Unspecific peroxygenases (UPOs) are fungal enzymes that attract significant attention for their ability to perform versatile oxyfunctionalization reactions using H2O2. Unlike other oxygenases, UPOs do not require additional reductive equivalents or electron transfer chains that complicate basic and applied research. Nevertheless, UPOs generally exhibit low to no heterologous production levels and only four UPO structures have been determined to date by crystallography limiting their usefulness and obstructing research. To overcome this bottleneck, we implemented a workflow that applies PROSS stability design to AlphaFold2 model structures of 10 unique and diverse UPOs followed by a signal peptide shuffling to enable heterologous production. Nine UPOs were functionally produced in Pichia pastoris, including the recalcitrant CciUPO and three UPOs derived from oomycetes the first nonfungal UPOs to be experimentally characterized. We conclude that the high accuracy and reliability of new modeling and design workflows dramatically expand the pool of enzymes for basic and applied research.

Unspecific peroxygenases (UPOs) perform oxy-functionalizations for a wide range of substrates utilizing H2O2 without the need for further reductive equivalents or electron transfer chains. Tailoring these promising enzymes toward industrial application was intensely pursued in the last decade with engineering campaigns addressing the heterologous expression, activity, stability, and improvements in chemo- and regioselectivity. One hitherto missing integral part was the targeted engineering of enantioselectivity for specific substrates with poor starting enantioselectivity. In this work, we present the engineering of the short-type MthUPO toward the enantiodivergent hydroxylation of the terpene model substrate, β-ionone. Guided by computational modeling, we designed a small smart library and screened it with a GC−MS setup. After two rounds of iterative protein evolution, the activity increased up to 17-fold and reached a regioselectivity of up to 99.6% for the 4-hydroxy-β-ionone. Enantiodivergent variants were identified with enantiomeric ratios of 96.6:3.4 (R) and 0.3:99.7 (S), respectively.

Introduction Liverworts are a group of non-vascular plants that possess unique metabolism not found in other plants. Many liverwort metabolites have interesting structural and biochemical characteristics, however the fluctuations of these metabolites in response to stressors is largely unknown. Objectives To investigate the metabolic stress-response of the leafy liverwort Radula complanata. Methods Five phytohormones were applied exogenously to in vitro cultured R. complanata and an untargeted metabolomic analysis was conducted. Compound classification and identification was performed with CANOPUS and SIRIUS while statistical analyses including PCA, ANOVA, and variable selection using BORUTA were conducted to identify metabolic shifts.Results It was found that R. complanata was predominantly composed of carboxylic acids and derivatives, followed by benzene and substituted derivatives, fatty acyls, organooxygen compounds, prenol lipids, and flavonoids. The PCA revealed that samples grouped based on the type of hormone applied, and the variable selection using BORUTA (Random Forest) revealed 71 identified and/or classified features that fluctuated with phytohormone application. The stress-response treatments largely reduced the production of the selected primary metabolites while the growth treatments resulted in increased production of these compounds. 4-(3-Methyl-2-butenyl)-5-phenethylbenzene-1,3-diol was identified as a biomarker for the growth treatments while GDP-hexose was identified as a biomarker for the stress-response treatments. Conclusion Exogenous phytohormone application caused clear metabolic shifts in Radula complanata that deviate from the responses of vascular plants. Further identification of the selected metabolite features can reveal metabolic biomarkers unique to liverworts and provide more insight into liverwort stress responses.

Engineering proteins and enzymes with the desired functionality has broad applications in molecular biology, biotechnology, biomedical sciences, health, and medicine. The vastness of protein sequence space and all the possible proteins it represents can pose a considerable barrier for enzyme engineering campaigns through directed evolution and rational design. The nonlinear effects of coevolution between amino acids in protein sequences complicate this further. Data-driven models increasingly provide scientists with the computational tools to navigate through the largely undiscovered forest of protein variants and catch a glimpse of the rules and effects underlying the topology of sequence space. In this review, we outline a complete theoretical journey through the processes of protein engineering methods such as directed evolution and rational design and reflect on these strategies and data-driven hybrid strategies in the context of sequence space. We discuss crucial phenomena of residue coevolution, such as epistasis, and review the history of models created over the past decade, aiming to infer rules of protein evolution from data and use this knowledge to improve the prediction of the structure− function relationship of proteins. Data-driven models based on deep learning algorithms are among the most promising methods that can account for the nonlinear phenomena of sequence space to some degree. We also critically discuss the available models to predict evolutionary coupling and epistatic effects (classical and deep learning) in terms of their capabilities and limitations. Finally, we present our perspective on possible future directions for developing data-driven approaches and provide key orientation points and necessities for the future of the fast-evolving field of enzyme engineering.

In recent years, the engineering of flexible loops to improve enzyme properties has gained attention in biocatalysis. Herein, we report a loop engineering strategy to improve the stability of the substrate access tunnels, which reveals the molecular mechanism between loops and tunnels. Based on the dynamic tunnel analysis of CYP116B3, five positions (A86, T91, M108, A109, T111) in loops B-B′ and B′-C potentially affecting tunnel frequent occurrence were selected and subjected to simultaneous saturation mutagenesis. The best variant 8G8 (A86T/T91L/M108N/A109M/T111A) for the dealkylation of 7-ethoxycoumarin and the hydroxylation of naphthalene was identified with considerably increased activity (134-fold and 9-fold) through screening. Molecular dynamics simulations showed that the reduced flexibility of loops B-B′ and B′-C was responsible for increasing the stability of the studied tunnel. The redesign of loops B-B′ and B′-C surrounding the tunnel entrance provides loop engineering with a powerful and likely general method to kick on/off the substrate/product transportation.

Enzymatic hydroxylation of activated and nonactivated sp3-carbons attracts keen interest from the chemistry community as it is one of the most challenging tasks in organic synthesis. Nature provides a vast number of enzymes with an enormous catalytic versatility to fulfill this task. Given that those very different enzymes have a distinct specificity in substrate scope, selectivity, activity, stability, and catalytic cycle, it is interesting to outline similarities and differences. In this Review, we intend to delineate which enzymes possess considerable advantages within specific issues. Heterologous production, crystal structure availability, enzyme engineering potential, and substrate promiscuity are essential factors for the applicability of these biocatalysts.

Unspecific peroxygenases (UPOs) enable oxyfunctionalizations of a broad substrate range with unparalleled activities. Tailoring these enzymes for chemo- and regioselective transformations represents a grand challenge due to the difficulties in their heterologous productions. Herein, we performed protein engineering in Saccharomyces cerevisiae using the MthUPO from Myceliophthora thermophila. More than 5300 transformants were screened. This protein engineering led to a significant reshaping of the active site as elucidated by computational modelling. The reshaping was responsible for the increased oxyfunctionalization activity, with improved kcat/Km values of up to 16.5-fold for the model substrate 5-nitro-1,3-benzodioxole. Moreover, variants were identified with high chemo- and regioselectivities in the oxyfunctionalization of aromatic and benzylic carbons, respectively. The benzylic hydroxylation was demonstrated to perform with enantioselectivities of up to 95% ee. The proposed evolutionary protocol and rationalization of the enhanced activities and selectivities acquired by MthUPO variants represent a step forward toward the use and implementation of UPOs in biocatalytic synthetic pathways of industrial interest.

IntroductionThe demand to develop efficient and reliable analytical methods for the quality control of nutraceuticals is on the rise, together with an increase in the legal requirements for safe and consistent levels of its active principles.ObjectiveTo establish a reliable model for the quality control of widely used Senna preparations used as laxatives and assess its phyto-equivalency.MethodsA comparative metabolomics approach via NMR and MS analyses was employed for the comprehensive measurement of metabolites and analyzed using chemometrics.ResultsUnder optimized conditions, 30 metabolites were simultaneously identified and quantified including anthraquinones, bianthrones, acetophenones, flavonoid conjugates, naphthalenes, phenolics, and fatty acids. Principal component analysis (PCA) was used to define relative metabolite differences among Senna preparations. Furthermore, quantitative 1H NMR (qHNMR) was employed to assess absolute metabolites levels in preparations. Results revealed that 6-hydroxy musizin or tinnevellin were correlated with active metabolites levels, suggesting the use of either of these naphthalene glycosides as markers for official Senna drugs authentication.ConclusionThis study provides the first comparative metabolomics approach utilizing NMR and UPLC–MS to reveal for secondary metabolite compositional differences in Senna preparations that could readily be applied as a reliable quality control model for its analysis.

IntroductionThe production of marine drugs in its normal habitats is often low and depends greatly on ecological conditions. Chemical synthesis of marine drugs is not economically feasible owing to their complex structures. Biotechnology application via elicitation represents a promising tool to enhance metabolites yield that has yet to be explored in soft corals.ObjectivesStudy the elicitation impact of salicylic acid (SA) and six analogues in addition to a systemic acquired resistance inducer on secondary metabolites accumulation in the soft coral Sarcophyton ehrenbergi along with the symbiont zooxanthellae and if SA elicitation effect is extended to other coral species S. glaucum and Lobophyton pauciliforum.MethodsPost elicitation in the three corals and zooxanthella, metabolites were extracted and analyzed via UHPLC-MS coupled with chemometric tools.ResultsMultivariate data analysis of the UHPLC-MS data set revealed clear segregation of SA, amino-SA, and acetyl-SA elicited samples. An increased level ca. 6- and 8-fold of the diterpenes viz., sarcophytonolide I, sarcophine and a C28-sterol, was observed in SA and amino-SA groups, respectively. Post elicitation, the level of diepoxy-cembratriene increased 1.5-fold and 2.4-fold in 1 mM SA, and acetyl-SA (aspirin) treatment groups, respectively. S. glaucum and Lobophyton pauciliforum showed a 2-fold increase of diepoxy-cembratriene levels.ConclusionThese results suggest that SA could function as a general and somewhat selective diterpene inducing signaling molecule in soft corals. Structural consideration reveals initial structure–activity relationship (SAR) in SA derivatives that seem important for efficient diterpene and sterol elicitation.

Thousands of articles using metabolomics approaches are published every year. With the increasing amounts of data being produced, mere description of investigations as text in manuscripts is not sufficient to enable re-use anymore: the underlying data needs to be published together with the findings in the literature to maximise the benefit from public and private expenditure and to take advantage of an enormous opportunity to improve scientific reproducibility in metabolomics and cognate disciplines. Reporting recommendations in metabolomics started to emerge about a decade ago and were mostly concerned with inventories of the information that had to be reported in the literature for consistency. In recent years, metabolomics data standards have developed extensively, to include the primary research data, derived results and the experimental description and importantly the metadata in a machine-readable way. This includes vendor independent data standards such as mzML for mass spectrometry and nmrML for NMR raw data that have both enabled the development of advanced data processing algorithms by the scientific community. Standards such as ISA-Tab cover essential metadata, including the experimental design, the applied protocols, association between samples, data files and the experimental factors for further statistical analysis. Altogether, they pave the way for both reproducible research and data reuse, including meta-analyses. Further incentives to prepare standards compliant data sets include new opportunities to publish data sets, but also require a little “arm twisting” in the author guidelines of scientific journals to submit the data sets to public repositories such as the NIH Metabolomics Workbench or MetaboLights at EMBL-EBI. In the present article, we look at standards for data sharing, investigate their impact in metabolomics and give suggestions to improve their adoption.