ACS Catalysis

L-3,4-dihydroxyphenylalanine (L-Dopa) is an important pharmaceutical for the treatment of Parkinsons disease and a precursor to numerous catechol-containing compounds. The flavin-dependent monooxygenase HpaBC is a promising biocatalyst for microbial L-Dopa production but exhibits limited native activity toward L-tyrosine. Although structure-based machine learning (ML) models have become increasingly popular for protein engineering, relatively few studies have systematically compared their performance or evaluated their integration into iterative engineering workflows. Here, we benchmarked multiple ML models for their ability to predict activity enhancing mutations in HpaBC. Experimentally validated single mutants were used to seed combinatorial design with EVOLVEpro, generating progressively improved higher-order variants. We next evaluated how expanding the EVOLVEpro training set with directed evolution derived variants influenced combinatorial predictions and finally explored an expanded sequence space by allowing combinations of both machine learning derived and directed evolution derived mutations. This workflow produced HpaBC variants with substantially improved activity. Although incorporating directed evolution data substantially altered EVOLVEpros predicted mutational trajectories, both training strategies converged on variants with comparable activities, demonstrating that distinct regions of sequence space can yield similarly optimized enzymes. Together, these results provide a systematic comparison of zero-shot ML models and establish an iterative framework for integrating machine learning with directed evolution to accelerate enzyme engineering.

Biocatalysis offers sustainable solutions to pressing challenges in chemical synthesis by exploiting the remarkable efficiency and selectivity of enzymes. Importantly, enzymes are able to accommodate non-native substrates and mediate transformations outside of their natural repertoire. Enzymes can be engineered for diverse applications by harnessing these promiscuous activities and optimizing them using directed evolution (DE). The success of a DE campaign, however, depends on the availability of a protein starting point that displays detectable levels of the desired function. To find a starting point, researchers often screen libraries of protein variants for novel activities, typically with low rates of success. Here, instead, we diversified the active site of a desirable parent protein and applied machine learning to generate informed, promiscuous libraries of protein variants. Specifically, we tested 26 different carbene and nitrene transfer reactions and used active learning-assisted directed evolution (ALDE) to generate optimized protoglobin variants with high activity across multiple reactions. We observed improvements in activity and selectivity for every reaction performed by the parent enzyme in at least one member of the ALDE-predicted libraries. Moreover, variants from these libraries can catalyze 5 out of 10 reactions not catalyzed by the parent protoglobin. These results indicate that supervised machine learning can help guide the construction of high-value enzyme libraries with expanded catalytic scope. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/740427v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@6d5cfborg.highwire.dtl.DTLVardef@1f38e0aorg.highwire.dtl.DTLVardef@f25fa2org.highwire.dtl.DTLVardef@64a0dc_HPS_FORMAT_FIGEXP M_FIG C_FIG

Engineered glycosynthases (GSs) are powerful biocatalysts for custom glycan synthesis, yet their optimization via directed evolution is severely constrained by bottlenecks in high-throughput screening for activated azido-sugar donors. Here, we demonstrate that chemical rescue (CR)--the azide-mediated restoration of hydrolytic activity in nucleophile-deficient mutants--serves as a predictive, high-throughput proxy for glycosynthase activity. Applying an azide-responsive Escherichia coli biosensor screen to a site-saturation mutagenesis library of Thermotoga maritima -L-fucosidase (TmAfc), we established a strong rank-order correlation between CR and GS activities in both crude lysates ({rho} = 0.73) and purified enzymes ({rho} = 0.95). Transition path sampling and QM/MM umbrella sampling revealed that both pathways proceed through a shared oxocarbenium-ion-like transition state ({Delta}G{ddagger} {approx} 8.7 kcal/mol), providing a structural and thermodynamic rationale for using CR to select for transition-state-stabilizing mutations. Biochemical characterization of top-performing variants yielded an engineered fucosynthase (TmAfc_D224G_N70D_T392S) exhibiting a nearly 100-fold enhancement in Vmax alongside altered regioselectivity. This two-tiered screening framework leverages cost-effective chemical rescue assays to streamline glycosynthase engineering for tailored glycans synthesis.

Acetyl-coenzyme A (CoA) is a central metabolic intermediate that links carbon and energy metabolism across all domains of life. The conversion of acetate and acetyl-CoA is carried out by three enzyme pathways: acetate kinase/phosphotransacetylase, ADP-forming acetyl-CoA synthetase, and AMP-forming acetyl-CoA synthetase (Acs). Acs enzymes serve critical physiological roles across diverse organisms generally by catalyzing a reversible two-step reaction forming acetyl-CoA and AMP from acetate and ATP. Isolated from the wastewater reclamation facility in Norman, Oklahoma, Syntrophus aciditrophicus strain SB (Sa) relies on an AMP-forming acetyl-CoA synthetase (SaAcs1) that favors synthesizing acetate and ATP from acetyl-CoA and AMP, in contrast to all previously characterized Acs enzymes. The origin of this preference and the structural determinants of both the thioester-forming step and catalytic directionality remain poorly understood. Here, we report a 2.2 [A] crystal structure of full-length SaAcs1 in the adenylation conformation with acetyl-AMP bound in the active site. Structural comparison to the extensively characterized Acs enzymes from Salmonella enterica (SeAcs) and Cryptococcus neoformans (CnAcs) revealed a displaced CoA-binding loop in SaAcs1. Enzymatic assays confirmed that SaAcs1 preferentially catalyzes the ATP-forming reaction. Site-directed mutagenesis demonstrated that reversion of two residues, G196 and T197, at the beginning of the CoA-binding loop to the consensus sequence repositions the loop and shifts catalytic preference toward the AMP-forming direction. Together, these results establish the CoA-binding loop and G196 and T197 as the primary structural determinants of directional preference in SaAcs1.

Enzymatic depolymerization of polyethylene terephthalate (PET) has advanced rapidly, alongside a growing volume of publicly available metagenomic data from microbial communities under sustained selective pressure from plastic exposure. Reasoning that such environments may harbor underexplored polyester-active enzymes, we developed a targeted mining workflow that screens exclusively plastic-associated datasets through multi-step bioinformatic filtering--integrating catalytic-motif screening, disulfide-topology validation, structural-similarity scoring, and phylogenetic profiling--to recover high-confidence PETase candidates. Applied to 271 plastic-associated metagenomes, the pipeline yielded 21 non-redundant candidates, several of which combine the Type I catalytic motif (GHSMGGGG) with Type II-like extended loops and secondary disulfide bonds. Two candidates were experimentally confirmed as PET hydrolases; the more active, PET-KR1, is a thermostable enzyme (Tm = 66.5 {degrees}C) that depolymerizes PET across a broad temperature range, with markedly higher productivity on powdered than on film substrate. PET-KR1 achieved optimal depolymerization at 50 {degrees}C, yet at 60-65 {degrees}C, where total yields declined, the product pool was more strongly enriched in the terminal monomer TPA, suggesting that thermostability and substrate accessibility are the primary targets for further engineering. Molecular dynamics simulations revealed a conserved hydrophobic binding network around the catalytic serine, consistent with established PETase substrate-recognition modes, and rational disulfide engineering raised the melting temperature by 3.5 {degrees}C, confirming amenability to further optimization. Overall, PET-KR1 expands the scaffold space available for PETase engineering, while the discovery workflow, built entirely on publicly available tools and open-access data, provides a reproducible strategy for metagenomic mining of novel PET-degrading enzymes toward biocatalytic PET recycling.

FuncLib and high-throughput FuncLib (htFuncLib) generate diverse, functional protein libraries using a stability-centered design; however, this substrate-independent approach lacks target-specific functional constraints. We developed a machine-learning-assisted enzyme-engineering (MLEE) workflow that adds substrate-specific functional information to htFuncLib through an initial screening and sequencing round. The system was benchmarked using previously published four-position fitness landscapes of three different proteins. The MLEE workflow successfully generated compact libraries enriched in globally high-fitness variants. After the initial training phase, an MLEE-enriched library of just 12 variants increased the hit rate for the global top-0.05% variants by 5- to 12-fold relative to the htFuncLib baseline. Screening a larger set of 96 variants recovered at least one of these top-performing enzymes in 61.3-99.4% of the simulations. We then applied MLEE to MthUPO-catalyzed {beta}-damascone hydroxylation. Across two rounds, 506 distinct variants were screened and sequenced. While the initial substrate-independent htFuncLib library yielded 14% of variants with activity above the wild type, the MLEE-enriched library increased this hit rate to 90% (97 of 108 variants) with activity above the wild type. The best variant increased the turnover number for 4-hydroxy-{beta}-damascone by 11.8-fold and achieved >99% regioisomeric excess. MLEE may bypass the need for transition-state models and reduce the effort required for obtaining high-activity variants. TABLE OF CONTENT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/739108v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@b1f79dorg.highwire.dtl.DTLVardef@1f77429org.highwire.dtl.DTLVardef@eb4b6dorg.highwire.dtl.DTLVardef@1a4e5ae_HPS_FORMAT_FIGEXP M_FIG C_FIG

Recent advances in the discovery, characterisation, and engineering of urethanases provide new opportunities for the sustainable biocatalytic degradation of polyurethane waste. A mechanistic understanding of enzyme-plastic interactions is essential for structure-based engineering to enhance urethanase activity. However, the extremely complex and hydrophobic nature of polyurethane makes it challenging to elucidate the structural basis of enzyme-plastic interactions. Here, we used a fragment-based approach to characterise the active sites of two novel urethanases with different catalytic scaffolds, employing both a crystallographic fragment-screening (FASE) campaign and soluble fragments of plastic-like analogues that mimic the substrate, transition state, or product. FASE identified new substrate-binding subpockets while interactions of plastic mimetics in the active site provided a mechanistic understanding of the recognition and binding of polyurethane fragments by these subpockets. These results highlight a diversity of binding modes among urethanases toward different polyurethane fragments.

Enzymes drive biological chemistry and offer greener routes to chemicals, materials and medicines, yet their broader use as biocatalysts is often limited by insufficient catalytic turnover. Improving turnover is hard: measured rate constants are scarce and protein sequence space is vast. Deep-learning models now predict the turnover number, Kcat, with growing accuracy, but they are typically applied after sequence generation to score or filter candidates, which separates the kinetic objective from the design itself. To bridge the gap between sequence generation and kinetic evaluation, we introduce CatESO, a differentiable sequence optimizer that enables direct, gradient-guided design of substrate-specific catalytic turnover. By backpropagating through a cross-modal Kcat predictor under continuous sequence relaxation, CatESO co-optimizes predicted catalytic activity, evolutionary plausibility and structural integrity in one end-to-end framework, using ESM-2 and ESMFold to keep designs evolutionarily plausible and foldable. Across seven stringent out-of-distribution enzymes spanning EC classes 1-7, CatESO raised model-predicted Kcat for the vast majority of designs, with a median predicted fold change of 1.52 while every variant retained a pLDDT above 70. Against RFdiffusion3-LigandMPNN pipeline and ZymCtrl, CatESO struck a better balance between predicted activity and structural confidence. By making substrate-conditioned kinetic objectives differentiable, CatESO carries differentiable protein design beyond structure- and binding-centred goals to enzyme catalytic function, giving a general route to function-oriented enzyme engineering.

Natural product (NPs) and their derivatives are a major source of small-molecule drugs, and the building blocks of these NPs are often amino acids. These include both proteinogenic and nonproteinogenic amino acids (NPAAs), the latter of which expand the structural diversity of NPs. Homologation, or the addition of a methylene group to the amino acid side chain, is one modification that generates NPAAs. If the natural homologation pathway can be characterized and engineered, it could be used to diversify NPs. In this study, we investigated the terminal enzyme of this pathway, HphB, to determine its substrate scope. HphB was tested with various substrates that differed in backbone and/or side chain structures relative to its natural substrate. The results showed that HphB exhibits high promiscuity toward substrates with different side chains while maintaining strict specificity for the substrate backbone. Comparative analysis with two homologous enzymes from primary metabolic pathways revealed that HphB displays markedly higher substrate promiscuity. Bioinformatics analysis and structural modeling suggest that this promiscuity arises from the absence of a "lid" over the active site, resulting in increased solvent exposure of the substrate side chain. This study highlights the unique substrate flexibility of HphB and is a step toward engineering the homologation pathway to generate amino acid derivatives.

Despite advances in de novo enzyme design, success has been largely limited to low energy barrier model reactions. Amide bonds such as those linking amino acids along the peptide backbone are stable for hundreds of years in neutral aqueous solution because of the high energy barrier to hydrolysis1. Here we describe the de novo design of enzymes which utilize an activated cysteine nucleophile to hydrolyze the polypeptide backbone in a sequence-dependent manner, with a success rate of 13/69=19% and rate enhancements over the background reaction (kcat/kuncat) of up to 3 x 107. The designed proteases have folds very different from proteases in nature (TM score < 0.50), and six crystal structures are very close to the design models (C RMSDs < 1.2 [A]), highlighting the capacity for generalization and the accuracy of the design methodology. Experimental and computational analyses suggest that the remaining gap in activity to the most active native cysteine proteases arises from imperfections in active site preorganization and substrate positioning. The designed proteases efficiently cleave their targets in mammalian cells, opening the door to a wide range of synthetic biology applications.

2-Deoxyribose-5-Phosphate Aldolase (DERA) is a key enzyme in the pentose phosphate pathway. Due to its C-C bond formation and stereoselective capabilities, DERA has been widely used for biocatalytic applications including the synthesis of chiral intermediates for antiviral and anticancer drugs. While protein engineering has expanded its substrate pool, improved yield, and enhanced stereoselectivity, the molecular basis of stereoselectivity remains unclear. Here, we determined the crystal structures of wildtype DERA from Geobacillus sp. and two of its variants with opposite stereoselectivity. Using a combination of structural biology, biochemistry, organic synthesis and molecular dynamic simulations, we show that the catalytic Lysine adopts two conformations and the Lysine conformation is a key determinant of DERA stereoselectivity. We also identified a mechanism of regulating stereoselectivity via a key amino acid. Using DERA from E. coli, we show that these findings are most likely conserved among bacteria.

Bacterial azabicyclocarbamates and related pyrrolizidine alkaloids play important roles in microbial interactions, and are scaffolds of therapeutic potential. Their biosynthesis involves a bimodular non-ribosomal peptide synthetase (NRPS), as well as a Baeyer-Villiger monooxygenase and tailoring enzymes, the latter contributing to the structural diversification of these compounds. Azetidomonamide A, a core metabolite produced by the major human opportunistic pathogen Pseudomonas aeruginosa, is a rare 4,7-bicyclocarbamate involved in modulating bacterial virulence that belongs to a unique family of natural products targeting ClpP proteases. In this study, we elucidated the full set of reactions leading to the 7-membered cyclocarbamate warhead, and reconstituted in vitro the biosynthesis of azetidomonamide A. Notably, this approach allowed for detailed characterization of a condensation (C) domain-catalyzed online dehydration via chemical capture of NRPS-tethered intermediates. Furthermore, we identified the dehydratase AzeD as the founding member of a distinct group of standalone proteins of the C domain family. Via combined structural, docking and biochemical analyses, we provided evidence that AzeDs catalytic mechanism is distinct from that of dehydrating C domains, further expanding the known chemistry of these key biosynthetic enzymes.

D-Ribitol-5-phosphate (Rbo5P) plays vital roles in bacterial and mammalian development. In mammals, Rbo5P is an essential component of the O-mannosyl glycan on -dystroglycan, and its defective biosynthesis causes a group of dystroglycanopathies. Supplementation with cytidine diphosphate ribitol (CDP-ribitol, CDP-Rbo) and its analogues has shown therapeutic potential for certain dystroglycanopathies. However, facile and scalable strategies to prepare CDP-Rbo remain unavailable. Here, we report a practical one-pot enzymatic strategy for gram-scale CDP-Rbo production. Starting from inexpensive ribitol, Rbo5P is first generated by L-ribulokinase (AraB) and then converted to CDP-Rbo by CDP-ribitol pyrophosphorylase (TarI) in 90% yield. To streamline the process, AraB and TarI were fused into a single bifunctional biocatalyst, AraB-TarI, which converts ribitol directly into CDP-Rbo on a gram-scale in a one-step reaction. Furthermore, a nucleotide recycling strategy was developed to lower the cost and increase the atom economy from 47% to 75%. This work provides a green and scalable route to CDP-Rbo and a reliable material supply for developing therapeutics against dystroglycanopathies.

Protein engineering requires efficient navigation of vast sequence spaces under limited evaluation budgets, especially when multiple properties must be optimized simultaneously. We developed Evolution-inspired Multi-Objective Bayesian Optimization (EvoMOBO), an active-learning framework that integrates path-dependent sequence generation, global competition among generated variants, and explicit multi-objective optimization. Benchmarking against state-of-the-art methods on complete steroid receptor DNA-binding domain and ParD3 antitoxin landscapes demonstrated robust target-region enrichment, Pareto-front advancement, and sequence diversity across two- and three-objective tasks. In the DBD landscape, simulation-derived geometric descriptors served as labels for both initialization and iterative updating, enriching variants with favorable measured activities without experimental labels. Building on this validation, we applied EvoMOBO to two enzyme-engineering tasks using simulation-derived mechanistic descriptors, with experiments reserved for final validation. For an old yellow enzyme (GkOYE), 16 of 26 tested variants outperformed the wild type, and the best increased non-native oxidative dehydrogenation conversion from 17.5% to 95%. For a formate oxidase (AoFOx), EvoMOBO identified aggregation-resistant variants, two of which nearly doubled diethyl phthalate degradation in a photoenzymatic cascade. Together, these results establish EvoMOBO as a modular framework for multi-objective protein engineering using experimental or mechanism-derived labels.

Kaitocephalin (KCP) is a fungal neuroactive natural product bearing a peptide-like yet nonpeptidic amino acid-derived scaffold in which amino acid-like units are connected by C-C bonds rather than peptide bonds. The enzymatic construction of this unusual scaffold has remained unresolved. Here, we identify KpbH as a PLP-dependent enzyme that couples pyrroline-5-carboxylate, generated from L-ornithine, with L-aspartate to form (2S,5R)-5-((S)-2-amino-2-carboxyethyl)pyrrolidine-2-carboxylic acid (ACPCA), which corresponds to the nonpeptidic Ala-Pro substructure of KCP. D2O-labeling experiments showed enzyme-controlled, solvent-derived deuterium incorporation at C7 of ACPCA, supporting a decarboxylative Mannich-type mechanism. Feeding of a deuterium-enriched ACPCA-containing reaction mixture to the KCP-producing fungus Eupenicillium shearii resulted in deuterium incorporation into KCP, linking ACPCA to KCP biosynthesis. These results identify KpbH as the first native PLP-dependent enzyme that catalyzes an L-aspartate-dependent decarboxylative Mannich-type C-C bond-forming reaction and reveal a biosynthetic strategy for constructing a noncanonical amino acid-like C-C bond scaffold. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/733665v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@a27fb7org.highwire.dtl.DTLVardef@6eea95org.highwire.dtl.DTLVardef@1eae086org.highwire.dtl.DTLVardef@13a92e9_HPS_FORMAT_FIGEXP M_FIG C_FIG

Flavin adenine dinucleotide-dependent glucose dehydrogenase (FAD-GDH) is a redox enzyme widely used in glucose monitoring, bioelectronic devices, and enzymatic biofuel cells because of its oxygen-independent catalysis and compatibility with electron-transfer processes. However, protein-based regulators that directly bind GDH and modulate its redox output remain underdeveloped. Here, we present an AI-guided strategy for developing a de novo protein inhibitor targeting FAD-GDH. GDH-targeting candidates generated through structure-based computational design were evaluated by yeast surface display and fluorescence-activated cell sorting, leading to the identification of FAD-GDH inhibitor-1 (FGI-1) as a GDH-targeting inhibitory scaffold. Purified His-MBP-FGI-1 reduced GDH-mediated DCIP reduction, demonstrating attenuation of GDH-derived redox output. Random mutagenesis followed by secondary FACS screening yielded evolved variants with increased GDH-binding signals and enhanced redox-output suppression, showing that the de novo inhibitory scaffold could be functionally tuned through experimental evolution. In addition, an FGI-1-based construct fused to a larger protein module retained GDH-output suppressive activity, and electrode-based measurements showed reduced GDH-derived current output. Because electrode-associated measurements may be influenced by protein-mediated surface shielding and altered electron-transfer accessibility, this decrease was interpreted conservatively as attenuation of GDH-derived electrochemical output rather than direct evidence of active-site inhibition. Together, this work establishes an AI-guided design-validation workflow for developing protein inhibitors that modulate FAD-GDH redox output and provides a foundation for protein-level control of enzyme output in biosensing and bioelectronic applications.

Poly(ethylene terephthalate) (PET) is one of the most widely produced plastics, and enzymatic depolymerization offers a promising route to closed-loop recycling under mild conditions. However, most known bacterial PET hydrolases belong to a conserved canonical-fold cutinase family, leaving much of alpha/beta-hydrolase diversity unexplored. Here, we mapped bacterial cutinase sequence space by combining bioinformatics-guided sequence selection with high-throughput secretion screening in Bacillus subtilis. A library of 1,120 genes encoding 954 unique bacterial cutinases, spanning canonical- and minimal-fold families, was screened for activity on Impranil DLN and semicrystalline PET. We identified 156 secreted cutinases with polyester activity, broadly distributed across sequence space, but only ten showed detectable PET hydrolysis, all from the canonical-fold family. These PET hydrolases were active at 40-50{degrees}C, preferred alkaline pH, and showed moderate thermostability. Our results demonstrate that PET activity is rare among bacterial cutinases and provide a scalable workflow for discovering diverse enzyme starting points.

Thiopeptides are ribosomally synthesized and post-translationally modified peptides (RiPPs) that form complex bioactive scaffolds through extensive enzymatic tailoring. The polyglycosylated thiopeptides persiathiacins, exhibit potent activity against multidrug-resistant Mycobacterium tuberculosis (Mtb) and methicillin-resistant Staphylococcus aureus (MRSA). The persiathiacin biosynthetic gene cluster encodes six cytochrome P450 (CYP) enzymes, but the logic of their oxidative modifications was unknown. Here, we establish a protoplast-based genetic system for Actinokineospora and systematically assign functions to all P450s. We demonstrate that PerX hydroxylates the central thiazole, PerV installs the third indole-core crosslink required for macrocyclization, and PerT, not PerU, catalyses indole N-hydroxylation. Combined gene inactivation and metabolite profiling reveal a hierarchical enzymatic sequence leading to the mature scaffold prior to sugar installation. Notably, the intermediate accumulating in the {Omega}perX mutant exhibits enhanced anti-M. tuberculosis potency compared to persiathiacin A (IC50 = 0.07 vs 1.5 g mL1). These results define the enzymatic logic and temporal organization of persiathiacin biosynthesis, providing a conceptual framework for rational diversification of complex thiopeptide natural products.

Developing enzymatic mechanisms for C-F bond formation remains a long-standing challenge. Here, we repurposed the biosynthetic nonheme Fe enzyme EgtB, which features a three-histidine facial triad, to catalyze C(sp3)-H fluorination reactions. Directed evolution of EgtB afforded two new-to-nature fluorine atom transferases with opposite enantiopreference, EgtBCHF1 and EgtBCHF2, with up to 28-fold improved total activity. In contrast to our previously evolved nonheme Fe fluorine atom transfer biocatalyst ACCOCHF, which contains a two-histidine-one-carboxylate facial triad, the evolved EgtBCHF variants displayed unexpected hydroxylation activity. 18O-labeling experiments showed that the hydroxy group originated from water rather than residual O2. Computational studies suggested that the three-histidine-supported Fe(III) center exhibits enhanced Lewis acidity compared to the two-histidine-one-carboxylate system, allowing deprotonation of Fe(III)-bound water to form a Fe(III)-OH species to catalyze radical hydroxylation. Primary coordination-sphere mutagenesis in EgtB and ACCO further supported the critical role of Fe coordination chemistry in controlling radical rebound reactivity and selectivity. Computational studies revealed that Fe coordination chemistry strongly influences both fluorine atom abstraction and radical rebound, with the intrinsic C-X (X = F, OH, and N3) bond forming radical rebound preference following the order N3 > OH > F. Furthermore, multivariate linear regression analysis revealed that fluorine atom abstraction is primarily governed by the intrinsic Fe-F bond strength, whereas fluorine rebound is predominantly controlled by the electronic structure of the Fe(III) intermediate. Together, these findings provide mechanistic insights into nonheme Fe enzymology and reprogramming toward selective radical rebound reactions, including challenging C-H fluorination. Table of Contents (TOC) O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/737789v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1ad85b2org.highwire.dtl.DTLVardef@1248bd4org.highwire.dtl.DTLVardef@58268dorg.highwire.dtl.DTLVardef@14b2da0_HPS_FORMAT_FIGEXP M_FIG C_FIG

Flavonoids constitute a large class of natural products widely investigated for their bioactive properties, with microbial production offering a potentially scalable alternative to plant extraction. However, achieving structural diversification of these compounds in microbial systems remains challenging, as modification of the flavonoid B-ring typically relies on downstream tailoring enzymes. An alternative strategy is to exploit the intrinsic promiscuity of the canonical flavanone biosynthesis pathway to introduce structural variation at an early stage. Here, we sought to improve microbial production of diverse flavanones by systematically leveraging pathway promiscuity. By constructing a combinatorial library of pathways comprising 4-coumarate-CoA ligase, chalcone synthase, and chalcone isomerase, we enabled the conversion of a panel of ring-substituted cinnamic acid precursors into ten natural and non-natural flavanones. In parallel, we established a genetically encoded biosensor based on the transcriptional regulator FdeR and demonstrated its responsiveness across all ten flavanones. Leveraging this biosensor for high-throughput screening, we performed directed evolution of chalcone synthases from Hordeum vulgare and Arabidopsis thaliana, identifying enzyme variants that led to improved production of O-methylated flavanones, including isosakuranetin, hesperetin, and homoeriodictyol, as well as fluoro-substituted flavanones. In addition, we demonstrated that specific variants of H. vulgare chalcone synthase promoted the formation of isoferuloyl-derived derailment products. Collectively, this work establishes the FdeR-based biosensor as a versatile platform for pathway and enzyme engineering, enabling efficient early-stage diversification of flavanones in microbial systems and providing insight into the mutational landscape of chalcone synthases.