ACS Catalysis

Active-site redesign frequently yields modest improvements because residues controlling physical steps like substrate binding and product release lie outside the active site. Efficient catalysis requires a cooperative catalytic network of residues that support both the chemical and physical steps of catalysis. Using ancestral hydroxynitrile lyase HNL1, an /{beta}-hydrolase with poor esterase activity, we tested this framework directly. Matching all active-site residues to a proficient esterase improved KM five-fold but left kcat unchanged, confirming that chemical machinery alone is insufficient. Activity-weighted sequence comparison (SigniSite) across ten homologous HNLs and esterases identified 38 positions disfavoring esterase activity. Experimental refinement yielded a minimal set of fifteen substitutions (HNL1-15) with [~]60-fold higher kcat and [~]400-fold higher kcat/KM. Single-substitution reversion analysis confirmed that all fifteen substitutions are essential and provided evidence for strong cooperativity between them. X-ray crystal structures of HNL1 and HNL1-15 reveal three coordinated structural changes: reshaping the substrate-binding pocket to favor productive ester binding, restoring access to the oxyanion hole, and opening an additional tunnel for product egress and water entry. These changes arise through backbone rearrangements and altered flexibility rather than direct active-site contacts, explaining why the responsible positions escape conservation-based detection. Because cooperativity masks individual contributions, engineering such networks may require step-specific assays -- measuring binding, acylation, or product release directly -- rather than screening composite kcat.

The de novo design of enzymes critically tests our understanding of natural enzymes and enables design of novel catalysts. Here, we identify the features responsible for the catalytic efficiency of a highly proficient de novo enzyme generated through computational design and optimized by directed evolution. Computational, spectroscopic, and biochemical studies reveal successfully designed features, including precise alignment of catalytic residues, transition state stabilization, and environmental tuning. In the most evolved enzyme, the binding of a transition state analog also led to widespread increases in backbone rigidity and conformational stability throughout the protein, except within a helix near the active site entrance, where the introduction of Gly and Pro increased dynamics and catalytic activity. Thus, the entire protein contributes to catalysis in the most optimized enzyme. These studies provide principles for designing efficient enzymes.

In Anabaena variabilis (Trichormus variabilis) phenylalanine ammonia-lyase (AvPAL), a conserved lid-like loop sits over the active site and has been studied both for its role in positioning a catalytic tyrosine and for its contribution to phenylalanine aminomutase (PAM) activity. While the active site architecture and substrate specificity of AvPAL have been extensively characterized, the dynamic behavior of this unstructured loop beyond its role in catalysis remains poorly understood. Here, we investigate the functional role of this loop by restricting its mobility through targeted interchain disulfide bond engineering. Three in-house approaches were designed to predict ideal cysteine residue pairs: (i) quantifying pair interaction energies via electrostatic and van der Waals forces, (ii) generating a contact map of residues within 5 [A] proximity, and (iii) implementing a machine-learning model trained on datasets from PDBCYS, SPX, and an internal database to rank cysteine pair likelihood within disulfide bond geometric constraints. Our machine-learning-guided strategy yielded a successful variant with complete oxidation efficiency in E. coli. Rigidification of this loop reveals that it also functions as a regulator of substrate specificity. Multi-scale molecular simulation analyses (molecular dynamics, metadynamics, quantum/molecular mechanics) reveal that this modification alters the active-site pocket by reducing the conformational dynamics of substrate binding. Our findings underscore the delicate balance between enzyme flexibility and catalytic efficiency, providing novel insights into the role of this understudied dynamic loop region in AvPAL.

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.

Iron(II)- and 2-oxoglutarate-dependent (Fe(II)/2OG) enzymes catalyze a wide range of C-H bond activation and functionalization reactions and play essential roles in biosynthesis and metabolic regulation. Despite extensive mechanistic studies, the principles governing selectivity between canonical hydroxylation and alternative transformations remain incompletely understood. Here, we investigate the catalytic mechanism of hyoscyamine 6{beta}-hydroxylase (H6H), a Fe(II)/2OG-dependent oxygenase that sequentially catalyzes the 6{beta}-hydroxylation of hyoscyamine followed by 6,7-exo-epoxidation of 6{beta}-hydroxyhyoscyamine to generate scopolamine. Combined molecular dynamics and QM/MM calculations reveal that an inline Fe(IV)-oxo intermediate initiates hydrogen atom abstraction from the substrate C7 position. The resulting Fe(III)-OH species subsequently deprotonates the substrate hydroxyl group in a process coupled to substrate coordination to the iron center and an in-line-to-off-line rearrangement of the Fe(III)-OH moiety. This coordination dynamics machinery is further supported by the observed chlorination reactivity on the same substrate. Importantly, this coordination switch favors epoxide formation over hydroxyl rebound, thereby directing the reaction toward selective epoxidation. Further computational analysis of the L290F variant demonstrates that steric constraints imposed by L290 are essential for suppressing hydroxylation, revealing a bidirectional regulatory mechanism governing epoxidation/hydroxylation selectivity. Whereas iron coordination dynamics promote epoxidation reactivity, precise substrate positioning and protein-derived steric effects suppress the competing hydroxylation pathway. These findings are consistent with available experimental observations and establish metal coordination dynamics as a key determinant of selective C-H functionalization in Fe(II)/2OG enzymes.

Human homogentisate 1,2-dioxygenase (HGD) catalyses the oxidative cleavage of homogentisic acid (HGA) to maleylacetoacetate (MAA), a key step in tyrosine degradation. Loss of HGD activity causes alkaptonuria (AKU), a rare inherited metabolic disorder characterized by toxic HGA accumulation. Current therapy with nitisinone lowers HGA levels but does not restore HGD function, motivating further investigation of HGD structure-function relationships. In this study, we applied the Knowledge Gaining Directed Evolution (KnowVolution) strategy to investigate how amino acid substitutions influence catalytic activity and structural integrity of human HGD. Catalytic activity was evaluated in Escherichia coli using an assay quantifying MAA formation over time. Across four KnowVolution phases, multiple substitutions were identified that modulated catalytic activity while preserving enzyme function. Notably, none of the influential substitutions were located within the catalytic pocket; instead, they occurred predominantly at surface-exposed or structural positions. Structural mapping, interface analysis, and computational stability predictions indicated that some substitutions contribute to hexamer stabilization, whereas others likely alter activity through indirect, non-catalytic mechanisms involving pocket remodelling. Combined substitutions showed non-additive effects that were either cooperative or antagonistic, demonstrating that their impact could not be predicted from individual contributions. Tunnel and pocket analyses showed that N31S, S54D and D86H produced a more compact hexamer, whereas a Q354P+P359E double mutant reduced catalytic pocket solvent accessibility and volume, supporting the observed activity differences. Overall, these findings demonstrate that HGD activity can be modulated by substitutions outside the catalytic pocket, providing new insight into HGD function and genotype-phenotype relationships underlying AKU.

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.

Directed evolution has long been constrained by complex screening hardware and labor-intensive workflows. Here, we report the first genuine test-tube screening platform that uses His6-tagged peptide-functionalized magnetic beads and Fe3+-decorated E. coli cells to establish a phenotype-genotype linkage, thereby decoupling ultrahigh-throughput screening from specialized instrumentation and democratizing directed evolution. The platform demonstrated a screening throughput of > 108 events s-1 and an enrichment factor of up to 63-fold. Using galactose oxidase as a model, we identified variants with up to a 26-fold increase in catalytic efficiency. Extensions to D-amino acid oxidase and alcohol oxidase yielded variants with up to 5383-fold and 25-fold improvements over their respective wildtypes after a single round of screening. These results highlight the platforms capacity to rapidly engineer H2O2-generating oxidases and to advance AI-driven enzyme design through rapid data generation.

The iron (Fe) nitrogenase drives bacterial methane (CH4) formation by converting carbon dioxide (CO2) to CH4 in a single enzymatic step. Enhancing the initial CH4 formation activity of Fe-nitrogenase and expanding the product spectrum to hydrocarbon chains could lead to a route for sustainable feedstock chemicals. Here, we performed the first directed evolution campaign on the Fe-nitrogenase aimed at optimizing the hydrocarbon production. We achieved an ~8-fold increase in CH4 formation by Fe-nitrogenase expressing Rhodobacter capsulatus cultures in three rounds of site-saturation mutagenesis. The best performing mutant (F362ManfD, Y85FanfD, T360SanfD) extends the in vivo product spectrum of the nitrogenase to ethane (C2H6) and exhibits 6-fold higher rates for CO production in vitro, whereas the formation of the undesirable byproduct formate was abolished. Electron microscopy-based structural analysis identified a methionine and water potentially stabilizing the transition state and fine-tuning the CO2 reduction mechanism and activity.

Thiol dioxygenases (TDOs) catalyze the incorporation of molecular oxygen into thiol metabolites and N-terminal cysteine residues of regulatory proteins, thereby playing critical roles in sulfur metabolism and oxygen sensing. Despite extensive study over the past two decades, the molecular basis for substrate recognition and the catalytic mechanism of TDOs remains controversial, owing to the scarcity of substrate-bound structures and direct evidence for catalytic intermediates. Herein, we present a comprehensive study of mercaptosuccinate dioxygenase (MSDO), a TDO originally identified in Variovorax paradoxus B4, using a combination of structural, biochemical, spectroscopic, and computational approaches. MSDO oxidizes both (S)- and (R)-mercaptosuccinate (MS) with similar Km values but exhibits approximately 2.5-fold higher turnover for the (S)-enantiomer. Crystal structures of MSDO reveal that both (S)- and (R)-MS coordinate the iron in a bidentate mode via their thiolate and proximal carboxylate groups, with the distal carboxylate adopting distinct orientations. Two active-site Arg residues recognize the substrate carboxylate groups and thereby stabilize a flexible C-terminal loop, underpinning a catalytic site gating mechanism in MSDO. EPR spectroscopy corroborates bidentate coordination, showing conversion of a high-spin {FeNO}7 complex to a low-spin species upon substrate binding. Time-resolved in crystallo reactions capture two key iron-bound intermediates, namely an unprecedented monooxygenated sulfenate and a dioxygenated sulfinate product. These structural snapshots are supported by DFT calculations that point to a stepwise oxygen atom transfer pathway. Computational analysis further accounts for the kinetic differences between the substrate enantiomers, as rationalized by structural comparisons, active-site geometry, and second coordination sphere interactions. Together, these results elucidate fundamental principles of TDO catalysis and advance our understanding of nonheme iron-dependent oxygen activation.

Enzymes are essential biocatalysts across diverse industries, driving demand for high-performing variants. Foundation models are attractive for guiding enzyme discovery, but often lack the resolution to model subtle variations driving function within homologous families. Navigating these rugged functional landscapes to identify elite variants remains challenging and experimentally costly, even when guided by such models. Here we show that coupling dense, family-specific experimental screening with targeted, sequence-based deep learning provides a data-efficient discovery strategy. We experimentally screened 1,513 natural homologues from an esterase superfamily (>7,500 assays) and used this functional landscape to train task-specific models that predict activity, thermostability, and substrate specificity from sequence alone. Prospective experimental validation of previously untested sequences demonstrated that these task-specific models significantly outperformed generalist pre-trained and physics-based models in enriching for target traits. Residue-level attribution further indicated that the models captured sequence patterns consistent with underlying structural features. Finally, retrospective simulations showed that iterative retraining compresses the search space, discovering 60% of top-tier hits using nearly half the samples required by pre-trained baseline models. Together, these results highlight that machine learning can provide mechanistic insight, and that integrating targeted data acquisition with iterative machine learning provides a more data-efficient discovery strategy than relying on generic model scale.

Protein language models (PLMs) provide a novel computational paradigm for deeply mining evolutionary information. Nevertheless, the discrepancy between the natural evolutionary fitness captured by their zero-shot predictions and actual industrial demands significantly constrains wet-lab success rates. To address this bottleneck, we developed CASPE, a light-weighted protein engineering platform consisting of the CAS and APCNet. CAS leverages gradient activation mapping and multi-layer attention matrices to transform the implicit representations of PLMs into explicit site-importance metrics. Working in tandem with APCNet, CASPE establishes a workflow encompassing the entire trajectory from site localization to residue prediction, which successfully overcomes the fitness misalignment issue, enabling the precise directed evolution of target protein properties. CASPE efficiently identifies thermostable (31-60%) and pH-stable (40-80%) mutants. Specialized models further boost its success in phytase evolution, significantly outperforming FoldX and ESM2-t33 in hit rates. By shifting from global saturated mutagenesis to targeted optimization of feature-relevant sites, CASPE streamlines enzyme evolution, yielding a higher discovery rate of beneficial mutants.

The de novo design of enzymes remains a central challenge, requiring consideration of catalytic mechanism and optimization across biochemical and biophysical criteria. To capture these criteria, we draw on principles from evolutionary biology. Here, we present dEVA (design by EVolutionary Algorithm), a multi-objective design framework for structure-based protein design. We apply dEVA to the zero-shot, de novo design of metalloenzymes by optimizing for the coordination sphere of catalytic metals. We fully characterize one of these designs: a bi-zinc metalloenzyme exhibiting promiscuous hydrolytic activity towards both phosphomonoesters and phosphodiesters. This design achieves a catalytic efficiency (kcat/KM) of up to 1500 M-1s-1 and a rate enhancement ((kcat/KM)/kw) of up to 3 x 1013, comparable to characterized natural phosphatases. dEVA offers a general and modular strategy for the programmable design of protein function without dependence on natural templates, predefined motif, or evolutionary information.

Enzyme adaptation toward novel substrates involves the rewiring of intramolecular residue networks, yet how this rewiring differs across multiple substrates, and how it underpins functional trade-offs and promiscuity, remains poorly understood. Here, we profile all 64 combinations of six key mutations in a phosphotriesterase across nine structurally diverse substrates spanning three chemical classes (organophosphates, esters, and lactones), thus generating a multi-dimensional map of epistasis and promiscuity within the phosphotriesterase's active site. We developed a statistically robust reference-based analysis pipeline incorporating error propagation and significance testing to move beyond global epistatic trends and resolve idiosyncratic, substrate-dependent intramolecular wiring in specific genetic backgrounds. Simulations confirm that this pipeline reliably identifies genuine higher-order epistatic interactions while minimizing false positives. We reveal that intramolecular network wiring varies substantially between substrates, even within the same chemical class, with notable divergences between the adaptive target substrate 2-naphthyl hexanoate and its shorter-chain ester analogs. Key higher-order networks, including d233E/h254R/l271F and l271F/f306I/i313F, exhibit substrate-specific epistatic signatures that discriminate between subtle structural features such as acyl chain length, leaving group identity, and heteroatom substitution. These substrate-dependent rewiring events account for observed functional trade-offs, particularly the strong anti-correlation between the adaptive and native substrates. Collectively, these findings demonstrate that comprehensive cross-substrate epistatic profiling, paired with rigorous statistical analysis, provides a powerful framework for dissecting the molecular basis of enzyme promiscuity and the trade-offs that define adaptive evolution.

Cytochrome P450s catalyze a diverse array of reactions including crosslinking of aromatic side chains in the biosynthesis of ribosomally synthesized and post-translationally modified peptides (RiPPs). ApyO is a cytochrome P450 enzyme that forms a C-C bond between two tyrosines in a YLY motif in the substrate ApyA, the precursor peptide of the RiPP aminopyruvatide. We utilized cell-free translation to generate ApyA variants and probe the substrate tolerance of ApyO. Through Alphafold-based modelling and in vitro assays, we show that ApyO accepts the 10 C-terminal residues of ApyA and requires a conserved Arg/Lys in the substrate peptide. Inspired by substrate sequences found in orthologous biosynthetic gene clusters, we substituted one of the tyrosine residues with a tryptophan and observed that ApyO catalyzed the formation of an N-C bond between the indole of Trp and the C{varepsilon}2 of Tyr. ApyO unexpectedly catalyzed formation of a C-O bond between the two tyrosine residues when we substituted the leucine residue in the YLY motif with tyrosine and tryptophan. We also show that a peptide containing a biaryl linkage and the C-terminal aminopyruvate displayed sub-nanomolar inhibitory activity against selected proteases. Overall, this study demonstrates plasticity in the manner of macrocyclization catalyzed by the P450 ApyO and provides a starting point for chemoenzymatic approaches towards producing diverse macrocyclic scaffolds.

O2-tolerance is a desirable property for [FeFe] hydrogenases, which are highly efficient H2-producing catalysts. While most such enzymes are highly sensitive to aerobic environments, a small number of explored representatives exhibit exceptional stability and even H2-producing activity under oxygenic conditions. However, the genetic signatures of the O2-tolerance in this class of enzymes remain largely unknown. To address this knowledge gap, we explored a close homologue of a well-characterized O2-tolerant [FeFe] hydrogenase from Clostridium beijerinckii (CbHydA1) - a hydrogenase from Terrisporobacter glycolicus (TgHydA1). Our investigation indeed confirms that TgHydA1 can transition to the O2-stable Hinact state, a hallmark of O2 tolerance. The surprising outcome is that despite the high amino acid similarity, TgHydA1 shows a substantially higher propensity to remain in the Hinact state than CbHydA1. Using protein film electrochemical experiments, we demonstrate that the root of this behavior lies in roughly tenfold slower reactivation rates than those of CbHydA1 at any applied potential. This degree and direction of variation in reactivation kinetics have not been observed before for any other O2-tolerant [FeFe] hydrogenases or their variants to date, uncovering a yet-to-be-explored facet of reactivity alteration available to these enzymes. Overall, the results presented here highlight the importance of a holistic analysis of [FeFe] hydrogenase sequences in the context of their interaction with O2 that encompasses the protein environment and properties of the auxiliary metallocofactors.

Harnessing enzyme specificity requires a thorough understanding of enzyme promiscuity, which determines enzymes catalytic scope; however, measuring this scope still relies heavily on labor-intensive analytical approaches. While data-driven approaches have emerged to predict the catalytic scope of enzymes, these methods continue to face challenges such as restricted datasets and insufficient integration of enzyme structural information and reaction transformations. Here, we introduce MAERM, an innovative mixed-attention model designed to predict enzyme-reaction matching relationships. Built on our MAERM-DB, a dataset with broad coverage of validated and chemoenzymatic catalysis data, MAERM utilizes a local-global attention module to integrate multimodal enzyme information with fine-grained reaction representations, thereby predicting enzyme-reaction matching probabilities. Results show that MAERM consistently outperforms all baselines, with an average F1-score of 0.984. Notably, on challenging test samples with less than 40% sequence identity to the training set, MAERM outperforms the second-ranked model by 5.9% in F1-score. In addition, MAERM achieves the highest top-10 success rate of 51.7% on Enzyme-405 and the highest balanced accuracy of 0.697 on BioCat-547, further supporting its generalizability in enzyme screening and chemoenzymatic catalysis. Finally, MAERM can serve as an efficient scoring module. When integrated with ProteinMPNN, MAERM has successfully guided novel enzyme design for two carbonyl reduction reactions, resulting in enhanced catalytic potential for the native substrate and demonstrating broad compatibility. Overall, MAERM has the potential to reduce the experimental cost of measuring enzymes catalytic scope, facilitate enzyme design, and ultimately accelerate the design-build-test-learn cycle in enzyme engineering.

Arylmalonate decarboxylase (AMDase) stereoselectively converts disubstituted malonates to chiral carboxylic acids, but its substrate spectrum is very limited regarding the size of the smaller substituent. Inspired by the observation that (S)-selective AMDase variants also convert larger substrates, we unlocked the synthesis of the (R)-enantiomers of -aryl and -alkenyl n-butanoic and n-pentanoic acids, respectively, in exquisite enantiopurity.

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are produced by biosynthetic enzymes that modify genetically encoded precursor peptide backbones and side chains. Genome mining and bioinformatics analyses targeting the multinuclear nonheme iron oxidative (MNIO) enzyme family led to the identification of a Streptomyces thermodiastaticus JCM 4840 RiPP biosynthetic gene cluster, the std cluster, which includes multiple biosynthetic enzymes and a precursor peptide containing a conserved SNKEWQE motif. Using in vitro approaches, we elucidated the modifications installed by the std biosynthetic enzymes. First, a YcaO-TfuA pair thioamidates the backbone of asparagine. Next, a peptidase S8/S53 domain fused to a NodU-like carbamoyltransferase that both carbamoylates the {varepsilon}-amino group of lysine to produce the non-proteinogenic amino acid homocitrulline and cleaves the C-terminal EWQE motif. Finally, a partner protein-MNIO pair bis-hydroxylates the {beta}- and {gamma}-carbon positions of the installed homocitrulline. The formation of homocitrulline and its subsequent modification are unprecedented in RiPP biosynthesis. Moreover, these findings expand the substrate scope of YcaO-TfuA enzymes and MNIOs and identify new roles for carbamoyl transferases in RiPP biosynthesis.

Modern protein design methods based on deep learning allow generation of customized protein scaffolds with diverse geometries and functionalities. Here, we capitalize on these recent advances to develop hyper-thermostable de novo CO2 reductases featuring a cobalt porphyrin IX cofactor (CoPPIX). CoPPIX containing enzymes were assembled in vivo through media supplementation with cobalt salts and assessed for photocatalytic CO2 reductase activity. We identified two cysteine-ligated designs that exhibit high activity (>1000 turnovers at rates of up to 25 min-1) while suppressing competing hydrogen evolution pathways. A 2.1 [A] crystal structure shows close agreement to the design model with the Co-Cys bond programmed as intended. This study showcases the power of computational protein design in developing artificial enzymes to activate challenging molecules such as CO2.