Enzymatic Reaction Research Articles

Understanding the Enzymatic Synthesis of a New Biolubricant: Decane-1,10-diyl bis(2-methylpentanoate).

The value of branched esters comes from the special properties they have in cold environments, which allow them to remain liquid over a wide range of temperatures. These properties make them useful for application in the cosmetic industry or as lubricant additives. This paper presents the studies carried out to ascertain the operational feasibility of the enzymatic esterification of 2-methylpentanoic acid (MPA) with 1,10-decanediol (DD), with the objective of obtaining a novel molecule: decane-1,10-diyl bis(2-methylpentanoate) (DDBMP). The enzymatic reaction is conducted in a thermostated batch reactor, utilizing the commercially available immobilized lipase Lipozyme® 435 in a solvent-free medium. The reaction conversion is determined by an acid number determination and a gas chromatographic analysis. The most optimal result is achieved at a temperature of 80 °C, a biocatalyst concentration of 2.5% (w/w), and a non-stoichiometric substrate relation. A preliminary economic study and the calculation of Green Metrics has established that the operation with a 30% molar excess of acid is the best option to obtain a product with 92.6% purity at a lower cost than the other options and in accordance with the 12 Principles of Green Chemistry. The synthetized diester has a viscosity index of 210, indicating that this new molecule can be used as a biolubricant at extreme temperatures.

Ranking Single Fluorescent Protein-Based Calcium Biosensor Performance by Molecular Dynamics Simulations.

Genetically encoded fluorescent biosensors (GEFBs) have become indispensable tools for visualizing biological processes in vivo. A typical GEFB is composed of a sensory domain (SD) that undergoes a conformational change upon ligand binding or enzymatic reaction; the SD is genetically fused with a fluorescent protein (FP). The changes in the SD allosterically modulate the chromophore environment whose spectral properties are changed. Single fluorescent (FP)-based biosensors, a subclass of GEFBs, offer a simple experimental setup; they are easy to produce in living cells, structurally stable, and simple to use due to their single-wavelength operation. However, they pose a significant challenge for structure optimization, especially concerning the length and residue content of linkers between the FP and SD, which affect how well the chromophore responds to conformational change in the SD. In this work, we use all-atom molecular dynamics simulations to analyze the dynamic properties of a series of calmodulin-based calcium biosensors, all with different FP-SD interaction interfaces and varying degrees of calcium binding-dependent fluorescence change. Our results indicate that biosensor performance can be predicted based on distribution of water molecules around the chromophore and shifts in hydrogen bond occupancies between the ligand-bound and ligand-free sensor structures.

Deep Learning-Driven Insights into Enzyme-Substrate Interaction Discovery.

Enzymes are ubiquitous catalysts with enormous application potential in biomedicine, green chemistry, and biotechnology. However, accurately predicting whether a molecule serves as a substrate for a specific enzyme, especially for novel entities, remains a significant challenge. Compared with traditional experimental methods, computational approaches are much more resource-efficient and time-saving, but they often compromise on accuracy. To address this, we introduce the molecule-enzyme interaction (MEI) model, a novel machine learning framework designed to predict the probability that a given molecule is a substrate for a specified enzyme with high accuracy. Utilizing a comprehensive data set that encapsulates extensive information on enzymatic reactions and enzyme sequences, the MEI model seamlessly combines atomic environmental data with amino acid sequence features through an advanced attention mechanism within a hierarchical neural network. Empirical evaluations have confirmed that the MEI model outperforms the current state-of-the-art model by at least 6.7% in prediction accuracy and 8.5% in AUROC, underscoring its enhanced predictive capabilities. Additionally, the MEI model demonstrates remarkable generalization across data sets of varying qualities and sizes. This adaptability is further evidenced by its successful application in diverse areas, such as predicting interactions within the CYP450 enzyme family and achieving an outstanding accuracy of 90.5% in predicting the enzymatic breakdown of complex plastics within environmental applications. These examples illustrate the model's ability to effectively transfer knowledge from coarsely annotated enzyme databases to smaller, high-precision data sets, robustly modeling both sparse and high-quality databases. We believe that this versatility firmly establishes the MEI model as a foundational tool in enzyme research with immense potential to extend beyond its original scope.

In situ characterization of amine‐forming enzymes shows altered oligomeric state

AbstractEnzyme stability can be measured in a number of ways, including melting temperature, activity retention, and size analysis. However, these measurements are often conducted in an idealized storage buffer and not in the relevant enzymatic reaction media. Particularly for reactions that occur in alkaline, volatile, and high ionic strength media, typical analyses using differential scanning calorimetry, light scattering, and sodium dodecyl‐sulfate polyacrylamide gel electrophoresis are not satisfactory to track the stability of these enzymes. In this work, we monitor the stability of engineered and native dehydrogenases that require a high amount of ammonia for their reaction to occur. We demonstrate the benefits of analyzing these enzymes in their reaction buffer, uncovering trends that were not observable in the typical phosphate storage buffer. This work provides a framework for analyzing the stability of many other enzymes whose reaction media is not suitable for traditional techniques. We introduce several strategies for measuring the melting temperature, oligomeric state, and activity of these enzymes in their reaction media. Further, we have identified opportunities for integration of computational tools into this workflow to engineer enzymes more effectively for solvent tolerance and improved stability.

Recent Advances in Biosensors Using Enzyme-Stabilized Gold Nanoclusters.

Recently, gold nanoclusters (AuNCs) have been widely used in biological applications due to their ultrasmall size, ranging within a few nanometers; large specific surface area; easy functionalization; unique fluorescence properties; and excellent conductivity. However, because they are unstable in solution, AuNCs require stabilization by using ligands such as dendrimers, peptides, DNA, and proteins. As a result, the properties of AuNCs and their formation are determined by the ligand, so the selection of the ligand is important. Of the many ligands implemented, enzyme-stabilized gold nanoclusters (enzyme-AuNCs) have attracted increasing attention for biosensor applications because of the excellent optical/electrochemical properties of AuNCs and the highly target-specific reactions of enzymes. In this review, we explore how enzyme-AuNCs are prepared, their properties, and the various types of enzyme-AuNC-based biosensors that use optical and electrochemical detection techniques. Finally, we discuss the current challenges and prospects of enzyme-AuNCs in biosensing applications. We expect this review to provide interdisciplinary knowledge about the application of enzyme-AuNC-based materials within the biomedical and environmental fields.

Mannich Base Derived from Lawsone Inhibits PKM2 and Induces Neoplastic Cell Death.

Background/Objectives: Pyruvate kinase M2, a central regulator of cancer cell metabolism, has garnered significant attention as a promising target for disrupting the metabolic adaptability of tumor cells. This study explores the potential of the Mannich base derived from lawsone (MB-6a) to interfere with PKM2 enzymatic activity both in vitro and in silico. Methods: The antiproliferative potential of MB-6a was tested using MTT assay in various cell lines, including SCC-9, Hep-G2, HT-29, B16-F10, and normal human gingival fibroblast (HGF). The inhibition of PKM2 mediated by MB-6a was assessed using an LDH-coupled assay and by measuring ATP production. Docking studies and molecular dynamics calculations were performed using Autodock 4 and GROMACS, respectively, on the tetrameric PKM2 crystallographic structure. Results: The Mannich base 6a demonstrated selective cytotoxicity against all cancer cell lines tested without affecting cell migration, with the highest selectivity index (SI) of 4.63 in SCC-9, followed by B16-F10 (SI = 3.9), Hep-G2 (SI = 3.4), and HT-29 (SI = 2.03). The compound effectively inhibited PKM2 glycolytic activity, leading to a reduction of ATP production both in the enzymatic reaction and in cells treated with this naphthoquinone derivative. MB-6a showed favorable binding to PKM2 in the ATP-bound monomers through docking studies (PDB ID: 4FXF; binding affinity scores ranging from -6.94 to -9.79 kcal/mol) and MD simulations, revealing binding affinities stabilized by key interactions including hydrogen bonds, halogen bonds, and hydrophobic contacts. Conclusions: The findings suggest that MB-6a exerts its antiproliferative activity by disrupting cell glucose metabolism, consequently reducing ATP production and triggering energetic collapse in cancer cells. This study highlights the potential of MB-6a as a lead compound targeting PKM2 and warrants further investigation into its mechanism of action and potential clinical applications.

Expanding PFAS Identification with Transformation Product Libraries: Nontargeted Analysis Reveals Biotransformation Products in Mice.

Per- and polyfluoroalkyl substances (PFAS) are widely used persistent synthetic chemicals that have been linked to adverse health effects. While the behavior of PFAS has been evaluated in the environment, our understanding of reaction products in mammalian systems is limited. This study identified biological PFAS transformation products and generated mass spectral libraries to facilitate an automated search and identification. The biological transformation products of 27 PFAS, spanning 5 chemical subclasses (alcohols, sulfonamides, carboxylic acids, ethers, and esters), were evaluated following enzymatic reaction with mouse liver S9 fractions. Four major pathways were identified by liquid chromatography-high-resolution mass spectrometry: glucuronidation, sulfation, dealkylation, and oxidation. Class-based fragmentation rules and associated PFAS transformation product libraries were generated and integrated into an automated nontargeted PFAS data analysis software (FluoroMatch). Fragmentation was additionally predicted for the potential transformation products of more than 2,500 PFAS in the EPA CompTox Chemicals Dashboard PFASSTRUCTv4. Generated mass spectral libraries were validated by applying FluoroMatch to a data set of urine from aqueous film-forming foam (AFFF)-dosed mice. Toxicity predictions showed identified PFAS transformation products to be potential developmental and mutagenic toxicants. This research enables more comprehensive PFAS characterization in biological systems, which will improve the assessment of exposures and evaluation of the associated health impacts.

Structural insights into the enzymatic breakdown of azomycin-derived antibiotics by 2-nitroimdazole hydrolase (NnhA)

The antibiotic 2-nitroimidazole (2NI) or azomycin, used for treating drug-resistant tuberculosis and imaging tumor hypoxia, requires activation by bacterial nitroreductases for its antibiotic and cytotoxic effect. Mycobacterium sp. JS330 produces 2-nitroimidazole nitrohydrolase (NnhA) that circumvents 2NI activation, conferring 2NI resistance by hydrolysing it to nitrite and imidazol-2-one (IM2O) instead. This study elucidates NnhA’s structure, catalytic mechanism, and evolutionary background within the guanidino-group modifying enzyme (GME) superfamily, aided by a more soluble protein variant engineered through directed evolution. Despite low sequence similarity and limited occurrence in a few soil-dwelling mycobacteria and Actinomycetota, NnhA maintains the α/β propeller fold characteristic of GME superfamily enzymes and forms an unusual hexameric ring structure formed by a trimer of domain-swapped dimers. The similarity of its active site to arginine deiminases (ADIs) and human dimethylarginine dimethylaminohydrolases (DDAHs), along with molecular dynamics simulations, suggests NnhA’s catalytic mechanism resembles the hydrolysis reactions of these related enzymes.

Efficient Enzymatic Glycan Engineering of Extracellular Vesicles Using Nanomaterial-Interfaced Microfluidics.

Extracellular vesicles (EVs) present a promising modality for numerous biological and medical applications, including therapeutics. Developing facile methods to engineer EVs is essential to meeting the rapidly expanding demand for various functionalized EVs in these applications. Herein, we developed a technology that integrates enzymatic glycoengineering and microfluidics for effective EV functionalization. This method builds on a 3D nanostructured microfluidic device to streamline a multiple-step EV engineering process, which involves a step of enzymatic reaction to install azido-sialic acid residues to glycans on EVs using a sialyltransferase and an azide-tagged sialyl donor followed by the attachment of various functionalities, such as biotin and fluorescent labels, to the resulting azido-glycans on EVs through a biocompatible click reaction. Compared to traditional EV engineering methods, we show that our technology improves the efficiency of EV glycoengineering while simplifying and expediting the workflow. Furthermore, we demonstrated the applicability of this technology to EVs derived from the cell lines of different cancer types, including A549, PC3, and COLO-1 cells. Overall, this EV engineering technology could provide a potentially useful tool for broad applications.

Conversion of pyridoxal to pyridoxamine with NH3 and H2 on nickel generates a protometabolic nitrogen shuttle under serpentinizing conditions.

Serpentinizing hydrothermal vents are likely sites for the origin of metabolism because they produce H2 as a source of electrons for CO2 reduction while depositing zero-valent iron, cobalt, and nickel as catalysts for organic reactions. Recent work has shown that solid-state nickel can catalyze the H2-dependent reduction of CO2 to various organic acids and their reductive amination with H2 and NH3 to biological amino acids under the conditions of H2-producing hydrothermal vents and that amino acid synthesis from NH3, H2, and 2-oxoacids is facile in the presence of Ni0. Such reactions suggest a metallic origin of metabolism during early biochemical evolution because single metals replace the function of over 130 enzymatic reactions at the core of metabolism in microbes that use the acetyl-CoA pathway of CO2 fixation. Yet solid-state catalysts tether primordial amino synthesis to a mineral surface. Many studies have shown that pyridoxal catalyzes transamination reactions without enzymes. Here we show that pyridoxamine, the NH2-transferring intermediate in pyridoxal-dependent transamination reactions, is generated from pyridoxal by reaction with NH3 (as little as 5 mm) and H2 (5 bar) on Ni0 as catalyst at pH 11 and 80 °C within hours. These conditions correspond to those in hydrothermal vents undergoing active serpentinization. The results indicate that at the origin of metabolism, pyridoxamine provided a soluble, organic amino donor for aqueous amino acid synthesis, mediating an evolutionary transition from NH3-dependent amino acid synthesis on inorganic surfaces to pyridoxamine-dependent organic reactions in the aqueous phase.

Extraction of E171 or E172 food additives from sugar-based or lipid-based complex matrices for primary particles identification by electron microscopy

Measurement of primary particle size distributions is fundamental to determining if a material should be classified as a nanomaterial according to the European Commission recommended definition. This is of particular relevance to alimentary products, where current regulation requires that any additives containing nanoforms be explicitly declared as such in the ingredients’ list. Currently, Electron Microscopy is the only instrumental technique able to reliably classify materials as nanomaterials. To verify the nanostatus of a material using this technique, it is essential to firstly extract the additives from the matrix in which they are dispersed, and then measure a wide size distribution while avoiding artefacts from the food matrix. In this operating procedure, extraction protocols, developed and applied for sugar-based or lipid-based food, are described. In particular, case studies for titanium dioxide E171 and iron oxide/iron hydroxide based materials such as E172 are considered. The protocols are based on gentle extraction of the additive materials and could, in principle, be used for other nanomaterials provided they are not soluble in water or ethanol and are resistant to enzymatic reactions. The size distribution of the extracted material can then be determined from images acquired using scanning and/or transmission electron microscopy.

Gain of pancreatic beta cell-specific SCD1 improves glucose homeostasis by maintaining functional beta cell mass under metabolic stress.

The key pancreatic beta cell transcription factor v-maf musculoaponeurotic fibrosarcoma oncogene homologue A (MafA) is critical for the maintenance of mature beta cell function and phenotype. The expression levels and/or activities of MafA are reduced when beta cells are chronically exposed to diabetogenic stress, such as hyperglycaemia (i.e. glucotoxicity). Interventional targets and adjuvant therapies to abate MafA loss in beta cells may provide evidence to support the effective treatment of diabetes. In this study, we aimed to investigate the function of stearoyl-CoA desaturase 1 (SCD1) in the stabilisation of MafA expression and activity in order to maintain functional beta cell mass, with a view to suppressing the development of type 2 diabetes. SCD1 expression levels were analysed in islets obtained from humans with type 2 diabetes, hyperglycaemic db/db mice, and a high-fat diet (HFD)-induced mouse model of diabetes. Pancreatic beta cell-specific Scd1 knockin (βSCD1KI) mice were generated to study the role of SCD1 in beta cell function and identity. The protein-to-protein interactions between SCD1 and MafA were detected in MIN6 and HEK293A cells. We used experiments including chromatin immunoprecipitation, cell-based ubiquitination assay and fatty acid composition analysis to investigate the specific molecular mechanism underlying the effect of SCD1 on the restoration of MafA and beta cell function under glucotoxic conditions. SCD1 expression was reduced in beta cells of humans with type 2 diabetes and in HFD-fed and db/db mice compared with healthy controls, which was attributed to glucotoxicity-induced Scd1 promoter histone deacetylation. Gain-of-function of SCD1 in beta cells improved insulin deficiency, glucose intolerance and beta cell dedifferentiation/transdifferentiation in the HFD-induced mouse model of diabetes. Mechanistically, SCD1 directly bound to the E3 ubiquitin ligase HMG-CoA reductase degradation 1 (HRD1) and stabilised nuclear MafA through interrupting MafA-HRD1 interactions in mouse islets and MIN6 cells, which inhibited the ubiquitination-mediated degradation of MafA. Moreover, the products of SCD enzyme reactions (mainly oleic acid) also alleviated glucotoxicity-mediated oxidative stress in MIN6 cells. Our findings indicate that SCD1 stabilises beta cell MafA both in desaturase-dependent and -independent manners, thus improving glucose homeostasis under metabolic stress. This provides a potential novel target for precision medicine for the treatment of diabetes.

Ultrasensitive SERS Profiling of Intracellular Hydrogen Peroxide Release Based on Enzymatic Amplification and Silent-Region Raman Reporter.

Surface-enhanced Raman scattering spectroscopy (SERS) has been widely applied to screen biomarkers and biological species due to its high sensitivity; however, severe background noise signals significantly limit its practical application. In this study, we develop an ultrasensitive SERS sensor to determine cellular oxidative stress based on the H2O2-induced enzymatic amplification and a silent-range Raman fingerprint. In the presence of horseradish peroxidase, H2O2 could trigger the coupling reaction of 4-hydroxythiophenol (4-MTP) with phenol-d5 to form a new compound, which can bind to the SERS substrate via the Au-S bond and generate the stable SERS signal with nearly zero background signals owing to the Raman-silent fingerprint of phenol-d5 at 2125 cm-1. The high-performance enzymatic reaction and the silent-range Raman fingerprint enable the ultrasensitive determination of H2O2 with a broad linear range of 5 × 10-9 to 1 × 10-3 M and a limit of detection as low as 1.6 nM. Taking advantage of its excellent sensitivity and anti-interference capability, the as-developed SERS sensor is employed to profile the triggered dynamic change of the intracellular H2O2 release, and further to testify the cancerous cells exhibiting a significantly higher level of intracellular oxidative stress than the healthy cells, and the different cell populations possessing the susceptible difference to additional oxidative stimulation. This study paves the way for improving SERS sensing capability through the silent-range fingerprint and signal amplification via an enzymatic reaction and reveals SERS as an effective tool for monitoring the endogenous oxidative behavior of living cells.

Water Migration through Enzyme Tunnels Is Sensitive to the Choice of Explicit Water Model.

The utilization of tunnels and water transport within enzymes is crucial for their catalytic function as water molecules can stabilize bound substrates and help with unbinding processes of products and inhibitors. Since the choice of water models for molecular dynamics simulations was shown to determine the accuracy of various calculated properties of the bulk solvent and solvated proteins, we have investigated if and to what extent water transport through the enzyme tunnels depends on the selection of the water model. Here, we focused on simulating enzymes with various well-defined tunnel geometries. In a systematic investigation using haloalkane dehalogenase as a model system, we focused on the well-established TIP3P, OPC, and TIP4P-Ew water models to explore their impact on the use of tunnels for water molecule transport. The TIP3P water model showed significantly faster migration, resulting in the transport of approximately 2.5 times more water molecules compared to that of the OPC and 1.7 times greater than that of the TIP4P-Ew. Finally, the transport was 1.4-fold more pronounced in TIP4P-Ew than in OPC. The increase in migration of TIP3P water molecules was mainly due to faster transit times through dehalogenase tunnels. We observed similar behavior in two different enzymes with buried active sites and different tunnel network topologies, i.e., alditol oxidase and cytochrome P450, indicating that our findings are likely not restricted to a particular enzyme family. Overall, this study showcases the critical importance of water models in comprehending the use of enzyme tunnels for small molecule transport. Given the significant role of water availability in various stages of the catalytic cycle and the solvation of substrates, products, and drugs, choosing an appropriate water model may be crucial for accurate simulations of complex enzymatic reactions, rational enzyme design, and predicting drug residence times.

One-Pot and Gram-Scale Synthesis of Fe-Based Nanozymes with Tunable O2 Activation Pathway and Specificity Between Associated Enzymatic Reactions.

Nanozymes have recently gained attention for their low cost and high stability. However, unlike natural enzymes, they often exhibit multiple enzyme-like activities, complicating their use in selective bioassays. Since H2O2 and O2 are common substrates in these reactions, controlling their activation-and thus reaction specificity-is crucial. Recent advances in tuning the chemical state of cerium have enabled control over H2O2 activation pathways for tunable peroxidase/haloperoxidase-like activities. In contrast, the control of O2 activation on an element in oxidase/laccase nanozymes and the impact of its chemical state on these activities remains unexplored. Herein, a facile one-pot method is presented for the gram-scale synthesis of Fe-based nanozymes with tunable compositions of Fe3O4 and Fe3C by adjusting preparation temperatures. The Fe3O4-containing samples exhibit superior laccase-like activity, while the Fe3C-containing counterparts demonstrate better oxidase-like activity. This divergent O2 activation behavior is linked to their surface Fe species: the abundant reactive Fe2+ in Fe3O4 promotes laccase-like activity via Fe3+-superoxo formation, whereas metallic Fe in Fe3C facilitates OH radical generation for oxidase-like activity. Controlled O2 activation pathways in these Fe-based nanozymes demonstrate improved sensitivity in the corresponding biomolecule detection, which should inform the design of nanozymes with enhanced activity and specificity.

The Effect of Reaction Temperature on the Quality of Oyster Enzymatic Hydrolysate Maillard Products

Oyster meat was used as raw material, which was enzymatically digested under specific conditions for 3h. Then the effect of reaction temperature on the sensory, color difference, reaction rate, and volatile flavor components of the oyster enzymatic hydrolyzates and glucose meridian reaction product was investigated. The study showed that the 3h enzymatic hydrolysis of oyster produced the best flavour and sensory score of the maillard reaction product at 110 (110HM), which had the absorbance value of 0.788 for A294, and 0.606 for A420. 54 compounds were identified from 110HM, which mainly contained 6 alcohols (26.40%), 3 ketones (1.80%), 8 aldehydes (8.38%), 6 esters (1.71%), 16 hydrocarbons (32.84%), and 15 heterocyclic and other species (22.82%).

Enzymatic Reaction Network-Driven Polymerization-Induced Transient Coacervation.

A living cell has a highly complex microenvironment whereas numerous enzyme-driven processes are active at once. These procedures are incredibly accurate and efficient, although comparable control has not yet been established in vitro. Here, we design an enzymatic reaction network (ERN) that combines antagonistic and orthogonal enzymatic networks to produce adjustable dynamics of ATP-fueled transient coacervation. Using horseradish peroxidase (HRP)-mediated Biocatalytic Atom Transfer Radical Polymerization (BioATRP), we synthesized poly(dimethylaminoethyl methacrylate), which subsequently formed coacervates with ATP. We rationally explored enzymatic control over coacervation and dissolution, using orthogonal and antagonistic enzyme pairs viz., alkaline phosphatase, Creatine phosphokinase, hexokinase, esterase, and urease. ATP-fuelled coacervates also demonstrate the enzymatic catalysis to prove its potential to be exploited as a cellular microreactor. Additionally, we developed ERN-polymerization-induced transient coacervation (ERN-PIC), with complete control over the system, polymerization, coacervation, and dissolution. Notably, the coacervation process itself determines functional properties, as seen in selective cargo uptake. The strategy offers cutting-edge biomimetic applications, and insights into cellular compartmentalization by bridging the gap between synthetic and biological systems. The development of temporally programmed coacervation is promising for the spatial arrangement of multienzyme cascades, and offers novel ideas on the architecture of artificial cells.

Enzymatic Reaction Network‐driven Polymerization‐induced Transient Coacervation

A living cell has a highly complex microenvironment whereas numerous enzyme‐driven processes are active at once. These procedures are incredibly accurate and efficient, although comparable control has not yet been established in vitro. Here, we design an enzymatic reaction network (ERN) that combines antagonistic and orthogonal enzymatic networks to produce adjustable dynamics of ATP‐fueled transient coacervation. Using horseradish peroxidase (HRP)‐mediated Biocatalytic Atom Transfer Radical Polymerization (BioATRP), we synthesized poly(dimethylaminoethyl methacrylate), which subsequently formed coacervates with ATP. We rationally explored enzymatic control over coacervation and dissolution, using orthogonal and antagonistic enzyme pairs viz., alkaline phosphatase, Creatine phosphokinase, hexokinase, esterase, and urease. ATP‐fuelled coacervates also demonstrate the enzymatic catalysis to prove its potential to be exploited as a cellular microreactor. Additionally, we developed ERN‐polymerization‐induced transient coacervation (ERN‐PIC), with complete control over the system, polymerization, coacervation, and dissolution. Notably, the coacervation process itself determines functional properties, as seen in selective cargo uptake. The strategy offers cutting‐edge biomimetic applications, and insights into cellular compartmentalization by bridging the gap between synthetic and biological systems. The development of temporally programmed coacervation is promising for the spatial arrangement of multienzyme cascades, and offers novel ideas on the architecture of artificial cells.

Chemoenzymatic Synthesis Planning Guided by Reaction Type Score.

Thanks to the growing interest in computer-aided synthesis planning (CASP), a wide variety of retrosynthesis and retrobiosynthesis tools have been developed in the past decades. However, synthesis planning tools for multistep chemoenzymatic reactions are still rare despite the widespread use of enzymatic reactions in chemical synthesis. Herein, we report a reaction type score (RTscore)-guided chemoenzymatic synthesis planning (RTS-CESP) strategy. Briefly, the RTscore is trained using a text-based convolutional neural network (TextCNN) to distinguish synthesis reactions from decomposition reactions and evaluate synthesis efficiency. Once multiple chemical synthesis routes are generated by a retrosynthesis tool for a target molecule, RTscore is used to rank them and find the step(s) that can be replaced by enzymatic reactions to improve synthesis efficiency. As proof of concept, RTS-CESP was applied to 10 molecules with known chemoenzymatic synthesis routes in the literature and was able to predict all of them with six being the top-ranked routes. Moreover, RTS-CESP was employed for 1000 molecules in the boutique database and was able to predict the chemoenzymatic synthesis routes for 554 molecules, outperforming ASKCOS, a state-of-the-art chemoenzymatic synthesis planning tool. Finally, RTS-CESP was used to design a new chemoenzymatic synthesis route for the FDA-approved drug Alclofenac, which was shorter than the literature-reported route and has been experimentally validated.

Parahydrogen Polarization in Reverse Micelles and Application to Sensing of Protein-Ligand Binding.

A medium containing reverse micelles supports non-hydrogenative parahydrogen induced polarization (nhPHIP) in the organic phase while solubilizing a protein in the aqueous phase. Strongly enhanced NMR signals from iridium hydride complexes report on a ligand, 4-amino-2-benzylaminopyrimidine, which crosses the phase boundary and interacts with the thiaminase protein TenA. The calculation of binding equilibria reveals a KD of 39.7 ± 8.9 μM for protein binding. The nanoscale separation of the two phases allows the separate optimization of the parahydrogen polarization and solubilization of a biological macromolecule. The reverse micelles may be used to study other biological questions using signal enhancement by parahydrogen polarization, such as enzyme reactions, protein-protein interactions, and protein binding epitopes.