Biocatalysis@TUDelft

Nature, Published online: 04 March 2026; doi:10.1038/d41586-026-00652-3

A workflow was developed to selectively capture bacterially produced compounds containing a reactive diazo chemical group. This enabled the discovery of two diazo-containing molecules from a bacterium that causes lung disease. Investigation of the bacterial synthesis of these molecules revealed an enzyme that constructs the diazo group, with broad synthetic applications.

This integrated study combines experimental enzyme kinetics with QM/MM simulations to map the catalytic mechanisms of four formate dehydrogenases at the atomic level. This approach reveals the key determinants of catalytic efficiency and guides the rational design of biocatalysts for effective CO₂ reduction—a crucial step towards sustainable biotechnology.

Four formate dehydrogenases (FDHs) from Pseudomonas sp. 101, Myceliophthora thermophila, Chaetomium thermophilum, and Ogataea parapolymorpha were recombinantly produced, purified, and characterized to investigate their catalytic properties and reaction mechanisms. The enzymes were studied for their ability to oxidize formate to carbon dioxide (CO₂) coupled with NAD⁺ reduction. In contrast, their CO₂ reduction activity was undetectable under the tested conditions. Oxidative reactions revealed significant differences in catalytic efficiency and substrate specificity, prompting further investigation through molecular dynamics (MD) simulations and quantum mechanics/molecular mechanics (QM/MM) ONIOM calculations. Structural models were derived from high-resolution structural data available for enzymes from Pseudomonas sp. 101 (pseFDH) and Chaetomium thermophilum (ctFDH) and extended to all four variants. Comparative analyses of the transition states revealed distinct interaction patterns within the active sites, allowing us to discriminate between high- and low-performing catalysts, in full agreement with the experimental k _cat values. These findings provide a mechanistic rationale for the observed disparities in catalytic performance and offer structural insights into the determinants of FDH activity. Notably, ctFDH emerged as a potential candidate for the development of CO₂-reducing reactions, with QM/MM data guiding the rational design of transition-state stabilizing mutations.

A recommendation and method to view the energy potentials of metabolites with reference to (green arrow) a biochemical precursor state (in green: water, Mg²⁺, phosphate, bicarbonate, ammonium, and sulfate, at pH = 7) rather than (the red arrow) the physical–chemical reference state (in grayish red) that is alien to biology, that is, O₂, H₂, N₂ at 1 atmosphere, graphite, and solid P, S, and Mg.

Chemical potentials (molar Gibbs energies) are usually extrapolated to the remote physical–chemical reference state and then stored. Subsequent use under in vivo conditions requires a similarly substantial, reverse extrapolation, again with significant potential errors. In order to shrink both extrapolations drastically and thereby enhance both biological meaning and accuracy, we propose a transformation to a more biological reference state: pH = 7, pMg = 3, 99.5% water, with 1 mm each of the additional ‘precursors’ inorganic phosphate, sulfate, ammonium, and bicarbonate, and with twin temperatures 37 and 25 °C, ionic strength 0.15 m and mm as concentration unit. These precursors substitute for reference compounds alien to biology such as H₂ at 1 bar, and solid graphite, sulfur, and phosphorus. The standard chemical potentials are herewith increased by the magnitudes of the chemical potentials of protons, Mg²⁺, water, and the four precursors, each multiplied by the number of corresponding atoms in the molecule. This defines standard ‘metabolic potentials’. We make these potentials findable and accessible as 1360 collated standard chemical potentials for 320 compounds of biochemical interest at the twin metabolic reference states. We do this for 3 reference pH's: We present the metabolic reference state as a convenient anchor, not a universal intracellular milieu. All datasets must continue to report the actual experimental state (T, pH, pMg, I, osmolarity, concentrations), yet aim at (also) reporting parameter values for this anchor state; we supply algorithms to transform between states. This preserves interoperability across diverse organelles, media and between enzymology and chemical engineering, while facilitating reuse.

4 enzymes team up in a cascade fashion to convert (waste) glycerol into (high value) l-erythrulose in high final concentration.

Upgrading glycerol (GLY), an abundant bio-based platform chemical, into high-value oxygenates is a cornerstone of integrated biorefineries. While chemo-catalytic routes typically suffer of a lack of selectivity, enzymatic approaches are often limited in productivity and robustness. Glycerol dehydrogenase (GDH) catalyzes the selective oxidation of glycerol into the valuable compound dihydroxyacetone (DHA). However, this biocatalytic reaction is hampered by strong product inhibition of the enzyme and by the requirement for the costly cofactor NAD⁺. The enzyme is also inhibited by the formed NADH. To overcome these limitations, we designed a biocatalytic cascade system. In this approach, fructose-6-phosphate aldolase (FSA_A129S) rapidly converts DHA, thereby preventing inhibition and funneling the reaction toward the formation of l-erythrulose, a stable, noninhibitory, and more valuable product. In addition, an optimized cofactor regeneration system based on NADH oxidase and catalase (NOX) is incorporated so that only a catalytic amount of NAD⁺ is required. All four enzymes are co-immobilized on a resin to create a multifunctional heterogeneous biocatalyst. Using this system, l-erythrulose is produced at concentrations up to 120 mM with complete selectivity.

This study explored the cascade depolymerization of a polyether-polyester polyurethane based on a combination of low-temperature thermal treatment and enzymatic hydrolysis. Heat pretreatment changed the physicochemical properties of polyurethane, followed by cutinase-catalyzed hydrolysis, leading to an increase in weight loss and the production of two intermediates, which were further hydrolyzed into the constituent monomer, 4,4^′-methylenedianiline (MDA) by the urethanase SP2.

Enzymatic depolymerization of postconsumer polyurethanes (PURs) offers a promising route for sustainable plastic waste management. However, the complex chemistry of PURs containing carbamate, ether, and ester bonds poses a challenge for such a biotechnological process. Here, we explored the deconstruction of a commercial polyether-polyester-PUR through a cascade depolymerization approach, in which a low-temperature thermal pretreatment (180°C, 4 h) was combined with tandem enzymatic hydrolysis. Heat treatment modified the polymer's physicochemical properties, enabling the cutinase HiC from Humicola insolens to cause more than 8% weight loss of the treated PUR films, versus less than 2% of the untreated control after 48 h incubation. Furthermore, the addition of the metagenomic urethanase SP2 completed the one-pot enzymatic cascade, achieving not only depolymerization to the constituent monomer, 4,4′-methylenedianiline (MDA), but also a nearly 3-fold increase in MDA yield compared to using SP2 alone. Docking studies highlighted HiC's specificity toward ester bonds in the PUR polymeric units, and two HiC variants further enhanced degradation within 24 h. Altogether, this work lays the foundation for future investigation and process design for the depolymerization of polyether-polyester-PURs and related materials by cascade enzymatic reactions.

We highlight how machine learning interatomic potentials (MLIPs), embedded within hybrid machine learning and molecular mechanics (ML/MM) frameworks, are transforming computational enzymology. By replacing the costly quantum mechanics (QM) region with reactive MLIPs trained on diverse datasets, ML/MM achieves near-QM accuracy with speedups on the order of thousands, enabling quantitative prediction of stereoselectivity and mutant effects in enzymatic catalysis. Key frontiers, including long-range corrections, field-aware embeddings, reaction modeling, and excited-state simulations, are discussed as pathways toward next-generation computational enzyme design.

We report here a series of iron(III) porphyrin-phenoxide complexes where secondary-sphere H-bonding interactions exert a large influence on geometry, spin state and redox properties by shifting the Fe(III)/Fe(II) redox couples and 1e-oxidation toward more positive potentials and thereby offering insights into enzymatic regulation for biological activities.

ABSTRACT

Hydrogen bonding (H-bonding) plays a pivotal role in regulating the chemical and electrochemical properties of metalloproteins by influencing substrate recognition, binding orientation, and active-site geometry. In heme enzymes, conserved H-bonding networks are directly linked to catalytic efficiency by modulating redox potentials and spin states of the iron center. Despite extensive studies on biological systems, the molecular origin of H-bonding effects on the electronic structure and redox properties of heme groups remains underexplored. We report here iron(III) porphyrin–phenoxide complexes where secondary-sphere H-bonding interactions exert a large influence on geometry, spin state, and redox properties. The H-bonding interactions elongate the axial Fe─O bond, contract the porphyrin core, and stabilize the intermediate-spin (S = 3/2) state of iron, while the absence of H-bonding favors the high-spin (S = 5/2) state. Similar effects are also observed in the iron(III)-chloro complex in which the axial ligand is engaged in secondary-sphere H-bonding interactions. Electrochemical studies reveal positive shifts in the Fe(III)/Fe(II) couple and 1e⁻ oxidation, highlighting H-bonding as a regulator of redox noninnocence. Supported by computational studies, our findings provide fundamental insights into the interplay between H-bonding, spin state, and redox chemistry, thereby offering insight into enzymatic regulation for its biological functions.

Poly({varepsilon}-caprolactone) (PCL) is a well-known biodegradable polyester and is among the few polyesters susceptible to degradation in marine environments; however, marine-derived PCL-degrading enzymes remain poorly characterized. Here, we searched for PCL-degrading enzymes from the marine bacterium Alloacanivorax gelatiniphagus JCM 18425 using a genome-based approach. Five candidate genes were predicted, and one encoded protein, designated Ag0826, was identified as a PCL depolymerase. Recombinant Ag0826 was expressed, purified, and biochemically characterized. The enzyme exhibited optimal activity at 35-40{degrees}C and pH 8.0, although it showed limited thermal stability. Substrate specificity was compared with that of leaf-branch compost cutinase (LCC), a well-characterized poly(ethylene terephthalate) (PET) hydrolase, using various polyesters. Both enzymes exhibited largely overlapping substrate ranges with respect to the presence or absence of monomer conversion activity across the tested substrates. Ag0826 slightly degraded PET to terephthalic acid, indicating potential PET-hydrolyzing activity; its conversion rate, however, was substantially lower than that of LCC, suggesting that Ag0826 exhibits a substrate preference differing from LCC. Phylogenetic analysis based on amino acid sequences revealed that Ag0826 formed a separate clade from LCC and IsPETase (from Ideonella sakaiensis). At a broader level, Ag0826 was positioned near HaloPETase1 (from Halopseudomonas pachastrellae), which has been proposed as a Type III PET hydrolase; in contrast, residues corresponding to the substrate-binding subsites were similar but not identical between the two enzymes. These results suggest that Ag0826 broadly belongs to the group of known PET hydrolases, yet it exhibits a partially distinct sequence profile even within this enzyme family.

Assembly-line enzymes carry out multi-step synthesis of diverse metabolites by using a handful of catalytic motifs in which minor structural differences control substrate specificity and reaction order. Here we examine differences in substrate binding to FabB and FabF, the two {beta}-ketoacyl-ACP synthases (KSs) responsible for fatty acid elongation in Escherichia coli, by exploring a peculiar mutational effect. In FabB, a blocking mutation in the acyl binding pocket yields a shifted, but broad product profile, while in FabF, the same mutation disrupts the binding of acyl chains longer than eight carbons (C8). X-ray crystal structures of the FabB mutant provide an explanation: a second, previously unobserved binding pocket allows medium-to-long acyl chains ([≥] C8) to bind with an alternate conformation. Molecular simulations suggest that this pocket is more stable in FabB than in FabF, where mutations reduce the catalytic competency of longer chains instead of shifting them to the alternate pocket. Our findings indicate that homologous KSs differ not only in their primary binding sites but also in the availability of alternative binding modes that can buffer against mutational effects and enable functional diversification.

With the rapid development of Large Language Models (LLMs) and Agent technologies, AI can assist in solving a variety of real-world problems across multiple domains, such as autonomous driving, drug discovery, and materials design. In this work, we present EnzySeek, an enzyme catalysis AI agent designed to assist researchers in enzyme catalysis simulations. First, we constructed a domain-specific knowledge base by curating thousands of papers related to enzyme catalysis. Second, we customized Model Context Protocol (MCP) interfaces for each step of the enzyme catalysis simulation workflow, enabling these functions to be invoked by LLMs. Finally, we configured an agent capable of simultaneously referencing past empirical studies on enzyme catalysis, autonomously executing tool calls, and analyzing as well as presenting the results. EnzySeeks capabilities cover multiple aspects, including protein structure prediction, molecular docking, system preparation and parameterization, molecular dynamics (MD) simulations, and QM/MM calculations. The conclusions drawn by EnzySeek are primarily based on the results of QM/MM calculations. We employed the semi-empirical quantum mechanical method GFN2-xTB to calculate the QM region of the system. Benchmark results indicate that the GFN2-xTB method can achieve high efficiency while maintaining accuracy. The EnzySeek agent is designed to continuously learn from newly published literature and past computational tasks. During its operation, every AI decision is manually verified and scored by human experts. This human-in-the-loop validation provides the AI with sufficient case-based support, ultimately contributing to the full automation of enzyme catalysis computations. All data generated during the simulations are compiled into a dataset, which is used to establish evaluation criteria specific to enzyme catalysis computational results.

Nature Synthesis, Published online: 27 February 2026; doi:10.1038/s44160-026-01003-w

Photoinduced ligand-to-metal charge transfer is used to enable abiotic cross-couplings in metalloenzymes. Engineering a 2-histidine metal site and substituting iron with nickel activates PsEFE for nickel-catalysed C(sp²)–S coupling reactions between thiols and aryl bromides. Directed evolution yielded metalloenzyme variants that can produce a range of thioethers with high efficiency.