Revealing the hidden functional diversity of an enzyme family

Design and implementation of aerobic and ambient CO2-reduction as an entry-point for enhanced carbon fixation

The direct reduction of CO2 into one-carbon molecules is key to highly efficient biological CO2-fixation. However, this strategy is currently restricted to anaerobic organisms and low redox potentials. In this study, we introduce the CORE cycle, a synthetic metabolic pathway that converts CO2 to formate at aerobic conditions and ambient CO2 levels, using only NADPH as a reductant. Combining theoretical pathway design and analysis, enzyme bioprospecting and high-throughput screening, modular assembly and adaptive laboratory evolution, we realize the CORE cycle in vivo and demonstrate that the cycle supports growth of E. coli by supplementing C1-metabolism and serine biosynthesis from CO2. We further analyze the theoretical potential of the CORE cycle as a new entry-point for carbon in photorespiration and autotrophy. Overall, our work expands the solution space for biological carbon reduction, offering a promising approach to enhance CO2 fixation processes such as photosynthesis, and opening avenues for synthetic autotrophy.

The Lipocone Superfamily: A Unifying Theme In Metabolism Of Lipids, Peptidoglycan And Exopolysaccharides, Inter-Organismal Conflicts And Immunity

Wnt proteins are critical signaling molecules in developmental processes across animals. Despite intense study, their evolutionary roots have remained enigmatic. Using sensitive sequence analysis and structure modeling, we establish that the Wnts are part of a vast assemblage of domains, the Lipocone superfamily, defined here for the first time. It includes previously studied enzymatic domains like the phosphatidylserine synthases (PTDSS1/2) and the TelC toxin domain from Streptococcus intermedius , the enigmatic VanZ proteins, the animal Serum Amyloid A (SAA) and a further host of uncharacterized proteins in a total of 30 families. Though the metazoan Wnts are catalytically inactive, we present evidence for a conserved active site across this superfamily, versions of which are consistently predicted to operate on head groups of either phospholipids or polyisoprenoid lipids, catalyzing transesterification and phosphate-containing head group severance reactions. We argue that this superfamily originated as membrane proteins, with one branch (including Wnt and SAA) evolving into soluble versions. By comprehensively analyzing contextual information networks derived from comparative genomics, we establish that they act in varied functional contexts, including regulation of membrane lipid composition, extracellular polysaccharide biosynthesis, and biogenesis of bacterial outer-membrane components, like lipopolysaccharides. On multiple occasions, members of this superfamily, including the bacterial progenitors of Wnt and SAA, have been recruited as effectors in biological conflicts spanning inter-organismal interactions and anti-viral immunity in both prokaryotes and eukaryotes. These findings establish a unifying theme in lipid biochemistry, explain the origins of Wnt signaling and provide new leads regarding immunity across the tree of life.

Deep Learning-Driven Insights into Enzyme-Substrate Interaction Discovery

Enzymes are ubiquitous catalysts with enormous application potential in biomedicine, green chemistry, and biotechnology. However, accurately predicting whether a molecule serves as a substrate for a specific enzyme, especially for novel entities, remains a significant challenge. Compared with traditional experimental methods, computational approaches are much more resource-efficient and time-saving, but they often compromise on accuracy. To address this, we introduce the molecule-enzyme interaction (MEI) model, a novel machine learning framework designed to predict the probability that a given molecule is a substrate for a specified enzyme with high accuracy. Utilizing a comprehensive data set that encapsulates extensive information on enzymatic reactions and enzyme sequences, the MEI model seamlessly combines atomic environmental data with amino acid sequence features through an advanced attention mechanism within a hierarchical neural network. Empirical evaluations have confirmed that the MEI model outperforms the current state-of-the-art model by at least 6.7% in prediction accuracy and 8.5% in AUROC, underscoring its enhanced predictive capabilities. Additionally, the MEI model demonstrates remarkable generalization across data sets of varying qualities and sizes. This adaptability is further evidenced by its successful application in diverse areas, such as predicting interactions within the CYP450 enzyme family and achieving an outstanding accuracy of 90.5% in predicting the enzymatic breakdown of complex plastics within environmental applications. These examples illustrate the model's ability to effectively transfer knowledge from coarsely annotated enzyme databases to smaller, high-precision data sets, robustly modeling both sparse and high-quality databases. We believe that this versatility firmly establishes the MEI model as a foundational tool in enzyme research with immense potential to extend beyond its original scope.

Riboflavin-induced photo-ATRP electrochemical strategy for detection of biomarker trypsin

The detection of trypsin and its inhibitors is important for both clinical diagnosis and disease treatment. Abnormal trypsin activity affects pancreatic function and leads to corresponding pathological changes in the body. Therefore, the study presented a riboflavin-induced photo-ATRP electrochemical assay of trypsin activity and its inhibitor, including detection of trypsin activity in real urine samples. Experiments were performed on indium tin oxide (ITO) electrodes modified with sulfhydryl groups of 3-mercaptopropionic acid, and target trypsin-specific cleavage of BSA-Au nanocluster (BSA-Au NCs) was followed by the modification of Au NCs to the electrodes using Au-S. The Au NCs immobilized monodeoxy-monomercapto-β-cyclodextrin@adamantan-2-amine (SH-β-CD@2-NH2-Ada) host-guest inclusion complexes to the electrode surfaces via Au-S. In a two-component photo-initiator system consisting of riboflavin as an initiator and ascorbic acid (AA) as a mild reducing agent under mild blue light radiation, a large number of electroactive substances were grafted onto the electrode surface to generate electrochemical signals. In addition, we have successfully realized the detection of clinical drug inhibitors of trypsin. The detection limit of the system is as low as 0.0024 ng/mL, which much littler than the average standard of trypsin in the patient's urine or serum. It's worth noting that this work will provide researchers with a different route to design electrochemical sensors based on non-covalent recognition strategies.

An aldolase-dependent phloroglucinol degradation pathway in Collinsella sp. zg1085

Phloroglucinol (1,3,5-trihydroxybenzene) is a key intermediate in the degradation of polyphenols such as flavonoids and hydrolysable tannins and can be used by certain bacteria as a carbon and energy source for growth. The identification of enzymes that participate in the fermentation of phloroglucinol to acetate and butyrate in Clostridia was recently reported. In this study, we present the discovery and characterization of a novel metabolic pathway for phloroglucinol degradation in the bacterium Collinsella sp. zg1085, from marmot respiratory tract. In both the Clostridial and Collinsella pathways, phloroglucinol is first reduced to dihydrophoroglucinol by the NADPH-dependent phloroglucinol reductase (PGR), followed by ring opening to form (S)-3-hydroxy-5-oxohexanoate by a Mn2+-dependent dihydrophloroglucinol cyclohydrolase (DPGC). In the Collinsella pathway, (S)-3-hydroxy-5-oxohexanoate is then cleaved to form malonate semialdehyde and acetone by a newly identified aldolase (HOHA). Finally, a NADP+-dependent malonate-semialdehyde dehydrogenase converts malonate semialdehyde to CO2 and acetyl-CoA, an intermediate in carbon and energy metabolism. Recombinant expression of the Collinsella PGR, DPGC, and HOHA in E. coli enabled the conversion of phloroglucinol into acetone, providing support for the proposed pathway. Experiments with Olsenella profusa, another bacterium containing the gene cluster of interest, show that the PGR, DPGC, HOHA, and MSDH are induced by phloroglucinol. Our findings add to the variety of metabolic pathways for the degradation of phloroglucinol, a widely distributed phenolic compound, in the anaerobic microbiome.IMPORTANCEPhloroglucinol is an important intermediate in the bacterial degradation of polyphenols, a highly abundant class of plant natural products. Recent research has identified key enzymes of the phloroglucinol degradation pathway in butyrate-producing anaerobic bacteria, which involves cleavage of a linear triketide intermediate by a beta ketoacid cleavage enzyme, requiring acetyl-CoA as a co-substrate. This paper reports a variant of the pathway in the lactic acid bacterium Collinsella sp. zg1085, which involves cleavage of the triketide intermediate by a homolog of deoxyribose-5-phosphate aldolase, highlighting the variety of mechanisms for phloroglucinol degradation by different anaerobic bacterial taxa.

Descriptor-augmented machine learning for enzyme-chemical interaction predictions

Descriptors play a pivotal role in enzyme design for the greener synthesis of biochemicals, as they could characterize enzymes and chemicals from the physicochemical and evolutionary perspective. This study examined the effects of various descriptors on the performance of Random Forest model used for enzyme-chemical relationships prediction. We curated activity data of seven specific enzyme families from the literature and developed the pipeline for evaluation the machine learning model performance using 10-fold cross-validation. The influence of protein and chemical descriptors was assessed in three scenarios, which were predicting the activity of unknown relations between known enzymes and known chemicals (new relationship evaluation), predicting the activity of novel enzymes on known chemicals (new enzyme evaluation), and predicting the activity of new chemicals on known enzymes (new chemical evaluation). The results showed that protein descriptors significantly enhanced the classification performance of model on new enzyme evaluation in three out of the seven datasets with the greatest number of enzymes, whereas chemical descriptors appear no effect. A variety of sequence-based and structure-based protein descriptors were constructed, among which the esm-2 descriptor achieved the best results. Using enzyme families as labels showed that descriptors could cluster proteins well, which could explain the contributions of descriptors to the machine learning model. As a counterpart, in the new chemical evaluation, chemical descriptors made significant improvement in four out of the seven datasets, while protein descriptors appear no effect. We attempted to evaluate the generalization ability of the model by correlating the statistics of the datasets with the performance of the models. The results showed that datasets with higher sequence similarity were more likely to get better results in the new enzyme evaluation and datasets with more enzymes were more likely beneficial from the protein descriptor strategy. This work provides guidance for the development of machine learning models for specific enzyme families.

A method for the systematic selection of enzyme panel candidates by solving the maximum diversity problem

Enzymes are being increasingly exploited for their potential as industrial biocatalysts. Establishing a portfolio of useful biocatalysts from large and diverse protein family is challenging and a systematic method for candidate selection promises to aid in this task. Moreover, accurate enzyme functional annotation can only be confidently guaranteed through experimental characterisation in the laboratory. The selection of catalytically diverse enzyme panels for experimental characterisation is also an important step for shedding light on the currently unannotated proteins in enzyme families. Current selection methods often lack efficiency and scalability, and are usually non-systematic. We present a novel algorithm for the automatic selection of subsets from enzyme families. A tabu search algorithm solving the maximum diversity problem for sequence identity was designed and implemented, and applied to three diverse enzyme families. We show that this approach automatically selects panels of enzymes that contain high richness and relative abundance of the known catalytic functions, and outperforms other methods such as k-medoids.

Biocatalytic reductive aminations with NAD(P)H-dependent enzymes: enzyme discovery, engineering and synthetic applications

Chiral amines are pivotal building blocks for the pharmaceutical industry. Asymmetric reductive amination is one of the most efficient and atom economic methodologies for the synthesis of optically active amines. Among the various strategies available, NAD(P)H-dependent amine dehydrogenases (AmDHs) and imine reductases (IREDs) are robust enzymes that are available from various sources and capable of utilizing a broad range of substrates with high activities and stereoselectivities. AmDHs and IREDs operate via similar mechanisms, both involving a carbinolamine intermediate followed by hydride transfer from the co-factor. In addition, both groups catalyze the formation of primary and secondary amines utilizing both organic and inorganic amine donors. In this review, we discuss advances in developing AmDHs and IREDs as biocatalysts and focus on evolutionary history, substrate scope and applications of the enzymes to provide an outlook on emerging industrial biotechnologies of chiral amine production.

Discovery of a non-canonical prototype long-chain monoacylglycerol lipase through a structure-based endogenous reaction intermediate complex

The identification and characterization of enzyme function is largely lacking behind the rapidly increasing availability of large numbers of sequences and associated high-resolution structures. This is often hampered by lack of knowledge on in vivo relevant substrates. Here, we present a case study of a high-resolution structure of an unusual orphan lipase in complex with an endogenous C18 monoacylglycerol ester reaction intermediate from the expression host, which is insoluble under aqueous conditions and thus not accessible for studies in solution. The data allowed its functional characterization as a prototypic long-chain monoacylglycerol lipase, which uses a minimal lid domain to position the substrate through a hydrophobic tunnel directly to the enzyme's active site. Knowledge about the molecular details of the substrate binding site allowed us to modulate the enzymatic activity by adjusting protein/substrate interactions, demonstrating the potential of our findings for future biotechnology applications.

Host macrocyclic acylcarnitines mediate symbiotic interactions between frogs and their skin microbiome

The host-microbiome associations occurring on the skin of vertebrates significantly influence hosts' health. However, the factors mediating their interactions remain largely unknown. Herein, we used integrated technical and ecological frameworks to investigate the skin metabolites sustaining a beneficial symbiosis between tree frogs and bacteria. We characterize macrocyclic acylcarnitines as the major metabolites secreted by the frogs' skin and trace their origin to an enzymatic unbalance of carnitine palmitoyltransferases. We found that these compounds colocalize with bacteria on the skin surface and are mostly represented by members of the Pseudomonas community. We showed that Pseudomonas sp. MPFS isolated from frogs' skin can exploit acylcarnitines as its sole carbon and nitrogen source, and this metabolic capability is widespread in Pseudomonas. We summarize frogs' multiple mechanisms to filter environmental bacteria and highlight that acylcarnitines likely evolved for another function but were co-opted to provide nutritional benefits to the symbionts.

Anaerobic phloroglucinol degradation by Clostridium scatologenes

Polyphenols are abundant in nature, and their anaerobic biodegradation by gut and soil bacteria is a topic of great interest. The O2 requirement of phenol oxidases is thought to explain the microbial inertness of phenolic compounds in anoxic environments, such as peatlands, termed the enzyme latch hypothesis. A caveat of this model is that certain phenols are known to be degraded by strict anaerobic bacteria, although the biochemical basis for this process is incompletely understood. Here, we report the discovery and characterization of a gene cluster in the environmental bacterium Clostridium scatologenes for the degradation phloroglucinol (1,3,5-trihydroxybenzene), a key intermediate in the anaerobic degradation of flavonoids and tannins, which constitute the most abundant polyphenols in nature. The gene cluster encodes the key C-C cleavage enzyme dihydrophloroglucinol cyclohydrolase, as well as (S)-3-hydroxy-5-oxo-hexanoate dehydrogenase and triacetate acetoacetate-lyase, which enable phloroglucinol to be utilized as a carbon and energy source. Bioinformatics studies revealed the presence of this gene cluster in phylogenetically and metabolically diverse gut and environmental bacteria, with potential impacts on human health and carbon preservation in peat soils and other anaerobic environmental niches. IMPORTANCE This study provides novel insights into the microbiota's anaerobic metabolism of phloroglucinol, a critical intermediate in the degradation of polyphenols in plants. Elucidation of this anaerobic pathway reveals enzymatic mechanisms for the degradation of phloroglucinol into short-chain fatty acids and acetyl-CoA, which are used as a carbon and energy source for bacterium growth. Bioinformatics studies suggested the prevalence of this pathway in phylogenetically and metabolically diverse gut and environmental bacteria, with potential impacts on carbon preservation in peat soils and human gut health.

A (S)-3-Hydroxybutyrate Dehydrogenase Belonging to the 3-Hydroxyacyl-CoA Dehydrogenase Family Facilitates Hydroxyacid Degradation in Anaerobic Bacteria

Ketone bodies, including acetoacetate, 3-hydroxybutyrate, and acetone, are produced in the liver of animals during glucose starvation. Enzymes for the metabolism of (R)-3-hydroxybutyrate have been extensively studied, but little is known about the metabolism of its enantiomer (S)-3-hydroxybutyrate. Here, we report the characterization of a novel pathway for the degradation of (S)-3-hydroxybutyrate in anaerobic bacteria. We identify and characterize a stereospecific (S)-3-hydroxylbutyrate dehydrogenase (3SHBDH) from Desulfotomaculum ruminis, which catalyzes the reversible NAD(P)H-dependent reduction of acetoacetate to form (S)-3-hydroxybutyrate. 3SHBDH also catalyzes oxidation of d-threonine (2R, 3S) and l-allo-threonine (2S, 3S), consistent with its specificity for β-(3S)-hydroxy acids. Isothermal calorimetry experiments support a sequential mechanism involving binding of NADH prior to (S)-3-hydroxybutyrate. Homologs of 3SHBDH are present in anaerobic fermenting and sulfite-reducing bacteria, and experiments with Clostridium pasteurianum showed that 3SHBDH, acetate CoA-transferase (YdiF), and (S)-3-hydroxybutyryl-CoA dehydrogenase (Hbd) are involved together in the degradation of (S)-3-hydroxybutyrate as a carbon and energy source for growth. (S)-3-hydroxybutyrate is a human metabolic marker and a chiral precursor for chemical synthesis, suggesting potential applications of 3SHBDH in diagnostics or the chemicals industry. IMPORTANCE (R)-3-hydroxybutyrate is well studied as a component of ketone bodies produced by the liver and of bacterial polyesters. However, the biochemistry of its enantiomer (S)-3-hydroxybutyrate is poorly understood. This study describes the identification and characterization of a stereospecific (S)-3-hydroxylbutyrate dehydrogenase and its function in a metabolic pathway for the degradation of (S)-3-hydroxybutyrate as a carbon and energy source in anaerobic bacteria. (S)-3-hydroxybutyrate is a mammalian metabolic marker and a precursor for chemical synthesis and bioplastics, suggesting potential applications of these enzymes in diagnostics and biotechnology.

Contrastive learning in protein language space predicts interactions between drugs and protein targets

Sequence-based prediction of drug-target interactions has the potential to accelerate drug discovery by complementing experimental screens. Such computational prediction needs to be generalizable and scalable while remaining sensitive to subtle variations in the inputs. However, current computational techniques fail to simultaneously meet these goals, often sacrificing performance of one to achieve the others. We develop a deep learning model, ConPLex, successfully leveraging the advances in pretrained protein language models ("PLex") and employing a protein-anchored contrastive coembedding ("Con") to outperform state-of-the-art approaches. ConPLex achieves high accuracy, broad adaptivity to unseen data, and specificity against decoy compounds. It makes predictions of binding based on the distance between learned representations, enabling predictions at the scale of massive compound libraries and the human proteome. Experimental testing of 19 kinase-drug interaction predictions validated 12 interactions, including four with subnanomolar affinity, plus a strongly binding EPHB1 inhibitor (KD = 1.3 nM). Furthermore, ConPLex embeddings are interpretable, which enables us to visualize the drug-target embedding space and use embeddings to characterize the function of human cell-surface proteins. We anticipate that ConPLex will facilitate efficient drug discovery by making highly sensitive in silico drug screening feasible at the genome scale. ConPLex is available open source at https://ConPLex.csail.mit.edu.

In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning

Nowadays, the vastly increasing demand for novel biotechnological products is supported by the continuous development of biocatalytic applications which provide sustainable green alternatives to chemical processes. The success of a biocatalytic application is critically dependent on how quickly we can identify and characterize enzyme variants fitting the conditions of industrial processes. While miniaturization and parallelization have dramatically increased the throughput of next-generation sequencing systems, the subsequent characterization of the obtained candidates is still a limiting process in identifying the desired biocatalysts. Only a few commercial microfluidic systems for enzyme analysis are currently available, and the transformation of numerous published prototypes into commercial platforms is still to be streamlined. This review presents the state-of-the-art, recent trends, and perspectives in applying microfluidic tools in the functional and structural analysis of biocatalysts. We discuss the advantages and disadvantages of available technologies, their reproducibility and robustness, and readiness for routine laboratory use. We also highlight the unexplored potential of microfluidics to leverage the power of machine learning for biocatalyst development.

Characterization of a novel β-alanine biosynthetic pathway consisting of promiscuous metabolic enzymes

Bacteria adapt to utilize the nutrients available in their environment through a sophisticated metabolic system composed of highly specialized enzymes. Although these enzymes can metabolize molecules other than those for which they evolved, their efficiency toward promiscuous substrates is considered too low to be of physiological relevance. Herein, we investigated the possibility that these promiscuous enzymes are actually efficient enough at metabolizing secondary substrates to modify the phenotype of the cell. For example, in the bacterium Acinetobacter baylyi ADP1 (ADP1), panD (coding for l-aspartate decarboxylase) encodes the only protein known to catalyze the synthesis of β-alanine, an obligate intermediate in CoA synthesis. However, we show that the ADP1 ΔpanD mutant could also form this molecule through an unknown metabolic pathway arising from promiscuous enzymes and grow as efficiently as the wildtype strain. Using metabolomic analyses, we identified 1,3-diaminopropane and 3-aminopropanal as intermediates in this novel pathway. We also conducted activity screening and enzyme kinetics to elucidate candidate enzymes involved in this pathway, including 2,4-diaminobutyrate aminotransferase (Dat) and 2,4-diaminobutyrate decarboxylase (Ddc) and validated this pathway in vivo by analyzing the phenotype of mutant bacterial strains. Finally, we experimentally demonstrate that this novel metabolic route is not restricted to ADP1. We propose that the occurrence of conserved genes in hundreds of genomes across many phyla suggests that this previously undescribed pathway is widespread in prokaryotes.

Protein Mediated Enzyme Immobilization

Enzyme immobilization is an essential technology for commercializing biocatalysis. It imparts stability, recoverability, and other valuable features that improve the effectiveness of biocatalysts. While many avenues to join an enzyme to solid phases exist, protein-mediated immobilization is rapidly developing and has many advantages. Protein-mediated immobilization allows for the binding interaction to be genetically coded, can be used to create artificial multienzyme cascades, and enables modular designs that expand the variety of enzymes immobilized. By designing around binding interactions between protein domains, they can be integrated into functional materials for protein immobilization. These materials are framed within the context of biocatalytic performance, immobilization efficiency, and stability of the materials. In this review, supports composed entirely of protein are discussed first, with systems such as cellulosomes and protein cages being discussed alongside newer technologies like spore-based biocatalysts and forizymes. Protein-composite materials such as polymersomes and protein-inorganic supraparticles are then discussed to demonstrate how protein-mediated strategies are applied to many classes of solid materials. Critical analysis and future directions of protein-based immobilization are then discussed, with a particular focus on both computational and design strategies to advance this area of research and make it more broadly applicable to many classes of enzymes.

Nitrogen Metabolism in Pseudomonas putida: Functional Analysis Using Random Barcode Transposon Sequencing

Pseudomonas putida KT2440 has long been studied for its diverse and robust metabolisms, yet many genes and proteins imparting these growth capacities remain uncharacterized. Using pooled mutant fitness assays, we identified genes and proteins involved in the assimilation of 52 different nitrogen containing compounds. To assay amino acid biosynthesis, 19 amino acid drop-out conditions were also tested. From these 71 conditions, significant fitness phenotypes were elicited in 672 different genes including 100 transcriptional regulators and 112 transport-related proteins. We divide these conditions into 6 classes, and propose assimilatory pathways for the compounds based on this wealth of genetic data. To complement these data, we characterize the substrate range of three promiscuous aminotransferases relevant to metabolic engineering efforts in vitro. Furthermore, we examine the specificity of five transcriptional regulators, explaining some fitness data results and exploring their potential to be developed into useful synthetic biology tools. In addition, we use manifold learning to create an interactive visualization tool for interpreting our BarSeq data, which will improve the accessibility and utility of this work to other researchers. IMPORTANCE Understanding the genetic basis of P. putida's diverse metabolism is imperative for us to reach its full potential as a host for metabolic engineering. Many target molecules of the bioeconomy and their precursors contain nitrogen. This study provides functional evidence linking hundreds of genes to their roles in the metabolism of nitrogenous compounds, and provides an interactive tool for visualizing these data. We further characterize several aminotransferases, lactamases, and regulators, which are of particular interest for metabolic engineering.

Aerobic Methoxydotrophy: Growth on Methoxylated Aromatic Compounds by Methylobacteriaceae

Pink-pigmented facultative methylotrophs have long been studied for their ability to grow on reduced single-carbon (C1) compounds. The C1 groups that support methylotrophic growth may come from a variety of sources. Here, we describe a group of Methylobacterium strains that can engage in methoxydotrophy: they can metabolize the methoxy groups from several aromatic compounds that are commonly the product of lignin depolymerization. Furthermore, these organisms can utilize the full aromatic ring as a growth substrate, a phenotype that has rarely been described in Methylobacterium. We demonstrated growth on p-hydroxybenzoate, protocatechuate, vanillate, and ferulate in laboratory culture conditions. We also used comparative genomics to explore the evolutionary history of this trait, finding that the capacity for aromatic catabolism is likely ancestral to two clades of Methylobacterium, but has also been acquired horizontally by closely related organisms. In addition, we surveyed the published metagenome data to find that the most abundant group of aromatic-degrading Methylobacterium in the environment is likely the group related to Methylobacterium nodulans, and they are especially common in soil and root environments. The demethoxylation of lignin-derived aromatic monomers in aerobic environments releases formaldehyde, a metabolite that is a potent cellular toxin but that is also a growth substrate for methylotrophs. We found that, whereas some known lignin-degrading organisms excrete formaldehyde as a byproduct during growth on vanillate, Methylobacterium do not. This observation is especially relevant to our understanding of the ecology and the bioengineering of lignin degradation.

An inventory of early branch points in microbial phosphonate biosynthesis

Microbial phosphonate biosynthetic machinery has been identified in ~5 % of bacterial genomes and encodes natural products like fosfomycin as well as cell surface decorations. Almost all biological phosphonates originate from the rearrangement of phosphoenolpyruvate (PEP) to phosphonopyruvate (PnPy) catalysed by PEP mutase (Ppm), and PnPy is often converted to phosphonoacetaldehyde (PnAA) by PnPy decarboxylase (Ppd). Seven enzymes are known or likely to act on either PnPy or PnAA as early branch points en route to diverse biosynthetic outcomes, and these enzymes may be broadly classified into three reaction types: hydride transfer, aminotransfer, and carbon-carbon bond formation. However, the relative abundance of these branch points in microbial phosphonate biosynthesis is unknown. Also unknown is the proportion of ppm-containing gene neighbourhoods encoding new branch point enzymes and potentially novel phosphonates. In this study we computationally sorted 434 ppm-containing gene neighbourhoods based on these seven branch point enzymes. Unsurprisingly, the majority (56 %) of these pathways encode for production of the common naturally occurring compound 2-aminoethylphosphonate (AEP) or a hydroxylated derivative. The next most abundant genetically encoded intermediates were phosphonoalanine (PnAla, 9.2 %), 2-hydroxyethylphosphonate (HEP, 8.5 %), and phosphonoacetate (PnAc, 6 %). Significantly, about 13 % of the gene neighbourhoods could not be assigned to any of the seven branch points and may encode novel phosphonates. Sequence similarity network analysis revealed families of unusual gene neighbourhoods including possible production of phosphonoacrylate and phosphonofructose, the apparent biosynthetic use of the C-P lyase operon, and a virus-encoded phosphonate. Overall, these results highlight the utility of branch point inventories to identify novel gene neighbourhoods and guide future phosphonate discovery efforts.

Machine learning modeling of family wide enzyme-substrate specificity screens

Biocatalysis is a promising approach to sustainably synthesize pharmaceuticals, complex natural products, and commodity chemicals at scale. However, the adoption of biocatalysis is limited by our ability to select enzymes that will catalyze their natural chemical transformation on non-natural substrates. While machine learning and in silico directed evolution are well-posed for this predictive modeling challenge, efforts to date have primarily aimed to increase activity against a single known substrate, rather than to identify enzymes capable of acting on new substrates of interest. To address this need, we curate 6 different high-quality enzyme family screens from the literature that each measure multiple enzymes against multiple substrates. We compare machine learning-based compound-protein interaction (CPI) modeling approaches from the literature used for predicting drug-target interactions. Surprisingly, comparing these interaction-based models against collections of independent (single task) enzyme-only or substrate-only models reveals that current CPI approaches are incapable of learning interactions between compounds and proteins in the current family level data regime. We further validate this observation by demonstrating that our no-interaction baseline can outperform CPI-based models from the literature used to guide the discovery of kinase inhibitors. Given the high performance of non-interaction based models, we introduce a new structure-based strategy for pooling residue representations across a protein sequence. Altogether, this work motivates a principled path forward in order to build and evaluate meaningful predictive models for biocatalysis and other drug discovery applications.

Predicting interactions between compounds and proteins represents a long-standing dream of drug discovery and protein engineering. Robust models of enzyme-substrate scope would dramatically advance our ability to design synthetic routes involving enzymatic catalysis. However, the lack of standardization between compound-protein interaction studies makes it difficult to evaluate the generalizability of such models. In this work we take a critical step forward by standardizing high-quality datasets measuring enzyme-substrate interactions, outlining rigorous evaluations, and proposing a new way to integrate structural information into protein representations. In testing previous modeling approaches, we highlight a surprising inability of existing models to effectively leverage compound-protein interactions to improve generalization, which challenges a perception in the literature. This establishes future opportunities for model development and integration of enzyme-substrate scope models into computer-aided synthesis planning software.

Discovery of Two Novel Oxidases Using a High-Throughput Activity Screen

Discovery of novel enzymes is a challenging task, yet a crucial one, due to their increasing relevance as chemical catalysts and biotechnological tools. In our work we present a high-throughput screening approach to discovering novel activities. A screen of 96 putative oxidases with 23 substrates led to the discovery of two new enzymes. The first enzyme, N-acetyl-D-hexosamine oxidase (EC 1.1.3.29) from Ralstonia solanacearum, is a vanillyl alcohol oxidase-like flavoprotein displaying the highest activity with N-acetylglucosamine and N-acetylgalactosamine. Before our discovery of the enzyme, its activity was an orphan one - experimentally characterized but lacking the link to amino acid sequence. The second enzyme, from an uncultured marine euryarchaeota, is a long-chain alcohol oxidase (LCAO, EC 1.1.3.20) active with a range of fatty alcohols, with 1-dodecanol being the preferred substrate. The enzyme displays no sequence similarity to previously characterised LCAOs, and thus is a completely novel representative of a protein with such activity.

Identification of putative essential protein domains from high-density transposon insertion sequencing

A first clue to gene function can be obtained by examining whether a gene is required for life in certain standard conditions, that is, whether a gene is essential. In bacteria, essential genes are usually identified by high-density transposon mutagenesis followed by sequencing of insertion sites (Tn-seq). These studies assign the term "essential" to whole genes rather than the protein domain sequences that encode the essential functions. However, genes can code for multiple protein domains that evolve their functions independently. Therefore, when essential genes code for more than one protein domain, only one of them could be essential. In this study, we defined this subset of genes as "essential domain-containing" (EDC) genes. Using a Tn-seq data set built-in Burkholderia cenocepacia K56-2, we developed an in silico pipeline to identify EDC genes and the essential protein domains they encode. We found forty candidate EDC genes and demonstrated growth defect phenotypes using CRISPR interference (CRISPRi). This analysis included two knockdowns of genes encoding the protein domains of unknown function DUF2213 and DUF4148. These putative essential domains are conserved in more than two hundred bacterial species, including human and plant pathogens. Together, our study suggests that essentiality should be assigned to individual protein domains rather than genes, contributing to a first functional characterization of protein domains of unknown function.

Ultrahigh-Throughput Detection of Enzymatic Alcohol Dehydrogenase Activity in Microfluidic Droplets with a Direct Fluorogenic Assay

The exploration of large DNA libraries of metagenomic or synthetic origin is greatly facilitated by ultrahigh‐throughput assays that use monodisperse water‐in‐oil emulsion droplets as sequestered reaction compartments. Millions of samples can be generated and analysed in microfluidic devices at kHz speeds, requiring only micrograms of reagents. The scope of this powerful platform for the discovery of new sequence space is, however, hampered by the limited availability of assay substrates, restricting the functions and reaction types that can be investigated. Here, we broaden the scope of detectable biochemical transformations in droplet microfluidics by introducing the first fluorogenic assay for alcohol dehydrogenases (ADHs) in this format. We have synthesized substrates that release a pyranine fluorophore (8‐hydroxy‐1,3,6‐pyrenetrisulfonic acid, HPTS) when enzymatic turnover occurs. Pyranine is well retained in droplets for >6 weeks (i. e. 14‐times longer than fluorescein), avoiding product leakage and ensuring excellent assay sensitivity. Product concentrations as low as 100 nM were successfully detected, corresponding to less than one turnover per enzyme molecule on average. The potential of our substrate design was demonstrated by efficient recovery of a bona fide ADH with an >800‐fold enrichment. The repertoire of droplet screening is enlarged by this sensitive and direct fluorogenic assay to identify dehydrogenases for biocatalytic applications.

Experimental and computational investigation of enzyme functional annotations uncovers misannotation in the EC 1.1.3.15 enzyme class

Only a small fraction of genes deposited to databases have been experimentally characterised. The majority of proteins have their function assigned automatically, which can result in erroneous annotations. The reliability of current annotations in public databases is largely unknown; experimental attempts to validate the accuracy within individual enzyme classes are lacking. In this study we performed an overview of functional annotations to the BRENDA enzyme database. We first applied a high-throughput experimental platform to verify functional annotations to an enzyme class of S-2-hydroxyacid oxidases (EC 1.1.3.15). We chose 122 representative sequences of the class and screened them for their predicted function. Based on the experimental results, predicted domain architecture and similarity to previously characterised S-2-hydroxyacid oxidases, we inferred that at least 78% of sequences in the enzyme class are misannotated. We experimentally confirmed four alternative activities among the misannotated sequences and showed that misannotation in the enzyme class increased over time. Finally, we performed a computational analysis of annotations to all enzyme classes in the BRENDA database, and showed that nearly 18% of all sequences are annotated to an enzyme class while sharing no similarity or domain architecture to experimentally characterised representatives. We showed that even well-studied enzyme classes of industrial relevance are affected by the problem of functional misannotation.

Correct annotation of genomes is crucial for our understanding and utilization of functional gene diversity, yet the reliability of current protein annotations in public databases is largely unknown. In our work we validated annotations to an S-2-hydroxyacid oxidase enzyme class (EC 1.1.3.15) by assessing activity of 122 representative sequences in a high-throughput screening experiment. From this dataset we inferred that at least 78% of the sequences in the enzyme class are misannotated, and confirmed four alternative activities among the misannotated sequences. We showed that the misannotation is widespread throughout enzyme classes, affecting even well-studied classes of industrial relevance. Overall, our study highlights the value of experimental and computational validation of predicted functions within individual enzyme classes.

Purification and Characterization of Nit phym , a Robust Thermostable Nitrilase From Paraburkholderia phymatum

Despite the success of some nitrilases in industrial applications, there is a constant demand to broaden the catalog of these hydrolases, especially robust ones with high operational stability. By using the criteria of thermoresistance to screen a collection of candidate enzymes heterologously expressed in Escherichia coli, the enzyme Nit _phym from the mesophilic organism Paraburkholderia phymatum was selected and further characterized. Its quick and efficient purification by heat treatment is of major interest for large-scale applications. The purified nitrilase displayed a high thermostability with 90% of remaining activity after 2 days at 30°C and a half-life of 18 h at 60°C, together with a broad pH range of 5.5-8.5. Its high resistance to various miscible cosolvents and tolerance to high substrate loadings enabled the quantitative conversion of 65.5 g⋅L^-1 of 3-phenylpropionitrile into 3-phenylpropionic acid at 50°C in 8 h at low enzyme loadings of 0.5 g⋅L^-1, with an isolated yield of 90%. This study highlights that thermophilic organisms are not the only source of industrially relevant thermostable enzymes and extends the scope of efficient nitrilases for the hydrolysis of a wide range of nitriles, especially trans-cinnamonitrile, terephthalonitrile, cyanopyridines, and 3-phenylpropionitrile.

Key amino acid residues in homoserine-acetyltransferase from M. tuberculosis give insight into the evolution of MetX family of enzymes - HAT, SAT and HST

Multiple sequence alignment of homoserine-acetyltransferases, serine-acetyltransferases and homoserine-succinyltransferases show they all belong to MetX family, having evolved from a common ancestor by conserving the catalytic site and substrate binding residues. The discrimination in the substrate selection arises due to the presence of substrate-specific residues lining the substrate-binding pocket. Mutation of Ala59 and Gly62 to Gly and Pro respectively in homoserine-acetyltransferase from M. tuberculosis resulted in a serine-acetyltransferase like enzyme as it acetylated both l-homoserine and l-serine. Homoserine-acetyltransferase from M. tuberculosis when mutated at positon 322 where Leu was converted to Arg, resulted in succinylation over acetylation of l-homoserine. Our studies establish the importance of the substrate binding residues in determining the type of activity possessed by MetX family, despite all of them having the same catalytic triad Ser-Asp-His. Hence key residues at the substrate binding pocket dictate whether the given enzyme shows predominant transferase or hydrolase activity.

Accelerating biological insight for understudied genes

The rapid expansion of genome sequence data is increasing the discovery of protein-coding genes across all domains of life. Annotating these genes with reliable functional information is necessary to understand evolution, to define the full biochemical space accessed by nature, and to identify target genes for biotechnology improvements. The vast majority of proteins are annotated based on sequence conservation with no specific biological, biochemical, genetic, or cellular function identified. Recent technical advances throughout the biological sciences enable experimental research on these understudied protein-coding genes in a broader collection of species. However, scientists have incentives and biases to continue focusing on well documented genes within their preferred model organism. This perspective suggests a research model that seeks to break historic silos of research bias by enabling interdisciplinary teams to accelerate biological functional annotation. We propose an initiative to develop coordinated projects of collaborating evolutionary biologists, cell biologists, geneticists, and biochemists that will focus on subsets of target genes in multiple model organisms. Concurrent analysis in multiple organisms takes advantage of evolutionary divergence and selection, which causes individual species to be better suited as experimental models for specific genes. Most importantly, multisystem approaches would encourage transdisciplinary critical thinking and hypothesis testing that is inherently slow in current biological research.

ZapG (YhcB/DUF1043), a novel cell division protein in gamma-proteobacteria linking the Z-ring to septal peptidoglycan synthesis

YhcB, a poorly understood protein conserved across gamma-proteobacteria, contains a domain of unknown function (DUF1043) and an N-terminal transmembrane domain. Here, we used an integrated approach including X-ray crystallography, genetics, and molecular biology to investigate the function and structure of YhcB. The Escherichia coli yhcB KO strain does not grow at 45 °C and is hypersensitive to cell wall–acting antibiotics, even in the stationary phase. The deletion of yhcB leads to filamentation, abnormal FtsZ ring formation, and aberrant septum development. The Z-ring is essential for the positioning of the septa and the initiation of cell division. We found that YhcB interacts with proteins of the divisome (e.g., FtsI, FtsQ) and elongasome (e.g., RodZ, RodA). Seven of these interactions are also conserved in Yersinia pestis and/or Vibrio cholerae. Furthermore, we mapped the amino acid residues likely involved in the interactions of YhcB with FtsI and RodZ. The 2.8 Å crystal structure of the cytosolic domain of Haemophilus ducreyi YhcB shows a unique tetrameric α-helical coiled-coil structure likely to be involved in linking the Z-ring to the septal peptidoglycan-synthesizing complexes. In summary, YhcB is a conserved and conditionally essential protein that plays a role in cell division and consequently affects envelope biogenesis. Based on these findings, we propose to rename YhcB to ZapG (Z-ring-associated protein G). This study will serve as a starting point for future studies on this protein family and on how cells transit from exponential to stationary survival.

The Link Between the Ecology of the Prokaryotic Rare Biosphere and Its Biotechnological Potential

Current research on the prokaryotic low abundance taxa, the prokaryotic rare biosphere, is growing, leading to a greater understanding of the mechanisms underlying organismal rarity and its relevance in ecology. From this emerging knowledge it is possible to envision innovative approaches in biotechnology applicable to several sectors. Bioremediation and bioprospecting are two of the most promising areas where such approaches could find feasible implementation, involving possible new solutions to the decontamination of polluted sites and to the discovery of novel gene variants and pathways based on the attributes of rare microbial communities. Bioremediation can be improved through the realization that diverse rare species can grow abundant and degrade different pollutants or possibly transfer useful genes. Further, most of the prokaryotic diversity found in virtually all environments belongs in the rare biosphere and remains uncultivatable, suggesting great bioprospecting potential within this vast and understudied genetic pool. This Mini Review argues that knowledge of the ecophysiology of rare prokaryotes can aid the development of future, efficient biotechnology-based processes, products and services. However, this promise may only be fulfilled through improvements in (and optimal blending of) advanced microbial culturing and physiology, metagenomics, genome annotation and editing, and synthetic biology, to name a few areas of relevance. In the future, it will be important to understand how activity profiles relate with abundance, as some rare taxa can remain rare and increase activity, whereas other taxa can grow abundant. The metabolic mechanisms behind those patterns can be useful in designing biotechnological processes.

XszenFHal, a novel tryptophan 5-halogenase from Xenorhabdus szentirmaii

Flavin-dependent halogenases (FHals) catalyse the halogenation of electron-rich substrates, mainly aromatics. Halogenated compounds have many applications, as pharmaceutical, agrochemicals or as starting materials for the synthesis of complex molecules. By exploring the sequenced bacterial diversity, we discovered and characterized XszenFHal, a novel FHal from Xenorhabdus szentirmaii, a symbiotic bacterium of entomopathogenic nematode. The substrate scope of XszenFHal was examined and revealed activities towards tryptophan, indole and indole derivatives, leading to the formation of the corresponding 5-chloro products. XszenFHal makes a valuable addition to the panel of flavin-dependent halogenases already discovered and enriches the potential for biotechnology applications by allowing access to 5-halogenated indole derivatives.

Enzymes from Marine Polar Regions and Their Biotechnological Applications

The microorganisms that evolved at low temperatures express cold-adapted enzymes endowed with unique catalytic properties in comparison to their mesophilic homologues, i.e., higher catalytic efficiency, improved flexibility, and lower thermal stability. Cold environments are therefore an attractive research area for the discovery of enzymes to be used for investigational and industrial applications in which such properties are desirable. In this work, we will review the literature on cold-adapted enzymes specifically focusing on those discovered in the bioprospecting of polar marine environments, so far largely neglected because of their limited accessibility. We will discuss their existing or proposed biotechnological applications within the framework of the more general applications of cold-adapted enzymes.

Exploring biochemical diversity in bacteria

The various descriptors of biochemical diversity and an evaluation of its status of knowledge are briefly outlined. Using a few examples from in house research projects, I illustrate strategies used to increase this knowledge. Because bacteria represent an extremely diverse domain of life and carry out the widest known range of biochemical transformations, this mini-review focusses on bacteria.

Continuous Culture Adaptation of Methylobacterium extorquens AM1 and TK 0001 to Very High Methanol Concentrations

The bio-economy relies on microbial strains optimized for efficient large scale production of chemicals and fuels from inexpensive and renewable feedstocks under industrial conditions. The reduced one carbon compound methanol, whose production does not involve carbohydrates needed for the feed and food sector, can be used as sole carbon and energy source by methylotrophic bacteria like Methylobacterium extorquens AM1. This strain has already been engineered to produce various commodity and high value chemicals from methanol. The toxic effect of methanol limits its concentration as feedstock to 1% v/v. We obtained M. extorquens chassis strains tolerant to high methanol via adaptive directed evolution using the GM3 technology of automated continuous culture. Turbidostat and conditional medium swap regimes were employed for the parallel evolution of the recently characterized strain TK 0001 and the reference strain AM1 and enabled the isolation of derivatives of both strains capable of stable growth with 10% methanol. The isolates produced more biomass at 1% methanol than the ancestor strains. Genome sequencing identified the gene metY coding for an O-acetyl-L-homoserine sulfhydrylase as common target of mutation. We showed that the wildtype enzyme uses methanol as substrate at elevated concentrations. This side reaction produces methoxine, a toxic homolog of methionine incorporated in polypeptides during translation. All mutated metY alleles isolated from the evolved populations coded for inactive enzymes, designating O-acetyl-L-homoserine sulfhydrylase as a major vector of methanol toxicity. A whole cell transcriptomic analysis revealed that genes coding for chaperones and proteases were upregulated in the evolved cells as compared with the wildtype, suggesting that the cells had to cope with aberrant proteins formed during the adaptation to increasing methanol exposure. In addition, the expression of ribosomal proteins and enzymes related to energy production from methanol like formate dehydrogenases and ATP synthases was boosted in the evolved cells upon a short-term methanol stress. D-lactate production from methanol by adapted cells overexpressing the native D-lactate dehydrogenase was quantified. A significant higher lactate yield was obtained compared with control cells, indicating an enhanced capacity of the cells resistant to high methanol to assimilate this one carbon feedstock more efficiently.

Characterization of l-Carnitine Metabolism in Sinorhizobium meliloti

l-Carnitine is a trimethylammonium compound mostly known for its contribution to fatty acid transport into mitochondria. In bacteria, it is synthesized from γ-butyrobetaine (GBB) and can be used as a carbon source. l-Carnitine can be formed directly by GBB hydroxylation or synthesized via a biosynthetic route analogous to fatty acid degradation. However, this multistep pathway has not been experimentally characterized. In this work, we identified by gene context analysis a cluster of l-carnitine anabolic genes next to those involved in its catabolism and proceeded to the complete in vitro characterization of l-carnitine biosynthesis and degradation in Sinorhizobium meliloti The five enzymes catalyzing the seven steps that convert GBB to glycine betaine are described. Metabolomic analysis confirmed the multistage synthesis of l-carnitine in GBB-grown cells but also revealed that GBB is synthesized by S. meliloti To our knowledge, this is the first report of aerobic GBB synthesis in bacteria. The conservation of l-carnitine metabolism genes in different bacterial taxonomic classes underscores the role of l-carnitine as a ubiquitous nutrient.IMPORTANCE The experimental characterization of novel metabolic pathways is essential for realizing the value of genome sequences and improving our knowledge of the enzymatic capabilities of the bacterial world. However, 30% to 40% of genes of a typical genome remain unannotated or associated with a putative function. We used enzyme kinetics, liquid chromatography-mass spectroscopy (LC-MS)-based metabolomics, and mutant phenotyping for the characterization of the metabolism of l-carnitine in Sinorhizobium meliloti to provide an accurate annotation of the corresponding genes. The occurrence of conserved gene clusters for carnitine metabolism in soil, plant-associated, and marine bacteria underlines the environmental abundance of carnitine and suggests this molecule might make a significant contribution to ecosystem nitrogen and carbon cycling.

Oxidative Pathways of Deoxyribose and Deoxyribonate Catabolism

Deoxyribose is one of the building blocks of DNA and is released when cells die and their DNA degrades. We identified a bacterium that can grow with deoxyribose as its sole source of carbon even though its genome does not contain any of the known genes for breaking down deoxyribose. By growing many mutants of this bacterium together on deoxyribose and using DNA sequencing to measure the change in the mutants’ abundance, we identified multiple protein-coding genes that are required for growth on deoxyribose. Based on the similarity of these proteins to enzymes of known function, we propose a 6-step pathway in which deoxyribose is oxidized and then cleaved. Diverse bacteria use a portion of this pathway to break down a related compound, deoxyribonate, which is a waste product of metabolism. Our study illustrates the utility of large-scale bacterial genetics to identify previously unknown metabolic pathways.

Using genome-wide mutant fitness assays in diverse bacteria, we identified novel oxidative pathways for the catabolism of 2-deoxy-d-ribose and 2-deoxy-d-ribonate. We propose that deoxyribose is oxidized to deoxyribonate, oxidized to ketodeoxyribonate, and cleaved to acetyl coenzyme A (acetyl-CoA) and glyceryl-CoA. We have genetic evidence for this pathway in three genera of bacteria, and we confirmed the oxidation of deoxyribose to ketodeoxyribonate in vitro. In Pseudomonas simiae, the expression of enzymes in the pathway is induced by deoxyribose or deoxyribonate, while in Paraburkholderia bryophila and in Burkholderia phytofirmans, the pathway proceeds in parallel with the known deoxyribose 5-phosphate aldolase pathway. We identified another oxidative pathway for the catabolism of deoxyribonate, with acyl-CoA intermediates, in Klebsiella michiganensis. Of these four bacteria, only P. simiae relies entirely on an oxidative pathway to consume deoxyribose. The deoxyribose dehydrogenase of P. simiae is either nonspecific or evolved recently, as this enzyme is very similar to a novel vanillin dehydrogenase from Pseudomonas putida that we identified. So, we propose that these oxidative pathways evolved primarily to consume deoxyribonate, which is a waste product of metabolism.

IMPORTANCE Deoxyribose is one of the building blocks of DNA and is released when cells die and their DNA degrades. We identified a bacterium that can grow with deoxyribose as its sole source of carbon even though its genome does not contain any of the known genes for breaking down deoxyribose. By growing many mutants of this bacterium together on deoxyribose and using DNA sequencing to measure the change in the mutants’ abundance, we identified multiple protein-coding genes that are required for growth on deoxyribose. Based on the similarity of these proteins to enzymes of known function, we propose a 6-step pathway in which deoxyribose is oxidized and then cleaved. Diverse bacteria use a portion of this pathway to break down a related compound, deoxyribonate, which is a waste product of metabolism. Our study illustrates the utility of large-scale bacterial genetics to identify previously unknown metabolic pathways.

Exploring natural biodiversity to expand access to microbial terpene synthesis

Background

Terpenes are industrially relevant natural compounds the biosynthesis of which relies on two well-established—mevalonic acid (MVA) and methyl erythritol phosphate (MEP)-pathways. Both pathways are widely distributed in all domains of life, the former is predominantly found in eukaryotes and archaea and the latter in eubacteria and chloroplasts. These two pathways supply isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP), the universal building blocks of terpenes.

Results

The potential to establish a semisynthetic third pathway to access these precursors has been investigated in the present work. We have tested the ability of a collection of 93 isopentenyl phosphate kinases (IPK) from the biodiversity to catalyse the double phosphorylation of isopentenol and dimethylallyl alcohol to give, respectively IPP and DMAPP. Five IPKs selected from a preliminary in vitro screening were evaluated in vivo in an engineered chassis E. coli strain producing carotenoids. The recombinant pathway leading to the synthesis of neurosporene and lycopene, allows a simple colorimetric assay to test the potential of IPKs for the synthesis of IPP and DMAPP starting from the corresponding alcohols. The best candidate identified was the IPK from Methanococcoides burtonii (UniProt ID: Q12TH9) which improved carotenoid and neurosporene yields ~ 18-fold and > 45-fold, respectively. In our lab scale conditions, titres of neurosporene reached up to 702.1 ± 44.7 µg/g DCW and 966.2 ± 61.6 µg/L. A scale up to 4 L in-batch cultures reached to 604.8 ± 68.3 µg/g DCW and 430.5 ± 48.6 µg/L without any optimisation shown its potential for future applications. Neurosporene was almost the only carotenoid produced under these conditions, reaching ~ 90% of total carotenoids both at lab and batch scales thus offering an easy access to this sophisticated molecule.

Conclusion

IPK biodiversity was screened in order to identify IPKs that optimize the final carotenoid content of engineered E. coli cells expressing the lycopene biosynthesis pathway. By simply changing the IPK and without any other metabolic engineering we improved the neurosporene content by more than 45 fold offering a new biosynthetic access to this molecule of upmost importance.

Electronic supplementary material

The online version of this article (10.1186/s12934-019-1074-4) contains supplementary material, which is available to authorized users.

Discovery of new levansucrase enzymes with interesting properties and improved catalytic activity to produce levan and fructooligosaccharides

Mining for new levansucrase enzymes with high levan production, transfructosylating activity, and thermal stability and studying their kinetics and acceptor specificity.

Structural Studies based on two Lysine Dioxygenases with Distinct Regioselectivity Brings Insights Into Enzyme Specificity within the Clavaminate Synthase-Like Family

Iron(II)/α-ketoacid-dependent oxygenases (αKAOs) are enzymes that catalyze the oxidation of unactivated C-H bonds, mainly through hydroxylation. Among these, those that are active towards amino-acids and their derivatives are grouped in the Clavaminate Synthase Like (CSL) family. CSL enzymes exhibit high regio- and stereoselectivities with strict substrate specificity. This study reports the structural elucidation of two new regiodivergent members, KDO1 and KDO5, active towards lysine, and the structural and computational analysis of the whole family through modelling and classification of active sites. The structures of KDO1 and KDO5 in complex with their ligands show that one exact position in the active site controls the regioselectivity of the reaction. Our results suggest that the substrate specificity and high stereoselectivity typical of this family is linked to a lid that closes up in order to form a sub-pocket around the side chain of the substrate. This dynamic lid is found throughout the family with varying sequence and length and is associated with a conserved stable dimeric interface. Results from this study could be a starting-point for exploring the functional diversity of the CSL family and direct in vitro screening in the search for new enzymatic activities.

Proteomic Profiling of Burkholderia thailandensis During Host Infection Using Bio-Orthogonal Noncanonical Amino Acid Tagging (BONCAT)

Burkholderia pseudomallei and B. mallei are the causative agents of melioidosis and glanders, respectively, and are often fatal to humans and animals. Owing to the high fatality rate, potential for spread by aerosolization, and the lack of efficacious therapeutics, B. pseudomallei and B. mallei are considered biothreat agents of concern. In this study, we investigate the proteome of Burkholderia thailandensis, a closely related surrogate for the two more virulent Burkholderia species, during infection of host cells, and compare to that of B. thailandensis in culture. Studying the proteome of Burkholderia spp. during infection is expected to reveal molecular mechanisms of intracellular survival and host immune evasion; but proteomic profiling of Burkholderia during host infection is challenging. Proteomic analyses of host-associated bacteria are typically hindered by the overwhelming host protein content recovered from infected cultures. To address this problem, we have applied bio-orthogonal noncanonical amino acid tagging (BONCAT) to B. thailandensis, enabling the enrichment of newly expressed bacterial proteins from virtually any growth condition, including host cell infection. In this study, we show that B. thailandensis proteins were selectively labeled and efficiently enriched from infected host cells using BONCAT. We also demonstrate that this method can be used to label bacteria in situ by fluorescent tagging. Finally, we present a global proteomic profile of B. thailandensis as it infects host cells and a list of proteins that are differentially regulated in infection conditions as compared to bacterial monoculture. Among the identified proteins are quorum sensing regulated genes as well as homologs to previously identified virulence factors. This method provides a powerful tool to study the molecular processes during Burkholderia infection, a much-needed addition to the Burkholderia molecular toolbox.

Revealing Unexplored Sequence-Function Space Using Sequence Similarity Networks

The rapidly expanding number of protein sequences found in public databases can improve our understanding of how protein functions evolve. However, our current knowledge of protein function likely represents a small fraction of the diverse repertoire that exists in nature. Integrative computational methods can facilitate the discovery of new protein functions and enzymatic reactions through the observation and investigation of the complex sequence-structure-function relationships within protein superfamilies. Here, we highlight the use of sequence similarity networks (SSNs) to identify previously unexplored sequence and function space. We exemplify this approach using the nitroreductase (NTR) superfamily. We demonstrate that SSN investigations can provide a rapid and effective means to classify groups of proteins, therefore exposing experimentally unexplored sequences that may exhibit novel functionality. Integration of such approaches with systematic experimental characterization will expand our understanding of the functional diversity of enzymes and their associated physiological roles.

Functional assignment of multiple catabolic pathways for D-apiose

Colocation of the genes encoding ABC, TRAP, and TCT transport systems and catabolic pathways for the transported ligand provides a strategy for discovering novel microbial enzymes and pathways. We screened solute-binding proteins (SBPs) for ABC transport systems and identified three that bind D-apiose, a branched pentose in the cell walls of higher plants. Guided by sequence similarity networks (SSNs) and genome neighborhood networks (GNNs), the identities of the SBPs enabled the discovery of four catabolic pathways for D-apiose with eleven previously unknown reactions. The new enzymes include D-apionate oxidoisomerase, which catalyzes hydroxymethyl group migration, as well as 3-oxo-isoapionate-4-phosphate decarboxylase and 3-oxo-isoapionate-4-phosphate transcarboxylase/hydrolase, which are RuBisCO-like proteins (RLPs). The web tools for generating SSNs and GNNs are publicly accessible ( http://efi.igb.illinois.edu/efi-est/ ), so similar 'genomic enzymology' strategies for discovering novel pathways can be used by the community.

Transcriptional Regulation of Carnitine Catabolism in Pseudomonas aeruginosa by CdhR

Pathogens must metabolize host-derived compounds during infection and properly regulate the responsible pathways. Carnitine is a common eukaryotic-associated quaternary amine compound that can be catabolized by Pseudomonas aeruginosa. Here we expand on our understanding of how this metabolic pathway is regulated and provide details on how carnitine catabolism is intertwined with glycine betaine catabolism at the level of transcriptional control.

The common environmental bacterium and opportunistic pathogen Pseudomonas aeruginosa encodes diverse metabolic pathways and associated regulatory networks allowing it to thrive in these different environments. In an effort to understand P. aeruginosa metabolism and detection of host-derived compounds, we previously identified CdhR and GbdR as members of the AraC transcription factor family that regulate catabolism of the quaternary amine compounds carnitine and glycine betaine, respectively. In this study, our goal was to further characterize regulation of carnitine catabolism by the transcription factor CdhR. CdhR binds in a concentration-dependent manner upstream of the carnitine catabolism operon promoter (P_{caiXcdhCABhocS}). We identified the CdhR binding site and determined that it overlaps with the GbdR binding site in the caiX-cdhR intergenic region. Carnitine catabolism is repressed by glucose and glycine betaine, and here we show this happens at the transcriptional level. Furthermore, we show that CdhR enhances its own expression and that GbdR contributes to cdhR expression by enhancing the level of basal expression. The intertwined regulation of caiX and cdhR transcription by GbdR and CdhR suggests that carnitine catabolism is under tight but tuneable control.

IMPORTANCE Pathogens must metabolize host-derived compounds during infection and properly regulate the responsible pathways. Carnitine is a common eukaryotic-associated quaternary amine compound that can be catabolized by Pseudomonas aeruginosa. Here we expand on our understanding of how this metabolic pathway is regulated and provide details on how carnitine catabolism is intertwined with glycine betaine catabolism at the level of transcriptional control.