts are prone to inhospitable physiochemical conditions. Encapsulating biocatalysts into a synthetic host matrix can improve their stability and activity, and broaden their operational conditions. In this Review, we summarize the emerging de novo approaches to encapsulating biocatalysts into synthetic matrixes. Here, de novo means that embedding of biocatalysts and construction of matrixes take place simultaneously. We discuss the advantages and limitations of the de novo approach. On the basis of the nature of the biocatalysts and the synthetic frameworks, we specifically focus on two aspects: (1) encapsulation of enzymes (in vitro) in metal-organic frameworks and (2) encapsulation of microbial electrocatalysts (in vivo) on the electrode. For both cases, we discuss how the encapsulation improves biocatalysts' performance (stability, viability, activity, and etc.). We also highlight the benefit of encapsulation in facilitating the transport of charge carriers in microbial electrocatalysis.

Jacques L, Bornscheuer UT, Syrén P. Biocatalysis in the Recycling Landscape for Synthetic Polymers and Plastics towards Circular Textiles

Although recovery of fibers from used textiles with retained material quality is desired, separation of individual components from polymer blends used in today's complex textile materials is currently not available at viable scale. Biotechnology could provide a solution to this pressing problem by enabling selective depolymerization of recyclable fibers of natural and synthetic origin, to isolate constituents or even recover monomers. We compiled experimental data for biocatalytic polymer degradation with a focus on synthetic polymers with hydrolysable links and calculated conversion rates to explore this path The analysis emphasizes that we urgently need major research efforts: beyond cellulose-based fibers, biotechnological-assisted depolymerization of plastics so far only works for polyethylene terephthalate, with degradation of a few other relevant synthetic polymer chains being reported. In contrast, by analyzing market data and emerging trends for synthetic fibers in the textile industry, in combination with numbers from used garment collection and sorting plants, it was shown that the use of difficult-to-recycle blended materials is rapidly growing. If the lack of recycling technology and production trend for fiber blends remains, a volume of more than 3400 Mt of waste will have been accumulated by 2030. This work highlights the urgent need to transform the textile industry from a biocatalytic perspective.

Targeted metabolite profiling as a top-down approach to uncover interspecies diversity and identify key conserved operational features in the Calvin-Benson cycle

Improving photosynthesis is a promising avenue to increase crop yield. This will be aided by better understanding of natural variance in photosynthesis. Profiling of Calvin-Benson cycle (CBC) metabolites provides a top-down strategy to uncover interspecies diversity in CBC operation. In a study of four C4 and five C3 species, principal components analysis separated C4 species from C3 species and also separated different C4 species. These separations were driven by metabolites that reflect known species differences in their biochemistry and pathways. Unexpectedly, there was also considerable diversity between the C3 species. Falling atmospheric CO2 and changing temperature, nitrogen, and water availability have driven evolution of C4 photosynthesis in multiple lineages. We propose that analogous selective pressures drove lineage-dependent evolution of the CBC in C3 species. Examples of species-dependent variation include differences in the balance between the CBC and the light reactions, and in the balance between regulated steps in the CBC. Metabolite profiles also reveal conserved features including inactivation of enzymes in low irradiance, and maintenance of CBC metabolites at relatively high levels in the absence of net CO2 fixation. These features may be important for photosynthetic efficiency in low light, fluctuating irradiance, and when stomata close due to low water availability.

Metallo-β-lactamases in the Age of Multidrug Resistance: From Structure and Mechanism to Evolution, Dissemination, and Inhibitor Design

Antimicrobial resistance is one of the major problems in current practical medicine. The spread of genes coding for resistance determinants among bacteria challenges the use of approved antibiotics, narrowing the options for treatment. Resistance to carbapenems, last resort antibiotics, is a major concern. Metallo-β-lactamases (MBLs) hydrolyze carbapenems, penicillins, and cephalosporins, becoming central to this problem. These enzymes diverge with respect to serine-β-lactamases by exhibiting a different fold, active site, and catalytic features. Elucidating their catalytic mechanism has been a big challenge in the field that has limited the development of useful inhibitors. This review covers exhaustively the details of the active-site chemistries, the diversity of MBL alleles, the catalytic mechanism against different substrates, and how this information has helped developing inhibitors. We also discuss here different aspects critical to understand the success of MBLs in conferring resistance: the molecular determinants of their dissemination, their cell physiology, from the biogenesis to the processing involved in the transit to the periplasm, and the uptake of the Zn(II) ions upon metal starvation conditions, such as those encountered during an infection. In this regard, the chemical, biochemical and microbiological aspects provide an integrative view of the current knowledge of MBLs.

Rubisco Adaptation Is More Limited by Phylogenetic Constraint Than by Catalytic Trade-off

Rubisco assimilates CO2 to form the sugars that fuel life on earth. Correlations between rubisco kinetic traits across species have led to the proposition that rubisco adaptation is highly constrained by catalytic trade-offs. However, these analyses did not consider the phylogenetic context of the enzymes that were analyzed. Thus, it is possible that the correlations observed were an artefact of the presence of phylogenetic signal in rubisco kinetics and the phylogenetic relationship between the species that were sampled. Here, we conducted a phylogenetically resolved analysis of rubisco kinetics and show that there is a significant phylogenetic signal in rubisco kinetic traits. We re-evaluated the extent of catalytic trade-offs accounting for this phylogenetic signal and found that all were attenuated. Following phylogenetic correction, the largest catalytic trade-offs were observed between the Michaelis constant for CO2 and carboxylase turnover (∼21-37%), and between the Michaelis constants for CO2 and O2 (∼9-19%), respectively. All other catalytic trade-offs were substantially attenuated such that they were marginal (<9%) or non-significant. This phylogenetically resolved analysis of rubisco kinetic evolution also identified kinetic changes that occur concomitant with the evolution of C4 photosynthesis. Finally, we show that phylogenetic constraints have played a larger role than catalytic trade-offs in limiting the evolution of rubisco kinetics. Thus, although there is strong evidence for some catalytic trade-offs, rubisco adaptation has been more limited by phylogenetic constraint than by the combined action of all catalytic trade-offs.

Integrating thermodynamic and enzymatic constraints into genome-scale metabolic models

Stoichiometric genome-scale metabolic network models (GEMs) have been widely used to predict metabolic phenotypes. In addition to stoichiometric ratios, other constraints such as enzyme availability and thermodynamic feasibility can also limit the phenotype solution space. Extended GEM models considering either enzymatic or thermodynamic constraints have been shown to improve prediction accuracy. In this paper, we propose a novel method that integrates both enzymatic and thermodynamic constraints in a single Pyomo modeling framework (ETGEMs). We applied this method to construct the EcoETM (E. coli metabolic model with enzymatic and thermodynamic constraints). Using this model, we calculated the optimal pathways for cellular growth and the production of 22 metabolites. When comparing the results with those of iML1515 and models with one of the two constraints, we observed that many thermodynamically unfavorable and/or high enzyme cost pathways were excluded from EcoETM. For example, the synthesis pathway of carbamoyl-phosphate (Cbp) from iML1515 is both thermodynamically unfavorable and enzymatically costly. After introducing the new constraints, the production pathways and yields of several Cbp-derived products (e.g. L-arginine, orotate) calculated using EcoETM were more realistic. The results of this study demonstrate the great application potential of metabolic models with multiple constraints for pathway analysis and phenotype prediction.

Filling in the Gaps in Metformin Biodegradation: a New Enzyme and a Metabolic Pathway for Guanylurea

The widely prescribed pharmaceutical metformin and its main metabolite, guanylurea, are currently two of the most common contaminants in surface and wastewater. Guanylurea often accumulates and is poorly, if at all, biodegraded in wastewater treatment plants. This study describes Pseudomonas mendocina strain GU, isolated from a municipal wastewater treatment plant, using guanylurea as its sole nitrogen source. The genome was sequenced with 36-fold coverage and mined to identify guanylurea degradation genes. The gene encoding the enzyme initiating guanylurea metabolism was expressed, and the enzyme was purified and characterized. Guanylurea hydrolase, a newly described enzyme, was shown to transform guanylurea to one equivalent (each) of ammonia and guanidine. Guanidine also supports growth as a sole nitrogen source. Cell yields from growth on limiting concentrations of guanylurea revealed that metabolism releases all four nitrogen atoms. Genes encoding complete metabolic transformation were identified bioinformatically, defining the pathway as follows: guanylurea to guanidine to carboxyguanidine to allophanate to ammonia and carbon dioxide. The first enzyme, guanylurea hydrolase, is a member of the isochorismatase-like hydrolase protein family, which includes biuret hydrolase and triuret hydrolase. Although homologs, the three enzymes show distinct substrate specificities. Pairwise sequence comparisons and the use of sequence similarity networks allowed fine structure discrimination between the three homologous enzymes and provided insights into the evolutionary origins of guanylurea hydrolase.IMPORTANCE Metformin is a pharmaceutical most prescribed for type 2 diabetes and is now being examined for potential benefits to COVID-19 patients. People taking the drug pass it largely unchanged, and it subsequently enters wastewater treatment plants. Metformin has been known to be metabolized to guanylurea. The levels of guanylurea often exceed that of metformin, leading to the former being considered a "dead-end" metabolite. Metformin and guanylurea are water pollutants of emerging concern, as they persist to reach nontarget aquatic life and humans, the latter if it remains in treated water. The present study has identified a Pseudomonas mendocina strain that completely degrades guanylurea. The genome was sequenced, and the genes involved in guanylurea metabolism were identified in three widely separated genomic regions. This knowledge advances the idea that guanylurea is not a dead-end product and will allow for bioinformatic identification of the relevant genes in wastewater treatment plant microbiomes and other environments subjected to metagenomic sequencing.

Resource uptake and the evolution of moderately efficient enzymes

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

Quantifying the taxonomic bias in enzymology

The ongoing biotechnological revolution is rooted in our knowledge of enzymes. However, metagenomics is showing how little we know about Earth's enzyme repertoire. Deep sequencing has revolutionized our view of the tree of life. The genomes of newly‐discovered organisms are replete with novel sequences, emphasizing the trove of enzyme structures and functions waiting to be explored by biochemists. Here, we sought to draw attention to the vastness of the “enzymatic dark matter” within the tree of life by placing enzymological knowledge in the context of phylogeny. We used kinetic parameters from the BRaunschweig ENzyme DAtabase (BRENDA) as our proxy for enzymological knowledge. Mapping 12,677 BRENDA entries onto the phylogenetic tree revealed that 55% of these data were from eukaryotes, even though they are the least diverse part of the tree. At the next taxonomic level, only four of 18 archaeal phyla and 24 of 111 bacterial phyla are represented in the BRENDA dataset. One phylum, the Proteobacteria, accounts for over half of all bacterial entries. Similarly, the supergroup Amorphea, which includes animals and fungi, contains over half the data on eukaryotes. Many major taxonomic groups are notable for their complete absence from BRENDA, including the ultra‐diverse bacterial Candidate Phyla Radiation. At the species level, five mammals (including human) contribute 15% of BRENDA entries. The taxonomic bias in enzymology is strong, but in the era of gene synthesis we now have the tools to address it. Doing so promises to enrich our biochemical understanding of life and uncover powerful new biocatalysts.

Symmetry-related residues as promising hotspots for the evolution of de novo oligomeric enzymes

Directed evolution has provided us with great opportunities and prospects in the synthesis of tailor-made proteins. It, however, often requires at least mid to high throughput screening, necessitating more effective strategies for laboratory evolution. We herein demonstrate that protein symmetry can be a versatile criterion for searching for promising hotspots for the directed evolution of de novo oligomeric enzymes. The randomization of symmetry-related residues located at the rotational axes of artificial metallo-β-lactamase yields drastic effects on catalytic activities, whereas that of non-symmetry-related, yet, proximal residues to the active site results in negligible perturbations. Structural and biochemical analysis of the positive hits indicates that seemingly trivial mutations at symmetry-related spots yield significant alterations in overall structures, metal-coordination geometry, and chemical environments of active sites. Our work implicates that numerous artificially designed and natural oligomeric proteins might have evolutionary advantages of propagating beneficial mutations using their global symmetry.

Symmetry-related residues located at the rotational axes can be promising hotspots for the evolution of de novo oligomeric enzymes even though they are distantly located from the active site pocket.

Comparative Biochemistry of Four Polyester (PET) Hydrolases*

The potential of bioprocessing in a circular plastic economy has strongly stimulated research into the enzymatic degradation of different synthetic polymers. Particular interest has been devoted to the commonly used polyester, poly(ethylene terephthalate) (PET), and a number of PET hydrolases have been described. However, a kinetic framework for comparisons of PET hydrolases (or other plastic-degrading enzymes) acting on the insoluble substrate has not been established. Herein, we propose such a framework, which we have tested against kinetic measurements for four PET hydrolases. The analysis provided values of k_cat and K_M , as well as an apparent specificity constant in the conventional units of M^-1 s^-1 . These parameters, together with experimental values for the number of enzyme attack sites on the PET surface, enabled comparative analyses. A variant of the PET hydrolase from Ideonella sakaiensis was the most efficient enzyme at ambient conditions; it relied on a high k_cat rather than a low K_M . Moreover, both soluble and insoluble PET fragments were consistently hydrolyzed much faster than intact PET. This suggests that interactions between polymer strands slow down PET degradation, whereas the chemical steps of catalysis and the low accessibility associated with solid substrate were less important for the overall rate. Finally, the investigated enzymes showed a remarkable substrate affinity, and reached half the saturation rate on PET when the concentration of attack sites in the suspension was only about 50 nM. We propose that this is linked to nonspecific adsorption, which promotes the nearness of enzyme and attack sites.

The activity of RubisCO and energy demands for its biosynthesis. Comparative studies with CO2-reductases

Most CO₂ on Earth is fixed into organic matter via reactions catalysed by enzymes called carboxylases. CO₂-fixation via carboxylases occurs in the Calvin-Benson-Bassham (CBB) cycle, and the crucial role in this cycle is played by RubisCO (D-ribulose 1,5-bisphosphate carboxylase/oxygenase). CO₂ can also be fixed by pathways, where a reduction of CO₂ to formate or carbon monoxide (CO) occurs. The latter reactions are performed by so-called CO₂-reductases e.g. formate dehydrogenase (FDH), carbon-monooxide (CO) dehydrogenase (CODH), and crotonyl-CoA reductase/carboxylase (CCR). In general, a simple model of enzymatic activity based only on a turnover rate of an enzyme for an appropriate substrate (k_cat) is insufficient. Based on estimated metabolic costs of each amino acid, the average energetic costs of amino acid biosynthesis (E_aa), and the total costs (E_T) for selected CO₂-fixing enzymes were analyzed concerning 1) k_cat for CO₂ (k_C), and 2) specificity factor (S_rel) for RubisCO. A comparison of E_aa and E_T to their k_C showed that CODH and FDHs do not need to be more efficient enzymes in CO₂ capturing pathways than some forms of RubisCO. CCR was the only both low-cost and highly active CO₂-fixing enzyme. The obtained results showed also that there exists an evolutionarily conserved trade-off between S_rel of RubisCOs and the energetic demands needed for their biosynthesis. Phylogenetic analysis demonstrated that RubisCO, CODH, FDH, and CCR are enzymes formed as a result of parallel evolution. Moreover, the kinetic parameters (k_C) of CO₂-fixing enzymes were plausibly optimized already at the early stages of life evolution on Earth.

Emergence of homochirality in large molecular systems

The selection of a single molecular handedness, or homochirality across all living matter, is a mystery in the origin of life. Frank's seminal model showed in the '50s how chiral symmetry breaking can occur in nonequilibrium chemical networks. However, an important shortcoming in this classic model is that it considers a small number of species, while there is no reason for the prebiotic system, in which homochirality first appeared, to have had such a simple composition. Furthermore, this model does not provide information on what could have been the size of the molecules involved in this homochiral prebiotic system. Here, we show that large molecular systems are likely to undergo a phase transition toward a homochiral state, as a consequence of the fact that they contain a large number of chiral species. Using chemoinformatics tools, we quantify how abundant chiral species are in the chemical universe of all possible molecules of a given length. Then, we propose that Frank's model should be extended to include a large number of species, in order to possess the transition toward homochirality, as confirmed by numerical simulations. Finally, using random matrix theory, we prove that large nonequilibrium reaction networks possess a generic and robust phase transition toward a homochiral state.

Dynamic Structural Biology Experiments at XFEL or Synchrotron Sources

Macromolecular crystallography (MX) leverages the methods of physics and the language of chemistry to reveal fundamental insights into biology. Often beautifully artistic images present MX results to support profound functional hypotheses that are vital to entire life science research community. Over the past several decades, synchrotrons around the world have been the workhorses for X-ray diffraction data collection at many highly automated beamlines. The newest tools include X-ray-free electron lasers (XFELs) located at facilities in the USA, Japan, Korea, Switzerland, and Germany that deliver about nine orders of magnitude higher brightness in discrete femtosecond long pulses. At each of these facilities, new serial femtosecond crystallography (SFX) strategies exploit slurries of micron-size crystals by rapidly delivering individual crystals into the XFEL X-ray interaction region, from which one diffraction pattern is collected per crystal before it is destroyed by the intense X-ray pulse. Relatively simple adaptions to SFX methods produce time-resolved data collection strategies wherein reactions are triggered by visible light illumination or by chemical diffusion/mixing. Thus, XFELs provide new opportunities for high temporal and spatial resolution studies of systems engaged in function at physiological temperature. In this chapter, we summarize various issues related to microcrystal slurry preparation, sample delivery into the X-ray interaction region, and some emerging strategies for time-resolved SFX data collection.

High throughput acoustic microfluidic mixer controls self-assembly of protein nanoparticles with tuneable sizes

HYPOTHESIS

Protein nanoparticles have attracted increased interest due to their broad applications ranging from drug delivery and vaccines to biocatalysts and biosensors. The morphology and the size of the nanoparticles play a crucial role in determining their suitability for different applications. Yet, effectively controlling the size of the nanoparticles is still a significant challenge in their manufacture. The hypothesis of this paper is that the assembly conditions and size of protein particles can be tuned via a mechanical route by simply modifying the mixing time and strength, while keeping the chemical parameters constant.

EXPERIMENTAL

We use an acoustically actuated, high throughput, ultrafast, microfluidic mixer for the assembly of protein particles with tuneable sizes. The performance of the acoustic micro-mixer is characterized via Laser Doppler Vibrometry and image processing. The assembly of protein nanoparticles is monitored by dynamic light scattering (DLS) and transmission electron microscopy (TEM).

FINDINGS

By changing actuation parameters, the turbulence and mixing in the microchannel can be precisely varied to control the initiation of protein particle assembly while the solution conditions of assembly (pH and ionic strength) are kept constant. Importantly, mixing times as low as 6 ms can be achieved for triggering protein assembly in the microfluidic channel. In comparison to the conventional batch process of assembly, the acoustic microfluidic mixer approach produces smaller particles with a more uniform size distribution, promising a new way to manufacture protein particles with controllable quality.

Recent results in time resolved serial femtosecond crystallography at XFELs

Time-resolved serial femtosecond crystallography (tr-SFX) methods exploit slurries of crystalline samples that range in size from hundreds of nanometers to a few tens of micrometers, at near-physiological temperature and pressure, to generate atomic resolution models and probe authentic function with the same experiment. 'Dynamic structural biology' is often used to encompass the research philosophy and techniques. Reaction cycles for tr-SFX studies are initiated by photons or ligand addition/mixing strategies, wherein the latter are potentially generalizable across enzymology. Thus, dynamic structural biology often creates stop-motion molecular movies of macromolecular function. In metal-dependent systems, complementary spectroscopic information can also be collected from the same samples and X-ray pulses, which provides even more detailed mechanistic insights. These types of experimental data also complement quantum mechanical and classical dynamics numerical calculations. Correlated structural-functional results will yield more detailed mechanistic insights and will likely translate into better drugs and treatments impacting human health, and better catalysis for clean energy and agriculture.