Site-Selective C-H Halogenation Using Flavin-Dependent Halogenases Identified via Family-Wide Activity Profiling

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.

Descriptor-augmented machine learning for enzyme-chemical interaction predictions

Descriptors play a pivotal role in enzyme design for the greener synthesis of biochemicals, as they could characterize enzymes and chemicals from the physicochemical and evolutionary perspective. This study examined the effects of various descriptors on the performance of Random Forest model used for enzyme-chemical relationships prediction. We curated activity data of seven specific enzyme families from the literature and developed the pipeline for evaluation the machine learning model performance using 10-fold cross-validation. The influence of protein and chemical descriptors was assessed in three scenarios, which were predicting the activity of unknown relations between known enzymes and known chemicals (new relationship evaluation), predicting the activity of novel enzymes on known chemicals (new enzyme evaluation), and predicting the activity of new chemicals on known enzymes (new chemical evaluation). The results showed that protein descriptors significantly enhanced the classification performance of model on new enzyme evaluation in three out of the seven datasets with the greatest number of enzymes, whereas chemical descriptors appear no effect. A variety of sequence-based and structure-based protein descriptors were constructed, among which the esm-2 descriptor achieved the best results. Using enzyme families as labels showed that descriptors could cluster proteins well, which could explain the contributions of descriptors to the machine learning model. As a counterpart, in the new chemical evaluation, chemical descriptors made significant improvement in four out of the seven datasets, while protein descriptors appear no effect. We attempted to evaluate the generalization ability of the model by correlating the statistics of the datasets with the performance of the models. The results showed that datasets with higher sequence similarity were more likely to get better results in the new enzyme evaluation and datasets with more enzymes were more likely beneficial from the protein descriptor strategy. This work provides guidance for the development of machine learning models for specific enzyme families.

Late-stage diversification of bacterial natural products through biocatalysis

Bacterial natural products (BNPs) are very important sources of leads for drug development and chemical novelty. The possibility to perform late-stage diversification of BNPs using biocatalysis is an attractive alternative route other than total chemical synthesis or metal complexation reactions. Although biocatalysis is gaining popularity as a green chemistry methodology, a vast majority of orphan sequenced genomic data related to metabolic pathways for BNP biosynthesis and its tailoring enzymes are underexplored. In this review, we report a systematic overview of biotransformations of 21 molecules, which include derivatization by halogenation, esterification, reduction, oxidation, alkylation and nitration reactions, as well as degradation products as their sub-derivatives. These BNPs were grouped based on their biological activities into antibacterial (5), antifungal (5), anticancer (5), immunosuppressive (2) and quorum sensing modulating (4) compounds. This study summarized 73 derivatives and 16 degradation sub-derivatives originating from 12 BNPs. The highest number of biocatalytic reactions was observed for drugs that are already in clinical use: 28 reactions for the antibacterial drug vancomycin, followed by 18 reactions reported for the immunosuppressive drug rapamycin. The most common biocatalysts include oxidoreductases, transferases, lipases, isomerases and haloperoxidases. This review highlights biocatalytic routes for the late-stage diversification reactions of BNPs, which potentially help to recognize the structural optimizations of bioactive scaffolds for the generation of new biomolecules, eventually leading to drug development.

Recent developments in the enzymatic modifications of steroid scaffolds

Steroids are an important family of bioactive compounds. Steroid drugs are renowned for their multifaceted pharmacological activities and are the second-largest category in the global pharmaceutical market. Recent developments in biocatalysis and biosynthesis have led to the increased use of enzymes to enhance the selectivity, efficiency, and sustainability for diverse modifications of steroids. This review discusses the advancements achieved over the past five years in the enzymatic modifications of steroid scaffolds, focusing on enzymatic hydroxylation, reduction, dehydrogenation, cascade reactions, and other modifications for future research on the synthesis of novel steroid compounds and related drugs, and new therapeutic possibilities.

ALDELE: All-Purpose Deep Learning Toolkits for Predicting the Biocatalytic Activities of Enzymes

Rapidly predicting enzyme properties for catalyzing specific substrates is essential for identifying potential enzymes for industrial transformations. The demand for sustainable production of valuable industry chemicals utilizing biological resources raised a pressing need to speed up biocatalyst screening using machine learning techniques. In this research, we developed an all-purpose deep-learning-based multiple-toolkit (ALDELE) workflow for screening enzyme catalysts. ALDELE incorporates both structural and sequence representations of proteins, alongside representations of ligands by subgraphs and overall physicochemical properties. Comprehensive evaluation demonstrated that ALDELE can predict the catalytic activities of enzymes, and particularly, it identifies residue-based hotspots to guide enzyme engineering and generates substrate heat maps to explore the substrate scope for a given biocatalyst. Moreover, our models notably match empirical data, reinforcing the practicality and reliability of our approach through the alignment with confirmed mutation sites. ALDELE offers a facile and comprehensive solution by integrating different toolkits tailored for different purposes at affordable computational cost and therefore would be valuable to speed up the discovery of new functional enzymes for their exploitation by the industry.

Discovery of Latent Cannabichromene Cyclase Activity in Marine Bacterial Flavoenzymes

Production of phytocannabinoids remains an area of active scientific interest due to the growing use of cannabis by the public and the underexplored therapeutic potential of the over 100 minor cannabinoids. While phytocannabinoids are biosynthesized by Cannabis sativa and other select plants and fungi, structural analogs and stereoisomers can only be accessed synthetically or through heterologous expression. To date, the bioproduction of cannabinoids has required eukaryotic hosts like yeast since key, native oxidative cyclization enzymes do not express well in bacterial hosts. Here, we report that two marine bacterial flavoenzymes, Clz9 and Tcz9, perform oxidative cyclization reactions on phytocannabinoid precursors to efficiently generate cannabichromene scaffolds. Furthermore, Clz9 and Tcz9 express robustly in bacteria and display significant tolerance to organic solvent and high substrate loading, thereby enabling fermentative production of cannabichromenic acid in Escherichia coli and indicating their potential for biocatalyst development.

Selective C-H Halogenation of Alkenes and Alkynes Using Flavin-Dependent Halogenases

Single component flavin-dependent halogenases (FDHs) possess both flavin reductase and FDH activity in a single enzyme. We recently reported that the single component FDH AetF catalyzes site-selective bromination and iodination of a variety of aromatic substrates and enantioselective bromolactonization and iodoetherification of styrenes bearing pendant carboxylic acid or alcohol substituents. Given this inherent reactivity and selectivity, we explored the utility of AetF as catalyst for alkene and alkyne C-H halogenation. We find that AetF catalyzes halogenation of a range of 1,1-disubstituted styrenes, often with high stereoselectivity. Despite the utility of haloalkenes for cross-coupling and other applications, accessing these compounds in a stereoselective manner typically requires functional group interconversion processes, and selective halogenation of 1,1'-disubstituted olefins remains rare. We also establish that AetF and homologues of this enzyme can halogenate terminal alkynes. Mutagenesis studies and deuterium kinetic isotope effects are used to support a mechanistic proposal involving covalent catalysis for halogenation of unactivated alkynes by AetF homologues. These findings expand the scope of FDH catalysis and continue to show the unique utility of single component FDHs for biocatalysis.

Structural and functional insights into the self-sufficient flavin-dependent halogenase

Flavin-dependent halogenases (FDHs) have tremendous applications in synthetic chemistry. A single-component FDH, AetF, exhibits both halogenase and reductase activities in a continuous polypeptide chain. AetF exhibits broad substrate promiscuity and catalyzes the two-step bromination of l-tryptophan (l-Trp) to produce 5-bromotryptophan (5-Br-Trp) and 5,7-dibromo-l-tryptophan (5,7-di-Br-Trp). To elucidate the mechanism of action of AetF, we solved its crystal structure in complex with FAD, FAD/NADP+, FAD/l-Trp, and FAD/5-Br-Trp at resolutions of 1.92-2.23 Å. The obtained crystal structures depict the unprecedented topology of single-component FDH. Structural analysis revealed that the substrate flexibility and dibromination capability of AetF could be attributed to its spacious substrate-binding pocket. In addition, highly-regulated interaction networks between the substrate-recognizing residues and 5-Br-Trp are crucial for the dibromination activity of AetF. Several Ala variants underwent monobromination with >98 % C5-regioselectivity toward l-Trp. These results reveal the catalytic mechanism of single-component FDH for the first time and contribute to efficient FDH protein engineering for biocatalytic halogenation.

Late-stage modification of bioactive compounds: Improving druggability through efficient molecular editing

Synthetic chemistry plays an indispensable role in drug discovery, contributing to hit compounds identification, lead compounds optimization, candidate drugs preparation, and so on. As Nobel Prize laureate James Black emphasized, "the most fruitful basis for the discovery of a new drug is to start with an old drug"1. Late-stage modification or functionalization of drugs, natural products and bioactive compounds have garnered significant interest due to its ability to introduce diverse elements into bioactive compounds promptly. Such modifications alter the chemical space and physiochemical properties of these compounds, ultimately influencing their potency and druggability. To enrich a toolbox of chemical modification methods for drug discovery, this review focuses on the incorporation of halogen, oxygen, and nitrogen-the ubiquitous elements in pharmacophore components of the marketed drugs-through late-stage modification in recent two decades, and discusses the state and challenges faced in these fields. We also emphasize that increasing cooperation between chemists and pharmacists may be conducive to the rapid discovery of new activities of the functionalized molecules. Ultimately, we hope this review would serve as a valuable resource, facilitating the application of late-stage modification in the construction of novel molecules and inspiring innovative concepts for designing and building new drugs.

An artificial nickel chlorinase based on the biotin-streptavidin technology

Herein, we report on an artificial nickel chlorinase (ANCase) resulting from anchoring a biotinylated nickel-based cofactor within streptavidin (Sav). The resulting ANCase was efficient for the chlorination of diverse C(sp3)-H bonds. Guided by the X-ray analysis of the ANCase, the activity of the artificial chlorinase could be significantly improved. This approach opens interesting perspectives for late-stage functionalization of organic intermediates as it complements biocatalytic chlorination strategies.

Site-selective chlorination of pyrrolic heterocycles by flavin dependent enzyme PrnC

Halogenation of pyrrole requires strong electrophilic reagents and often leads to undesired polyhalogenated products. Biocatalytic halogenation is a highly attractive approach given its chemoselectivity and benign reaction conditions. While there are several reports of enzymatic phenol and indole halogenation in organic synthesis, corresponding reports on enzymatic pyrrole halogenation have been lacking. Here we describe the in vitro functional and structural characterization of PrnC, a flavin-dependent halogenase that can act on free-standing pyrroles. Computational modeling and site mutagenesis studies identified three key residues in the catalytic pocket. A moderate resolution map using single-particle cryogenic electron microscopy reveals PrnC to be a dimer. This native PrnC can halogenate a library of structurally diverse pyrrolic heterocycles in a site-selective manner and be applied in the chemoenzymatic synthesis of a chlorinated analog of the agrochemical fungicide Fludioxonil.

Recent Developments and Applications of Biocatalytic and Chemoenzymatic Synthesis for the Generation of Diverse Classes of Drugs

Biocatalytic and chemoenzymatic biosynthesis are powerful methods of organic chemistry that use enzymes to execute selective reactions and allow the efficient production of organic compounds. The advantages of these approaches include high selectivity, mild reaction conditions, and the ability to work with complex substrates. The utilization of chemoenzymatic techniques for the synthesis of complicated compounds has lately increased dramatically in the area of organic chemistry. Biocatalytic technologies and modern synthetic methods are utilized synergistically in a multi-step approach to a target molecule under this paradigm. Chemoenzymatic techniques are promising for simplifying access to essential bioactive compounds because of the remarkable regio- and stereoselectivity of enzymatic transformations and the reaction diversity of modern organic chemistry. Enzyme kits may include ready-to-use, reproducible biocatalysts. Its use opens up new avenues for the synthesis of active therapeutic compounds and aids in drug development by synthesizing active components to construct scaffolds in a targeted and preparative manner. This study summarizes current breakthroughs as well as notable instances of biocatalytic and chemoenzymatic synthesis. To assist organic chemists in the use of enzymes for synthetic applications, it also provides some basic guidelines for selecting the most appropriate enzyme for a targeted reaction while keeping aspects like cofactor requirement, solvent tolerance, use of whole cell or isolated enzymes, and commercial availability in mind.

One-pot selective biosynthesis of Tyrian purple in Escherichia coli

Tyrian purple (6,6'-Dibromoindigo) is an ancient precious dye, which possesses remarkable properties as a biocompatible semiconductor material. Recently, biosynthesis has emerged as an alternative for the sustainable production of Tyrian purple from a natural substrate. However, the selectivity issue in enzymatic tryptophan (Trp) and bromotryptophan (6-Br-Trp) degradation was an obstacle for obtaining high-purity Tyrian purple in a single cell biosynthesis. In this study, we present a simplified one-pot process for the production of Tyrian purple from Trp in Escherichia coli (E. coli) using Trp 6-halogenase from Streptomyces toxytricini (SttH), tryptophanase from E. coli (TnaA) and a two-component indole oxygenase from Providencia Rettgeri GS-2 (GS-C and GS-D). To enhance the in vivo solubility and activity of SttH and flavin reductase (Fre) fusion enzyme (Fre-L3-SttH), a chaperone system of GroEL/GroES (pGro7) was introduced in addition to the implementation of a set of optimization strategies, including fine-tuning the expression vector, medium, concentration of bromide salt and inducer. To overcome the selectivity issue and achieve a higher conversion yield of Tyrian purple with minimal indigo formation, we applied the λpL/pR-cI857 thermoinducible system to temporally control the bifunctional fusion enzyme of TnaA and monooxygenase GS-C (TnaA-L3-GS-C). Through optimization of the fermentation process, we were able to achieve a Tyrian purple titer of 44.5 mg L-1 with minimal indigo byproduct from 500 μM Trp. To the best of our knowledge, this is the first report of the selective production of Tyrian purple in E. colivia a one-pot process.

Unifying and versatile features of flavin-dependent monooxygenases: Diverse catalysis by a common C4a-(hydro)peroxyflavin

Flavin-dependent monooxygenases (FDMOs) are known for their remarkable versatility and for their crucial roles in various biological processes and applications. Extensive research has been conducted to explore the structural and functional relationships of FDMOs. The majority of reported FDMOs utilize C4a-(hydro)peroxyflavin as a reactive intermediate to incorporate an oxygen atom into a wide range of compounds. This review discusses and analyzes recent advancements in our understanding of the structural and mechanistic features governing the enzyme functions. State-of-the-art discoveries related to common and distinct structural properties governing the catalytic versatility of the C4a-(hydro)peroxyflavin intermediate in selected FDMOs are discussed. Specifically, mechanisms of hydroxylation, dehalogenation, halogenation, and light-emitting reactions by FDMOs are highlighted. We also provide new analysis based on the structural and mechanistic features of these enzymes to gain insights into how the same intermediate can be harnessed to perform a wide variety of reactions. Challenging questions to obtain further breakthroughs in the understanding of FDMOs are also proposed.

Multiplexed Assessment of Promiscuous Non-Canonical Amino Acid Synthase Activity in a Pyridoxal Phosphate-Dependent Protein Family

Pyridoxal phosphate (PLP)-dependent enzymes afford access to a variety of non-canonical amino acids (ncAAs), which are premier buildings blocks for the construction of complex bioactive molecules. The vinylglycine ketimine (VGK) subfamily of PLP-dependent enzymes plays a critical role in sulfur metabolism and is home to a growing set of secondary metabolic enzymes that synthesize γ-substituted ncAAs. Identification of VGK enzymes for biocatalysis faces a distinct challenge because the subfamily contains both desirable synthases as well as lyases that break down ncAAs. Some enzymes have both activities, which may contribute to pervasive mis-annotation. To navigate this complex functional landscape, we used a substrate multiplexed screening approach to rapidly measure the substrate promiscuity of 40 homologs in the VGK subfamily. We found that enzymes involved in transsulfuration are less likely to have promiscuous activities and often possess undesirable lyase activity. Enzymes from direct sulfuration and secondary metabolism generally had a high degree of substrate promiscuity. From this cohort, we identified an exemplary γ-synthase from Caldicellulosiruptor hydrothermalis (CahyGS). This enzyme is thermostable and has high expression (~400 mg protein per L culture), enabling preparative scale synthesis of thioether containing ncAAs. When assayed with l-allylglycine, CahyGS catalyzes a stereoselective γ-addition reaction to afford access to a unique set of γ-methyl branched ncAAs. We determined high-resolution crystal structures of this enzyme that define an open-close transition associated with ligand binding and set the stage for future engineering within this enzyme subfamily.

Enabling Broader Adoption of Biocatalysis in Organic Chemistry

Biocatalysis is becoming an increasingly impactful method in contemporary synthetic chemistry for target molecule synthesis. The selectivity imparted by enzymes has been leveraged to complete previously intractable chemical transformations and improve synthetic routes toward complex molecules. However, the implementation of biocatalysis in mainstream organic chemistry has been gradual to this point. This is partly due to a set of historical and technological barriers that have prevented chemists from using biocatalysis as a synthetic tool with utility that parallels alternative modes of catalysis. In this Perspective, we discuss these barriers and how they have hindered the adoption of enzyme catalysts into synthetic strategies. We also summarize tools and resources that already enable organic chemists to use biocatalysts. Furthermore, we discuss ways to further lower the barriers for the adoption of biocatalysis by the broader synthetic organic chemistry community through the dissemination of resources, demystifying biocatalytic reactions, and increasing collaboration across the field.

Non-Native Site-Selective Enzyme Catalysis

The ability to site-selectively modify equivalent functional groups in a molecule has the potential to streamline syntheses and increase product yields by lowering step counts. Enzymes catalyze site-selective transformations throughout primary and secondary metabolism, but leveraging this capability for non-native substrates and reactions requires a detailed understanding of the potential and limitations of enzyme catalysis and how these bounds can be extended by protein engineering. In this review, we discuss representative examples of site-selective enzyme catalysis involving functional group manipulation and C-H bond functionalization. We include illustrative examples of native catalysis, but our focus is on cases involving non-native substrates and reactions often using engineered enzymes. We then discuss the use of these enzymes for chemoenzymatic transformations and target-oriented synthesis and conclude with a survey of tools and techniques that could expand the scope of non-native site-selective enzyme catalysis.

Asymmetric catalysis by flavin-dependent halogenases

In nature, flavin-dependent halogenases (FDHs) catalyze site-selective chlorination and bromination of aromatic natural products. This ability has led to extensive efforts to engineer FDHs for selective chlorination, bromination, and iodination of electron rich aromatic compounds. On the other hand, FDHs are unique among halogenases and haloperoxidases that exhibit catalyst-controlled site selectivity in that no examples of enantioselective FDH catalysis in natural product biosynthesis have been characterized. Over the past several years, our group has established that FDHs can catalyze enantioselective reactions involving desymmetrization, atroposelective halogenation, and halocyclization. Achieving high activity and selectivity for these reactions has required extensive mutagenesis and mitigation of problems resulting from hypohalous acid generated during FDH catalysis. The single-component flavin reductase/FDH AetF is unique among the wild type enzyme we have studied in that it provides high activity and selectivity toward several asymmetric transformations. These results highlight the ability of FDH active sites to tolerate different substrate topologies and suggest that they could be useful for a broad range of oxidative halogenations.

Contrastive learning in protein language space predicts interactions between drugs and protein targets

Sequence-based prediction of drug-target interactions has the potential to accelerate drug discovery by complementing experimental screens. Such computational prediction needs to be generalizable and scalable while remaining sensitive to subtle variations in the inputs. However, current computational techniques fail to simultaneously meet these goals, often sacrificing performance of one to achieve the others. We develop a deep learning model, ConPLex, successfully leveraging the advances in pretrained protein language models ("PLex") and employing a protein-anchored contrastive coembedding ("Con") to outperform state-of-the-art approaches. ConPLex achieves high accuracy, broad adaptivity to unseen data, and specificity against decoy compounds. It makes predictions of binding based on the distance between learned representations, enabling predictions at the scale of massive compound libraries and the human proteome. Experimental testing of 19 kinase-drug interaction predictions validated 12 interactions, including four with subnanomolar affinity, plus a strongly binding EPHB1 inhibitor (KD = 1.3 nM). Furthermore, ConPLex embeddings are interpretable, which enables us to visualize the drug-target embedding space and use embeddings to characterize the function of human cell-surface proteins. We anticipate that ConPLex will facilitate efficient drug discovery by making highly sensitive in silico drug screening feasible at the genome scale. ConPLex is available open source at https://ConPLex.csail.mit.edu.

In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning

Nowadays, the vastly increasing demand for novel biotechnological products is supported by the continuous development of biocatalytic applications which provide sustainable green alternatives to chemical processes. The success of a biocatalytic application is critically dependent on how quickly we can identify and characterize enzyme variants fitting the conditions of industrial processes. While miniaturization and parallelization have dramatically increased the throughput of next-generation sequencing systems, the subsequent characterization of the obtained candidates is still a limiting process in identifying the desired biocatalysts. Only a few commercial microfluidic systems for enzyme analysis are currently available, and the transformation of numerous published prototypes into commercial platforms is still to be streamlined. This review presents the state-of-the-art, recent trends, and perspectives in applying microfluidic tools in the functional and structural analysis of biocatalysts. We discuss the advantages and disadvantages of available technologies, their reproducibility and robustness, and readiness for routine laboratory use. We also highlight the unexplored potential of microfluidics to leverage the power of machine learning for biocatalyst development.

Enzymatic Late-Stage Halogenation of Peptides

The late-stage site-selective derivatisation of peptides has many potential applications in structure-activity relationship studies and postsynthetic modification or conjugation of bioactive compounds. The development of orthogonal methods for C-H functionalisation is crucial for such peptide derivatisation. Among them, biocatalytic methods are increasingly attracting attention. Tryptophan halogenases emerged as valuable catalysts to functionalise tryptophan (Trp), while direct enzyme-catalysed halogenation of synthetic peptides is yet unprecedented. Here, it is reported that the Trp 6-halogenase Thal accepts a wide range of amides and peptides containing a Trp moiety. Increasing the sequence length and reaction optimisation made bromination of pentapeptides feasible with good turnovers and a broad sequence scope, while regioselectivity turned out to be sequence dependent. Comparison of X-ray single crystal structures of Thal in complex with d-Trp and a dipeptide revealed a significantly altered binding mode for the peptide. The viability of this bioorthogonal approach was exemplified by halogenation of a cyclic RGD peptide.

Naturally Occurring Organohalogen Compounds-A Comprehensive Review

The present volume is the third in a trilogy that documents naturally occurring organohalogen compounds, bringing the total number-from fewer than 25 in 1968-to approximately 8000 compounds to date. Nearly all of these natural products contain chlorine or bromine, with a few containing iodine and, fewer still, fluorine. Produced by ubiquitous marine (algae, sponges, corals, bryozoa, nudibranchs, fungi, bacteria) and terrestrial organisms (plants, fungi, bacteria, insects, higher animals) and universal abiotic processes (volcanos, forest fires, geothermal events), organohalogens pervade the global ecosystem. Newly identified extraterrestrial sources are also documented. In addition to chemical structures, biological activity, biohalogenation, biodegradation, natural function, and future outlook are presented.

The Single-Component Flavin Reductase/Flavin-Dependent Halogenase AetF is a Versatile Catalyst for Selective Bromination and Iodination of Arenes and Olefins

Flavin-dependent halogenases (FDHs) natively catalyze selective halogenation of electron rich aromatic and enolate groups. Nearly all FDHs reported to date require a separate flavin reductase to supply them with FADH2 , which complicates biocatalysis applications. In this study, we establish that the single component flavin reductase/flavin dependent halogenase AetF catalyzes halogenation of a diverse set of substrates using a commercially available glucose dehydrogenase to drive its halogenase activity. High site selectivity, activity on relatively unactivated substrates, and high enantioselectivity for atroposelective bromination and bromolactonization was demonstrated. Site-selective iodination and enantioselective cycloiodoetherification was also possible using AetF. The substrate and reaction scope of AetF suggest that it has the potential to greatly improve the utility of biocatalytic halogenation.

Indole and azaindole halogenation catalyzed by the RebH enzyme variant 3-LSR utilizing co-purified E. coli reductase

Biocatalytic C-H halogenation is becoming increasingly attractive due to excellent catalyst-controlled selectivity and environmentally benign reaction conditions. Significant efforts have been made on enzymatic halogenation of industrial arenes in a cost-effective manner. Here we report an unprecedented enzymatic halogenation of a panel of industrially important indole, azaindole and anthranilamide derivatives using a thermostable RebH variant without addition of any external flavin reductase enzyme. The reactions were catalyzed by the RebH variant 3-LSR enzyme with the help of a co-purified E. coli reductase identified as alkyl hydroperoxide reductase F (AhpF).

The Time and Place for Nature in Drug Discovery

The case for a renewed focus on Nature in drug discovery is reviewed; not in terms of natural product screening, but how and why biomimetic molecules, especially those produced by natural processes, should deliver in the age of artificial intelligence and screening of vast collections both in vitro and in silico. The declining natural product-likeness of licensed drugs and the consequent physicochemical implications of this trend in the context of current practices are noted. To arrest these trends, the logic of seeking new bioactive agents with enhanced natural mimicry is considered; notably that molecules constructed by proteins (enzymes) are more likely to interact with other proteins (e.g., targets and transporters), a notion validated by natural products. Nature's finite number of building blocks and their interactions necessarily reduce potential numbers of structures, yet these enable expansion of chemical space with their inherent diversity of physical characteristics, pertinent to property-based design. The feasible variations on natural motifs are considered and expanded to encompass pseudo-natural products, leading to the further logical step of harnessing bioprocessing routes to access them. Together, these offer opportunities for enhancing natural mimicry, thereby bringing innovation to drug synthesis exploiting the characteristics of natural recognition processes. The potential for computational guidance to help identifying binding commonalities in the route map is a logical opportunity to enable the design of tailored molecules, with a focus on "organic/biological" rather than purely "synthetic" structures. The design and synthesis of prototype structures should pay dividends in the disposition and efficacy of the molecules, while inherently enabling greener and more sustainable manufacturing techniques.

Enzyme Activity Prediction of Sequence Variants on Novel Substrates using Improved Substrate Encodings and Convolutional Pooling

Protein engineering is currently being revolutionized by deep learning applications, especially through natural language processing (NLP) techniques. It has been shown that state-of-the-art self-supervised language models trained on entire protein databases capture hidden contextual and structural information in amino acid sequences and are capable of improving sequence-to-function predictions. Yet, recent studies have reported that current compound-protein modeling approaches perform poorly on learning interactions between enzymes and substrates of interest within one protein family. We attribute this to low-grade substrate encoding methods and over-compressed sequence representations received by downstream predictive models. In this study, we propose a new substrate-encoding based on Extended Connectivity Fingerprints (ECFPs) and a convolutional-pooling of the sequence embeddings. Through testing on an activity profiling dataset of haloalkanoate dehalogenase superfamily that measures activities of 218 phosphatases against 168 substrates, we show substantial improvements in predictive performances of compound-protein interaction modeling. In addition, we also test the workflow on three other datasets from the halogenase, kinase and aminotransferase families and show that our pipeline achieves good performance on these datasets as well. We further demonstrate the utility of this downstream model architecture by showing that it achieves good performance with six different protein embeddings, including ESM-1b (Rives et al., 2021), TAPE (Rao et al., 2019), ProtBert, ProtAlbert, ProtT5, and ProtXLNet (Elnaggar et al., 2021). This study provides a new workflow for activity prediction on novel substrates that can be used to engineer new enzymes for sustainability applications.

Finding Superior Biocatalysts via Homolog Screening

Many enzymes possess high catalytic efficiency and selectivity that far surpass classical organic or organometallic catalysts. However, the initial starting enzyme for a given transformation does not always possess the right properties needed for broad utilization. Searching in genome/protein sequence libraries for homologs, aided with powerful bioinformatic tools developed in recent years, provides an avenue to identify superior biocatalysts. Herein, we highlight several case studies to illustrate the power of this concept. A brief discussion on its complementarity with contemporary approaches in protein engineering (such as directed evolution) and possible future developments is also provided.

Analysis of Laboratory-Evolved Flavin-Dependent Halogenases Affords a Computational Model for Predicting Halogenase Site Selectivity

Flavin-dependent halogenases (FDHs) catalyze selective halogenation of electron-rich aromatic compounds without the need for harsh oxidants required by conventional oxidative halogenation reactions. Predictive models for halogenase site selectivity could greatly improve their utility for chemical synthesis. Toward this end, we analyzed the structures and selectivity of three halogenase variants evolved to halogenate tryptamine with orthogonal selectivity. Crystal structures and reversion mutations revealed key residues involved in altering halogenase selectivity. Density functional theory calculations and molecular dynamics simulations are both consistent with hypohalous acid as the active halogenating species in FDH catalysis. This model was used to accurately predict the site selectivity of halogenase variants toward different synthetic substrates, providing a valuable tool for implementing halogenases in biocatalysis efforts.

Directed Evolution of Flavin-Dependent Halogenases for Site- and Atroposelective Halogenation of 3-Aryl-4(3H)-Quinazolinones via Kinetic or Dynamic Kinetic Resolution

In this study, we engineer a variant of the flavin-dependent halogenase RebH that catalyzes site- and atroposelective halogenation of 3-aryl-4(3H)-quinazolinones via kinetic or dynamic kinetic resolution. The required directed evolution uses a combination of random and site-saturation mutagenesis, substrate walking using two probe substrates, and a two-tiered screening approach involving the analysis of variant conversion and then enantioselectivity of improved variants. The resulting variant, 3-T, provides >99:1 e.r. for the (M)-atropisomer of the major brominated product, 25-fold improved conversion, and 91-fold improved site selectivity relative to the parent enzyme on the probe substrate used in the final rounds of evolution. This high activity and selectivity translate well to several additional substrates with varied steric and electronic properties. Computational modeling and docking simulations are used to rationalize the effects of key mutations on substrate binding. Given the range of substrates that have been used for atroposelective synthesis via electrophilic halogenation in the literature, these results suggest that flavin-dependent halogenases (FDHs) could find many additional applications for atroposelective catalysis. More broadly, this study highlights how RebH can be engineered to accept structurally diverse substrates that enable its use for enantioselective catalysis.

From Tryptophan to Toxin: Nature's Convergent Biosynthetic Strategy to Aetokthonotoxin

Aetokthonotoxin (AETX) is a cyanobacterial neurotoxin that causes vacuolar myelinopathy, a neurological disease that is particularly deadly to bald eagles in the United States. The recently characterized AETX is structurally unique among cyanotoxins and is composed of a pentabrominated biindole nitrile. Herein we report the discovery of an efficient, five-enzyme biosynthetic pathway that the freshwater cyanobacterium Aetokthonos hydrillicola uses to convert two molecules of tryptophan to AETX. We demonstrate that the biosynthetic pathway follows a convergent route in which two functionalized indole monomers are assembled and then reunited by biaryl coupling catalyzed by the cytochrome P450 AetB. Our results revealed enzymes with novel biochemical functions, including the single-component flavin-dependent tryptophan halogenase AetF and the iron-dependent nitrile synthase AetD.

Machine learning modeling of family wide enzyme-substrate specificity screens

Biocatalysis is a promising approach to sustainably synthesize pharmaceuticals, complex natural products, and commodity chemicals at scale. However, the adoption of biocatalysis is limited by our ability to select enzymes that will catalyze their natural chemical transformation on non-natural substrates. While machine learning and in silico directed evolution are well-posed for this predictive modeling challenge, efforts to date have primarily aimed to increase activity against a single known substrate, rather than to identify enzymes capable of acting on new substrates of interest. To address this need, we curate 6 different high-quality enzyme family screens from the literature that each measure multiple enzymes against multiple substrates. We compare machine learning-based compound-protein interaction (CPI) modeling approaches from the literature used for predicting drug-target interactions. Surprisingly, comparing these interaction-based models against collections of independent (single task) enzyme-only or substrate-only models reveals that current CPI approaches are incapable of learning interactions between compounds and proteins in the current family level data regime. We further validate this observation by demonstrating that our no-interaction baseline can outperform CPI-based models from the literature used to guide the discovery of kinase inhibitors. Given the high performance of non-interaction based models, we introduce a new structure-based strategy for pooling residue representations across a protein sequence. Altogether, this work motivates a principled path forward in order to build and evaluate meaningful predictive models for biocatalysis and other drug discovery applications.

Predicting interactions between compounds and proteins represents a long-standing dream of drug discovery and protein engineering. Robust models of enzyme-substrate scope would dramatically advance our ability to design synthetic routes involving enzymatic catalysis. However, the lack of standardization between compound-protein interaction studies makes it difficult to evaluate the generalizability of such models. In this work we take a critical step forward by standardizing high-quality datasets measuring enzyme-substrate interactions, outlining rigorous evaluations, and proposing a new way to integrate structural information into protein representations. In testing previous modeling approaches, we highlight a surprising inability of existing models to effectively leverage compound-protein interactions to improve generalization, which challenges a perception in the literature. This establishes future opportunities for model development and integration of enzyme-substrate scope models into computer-aided synthesis planning software.

Biocatalytic Alkylation Chemistry: Building Molecular Complexity with High Selectivity

Biocatalysis has traditionally been viewed as a field that primarily enables access to chiral centers. This includes the synthesis of chiral alcohols, amines and carbonyl compounds, often through functional group interconversion via hydrolytic or oxidation-reduction reactions. This limitation is partly being overcome by the design and evolution of new enzymes. Here, we provide an overview of a recently thriving research field that we summarize as biocatalytic alkylation chemistry. In the past 3-4 years, numerous new enzymes have been developed that catalyze sp3 C-C/N/O/S bond formations. These enzymes utilize different mechanisms to generate molecular complexity by coupling simple fragments with high activity and selectivity. In many cases, the engineered enzymes perform reactions that are difficult or impossible to achieve with current small-molecule catalysts such as organocatalysts and transition-metal complexes. This review further highlights that the design of new enzyme function is particularly successful when off-the-shelf synthetic reagents are utilized to access non-natural reactive intermediates. This underscores how biocatalysis is gradually moving to a field that build molecules through selective bond forming reactions.

Discovery and Development of Promiscuous O-Glycan Hydrolases for Removal of Intact Sialyl T-Antigen

Mucin-type O-glycosylation (O-glycosylation) is a common post-translational modification that confers distinct biophysical properties to proteins and plays crucial roles in intercellular signaling. Yet, despite the importance of O-glycans, relatively few tools exist for their analysis and modification. In particular, there is a need for enzymes that can cleave the wide range of O-glycan structures found on protein surfaces, to facilitate glycan profiling and editing. Through functional metagenomic screening of the human gut microbiome, we discovered endo-O-glycan hydrolases from CAZy family GH101 that are capable of slowly cleaving the intact sialyl T-antigen trisaccharide (a ubiquitous O-glycan structure in humans) in addition to their primary activity against the T-antigen disaccharide. We then further explored this sequence space through phylogenetic profiling and analysis of representative enzymes, revealing large differences in the levels of this promiscuous activity between enzymes within the family. Through structural and sequence analysis, we identified active site residues that modulate specificity. Through subsequent rational protein engineering, we improved the activity of an enzyme identified by phylogenetic profiling sufficiently that substantial removal of the intact sialyl T-antigen from proteins could be readily achieved. Our best sialyl T-antigen hydrolase mutant, SpGH101 Q868G, is further shown to function on a number of proteins, tissues, and cells. Access to this enzyme opens up improved methodologies for unraveling the glycan code.

Phage-Assisted Continuous Evolution and Selection of Enzymes for Chemical Synthesis

Ligand-dependent biosensors are valuable tools for coupling the intracellular concentrations of small molecules to easily detectable readouts such as absorbance, fluorescence, or cell growth. While ligand-dependent biosensors are widely used for monitoring the production of small molecules in engineered cells and for controlling or optimizing biosynthetic pathways, their application to directed evolution for biocatalysts remains underexplored. As a consequence, emerging continuous evolution technologies are rarely applied to biocatalyst evolution. Here, we develop a panel of ligand-dependent biosensors that can detect a range of small molecules. We demonstrate that these biosensors can link enzymatic activity to the production of an essential phage protein to enable biocatalyst-dependent phage-assisted continuous evolution (PACE) and phage-assisted continuous selection (PACS). By combining these phage-based evolution and library selection technologies, we demonstrate that we can evolve enzyme variants with improved and expanded catalytic properties. Finally, we show that the genetic diversity resulting from a highly mutated PACS library is enriched for active enzyme variants with altered substrate scope. These results lay the foundation for using phage-based continuous evolution and selection technologies to engineer biocatalysts with novel substrate scope and reactivity.

Halogenases: a palette of emerging opportunities for synthetic biology-synthetic chemistry and C-H functionalisation

The enzymatic generation of carbon-halogen bonds is a powerful strategy used by both nature and synthetic chemists to tune the bioactivity, bioavailability and reactivity of compounds, opening up the opportunity for selective C-H functionalisation. Genes encoding halogenase enzymes have recently been shown to transcend all kingdoms of life. These enzymes install halogen atoms into aromatic and less activated aliphatic substrates, achieving selectivities that are often challenging to accomplish using synthetic methodologies. Significant advances in both halogenase discovery and engineering have provided a toolbox of enzymes, enabling the ready use of these catalysts in biotransformations, synthetic biology, and in combination with chemical catalysis to enable late stage C-H functionalisation. With a focus on substrate scope, this review outlines the mechanisms employed by the major classes of halogenases, while in parallel, it highlights key advances in the utilisation of the combination of enzymatic halogenation and chemical catalysis for C-H activation and diversification.

Comparative genomic analysis of azasugar biosynthesis

Azasugars are monosaccharide analogs in which the ring oxygen is replaced with a nitrogen atom. These well-known glycosidase inhibitors are of interest as therapeutics, yet several aspects of azasugars remain unknown including their distribution, structural diversity, and chemical ecology. The hallmark signature of bacterial azasugar biosynthesis is a three gene cluster (3GC) coding for aminotransferase, phosphatase, and dehydrogenase enzymes. Using the bioinformatics platform Enzyme Similarity Tool (EST), we identified hundreds of putative three gene clusters coding for azasugar production in microbial species. In the course of this work, we also report a consensus sequence for the aminotransferase involved in azasugar biosynthesis as being: SGNXFRXXXFPNXXXXXXXLXVPXPYCXRC. Most clusters are found in Bacillus and Streptomyces species which typically inhabit soil and the rhizosphere, but some clusters are found with diverse species representation such as Photorhabdus and Xenorhabdus which are symbiotic with entomopathogenic nematodes; the human skin commensal Cutibacterium acnes, and the marine Bacillus rugosus SPB7, a symbiont to the sea sponge Spongia officinalis. This pan-taxonomic survey of the azasugar 3GC signature may lead to the identification of new azasugar producers, facilitate studies of their natural functions, and lead to new potential therapeutics.

Concise Chemoenzymatic Synthesis of Fasamycin A

We report the development of a chemoenzymatic approach toward fasamycin A, a halogenated naphthacenoid that exhibits activities against methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococcus faecalis. The synthesis was accomplished in a convergent manner: two fragments were combined together in a Sammes annulation to afford a dimethylnaphthacenone system. Finally, an enzymatic halogenation was employed to introduce the requisite chlorine substituent of the natural product at a late stage.

State-of-the-Art Biocatalysis

The use of enzyme-mediated reactions has transcended ancient food production to the laboratory synthesis of complex molecules. This evolution has been accelerated by developments in sequencing and DNA synthesis technology, bioinformatic and protein engineering tools, and the increasingly interdisciplinary nature of scientific research. Biocatalysis has become an indispensable tool applied in academic and industrial spheres, enabling synthetic strategies that leverage the exquisite selectivity of enzymes to access target molecules. In this Outlook, we outline the technological advances that have led to the field's current state. Integration of biocatalysis into mainstream synthetic chemistry hinges on increased access to well-characterized enzymes and the permeation of biocatalysis into retrosynthetic logic. Ultimately, we anticipate that biocatalysis is poised to enable the synthesis of increasingly complex molecules at new levels of efficiency and throughput.

Enzymkatalysierte späte Modifizierungen: Besser spät als nie

Die Enzymkatalyse gewinnt zunehmend an Bedeutung in der Synthesechemie. Die durch Bioinformatik und Enzym‐Engineering stetig wachsende Zahl von Biokatalysatoren eröffnet eine große Vielfalt selektiver Reaktionen. Insbesondere für späte Funktionalisierungsreaktionen ist die Biokatalyse ein geeignetes Werkzeug, das oftmals der konventionellen De‐novo‐Synthese überlegen ist. Enzyme haben sich als nützlich erwiesen, um funktionelle Gruppen direkt in komplexe Molekülgerüste einzuführen sowie für die rasche Diversifizierung von Substanzbibliotheken. Biokatalytische Oxyfunktionalisierungen, Halogenierungen, Methylierungen, Reduktionen und Amidierungen sind von besonderem Interesse, da diese Strukturmotive häufig in Pharmazeutika vertreten sind. Dieser Aufsatz gibt einen Überblick über die Stärken und Schwächen der enzymkatalysierten späten Modifizierungen durch native und optimierte Enzyme in der Synthesechemie. Ebenso werden wichtige Beispiele in der Wirkstoffentwicklung hervorgehoben.

Enzymatic Late-Stage Modifications: Better Late Than Never

Enzyme catalysis is gaining increasing importance in synthetic chemistry. Nowadays, the growing number of biocatalysts accessible by means of bioinformatics and enzyme engineering opens up an immense variety of selective reactions. Biocatalysis especially provides excellent opportunities for late-stage modification often superior to conventional de novo synthesis. Enzymes have proven to be useful for direct introduction of functional groups into complex scaffolds, as well as for rapid diversification of compound libraries. Particularly important and highly topical are enzyme-catalysed oxyfunctionalisations, halogenations, methylations, reductions, and amide bond formations due to the high prevalence of these motifs in pharmaceuticals. This Review gives an overview of the strengths and limitations of enzymatic late-stage modifications using native and engineered enzymes in synthesis while focusing on important examples in drug development.

Recent Advances in Transition-Metal-Free Late-Stage C-H and N-H Arylation of Heteroarenes Using Diaryliodonium Salts

Transition-metal-free direct arylation of C-H or N-H bonds is one of the key emerging methodologies that is currently attracting tremendous attention. Diaryliodonium salts serve as a stepping stone on the way to alternative environmentally friendly and straightforward pathways for the construction of C-C and C-heteroatom bonds. In this review, we emphasize the recent synthetic advances of late-stage C(sp2)-N and C(sp2)-C(sp2) bond-forming reactions under metal-free conditions using diaryliodonium salts as arylating reagent and its applications to the synthesis of new arylated bioactive heterocyclic compounds.

Flavin-dependent halogenases catalyze enantioselective olefin halocyclization

Halocyclization of alkenes is a powerful bond-forming tool in synthetic organic chemistry and a key step in natural product biosynthesis, but catalyzing halocyclization with high enantioselectivity remains a challenging task. Identifying suitable enzymes that catalyze enantioselective halocyclization of simple olefins would therefore have significant synthetic value. Flavin-dependent halogenases (FDHs) catalyze halogenation of arene and enol(ate) substrates. Herein, we reveal that FDHs engineered to catalyze site-selective aromatic halogenation also catalyze non-native bromolactonization of olefins with high enantioselectivity and near-native catalytic proficiency. Highly selective halocyclization is achieved by characterizing and mitigating the release of HOBr from the FDH active site using a combination of reaction optimization and protein engineering. The structural origins of improvements imparted by mutations responsible for the emergence of halocyclase activity are discussed. This expansion of FDH catalytic activity presages the development of a wide range of biocatalytic halogenation reactions.

Catalytic enantioselective halocyclization of alkenes is an important bond forming tool and a key step in natural product biosynthesis, but so far no examples of the enzymatic counterpart of this reaction on simple achiral olefins have been reported. Here, the authors describe examples of engineered flavin-dependent halogenases that catalyze halolactonization of olefins with high enantioselectivity and near-native catalytic activity.

Genomic Determinants Encode the Reactivity and Regioselectivity of Flavin-Dependent Halogenases in Bacterial Genomes and Metagenomes

Halogenases create diverse natural products by utilizing halide ions and are of great interest in the synthesis of potential pharmaceuticals and agrochemicals. An increasing number of halogenases discovered in microorganisms are annotated as flavin-dependent halogenases (FDHs), but their chemical reactivities are markedly different and the genomic contents associated with such functional distinction have not been revealed yet. Even though the reactivity and regioselectivity of FDHs are essential in the halogenation activity, these FDHs are annotated inaccurately in the protein sequence repositories without characterizing their functional activities. We carried out a comprehensive sequence analysis and biochemical characterization of FDHs. Using a probabilistic model that we built in this study, FDHs were discovered from 2,787 bacterial genomes and 17 sediment metagenomes. We analyzed the essential genomic determinants that are responsible for substrate binding and subsequent reactions: four flavin adenine dinucleotide-binding, one halide-binding, and four tryptophan-binding sites. Compared with previous studies, our study utilizes large-scale genomic information to propose a comprehensive set of sequence motifs that are related to the active sites and regioselectivity. We reveal that the genomic patterns and phylogenetic locations of the FDHs determine the enzymatic reactivities, which was experimentally validated in terms of the substrate scope and regioselectivity. A large portion of publicly available FDHs needs to be reevaluated to designate their correct functions. Our genomic models establish comprehensive links among genotypic information, reactivity, and regioselectivity of FDHs, thereby laying an important foundation for future discovery and classification of novel FDHs.

IMPORTANCE Halogenases are playing an important role as tailoring enzymes in biosynthetic pathways. Flavin-dependent tryptophan halogenases (Trp-FDHs) are among the enzymes that have broad substrate scope and high selectivity. From bacterial genomes and metagenomes, we found highly diverse halogenase sequences by using a well-trained profile hidden Markov model built from the experimentally validated halogenases. The characterization of genotype, steady-state activity, substrate scope, and regioselectivity has established comprehensive links between the information encoded in the genomic sequence and reactivity of FDHs reported here. By constructing models for accurate and detailed sequence markers, our work should guide future discovery and classification of novel FDHs.

Endogenous Enzymes Enable Antimicrobial Activity

In light of the continued threat of antimicrobial-resistant bacteria, new strategies to expand the repertoire of antimicrobial compounds are necessary. Prodrugs are an underexploited strategy in this effort. Here, we report on the enhanced antimicrobial activity of a prodrug toward bacteria having an enzyme capable of its activation. A screen led us to the sulfurol ester of the antibiotic trans-3-(4-chlorobenzoyl)acrylic acid. An endogenous esterase makes Mycolycibacterium smegmatis sensitive to this prodrug. Candidate esterases were identified, and their heterologous production made Escherichia coli sensitive to the ester prodrug. Taken together, these data suggest a new approach to the development of antimicrobial compounds that takes advantage of endogenous enzymatic activities to target specific bacteria.

AoiQ Catalyzes Geminal Dichlorination of 1,3-Diketone Natural Products

Enzymes that can perform halogenation of aliphatic carbons are of significant interest to the synthetic and biocatalysis communities. Here we describe the characterization of AoiQ, a single-component flavin-dependent halogenase (FDH) that catalyzes gem-dichlorination of 1,3-diketone substrates in the biosynthesis of dichlorodiaporthin. AoiQ represents the first biochemically reconstituted FDH that can halogenate an enolizable sp³-hybridized carbon atom.