Synthetic and Systems Biotechnology

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.

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.

Numerous CRISPR-Cas systems have been developed for molecular detection of genetic elements exploiting the trans-cleavage activity of LbCas12a combined with short fluorescent probes. Alongside, a large variability of buffer conditions has been reported. However, how solution chemistry balances enzymatic turnover, signal stability, and detection performance have not been systematically defined. Here, we evaluated how the interplay between anions (chloride vs. acetate), additives, sequence probes, and ionic strength contributes to LbCas12a trans-cleavage activity. Buffer composition screening revealed that chloride-based buffers maintain a low background but slow catalytic rates, meanwhile acetate-based buffers accelerate enzymatic turnover with and without the target DNA sequence, increasing non-specific signals. Reducing agents and surfactants were the best enhancer combinations, improving cleavage performance by increasing ternary complex formation (Km). High salt concentrations only slightly improved the reaction kinetics. Finally, the 5FAM-TTATT-3BHQ-1 reporter showed the best balance between signal intensity and nonspecific background. Our findings indicate that LbCas12a tolerates a vast buffer composition landscape, maintaining robust trans-cleavage activity and detection performance. However, conditions that enhance catalytic activity simultaneously increase nonspecific probe cleavage and higher background, resulting in lower detection performance. Therefore, assay optimization should prioritize detection performance rather than maximal fluorescence output, where solution chemistry is a critical determinant.

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.

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.

Drug targets and vaccines for bacterial diseases are challenging to develop. Computational subtractive genomics and reverse vaccinology offer a viable substitute for conventional drug and vaccine development approaches. In recent years, Pasteurella multocida, a Gram-negative pathogen responsible for severe infections in humans along with major diseases in livestock and poultry, has developed resistance to multiple drugs. As a result, there is an urgent demand for research into reliable, efficient, highly potent, and cost-effective strategies to combat the rise of Pasteurella multocida infections. To address this, the present study integrated an in silico subtractive genomics and reverse vaccinology workflow to pinpoint proteins with potential value as both drug and vaccine targets. From the complete Pasteurella multocida Pm70 proteome, we identified 446 proteins that had no similarities with human proteins, out of which 129 were found to be essential for the pathogens survival. These 129 proteins are therefore potential candidates for drug development. Further metabolic pathway analysis, virulence potential, subcellular localization, and structural analyses revealed two outer membrane proteins, namely, 47 kDa outer membrane protein and lipopolysaccharide-assembly protein LptD, as promising candidates for therapeutic development. Subsequent antigenicity analysis revealed both outer membrane proteins as potential vaccine targets, exhibiting highly favorable antigenicity scores. Candidate B- and T-cell epitopes were systematically mapped from these two targets based on antigenicity, immunogenicity, toxicity, and allergenicity potential and only high-confidence epitopes were assembled using adjuvants and class-specific linkers to construct the final multi-epitope vaccine (MEV). The designed MEV construct was demonstrated to be highly antigenic, non-toxic and non-allergenic to humans, while possessing favorable structural and physicochemical properties, and exhibited strong binding interactions with human toll-like receptors with minimal molecular deformability. Although further experimental validation is recommended to fully assess their efficacy, the present study establishes a solid foundation for the development of novel therapeutics and vaccine candidates against P. multocida, ultimately contributing to the reduction of its rising infections.

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.

Genome transplantation is a key technology for synthetic genomics, enabling entire genomes to be transferred into recipient cells. Despite its importance, genome transplantation remains confined to a small number of wall-less Mollicute species and is often characterized by low efficiencies, limiting the development and testing of synthetic genomes. Because genome transplantation relies on the same polyethylene glycol mediated DNA delivery process used for plasmid transformation, improving DNA uptake is an important step toward more efficient transplantation systems. Here, we systematically optimized polyethylene glycol-mediated transformation in Mesoplasma florum, a fast-growing, non-pathogenic Mollicute with a reduced genome that represents an attractive chassis for synthetic genomics. We evaluated 20 parameters spanning cell physiology, DNA preparation, membrane conditioning, and recovery conditions. DNA topology emerged as the strongest determinant of transformation efficiency, with highly compacted DNA preparations producing up to three orders of magnitude more transformants than conventional plasmid preparations. Growth phase and polyethylene glycol concentration also strongly influenced transformation outcomes. To identify potential recipients for future genome transplantation experiments, we further screened ten strains belonging to the Mesoplasma lineage. Transformation efficiencies varied widely among strains, and Mesoplasma entomophilum W17 emerged as a particularly promising candidate. Moreover, W17 supported replication of plasmids carrying the M. florum oriC, suggesting compatibility between the replication systems of the two species. Together, these results establish an improved transformation workflow for M. florum, identify DNA topology as a major determinant of DNA uptake, and reveal a new candidate recipient for future genome transplantation studies. These advances provide practical tools and biological insights for synthetic genome engineering in Mollicutes.

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.

CRISPR-Cas12a-based diagnostics are promising for field detection yet are often constrained by two step workflows or laborious optimization of one-pot reactions. Herein, we present a universal one-pot RPA/CRISPR-Cas12a strategy using base-mismatched crRNA in the seed region (positions 1-6). Introducing of single-nucleotide mismatches (complementary or error) into crRNA suppresses cis-cleavage activity, enabling amplification and detection in a single tube without PAM re-design or costly modified crRNA. Molecular dynamics (MD) simulations indicated that moderate potential energy (PE) variation correlates with improved screening performance, providing guidance for the selection of effective mismatches. This strategy successfully converted established two-step assays for Burkholderia pseudomallei and Brucella into one-pot formats, achieving detection of targets at 10 copies/L within 30 min with good specificity. Lyophilized pellets supported field testing on lithium-ion battery-powered devices, and experiments with simulated soil samples confirm the robustness of the approach. Together, this cost-effective, adaptable strategy can streamline RPA-CRISPR diagnostics for infectious disease surveillance.

Forster resonance energy transfer (FRET) is commonly used to monitor protein-protein interactions in situ. The high spatiotemporal resolution and facile implementation inside complex molecular environments have spearheaded FRET's widespread adoption in biosensing. Despite these advantages, current FRET biosensors are largely restricted to the detection of the presence/absence of individual inputs and are thus unable to sense several multiplexable inputs simultaneously within complex milieu of biological environments. In this work, we introduce a generalizable strategy to construct genetically encoded protein-based FRET biosensors capable of recognizing multiple inputs following Boolean logic-type (YES/OR/AND) operations. These topologically specified FRET sensors powerfully expand the input capacity in sensing protein-protein interactions while providing a user-programmable platform for monitoring heterogeneous biological activities both in vitro and in living cells.

Non-small-cell lung cancer (NSCLC) diagnosis is challenged owing to the need for reliable, non-invasive biomarkers. Circulating cell-free microRNAs (ccf-miRNAs) are promising candidates, but their low levels and short sequences in blood complicate detection. In this study, a novel nano-hybrid fluorescent array was developed to enable rapid and sensitive identification of NSCLC-related ccf-miRNAs. The array consists of poly(T)-modified fluorescent nano-polystyrene beads for capturing target miRNAs, and fluorophore-labeled, sequence-specific locked nucleic acid (LNA) probes for their detection. This design forms a sandwich structure with the target miRNA. Nano cytometry and fluorescence microscopy assessed both the capture and specificity of detection. The assay was tested on plasma samples for miR-16-5p and U6, evaluating selectivity, sensitivity, and reproducibility. The results showed that the poly(T)-modified beads efficiently captured the target miRNAs, and the LNA probes accurately distinguished the sequences. The assay enabled direct detection of miR-16-5p and U6 from plasma without amplification, demonstrating high selectivity, sensitivity, and reproducibility. Combining enrichment with nano-polystyrene beads and sequence-specific LNA probes addressed the main challenges of ccf-miRNA detection, namely their low abundance and short sequence length. As ccf-miRNAs are linked to NSCLC progression, this method could assist in early diagnosis, disease monitoring, and assessment of treatment response. The target-independent nature of the poly(T) capture layer allows for easy calibration to new miRNAs by changing the LNA probes, rendering it suitable for multiplex detection in clinical settings. However, further preclinical and clinical studies are needed before adoption in routine practice. This nano-hybrid fluorescent array presents a rapid and reliable approach for detecting low-abundance ccf-miRNAs in plasma. Its encouraging performance and flexible design show potential for future application in NSCLC diagnosis and monitoring, subject to additional confirmation.

The base excision repair pathway removes small base lesions from DNA and is initiated in humans by one of eleven DNA glycosylases with overlapping substrate specificities. Despite their central role in genome maintenance and transcription, systematic datasets describing small-molecule binders or inhibitors of DNA glycosylases are lacking. Here, through a collaborative effort across Scandinavia, we establish a high-throughput biochemical assay platform for human DNA glycosylases and use it to generate a systematic small-molecule screening dataset. A chemogenomic library was screened against nine glycosylases with assays of sufficient quality for hit identification, yielding primary screening and concentration-response data and identifying multiple previously unreported inhibitors. All primary and processed data, along with detailed assay protocols and metadata, are publicly available to support reuse in chemical biology, further compound optimization, assay development and comparative studies of DNA repair enzymes.

With the increase of recycled water to offset water demand, the potential possibility to spread contagious RNA viruses, such as Coxsackievirus B3, increases. However, detection of viral particles remains challenging because of low viral concentrations in wastewater and high mutation rates of the RNA virus. Robust monitoring is needed with low cost and low infrastructure technologies to increase accessibility of monitoring technologies worldwide. Novel viral detection methods have been developed, based on synthetic riboswitches that bind to the target virus and trigger a reporter gene, thus amplifying the detection signals. Such monitoring technologies can leverage machine learning to optimize the candidate nucleic acid sequences used for detection. To support the design of effective riboswitches, we present a machine learning model for classifying riboswitch performance, integrating RNA sequence data with secondary structural features based on free energy calculations and parameters that represent single strandedness. This model uses a sparsely gated Mixture of Experts (MoE) architecture to route sequence and thermodynamic features to specialized experts, achieving strong generalization performance across cross-validation and held-out testing. When evaluated against baseline Decision Tree, Random Forest, Gaussian Naive Bayes, and Dense Neural Network classifiers, the MoE model demonstrated near-zero classification errors. Furthermore, post-hoc feature importance and k-mer analyzes reveal that near-perfect predictive performance in silico is strongly driven by sequence constructs in addition to generalizable folding mechanics. An ablation study of numerical feature and sequence inputs showed that while the Dense Neural Network also achieved high accuracy across all ablation conditions, the MoE architecture was retained as the primary model because its specialized subnetworks do not require all model parameters to be activated offering a potential computational advantage.

Microalgae are promising biological platforms for sustainable energy production because they can convert photosynthetic activity into electricity in biophotovoltaic (BPV) systems. Identifying strains that grow well at elevated temperatures and under reduced irradiance is an important step toward BPV applications in seasonally variable environments. Microalgae were isolated from Seondong Lake (Seoul, Republic of Korea) during three sampling periods and screened for growth at different temperatures. Among 45 isolates, KHUC-11 showed the highest weighted growth index based on growth rates at 25, 30, and 35 under dark conditions and was selected for further characterization. Phylogenetic reconstruction of the 45S rRNA gene cluster, internal transcribed spacer 2 (ITS2) secondary-structure comparison, and morphological characterization supported the description of KHUC-11 as Desmodesmus ecotolerans sp. nov. D. ecotolerans produced more biomass than Chlamydomonas reinhardtii CC-125 under both moderate- and low-light conditions. Under low light, its final dry biomass concentrations were 1.81 and 1.58 times those of CC-125 under mixotrophic and photoautotrophic conditions, respectively. Despite its smaller cell size, D. ecotolerans showed higher photosynthetic performance than CC-125, and screen-printed electrode (SPE)-based electrochemical assays demonstrated enhanced anodic current generation and CV-derived estimated power output. Taken together, these findings identify D. ecotolerans as a novel climate-resilient Desmodesmus species with bioelectrochemical properties that support further evaluation for BPV applications.

Translation initiation is a critical stage of protein synthesis in prokaryotes, where ribosomes assemble on mRNA to begin decoding genetic information. The interactions between ribosomal RNA (rRNA) and mRNA significantly influence this process, with features like ribosomal binding site (RBS) affecting ribosomal dynamics. However, direct experimental validation of these types of interaction is yet to be provided. In this study, we developed an optimized small sub-unit sequencing (SSU-seq) protocol for Escherichia coli, enabling the detailed examination of ribosomal complexes and subunits dynamics. Unlike Ribo-seq, which primarily maps fully assembled ribosomes, SSU-seq captures both ribosomal subunits and intact ribosomes, offering a comprehensive view of translation initiation and elongation events. By integrating experimental data with predictive models, our study advances the understanding of translation regulation in prokaryotes and underscores the utility of SSU-seq in decoding the complexities of ribosome-mRNA interactions. Our analysis reveals distinct patterns of small subunit distribution across the entire mRNAs with higher density near positions with stronger rRNA-mRNA hybridization. The results may suggest that rRNA-mRNA hybridization positions along the entire transcript facilitate efficient start codon recognition by maintaining proximity between the small subunit and the mRNA.

L-Threonine is an essential amino acid widely used in the feed, food, and pharmaceutical industries, and microbial fermentation is now the dominant route for its production. Further increases in producer titers depend on random mutagenesis coupled with high-throughput screening (HTS), but progress has been limited by the lack of a biosensor with a broad operating range, high sensitivity, and target specificity. Here, we show that L-threonine induces the tryptophan-repressed trp promoter of Escherichia coli, a response that, to our knowledge, has not been described previously. Deletion analysis showed that both the trpO operator and the trpL attenuation element contribute to induction, and that the lowest basal expression occurs only when both elements are present. By optimizing the ribosome-binding site, the promoter -10 region, and TrpR expression, we constructed an L-threonine biosensor on a pACYC backbone. The sensor responded linearly to L-threonine from 0.1 to 30 g/L (R2 = 0.9895), with an induction ratio of up to 51.7-fold. Among the 20 proteinogenic amino acids tested, only L-threonine produced a detectable response. The biosensor distinguished strains with different L-threonine production capacities and guided the directed evolution of homoserine kinase (ThrB) at a conserved motif, yielding variants with brighter reporter fluorescence and increased L-threonine production. This work provides a high-performance biosensor based on native E. coli regulatory elements for screening L-threonine overproducers.

Over the past decade, aptamers have emerged as promising therapeutics, with cancer therapeutics as a major research area. Existing computational methods, however, either predict aptamer-target pairs or design aptamers against a specific target. In this study, we present AntiCapt, a computational method for identifying single-stranded DNA (ssDNA) anticancer aptamers (ACAs) using sequence descriptors and fine-tuned nucleotide language models. The main dataset comprises 1,021 experimentally validated ACAs and an equal number of non-aptamer sequences. Comparative analysis revealed distinct patterns in the nucleotide, dinucleotide, and trinucleotide compositions associated with ACAs. We developed machine learning (ML) models using composition, autocorrelation, binary profiles and structural features. Among these, the best-performing composition-based model achieved an AUC of 0.93 on an independent dataset, while chemical descriptor-and structural feature-based models achieved a maximum AUC of 0.89. ML models using pretrained and fine-tuned NLM embeddings were also developed, with fine-tuned HyenaDNA embeddings achieving the highest performance, with an independent AUC of 0.94. Furthermore, we developed a model for discriminating anticancer aptamers from general aptamers. The best-performing models were integrated into AntiCapt, a web server and a standalone tool for predicting, designing, and genome-scale scanning anticancer aptamers (https://webs.iiitd.edu.in/raghava/anticapt/).

In the enzymatic toolbox of depollution, hydrolytic dehalogenases are of particular interest for the bioremediation of organohalogenated compounds. To better identify and characterize these enzymes, it is important to develop appropriate sensing methods. Fluorescent probes for dehalogenase detection have been developed in the past years, however, to this date none were available for application using the common FITC parameters (i.e. excitation laser at 488 nm). To address this limitation, we herein report a new fluorescent probe, COU4-Cl, excitable in the violet (405 nm) and blue (488 nm) wavelengths adapted for the detection and characterization of haloalkane dehalogenases. Its applicability is discussed and evaluated in comparison to the previously described COU2-Cl probe only excitable in the violet (405 nm). This study shows the advantages and complementarity of the two probes, and further demonstrate their application for the discrimination of dehalogenase activity in whole cell assays.

[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.