Protein Engineering Research Articles

Graph masked self-distillation learning for prediction of mutation impact on protein–protein interactions

Assessing mutation impact on the binding affinity change (ΔΔG) of protein–protein interactions (PPIs) plays a crucial role in unraveling structural-functional intricacies of proteins and developing innovative protein designs. In this study, we present a deep learning framework, PIANO, for improved prediction of ΔΔG in PPIs. The PIANO framework leverages a graph masked self-distillation scheme for protein structural geometric representation pre-training, which effectively captures the structural context representations surrounding mutation sites, and makes predictions using a multi-branch network consisting of multiple encoders for amino acids, atoms, and protein sequences. Extensive experiments demonstrated its superior prediction performance and the capability of pre-trained encoder in capturing meaningful representations. Compared to previous methods, PIANO can be widely applied on both holo complex structures and apo monomer structures. Moreover, we illustrated the practical applicability of PIANO in highlighting pathogenic mutations and crucial proteins, and distinguishing de novo mutations in disease cases and controls in PPI systems. Overall, PIANO offers a powerful deep learning tool, which may provide valuable insights into the study of drug design, therapeutic intervention, and protein engineering.

Nucleotide-mediated modulation of chemoselective protein functionalization in a liquid-like condensed phase

Liquid-like protein condensates are ubiquitous in cellular system and are increasingly recognized for their roles in physiological processes. Condensed phase harbors distinctive chemical microenvironment, markedly different than dilute aqueous phase. Herein, we demonstrate chemoselective modification pattern of nucleophilic canonical amino acid sidechains (namely – cysteine, tyrosine and lysine) of the protein towards 4-chloro-7-nitrobenzofurazan in the dilute and condensed phase. We also delineate how the effect of nucleotides and their in situ enzymatic dissociation temporally modulate the protein condensate’s pH and the protein’s corresponding chemoselective modification. We have shown that the pH of the condensate decreases in the presence of nucleoside triphosphate, whereas it increases in the presence of nucleoside monophosphates or phosphate ion. For instance, we find lysine-specific modification gets inhibited in the presence of adenosine triphosphate (ATP), but significantly enhanced in the presence of monophosphates. This feature enables us to gain temporal control over dynamic change in protein functionalization via enzymatic ATP hydrolysis. Overall, this work substantiates the alteration in pH-responsiveness of Brønsted basicity of a protein’s ε-amine in the condensed phase. Furthermore, this environment sensitivity in chemoselective protein functionalization in condensed phase will be important in adaptable protein engineering to the chemical biology of protein phase separation.

Engineered protein subunit COVID19 vaccine is as immunogenic as nanoparticles in mouse and hamster models

Initial studies on the immunogenicity of SARS-CoV-2 (CoV-2) S glycoprotein (“spike”) as a protein subunit vaccine suggested sub-optimal efficacy in mammals. Although protein engineering efforts have produced CoV-2 spike protein sequences with greatly improved immunogenicity, additional strategies for improving the immunogenicity of CoV-2 protein subunit vaccines are scaffolding and the use of adjuvants. Comparisons of the effectiveness of engineered protein-only and engineered protein-nanoparticles vaccines have been rare. To explore this knowledge gap, we inoculated mice with two doses of either sequence-optimized trimeric spike protein or one of several sequence-optimized spike nanoparticles. We measured their immune response up to two months after the first dose. We also measured the immune response and protection against live virus in hamsters inoculated with either sequence-optimized trimeric spike protein or a liposome-based sequence-optimized spike nanoparticle. We found that in the presence of adjuvant, the antibody and neutralization titers elicited by spike-nanoparticles were not significantly greater than those elicited by spike-only in mice, even at doses as low as 0.1 µg/animal. Hamsters vaccinated with spike-only or spike-nanoparticles were equally protected from live virus one month after their first inoculation. These results suggest that sequence-optimized protein subunit vaccines in the form of individual prefusion-stabilized trimers can be as effective in improving immunogenicity as scaffolded forms.

Improving the prediction of protein stability changes upon mutations by geometric learning and a pre-training strategy.

Accurate prediction of protein mutation effects is of great importance in protein engineering and design. Here we propose GeoStab-suite, a suite of three geometric learning-based models-GeoFitness, GeoDDG and GeoDTm-for the prediction of fitness score, ΔΔG and ΔTm of a protein upon mutations, respectively. GeoFitness engages a specialized loss function to allow supervised training of a unified model using the large amount of multi-labeled fitness data in the deep mutational scanning database. To further improve the downstream tasks of ΔΔG and ΔTm prediction, the encoder of GeoFitness is reutilized as a pre-trained module in GeoDDG and GeoDTm to overcome the challenge of lacking sufficient labeled data. This pre-training strategy, in combination with data expansion, markedly improves model performance and generalizability. In the benchmark test, GeoDDG and GeoDTm outperform the other state-of-the-art methods by at least 30% and 70%, respectively, in terms of the Spearman correlation coefficient.

Rational and Semirational Approaches for Engineering Salicylate Production in Escherichia coli.

Salicylate plays a pivotal role as a pharmaceutical intermediate in drugs, such as aspirin and lamivudine. The low catalytic efficiency of key enzymes and the inherent toxicity of salicylates to cells pose significant challenges to large-scale microbial production. In this study, we introduced the salicylate synthase Irp9 into an l-phenylalanine-producing Escherichia coli, constructing the shortest salicylate biosynthetic pathway. Subsequent protein engineering increased the catalytic efficiency of Irp9 by 33.5%. Furthermore, by integrating adaptive evolution with transcriptome analysis, we elucidated the crucial mechanism of efflux proteins in salicylate tolerance. The elucidation of this mechanism guided us in the targeted modification of these transport proteins, achieving a reported maximum level of 3.72 g/L of salicylate in a shake flask. This study highlights the importance of efflux proteins for enhancing the productivity of microbial cell factories in salicylate production, which also holds potential for application in the green synthesis of other phenolic acids.

Functional characterization and unraveling the structural determinants of novel steroid hydroxylase CYP154C7 from Streptomyces sp. PAMC26508

This study characterized cytochrome P450 enzyme CYP154C7 from Streptomyces sp. PAMC26508, emphasizing its capability to hydroxylate steroids, especially at the 16α-position. The enzymatic assay of CYP154C7 demonstrated effective conversion across a pH range of 7.2–7.6, with optimal activity at 30 °C in the Pdx/PdR plus NADH system. Kinetic analysis on most converted steroids (androstenedione and adrenosterone) was performed which shows a greater affinity for androstenedione (Km, 11.06 ± 1.903 μM; Vmax, 0.0062 ± 0.0002 sec−1) compared to adrenosterone (Km, 34.50 ± 6.2 μM; Vmax, 0.0119 ± 0.0007 sec−1). A whole-cell system in Escherichia coli, overexpressing recombinant CYP154C7, achieved substantial conversion for steroids, indicating that CYP154C7 can also be used as a potential whole-cell biocatalyst. To gain structural insights, homology models of CYP154C7 and its homologs were constructed using CYP154C5 (PDB ID: 6TO2), refined, validated, and used for docking studies. Comparative docking analysis suggests that lysine (K236) in the active site and tyrosine (Y197) in the substrate access channel of CYP154C7 are crucial for substrate selectivity and catalytic efficiency. This study suggests that CYP154C7 could be a promising candidate for developing modified steroids, providing valuable insights for protein engineering to design commercially useful CYP steroid hydroxylases with diverse substrate specificities.

Structural analysis of the stable form of fibroblast growth factor 2 – FGF2-STAB

Fibroblast growth factor 2 (FGF2) is a signaling protein that plays a significant role in tissue development and repair. FGF2 binds to fibroblast growth factor receptors (FGFRs) alongside its co-factor heparin, which protects FGF2 from degradation. The binding between FGF2 and FGFRs induces intracellular signaling pathways such as RAS-MAPK, PI3K-AKT, and STAT. FGF2 has strong potential for application in cell culturing, wound healing, and cosmetics but the potential is severely limited by its low protein stability. The thermostable variant FGF2-STAB was constructed by computer-assisted protein engineering to overcome the natural limitation of FGF2. Previously reported characterization of FGF2-STAB revealed an enhanced ability to induce MAP/ERK signaling while having a lower dependence on heparin when compared with FGF2-wt. Here we report the crystal structure of FGF2-STAB solved at 1.3 Å resolution. Protein stabilization is achieved by newly formed hydrophobic interactions, polar contacts, and one additional hydrogen bond. The overall structure of FGF2-STAB is similar to FGF2-wt and does not reveal information on the experimentally observed lower dependence on heparin. A noticeable difference in flexibility in the receptor binding region can explain the differences in signaling between FGF2-STAB and its wild-type counterpart. Our structural analysis provided molecular insights into the stabilization and unique biological properties of FGF2-STAB.

Genetically encoded green fluorescent sensor for probing sulfate transport activity of solute carrier family 26 member a2 (Slc26a2) protein

Genetically encoded fluorescent biosensors became indispensable tools for biological research, enabling real-time observation of physiological processes in live cells. Recent protein engineering efforts have resulted in the generation of a large variety of fluorescent biosensors for a wide range of biologically relevant processes, from small ions to enzymatic activity and signaling pathways. However, biosensors for imaging sulfate ions, the fourth most abundant physiological anion, in mammalian cells are still lacking. Here, we report the development and characterization of a green fluorescent biosensor for sulfate named Thyone. Thyone, derived through structure-guided design from bright green fluorescent protein mNeonGreen, exhibited a large negative fluorescence response upon subsecond association with sulfate anion with an affinity of 11 mM in mammalian cells. By integrating mutagenesis analyses with molecular dynamics simulations, we elucidated the molecular mechanism of sulfate binding and revealed key amino acid residues responsible for sulfate sensitivity. High anion selectivity and sensitivity of Thyone allowed for imaging of sulfate anion transients mediated by sulfate transporter heterologously expressed in cultured mammalian cells. We believe that Thyone will find a broad application for assaying the sulfate transport in mammalian cells via anion transporters and exchangers.

Distinct Regions within SAP25 Recruit O-Linked Glycosylation, DNA Demethylation, and Ubiquitin Ligase and Hydrolase Activities to the Sin3/HDAC Complex.

Sin3 is an evolutionarily conserved repressor protein complex mainly associated with histone deacetylase (HDAC) activity. Many proteins are part of Sin3/HDAC complexes, and the function of most of these members remains poorly understood. SAP25, a previously identified Sin3A associated protein of 25 kDa, has been proposed to participate in regulating gene expression programs involved in the immune response but the exact mechanism of this regulation is unclear. SAP25 is not expressed in HEK293 cells, which hence serve as a natural knockout system to decipher the molecular functions uniquely carried out by this Sin3/HDAC subunit. Using molecular, proteomic, protein engineering, and interaction network approaches, we show that SAP25 interacts with distinct enzymatic and regulatory protein complexes in addition to Sin3/HDAC. Additional proteins uniquely recovered from the Halo-SAP25 pull-downs included the SCF E3 ubiquitin ligase complex SKP1/FBXO3/CUL1 and the ubiquitin carboxyl-terminal hydrolase 11 (USP11). Furthermore, mutational analysis demonstrates that distinct regions of SAP25 participate in its interaction with USP11, OGT/TETs, and SCF(FBXO3). These results suggest that SAP25 may function as an adaptor protein to coordinate the assembly of different enzymatic complexes to control Sin3/HDAC-mediated gene expression. The data were deposited with the MASSIVE repository with the identifiers MSV000093576 and MSV000093553.

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

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

Computer-Aided Flexible Loops Engineering of Glutamate Dehydrogenase for Asymmetric Synthesis of Chiral Pesticides l-phosphinothricin.

The access to the enantiopure noncanonical amino acid l-phosphinothricin (l-PPT) by applying biocatalysts is highly appealing in organic chemistry. In this study, a NADH-dependent glutamate dehydrogenase from Lachnospiraceae bacterium (LbGluDH) was chosen for the asymmetric synthesis of l-PPT. Three flexible loops undergoing big conformational shifts during the catalysis were identified and rationally engineered following the initial mutagenesis. The enzyme's specific activity toward the key precursor of l-PPT, 2-oxo-4-[(hydroxy) (methyl) phosphinyl] butyric acid (PPO), was improved from negligible to 9 U/mg, and the Km value was reduced to 17 mM. The computational analysis showed that the modified loops broadened the enzyme's narrow tunnels, allowing the substrate to access the binding pocket and get closer to the crucial residue D165, thereby enhancing the catalytic process. Utilizing the variant as the catalyst, the preparation of l-PPT achieved a 100% conversion rate within 60 min, coupled with a stereoselectivity exceeding 99.9%, demonstrating its practical capacity for industrial application. Similar enhancement in catalytic activity was obtained applying the same strategy to a typical NADH-dependent GluDH from Pyrobaculum islandicum (PisGluDH), indicating the effectiveness of our strategy for the protein engineering of GluDHs targeted to the biosynthesis of unnatural compounds.

Gas Tunnel Engineering of Prolyl Hydroxylase Reprograms Hypoxia Signaling in Cells

AbstractCells have evolved intricate mechanisms for recognizing and responding to changes in oxygen (O2) concentrations. Here, we have reprogrammed cellular hypoxia (low O2) signaling via gas tunnel engineering of prolyl hydroxylase 2 (PHD2), a non‐heme iron dependent O2 sensor. Using computational modeling and protein engineering techniques, we identify a gas tunnel and critical residues therein that limit the flow of O2 to PHD2’s catalytic core. We show that systematic modification of these residues can open the constriction topology of PHD2’s gas tunnel. Using kinetic stopped‐flow measurements with NO as a surrogate diatomic gas, we demonstrate up to 3.5‐fold enhancement in its association rate to the iron center of tunnel‐engineered mutants. Our most effectively designed mutant displays 9‐fold enhanced catalytic efficiency (kcat/KM=830±40 M−1 s−1) in hydroxylating a peptide mimic of hypoxia inducible transcription factor HIF‐1α, as compared to WT PHD2 (kcat/KM=90±9 M−1 s−1). Furthermore, transfection of plasmids that express designed PHD2 mutants in HEK‐293T mammalian cells reveal significant reduction of HIF‐1α and downstream hypoxia response transcripts under hypoxic conditions of 1 % O2. Overall, these studies highlight activation of PHD2 as a new pathway to reprogram hypoxia responses and HIF signaling in cells.

Gas Tunnel Engineering of Prolyl Hydroxylase Reprograms Hypoxia Signaling in Cells.

Cells have evolved intricate mechanisms for recognizing and responding to changes in oxygen (O2) concentrations. Here, we have reprogrammed cellular hypoxia (low O2) signaling via gas tunnel engineering of prolyl hydroxylase 2 (PHD2), a non-heme iron dependent O2 sensor. Using computational modeling and protein engineering techniques, we identify a gas tunnel and critical residues therein that limit the flow of O2 to PHD2's catalytic core. We show that systematic modification of these residues can open the constriction topology of PHD2's gas tunnel. Using kinetic stopped-flow measurements with NO as a surrogate diatomic gas, we demonstrate up to 3.5-fold enhancement in its association rate to the iron center of tunnel-engineered mutants. Our most effectively designed mutant displays 9-fold enhanced catalytic efficiency (kcat/KM=830±40 M-1 s-1) in hydroxylating a peptide mimic of hypoxia inducible transcription factor HIF-1α, as compared to WT PHD2 (kcat/KM=90±9 M-1 s-1). Furthermore, transfection of plasmids that express designed PHD2 mutants in HEK-293T mammalian cells reveal significant reduction of HIF-1α and downstream hypoxia response transcripts under hypoxic conditions of 1 % O2. Overall, these studies highlight activation of PHD2 as a new pathway to reprogram hypoxia responses and HIF signaling in cells.

SUPREM: an engineered non-site-specific m6A RNA methyltransferase with highly improved efficiency.

N 6-Methyladenine(m6A) RNA methylation plays a key role in RNA processing and translational regulation, influencing both normal physiological and pathological processes. Yet, current techniques for studying RNA methylation struggle to isolate the effects of individual m6A modifications. Engineering of RNA methyltransferases (RNA MTases) could enable development of improved synthetic biology tools to manipulate RNA methylation, but it is challenging due to limited understanding of structure-function relationships in RNA MTases. Herein, using ancestral sequence reconstruction, we explore the sequence space of the bacterial DNA methyltransferase EcoGII (M.EcoGII), a promising target for protein engineering due to its lack of sequence specificity and its residual activity on RNA. We thereby created an efficient non-specific RNA MTase termed SUPer RNA EcoGII Methyltransferase (SUPREM), which exhibits 8-fold higher expression levels, 7°C higher thermostability and 12-fold greater m6A RNA methylation activity compared with M.EcoGII. Immunofluorescent staining and quantitativeliquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis confirmed SUPREM's higher RNA methylation activity compared with M.EcoGII in mammalian cells. Additionally, Nanopore direct RNA sequencing highlighted that SUPREM is capable of methylating a larger number of RNA methylation sites than M.EcoGII. Through phylogenetic and mutational analysis, we identified a critical residue for the enhanced RNA methylation activity of SUPREM. Collectively, our findings indicate that SUPREM holds promise as a versatile tool for in vivo RNA methylation and labeling.

Reshaping the substrate-binding pocket of acyl-ACP reductase to enhance the production of sustainable aviation fuel in Escherichia coli.

To reduce carbon emissions and address environmental concerns, the aviation industry is exploring the use of sustainable aviation fuel (SAF) as an alternative to traditional fossil fuels. In this context, bio-alkane is considered a potentially high-value solution. The present study focuses on the enzymes acyl-acyl carrier protein [ACP] reductase (AAR) and aldehyde-deformylating oxygenase (ADO), which are crucial enzymes for alka(e)ne biosynthesis. By using protein engineering techniques, including semi-rational design and site-directed mutagenesis, we aimed to enhance the substrate specificity of AAR and improve alkane production efficiency. The co-expression of a modified AAR (Y26G/Q40M mutant) with wild-type ADO in Escherichia coli significantly increased alka(e)ne production from 28.92 mg/L to 167.30 mg/L, thus notably demonstrating a 36-fold increase in alkane yield. This research highlights the potential of protein engineering in optimizing SAF production, thereby contributing to the development of more sustainable and efficient SAF production methods and promoting greener air travel.

Large-scale annotation of biochemically relevant pockets and tunnels in cognate enzyme–ligand complexes

Tunnels in enzymes with buried active sites are key structural features allowing the entry of substrates and the release of products, thus contributing to the catalytic efficiency. Targeting the bottlenecks of protein tunnels is also a powerful protein engineering strategy. However, the identification of functional tunnels in multiple protein structures is a non-trivial task that can only be addressed computationally. We present a pipeline integrating automated structural analysis with an in-house machine-learning predictor for the annotation of protein pockets, followed by the calculation of the energetics of ligand transport via biochemically relevant tunnels. A thorough validation using eight distinct molecular systems revealed that CaverDock analysis of ligand un/binding is on par with time-consuming molecular dynamics simulations, but much faster. The optimized and validated pipeline was applied to annotate more than 17,000 cognate enzyme–ligand complexes. Analysis of ligand un/binding energetics indicates that the top priority tunnel has the most favourable energies in 75% of cases. Moreover, energy profiles of cognate ligands revealed that a simple geometry analysis can correctly identify tunnel bottlenecks only in 50% of cases. Our study provides essential information for the interpretation of results from tunnel calculation and energy profiling in mechanistic enzymology and protein engineering. We formulated several simple rules allowing identification of biochemically relevant tunnels based on the binding pockets, tunnel geometry, and ligand transport energy profiles.Scientific contributionsThe pipeline introduced in this work allows for the detailed analysis of a large set of protein–ligand complexes, focusing on transport pathways. We are introducing a novel predictor for determining the relevance of binding pockets for tunnel calculation. For the first time in the field, we present a high-throughput energetic analysis of ligand binding and unbinding, showing that approximate methods for these simulations can identify additional mutagenesis hotspots in enzymes compared to purely geometrical methods. The predictor is included in the supplementary material and can also be accessed at https://github.com/Faranehhad/Large-Scale-Pocket-Tunnel-Annotation.git. The tunnel data calculated in this study has been made publicly available as part of the ChannelsDB 2.0 database, accessible at https://channelsdb2.biodata.ceitec.cz/.

A fitness distribution law for amino-acid replacements.

The effect of replacing the amino acid at a given site in a protein is difficult to predict. Yet, evolutionary comparisons have revealed highly regular patterns of interchangeability between pairs of amino acids, and such patterns have proved enormously useful in a range of applications in bioinformatics, evolutionary inference, and protein design. Here we reconcile these apparently contradictory observations using fitness data from over 350,000 experimental amino acid replacements. Almost one-quarter of the 20 × 19 = 380 types of replacements have broad distributions of fitness effects (DFEs) that closely resemble the background DFE for random changes, indicating an overwhelming influence of protein context in determining mutational effects. However, we also observe that the 380 pair-specific DFEs closely follow a maximum entropy distribution, specifically a truncated exponential distribution. The shape of this distribution is determined entirely by its mean, which is equivalent to the chance that a replacement of the given type is fitter than a random replacement. In this type of distribution, modest deviations in the mean correspond to much larger changes in the probability of falling in the far right tail, so that modest differences in mean exchangeability may result in much larger differences in the chance of a highly fit mutation. Indeed, we show that under the assumption that purifying selection filters out the vast majority of mutations, the maximum entropy distributions of fitness effects inferred from deep mutational scanning experiments predict the characteristic patterns of amino acid change observed in molecular evolution. These maximum entropy distributions of mutational effects not only provide a tuneable model for molecular evolution, but also have implications for mutational effect prediction and protein engineering.

Advancements and Applications of Artificial Intelligence in Pharmaceutical Sciences: A Comprehensive Review

: Artificial intelligence (AI) has revolutionized the pharmaceutical industry, improving drug discovery, development, and personalized patient care. Through machine learning (ML), deep learning, natural language processing (NLP), and robotic automation, AI has enhanced efficiency, accuracy, and innovation in the field. The purpose of this review is to shed light on the practical applications and potential of AI in various pharmaceutical fields. These fields include medicinal chemistry, pharmaceutics, pharmacology and toxicology, clinical pharmacy, pharmaceutical biotechnology, pharmaceutical nanotechnology, pharmacognosy, and pharmaceutical management and economics. By leveraging AI technologies such as ML, deep learning, NLP, and robotic automation, this review delves into the role of AI in enhancing drug discovery, development processes, and personalized patient care. It analyzes AI's impact in specific areas such as drug synthesis planning, formulation development, toxicology predictions, pharmacy automation, and market analysis. Artificial intelligence integration into pharmaceutical sciences has significantly improved medicinal chemistry, drug discovery, and synthesis planning. In pharmaceutics, AI has advanced personalized medicine and formulation development. In pharmacology and toxicology, AI offers predictive capabilities for drug mechanisms and toxic effects. In clinical pharmacy, AI has facilitated automation and enhanced patient care. Additionally, AI has contributed to protein engineering, gene therapy, nanocarrier design, discovery of natural product therapeutics, and pharmaceutical management and economics, including marketing research and clinical trials management. Artificial intelligence has transformed pharmaceuticals, improving efficiency, accuracy, and innovation. This review highlights AI's role in drug development and personalized care, serving as a reference for professionals. The future promises a revolutionized field with AI-driven methodologies.

Challenges and perspectives in using unspecific peroxygenases for organic synthesis

In the past 20 years, unspecific peroxygenases (UPOs) have emerged as promising biocatalysts for various organic transformations. Particularly, we have witnessed great attention being paid to the screening of new enzymes and expansion of the substrates/products. However, challenges such as enzyme stability, low turnover numbers, and substrate specificity hinder their widespread utilization in practical organic synthesis. This review article provides a concrete and mini-overview of the challenges associated with using UPOs in organic synthesis and discusses strategies for enzyme engineering to overcome these limitations. The article highlights recent advancements in UPO research and presents potential solutions to enhance their catalytic efficiency, stability, substrate specificity, and regioselectivity. Additionally, the review outlines the current methodologies employed for directed evolution and protein engineering of UPOs, along with computational modeling approaches for rational enzyme design. By addressing the challenges and exploring avenues for enzyme engineering, this review aims to shed light on the prospects of UPOs in organic synthesis.

Enantiocomplementary Synthesis of Tetrahydrofurans by Engineered Halohydrin Dehalogenases

AbstractChiral tetrahydrofurans are prevalent structural motifs in biologically active compounds, approved drugs and natural products, but relevant enantiocomplementary synthesis remains underdeveloped. In this study, wild type halohydrin dehalogenase HheC from Agrobacterium radiobacter AD1, was first discovered to enable the intramolecular substitution of δ‐halohydrins for the formation of tetrahydrofurans but with unsatisfied activity and stereoselectivity. Through structure‐guided protein engineering, two completely enantiocomplementary variants T134M and P84L/W139A of HheC were obtained for the synthesis of tetrahydrofurans by the kinetic resolution of δ‐halohydrins (both giving products with up to 99% e.e., E>200). This research unveiled the halohydrin dehalogenases’ catalytic ability to form chiral cyclic ethers larger ring size than three‐membered epoxides, which has hitherto been unexplored, and provided an enzymatic synthesis access to valuably enantiocomplementary tetrahydrofurans.