ACS Catalysis

Enzymatic sulfation offers a green-chemistry route to the selective sulfation of phenolic and other hydroxylated compounds. Bacterial arylsulfate sulfotransferases (ASSTs) are particularly attractive biocatalysts because they use simple aryl sulfate donors, such as p-nitrophenyl sulfate (pNPS), instead of the complex and labile cofactor PAPS required by conventional sulfotransferases. However, their rational application and engineering remain limited because the molecular principles controlling substrate recognition, catalytically competent donor and acceptor positioning, covalent sulfo-enzyme formation, and sulfo transfer are not fully understood. In particular, it has remained unclear how conserved active-site histidines cooperate during catalysis and how active-site preorganization promotes efficient sulfo transfer. Here, we address these questions for the newly characterized ASST (ASTB) from Desulfitobacterium hafniense clade 6 (Dh_ASST_6) by combining X-ray crystallography, steady-state enzyme kinetics, and quantum mechanics/molecular mechanics (QM/MM) simulations. Crystal structures of Dh_ASST_6 in the apo state and donor-soaked state reveal a monomeric {beta}-propeller enzyme with a histidine-centered active site and directly identify His350 as the transient sulfo carrier. Kinetic analysis shows efficient turnover and supports a ping-pong bi-bi mechanism, consistent with formation of a covalent sulfo-enzyme intermediate. Steered QM/MM simulations of the second catalytic step show that sulfuryl transfer from sulfated His350 to the phenolic acceptor proceeds through a proton-coupled, multistep pathway in which His283 assists acceptor activation by proton transfer. Active-site preorganization precedes the main bond-reorganization event and generates a near-attack geometry in which acceptor alignment, proton transfer, and stabilization of the sulfo group by a conserved hydrogen-bonding network jointly promote sulfo transfer. Free-energy reconstruction gives a low barrier of approximately 9.6 kcal/ mol, in agreement with efficient enzymatic turnover. The structural, kinetic, and computational data support a complete catalytic cycle for Dh_ASST_6 based on a cooperative two-histidine mechanism. This model establishes catalytically competent acceptor positioning and preservation of the histidine-centered hydrogen-bonding network as key design principles for ASST engineering. These features provide concrete targets for expanding substrate scope and tuning donor or acceptor preference while maintaining efficient sulfuryl transfer.

De novo-designed enzymes have recently achieved high catalytic activity and stereoselectivity while demonstrating exceptional thermostability in entirely novel protein scaffolds. Among these, -helical barrel protein scaffolds are attractive structures for biocatalysis due to their structural simplicity, high thermostability, and rationalizable sequence patterning. However, enabling major structural reengineering of these scaffolds while maintaining the structure, stability and catalytic activity while also improving soluble protein production remain major challenges and pose the fundamental question how engineerable a de novo backbone-sequence pair is. Here, we combine deep learning based and classic computational protein design to modify and optimize de novo -helical barrel biocatalysts. Using the previously reported six-helical barrel 6H5L as a model scaffold, AlphaFold2-guided RosettaRemodel enabled the design of a truncated variant, whose crystal structure closely matches the design model. Additional sequence-redesign using ProteinMPNN generated a variant with a tenfold increase of soluble protein yield in Escherichia coli. Biochemical, biophysical, and structural analyses showed that both variants retained the overall barrel architecture, high thermal stability, and catalytic activity for both purified protein and whole-cell systems. Detailed kinetic analysis on the variants showed both variation in kcat and Km, reflecting changes in catalytic turnover and substrate binding. Together, these approaches provide new insights and possibilities for the further engineering of functional de novo -helical barrels, their ability to withstand dramatically large sequence changes and their broader application in biocatalysis and biotechnology.

Sulfation plays an important role in many metabolic processes such as substrate detoxification, solubilization or activation. The enzymes responsible for these reactions, sulfotransferases, occur in all domains of life and can be classified in two major, evolutionary independent groups, PAPS dependent and PAPS independent or arysulfate sulfotransferases (ASST) enzymes. Besides the fact that they play key roles in biological functions, sulfotransferases can be interesting enzymatic tools to selectively sulfate specific positions of various metabolites. To this end we need to understand the relation between sequence, structure and function. Actually, eukaryotic PAPS dependent sulfotransferases have extensively been studied, while we largely lack knowledge about the diversity of bacterial PAPS-independent ASSTs. We have conducted a multidisciplinary study of a bacterial sulfotransferase from Salmonella typhimurium, revealing the importance of active site residues for activity and guiding substrate specificity. Based on the crystal structure in complex with substrate, we used molecular docking combined with Quantum Mechanics/Molecular Mechanics (QM/MM) free energy calculations to investigate the reaction catalytic mechanism with a focus on the role of specific active site residues in substrate specificity. Our results show that the catalytic mechanism involving three conserved histidines is common with previously determined ASSTs, and highlight a specific pair of aromatic residues, Phe174 and Tyr557, that play an important role in defining the substrate specificity in ASST clade 5. These findings enhance our general understanding of how substrate specificity is attained and has the potential to guide further enzyme engineering to expand the acceptor scope of ASSTs.

Engineering proteins at the scale of modern high-throughput screening technologies remains challenging because most experimental libraries sample only limited regions of sequence space while maintaining relatively low levels of functional diversity. Although recombination-based strategies can introduce substantially larger sequence changes than conventional mutagenesis approaches, it is unclear whether highly recombined enzymes can retain catalytic competence when library sizes approach millions to billions of variants. To investigate this question, we constructed a recombination library by combining ten sequence blocks from ten bacterial PETases, generating a theoretical diversity of 10^8 chimeric enzymes. Screening of yeast-displayed libraries using a fluorophosphonate probe for catalytic-serine reactivity identified substantial populations of catalytically competent variants despite extensive sequence recombination and dozens of amino acid substitutions per chimera. Approximately 2% of sampled variants possessed active-site serine reactivity, corresponding to an estimated 10 catalytically competent enzymes within the full theoretical library. Deep sequencing showed that functional populations remained broadly distributed across sequence space following selection and contained multiple enriched recombination patterns spanning the PETase structure. Secondary lysate screening and enzyme-normalized activity measurements further identified multiple purified PETase chimeras with soluble esterase activity comparable to or greater than the wild type Ideonella sakaiensis PETase on a chromogenic surrogate substrate. Together, these results demonstrate that large-scale recombination can generate millions of diverse and catalytically competent enzymes while remaining compatible with modern high-throughput screening workflows. More broadly, this work establishes recombination-based diversity generation as a scalable strategy for protein engineering and large-scale sequence-function data generation.

Despite their intriguing native metalloenzymology, naturally occurring copper enzymes remain largely underexploited for new-to-nature biocatalytic reactions. Herein, we report the systematic investigation and reprogramming of natural copper enzymes to catalyze unnatural free radical (pseudo)halogenation reaction in a highly enantioselective fashion. Evaluating Cu enzymes in the decarboxylative azidation of redox-active esters revealed activity across multiple Cu enzyme families, with type III binuclear Cu enzymes, particularly the Bacillus megaterium tyrosinase (BmTyr), exhibiting superior activity and enantioselectivity across both stabilized and unstabilized alkyl radicals upon further engineering. The strong halide binding affinity of the binuclear Cu system also enabled challenging enantioconvergent bromination, chlorination and isothiocyanation reactions, which remained inaccessible to repurposed nonheme Fe enzymes. Further EPR and UV-visible spectroscopic analyses confirmed the coupled binuclear nature of wild-type and engineered bacterial tyrosinases and provided insights into the origin of their enhanced activity. Collectively, this study establishes binuclear copper enzymes as a powerful platform for new-to-nature stereoselective radical reactions, expanding the scope of metalloenzyme catalysis beyond mononuclear systems.

The standard numbering scheme for class B beta-lactamases (metallo-beta-lactamases, MBLs), the BBL scheme, was proposed by Galleni et al. in 2001 and updated by Garau et al. in 2004 from the small number of MBL structures then solved. Hundreds of additional class B sequences have since been catalogued, but most have never been structurally solved, so whether BBL correspondences hold across this larger diversity has not been directly tested. Following the precedent set for the sibling class D enzymes, we used ColabFold (AlphaFold2/MMseqs2) to model 87 class B families across subclasses B1, B2, and B3 lacking an experimental structure, after validating the pipeline against an eight-enzyme benchmark spanning all three subclasses (RMSD to experimental structures 0.35-0.46 A). Applying BBL zinc-ligand numbering to the 87 models via paired sequence- and structure-based residue-transfer methods, the canonical zinc-coordination motif transferred cleanly in the large majority of families, and 11 of the 16 sequence-level exceptions were accounted for by a literature-supported biological cause (a subclass-appropriate substitution or a previously proposed, unconfirmed fourth subgroup), while two motif-retaining families showed predicted local structural divergence at the active-site loop, flagged by structural superposition, that could not be resolved to exact zinc-ligand numbering and warrants direct experimental follow-up. These results show that the BBL scheme, formulated before metagenomic sequencing or deep-learning structure prediction existed, generalizes robustly into the structural space these technologies have since opened, and identify a small set of variants whose active sites merit experimental attention.

Catalyst-controlled stereodivergent synthesis of acyclic molecules bearing multiple stereogenic centers remains a longstanding challenge in asymmetric catalysis, particularly through intermolecular radical C-C coupling. Here, we report the directed evolution of pyridoxal 5'-phosphate (PLP)-dependent biocatalysts for the stereodivergent synthesis of non canonical amino acids bearing either skipped , {gamma}-stereochemical dyads or contiguous , {beta}, {gamma}-stereochemical triads. Starting from a thermophilic tryptophan synthase {beta}-subunit, protein engineering generated complementary enzyme variants that selectively construct three of the four possible , {gamma}-stereoisomers through biocatalyst-controlled reversal of both remote {gamma}-stereochemistry and proximal -stereochemistry. These engineered enzymes catalyzed intermolecular radical C-C bond formation with a broad range of secondary alkyltrifluoroborates, affording , {gamma}-disubstituted amino acids in excellent yields and stereoselectivities. Furthermore, directed evolution enabled efficient {beta}-dehydroxylative radical coupling of the non-native threonine-derived aminoacrylate intermediate, providing amino acids bearing contiguous , {beta}, {gamma}-stereochemical triads with up to 96:4:<0.1:<0.1 d.r. and >99:1 e.r.. To further demonstrate the synthetic utility of this platform, the stereochemically enriched amino acids were transformed into valuable {gamma}-chiral amines through engineered PLP dependent tryptophan decarboxylases, establishing a dual PLP enzyme based biocatalytic cascade for the enantiodivergent synthesis of privileged amine scaffolds. Molecular dynamics simulations reveal that enzyme controlled {gamma}-stereoselectivity originates from active-site remodeling that modulates {pi}-{pi} interactions and steric contacts governing the facial selectivity of the radical addition step. This work expands the synthetic capabilities of radical biocatalysis by enabling catalyst-controlled stereodivergent assembly of complex acyclic stereochemical arrays and establishes engineered PLP enzymes as versatile catalysts for the synthesis of stereochemically complex amino acids and chiral amines.

Despite rapid advances in new-to-nature biocatalysis, trimolecular enzymatic reactions that generate previously inaccessible molecular entities, particularly those bearing well-defined, congested stereochemical dyads, remain exceedingly rare. Here, we report a novel diastereo- and enantioselective photobiocatalytic three-component radical coupling unknown in both organic chemistry and enzymology, enabled by cooperative catalysis employing an evolved pyridoxal biocatalyst and a transition-metal photosensitizer. Directed evolution using high-throughput photobiocatalysis furnished two threonine aldolase variants, enabling the stereoselective assembly of contiguous tri- and tetrasubstituted stereocenters as well as vicinal tetrasubstituted stereocenters, which were long regarded as a challenging goal for stereoselective radical chemistry and asymmetric catalysis. Furthermore, a variety of radical precursors, including alpha-iodoesters, alpha-iodoamides, alpha-iodoketones, and alpha-iodonitriles, could be transformed into structurally diverse, densely substituted non-canonical amino acid derivatives via C-H functionalization with excellent stereocontrol. Leveraging the broad substrate compatibility of engineered pyridoxal biocatalysts, combinatorial biocatalysis was achieved by simultaneously varying all three coupling partners, delivering products with a 98% success rate. Collectively, this diversity-generating multicomponent photobiocatalytic radical coupling afforded a powerful strategy for addressing long-standing challenges in asymmetric catalysis while enabling access to valuable stereochemically complex small molecules with applications in medicinal chemistry.

The enzymatic oxidation of 5-hydroxymethylfurfural (HMF) to 2,5-furandicarboxylic acid (FDCA) is a key step in the sustainable production of polyethylene furanoate (PEF), yet the final oxidation of 5-formyl-2-furancarboxylic acid (FFCA) to FDCA remains the principal bottleneck for industrial application. Although hydroxymethylfurfural oxidases (HMFOs) can catalyze this transformation, naturally occurring enzymes with high FFCA-oxidizing activity remain largely unexplored. Here, we present an iterative computational bioprospecting campaign to identify natural HMFOs with improved activity toward this rate-limiting reaction. Across four successive rounds, the computational workflow evolved from conservative homology-based screening to large-scale sequence mining assisted by machine learning, with each iteration redesigned according to the principal limitations identified through molecular simulations and experimental characterization. This adaptive strategy led to the discovery of several natural HMFOs with substantially improved FFCA oxidation activity, including candidates whose performance under the tested conditions approached that of the engineered benchmark 8BxHMFO. Beyond the identification of promising biocatalysts, the study demonstrates the value of continuous integration of computational prediction and experimental feedback for refining enzyme discovery campaigns, and provides practical insights into balancing catalytic prioritization, sequence diversity, developability, and computational scalability in future simulation-guided bioprospecting efforts.

[FeFe]-hydrogenases are molecular hydrogen (H2) converting enzymes that employ a hexanuclear iron-complex, the H-cluster, as catalytic cofactor, and a proton transfer pathway (PTP) that allows efficient proton coupled electron transfer (PCET). Recent phylogenetic analyses revealed different groups of [FeFe]-hydrogenases. Although only very few members from other groups have been characterized, current knowledge suggests high catalytic activity is predominantly associated with group A members. Here, we show that metal ions inhibit group A [FeFe]-hydrogenases by binding to three specific residues at the entrance of the PTP. Exchanging residue H565 of Clostridium pasteurianum CpI results in a metal-insensitive protein variant with wildtype like activity. In contrast, exchanging S320 and H569 in CpI, and their counterparts in additional group A [FeFe]-hydrogenases results in enzymes with strongly decreased activities and large overpotential requirements. These features are consistent with important roles of both residues in catalytic proton transfer. CpI structures reveal that H569, locally anchored by E278, can be present in two conformations so that we propose a histidine-dependent pendulum mechanism for exchanging protons between bulk solvent and the PTP, which is well supported by theoretical calculations. By that, our study widens information on how the mobility of histidine, governed by residues in the vicinity, contributes to the fundamental concept of PCET.

Azo dyes are widespread in the environment, and humans ingest a range of these dyes present in food and drugs. The human gut microbiome significantly impacts the fate of ingested azo dyes primarily through the reduction of the azo bond (R-N=N-R'). Flavin-dependent azoreductases have been considered the key enzymatic drivers of this process. To challenge this paradigm, we constructed a sequence similarity network of known azoreductases and focused on AzoA of Klebsiella pneumoniae, a central representative within the gut microbiome cluster. Unexpectedly, K. pneumoniae AzoA displayed relatively low in vitro activity against a range of azo compounds. While the physiological implications remain unclear, these findings are consistent with the possibility that non-enzymatic mechanisms contribute substantially to azo compound reduction in the gut. Furthermore, structural characterization of AzoA, as a holoenzyme and in its substrate-bound state, revealed a previously unrecognized ligand-coupled mechanism involving remodeling of the active site and oligomeric state.

The 4-amidopentadienoate (APD) motif is a rare and bioactive pharmacophore found in the cyclic lipodepsipeptide natural products BE-43547, rakicidins, microtermolides, and related compounds. Although several biosynthetic gene clusters associated with this family have been identified, the enzymatic origin of the APD warhead has remained unknown. Here, we investigated APD formation using NRPS engineering to install the unusual APD-associated polyketide synthase (PKS) module from the BE-43547 biosynthetic pathway into heterologous nonribosomal peptide synthetase/polyketide synthase (NRPS/PKS) assembly lines expressed in Escherichia coli. The engineered hybrids produced the designed lipopeptide containing the APD moiety, demonstrating that the core biosynthetic machinery is sufficient for APD installation and that no trans-acting enzymes are required. Total synthesis of the target compound enabled structural validation by co-elution, MS/MS analysis, and NMR spectroscopy. Production titers of 59 ug/L were achieved in the heterologous host. Systematic mutagenesis further revealed that the conserved dehydratase His-Asp catalytic dyad is essential for APD formation, whereas an additional highly conserved histidine is dispensable. These findings establish the biosynthetic origin of the APD warhead and provide a foundation for incorporating this privileged pharmacophore into engineered peptide scaffolds.

The valorisation of biological polymers requires microbial systems that can both access recalcitrant substrates and convert the resulting carbon into useful products. Although microbial genome and metagenome resources provide an expanding reservoir of candidate depolymerizing and modifying enzymes, most discovery workflows remain disconnected from enzyme optimisation and host adaptation. Here we present a coupled sequence-based enzyme discovery, enzyme evolution and synthetic yeast chassis adaptation strategy for microbial biopolymer valorisation. Focusing on laccases for the depolymerisation of lignin as a proof of concept, we combine sequence data mining for enzyme discovery, modular yeast surface display for functional screening, directed evolution for enzyme optimisation and synthetic yeast genome diversification for chassis improvement. In our study, surface display enabled functional benchmarking and recovery of improved laccase variants and synthetic-genome-enabled diversification provided a route to explore host configurations that influence display and enzyme performance. By integrating enzyme-level and chassis-level optimisation, this framework addresses a central bottleneck in converting microbial biodiversity by computational sequence repository mining into deployable biomanufacturing systems. Our results establish laccases as tractable entry points for oxidative biopolymer conversion and provide a generalizable platform for engineering yeast systems for sustainable carbon valorisation.

De novo enzyme design requires sequences whose structures reproduce prescribed catalytic geometries. This remains challenging for active sites that couple multiple catalytic residues, cofactors and substrates. Here we introduce the Catalytic Guidance Engine (CaGE), which optimizes designed sequences to satisfy complex sets of functional constraints by alternating constraint guided structure prediction trajectories with sequence design, gradually fading the strength of the constraint information over cycles to nudge the designed sequence towards satisfying all the constraints. We train RF3 to accept atom-pair distances and other design conditions, and use classifier-free guidance to fade their influence over multiple design cycles. We also develop training-free motif guidance protocols which operate at inference time by directly adjusting selected atoms towards the design target geometry during diffusion sampling. CaGE improves catalytic-motif geometry across a diverse enzyme benchmark and yields sequences that recover complex target active sites in independent AF3 predictions. We experimentally demonstrate the power of CaGE by designing active enzymes with complex catalytic sites: a nonheme-iron design positions four active-site residues around an iron centre and a transition-state ligand, and a PLP-dependent design positions four catalytic residues around a PLP cofactor with multiple functional groups. Active site substitutions and a 2.7[A] crystal structure support the designed mechanisms and structures. Thus, CaGE provides a route to functional de novo enzymes with complex active sites.

Antibiotic resistance driven by {beta}-lactamases has become a major threat to public health. Here, we propose a computational protocol for rapid estimation of {beta}-lactamase activity towards carbapenem antibiotics from sequence alone. Starting from AI-predicted acylenzyme complexes, only approximate transition state ensembles are sampled to compute catalytic electric fields, which strongly correlate with experimental activation barriers (R2 = 0.8). Compared with full quantum mechanics/molecular mechanics (QM/MM) reaction simulations, the protocol substantially reduces the amount of configurational sampling and thus computational cost. Our protocol enables efficient identification of carbapenem breakdown efficiency, further highlighting the important role of electric fields in enzyme catalysis.

Lipoate is an essential protein-bound cofactor attached to conserved lysine residues in structurally related acceptor proteins, including GcvH of the glycine cleavage system and LbpA proteins required for sulfur oxidation by the sulfur-oxidizing heterodisulfide-like (sHdr) complex. The coexistence of distinct lipoylation pathways and acceptor proteins raises the question of how the enzymes initiating lipoate attachment discriminate between homologous but functionally distinct substrates. We systematically tested lipoate:protein ligases and potential acceptor proteins from five representatives of the bacterial phyla Pseudomonadota and Aquificota using native gel-shift assays and MALDI-TOF mass spectrometry. The canonical Escherichia coli lipoate:protein ligase Lpl(AB) preferentially modified GcvH proteins but showed little or no activity toward LbpAs. In contrast, sulfur oxidation-associated lipoate:protein ligases, termed sLpl(AB), which function in the sLpl(AB)-LipS1/LipS2 lipoate assembly pathway, strongly preferred LbpA proteins and generally failed to modify GcvH. Conserved cysteine residues characteristic of LbpAs were dispensable for recognition by sLpl(AB). Instead, comparison of amino acid sequences and electrostatic surfaces revealed predominantly acidic interaction surfaces in GcvH proteins and corresponding basic regions in LbpAs. Accordingly, replacement of selected basic regions in Roseovarius mucosus LbpA2 by acidic residues reduced modification by its cognate sLpl(AB) and simultaneously enabled weak modification by E. coli Lpl(AB). These findings establish electrostatic surface properties as major determinants of substrate discrimination by lipoate ligases and show that recognition depends on distributed features of the folded acceptor protein rather than solely on the sequence surrounding the lipoylated lysine.

Protein-cofactor selectivity prediction is important for enzyme annotation, metabolic engineering, and biocatalyst discovery, but it remains challenging because experimentally supported associations are limited and unevenly distributed, and because biologically relevant differences can be subtler than those captured by general protein-ligand interaction models. Here, we present MINT (Multimodal Interaction Network with Teacher supervision), a multimodal framework for cofactor specificity prediction. MINT combines protein sequence features with cofactor sequence and graph representations and is trained with a gradual adaptation strategy that transfers useful information from a pretrained protein-ligand interaction model to the cofactor prediction task. On a fixed balanced held-out test set, MINT achieved the best overall performance among all compared methods (AUC = 0.932), and this ranking was reproduced in an independent family-stratified evaluation at the natural positive rate (pooled AUC = 0.939). Further analyses suggest that the pretrained model provides transferable structural information, while the gradual training scheme helps adapt that information to cofactor specificity. In a case study of the chemically near-identical NAD/NADP pair, MINT recovered 95.0-98.6% of true binders and resolved a single-phosphate-level distinction with direct relevance to engineering enzyme cofactor specificity. These results show that gradual transfer learning is an effective approach to cofactor specificity prediction when annotation is limited.

Glucoamylase is essential for industrial starch saccharification, but the limited thermostability and near-neutral pH tolerance of fungal glucoamylases necessitate cooling and acidification of liquefied starch. Here, we developed an artificial intelligence-guided strategy to simultaneously improve the thermostability, pH tolerance, and catalytic activity of glucoamylase from Penicillium oxalicum (PoGA). Two property-specific machine-learning models, CASPE-T and CASPE-A, identified substitutions associated with thermostability and pH tolerance, respectively. Experimental screening identified beneficial substitutions in 11 of 21 CASPE-T and 12 of 22 CASPE-A candidates. Folding-energy-guided recombination integrated the two traits while maintaining structural compatibility. The optimal variant, PoGA T513E/Q305N, exhibited 2.21-fold higher specific activity than the wild type, with half-life extended from 22.3 to 57.9 min at 60 degrees C and from 16.6 to 64.7 min at pH 8.0. Molecular dynamics simulations attributed these improvements to reinforcement of high-occupancy hydrogen-bonding networks, suppression of conformational fluctuations in the linker and carbohydrate-binding module, enhanced long-range dynamic coordination, and preservation of a compact catalytic architecture. At 60 degrees C and pH 6.5 without acidification, PoGA T513E/Q305N produced 219.9 g/L glucose and achieved 89.1% starch conversion, 31.4% higher than the wild type. This work provides an efficient framework for multi-objective enzyme engineering and sustainable starch biorefining.

Electromicrobial production (EMP) technologies aim to combine renewable electricity, CO2, and engineered microbes to make energy-dense molecules at efficiencies exceeding photosynthesis. CO2 can be electrochemically reduced to formate, which is far easier to handle at the bench than H2 or an electrode, but formate carries only two electrons per carbon against the six in a biofuel. The remaining electrons must come from oxidizing additional formate, from H2 oxidation, or from extracellular electron uptake (EEU), and no rigorous comparison of these options coupled to the choice of carbon assimilation pathway currently exists. We calculate upper-limit efficiencies for butanol production by six carbon assimilation pathways, each paired with all three electron delivery mechanisms, using electrochemical parameters drawn from a survey of the recent literature. Electrical to butanol energy conversion efficiencies range from 35.5 to 51.7%, corresponding to solar-to-fuel efficiencies of 11.7 to 17%, so even the least efficient route exceeds the 8% theoretical ceiling of algal photosynthesis. The serine variant of the reductive glycine pathway reaches an electrical energy conversion efficiency of when using H2 oxidation, within 1.9 points of the most efficient pathway, and is the only high-efficiency option that tolerates O2. This makes an EMP system that combines electron delivery by formate coupled with the serine variant of reductive glycine pathway highly attractive, as it presents few barriers to rapid, iterative engineering in the lab, and a high theoretical ceiling. Drawing both carbon and electrons from formate costs 6.2 points against H2 at a state-of-the-art whole-cell voltage (2.2 V). However, this small penalty is amplified three-fold by any rise in the CO2-to-formate cell voltage, and reaches 13.5 points at the highest whole-cell voltages reported for scaled-up CO2-to-formate electrolyzers, where formate-only operation falls to 11.2 electrical-to-fuel and 3.7% solar-to-fuel efficiency, below the ceiling of photosynthesis, against 24.7 and 8.1% for H2 (only just above algal photosynthesis). Our choice between a formate-only system and one coupled to H2 oxidation or EEU therefore depends on our belief about the trajectory of CO2 reduction technology. If whole-cell voltages continue to fall at the rate of the past decade, formate alone is the right target, and the simplicity of its workflow is bought at low cost. However, if that improvement plateaus, the electron delivery mechanism must be swappable, and a system should be designed from the outset so that it can be. At the US Department of Energy SunShot target of 2 cents per kilowatt hour, the electricity to make a US gallon of butanol costs $1.40 for a formate-only system at the state of the art, rising to $5.45 at the highest scaled-up electrolyzer voltage reported, against $1.23 and $2.47 for formate and H2 system.

The unprecedented rise in greenhouse gases such as carbon dioxide (CO2), and their detrimental effects on the atmosphere have intensified the current climate emergency. This challenge has inspired the development of novel carbon capture and utilisation (CCU) technologies, specifically microbial electrosynthesis (MES), for the bioelectrochemical fixation of CO2 into commodity chemical compounds (CCCs) using microbial biocatalysts. In this study, we systematically enriched and maintained robust mixed microbial communities of electroactive bacteria (EAB) from wastewater treatment sludge to serve as biocatalysts for MES. The MES performance of these biocatalysts under different operational conditions in bioelectrochemical systems (BES) reactors were investigated. BES reactors with enriched biocatalysts incubated and maintained under controlled conditions produced higher CCCs yields than those developed at ambient temperatures, while strict anaerobic operations further improved the MES efficiency compared to aerobic conditions. At an applied potential of -1000 mV vs Ag/AgCl, CO2 conversion reached up to 88.11%, significantly outperforming the lower potential (-600 mV) operation. These findings underscore the importance of systematic development of robust and stable biocatalysts, as well as maintaining strict anaerobic conditions, for achieving efficient MES performance, thereby demonstrating the potential of our approach for scalable industrial CCU applications.