Variety Of Enzymes Research Articles

Dirichlet latent modelling enables effective learning and sampling of the functional protein design space

Engineering proteins with desired functions and biochemical properties is pivotal for biotechnology and drug discovery. While computational methods based on evolutionary information are reducing the experimental burden by designing targeted libraries of functional variants, they still have a low success rate when the desired protein has few or very remote homologous sequences. Here we propose an autoregressive model, called Temporal Dirichlet Variational Autoencoder (TDVAE), which exploits the mathematical properties of the Dirichlet distribution and temporal convolution to efficiently learn high-order information from a functionally related, possibly remotely similar, set of sequences. TDVAE is highly accurate in predicting the effects of amino acid mutations, while being significantly 90% smaller than the other state-of-the-art models. We then use TDVAE to design variants of the human alpha galactosidase enzymes as potential treatment for Fabry disease. Our model builds a library of diverse variants which retain sequence, biochemical and structural properties of the wildtype protein, suggesting they could be suitable for enzyme replacement therapy. Taken together, our results show the importance of accurate sequence modelling and the potential of autoregressive models as protein engineering and analysis tools.

Modulating Polymerase Activity Through Light-Oxygen-Voltage Domain Insertion.

Biochemical reaction networks adapt to environmental conditions by sensing chemical or physical stimuli and using tightly controlled mechanisms. While most signals come from molecules, many cells can also sense and respond to light. Among the biomolecular structures that enable light sensing, we selected a light-oxygen-voltage (LOV) domain in a previous study that tested the engineering of novel regulatory mechanisms into a nucleic acid polymerase. In this follow-up study, we studied the activities of previously selected variants in kinetic detail, and we generated additional LOV-polymerase fusion variants based on further insertion criteria. Our results provide mechanistic insights into how LOV domain insertion influences polymerase activity in a light-responsive manner: All active and photoresponsive enzyme variants studied by us to date were partially inhibited (i. e., "turned off") after irradiation with blue light at 470 nm, which can be explained by specific obstructions of the polymerase entry or exit structures (substrate entry channels or product exit channels, or both). Although the effects observed are moderate, we anticipate further engineering strategies that could be used to improve the extent of switchability and possibly to develop a "turn-on mode" insertion.

Manufacturing of the highly active thermophile PETases PHL7 and PHL7mut3 using Escherichia coli

BackgroundThe global plastic waste crisis requires combined recycling strategies. One approach, enzymatic degradation of PET waste into monomers, followed by re-polymerization, offers a circular economy solution. However, challenges remain in producing sufficient amounts of highly active PET-degrading enzymes without costly downstream processes.ResultsUsing the growth-decoupled enGenes eX-press V2 E. coli strain, pH, induction strength and feed rate were varied in a factorial-based optimization approach, to find the best-suited production conditions for the PHL7 enzyme. This led to a 40% increase in activity of the fermentation supernatant. Optimization of the expression construct resulted in a further 4-fold activity gain. Finally, the identified improvements were applied to the production of the more active and temperature stable enzyme variant, PHL7mut3. The unpurified fermentation supernatant of the PHL7mut3 fermentation was able to completely degrade our PET film sample after 16 h of incubation at 70 °C at an enzyme loading of only 0.32 mg enzyme per g of PET.ConclusionsIn this research, we present an optimized process for the extracellular production of thermophile and highly active PETases PHL7 and PHL7mut3, eliminating the need for costly purification steps. These advancements support large-scale enzymatic recycling, contributing to solving the global plastic waste crisis.

Machine learning guided rational design of a non-heme iron-based lysine dioxygenase improves its total turnover number.

Highly selective C-H functionalization remains an ongoing challenge in organic synthetic methodologies. Biocatalysts are robust tools for achieving these difficult chemical transformations. Biocatalyst engineering has often required directed evolution or structure-based rational design campaigns to improve their activities. In recent years, machine learning has been integrated into these workflows to improve the discovery of beneficial enzyme variants. In this work, we combine a structure-based machine-learning algorithm with classical molecular dynamics simulations to down select mutations for rational design of a non-heme iron-dependent lysine dioxygenase, LDO. This approach consistently resulted in functional LDO mutants and circumvents the need for extensive study of mutational activity before-hand. Our rationally designed single mutants purified with up to 2-fold higher yields than WT and displayed higher total turnover numbers (TTN). Combining five such single mutations into a pentamutant variant, LPNYI LDO, leads to a 40% improvement in the TTN (218±3) as compared to WT LDO (TTN = 160±2). Overall, this work offers a low-barrier approach for those seeking to synergize machine learning algorithms with pre-existing protein engineering strategies.

B-124 Structure-based Variant Effect Prediction and Design of Functional CYP2C9 Variants by Virtual Evolution

Abstract Background The decryption of massive variants of uncertain significance (VUS) discovered by sequencing poses the main challenge in the post-sequencing era. VUS also resulted in a particular problem in the field of pharmacogenomics, which can be epitomized by drug-metabolizing enzymes, such as CYP2C9. Therefore, an accurate model that incorporates the inner connection between massive protein variants and their function alterations is crucial for clinical annotation of human variome and the virtual evolution of enzyme engineering for biotherapeutics. Methods We developed a model named variant effect recognition network for CYP2C9 (vERnet-P), which learned features from AlphaFold2-predicted protein structures and predicted the enzyme activity of missense single-nucleotide variants in CYP2C9. The two crucial strategies of vERnet-P are the construction of amino acid interaction networks and the application of deep learning. Therefore, vERnet-P leveraged highly accurate protein structures predicted by AlphaFold2, combined with novel techniques for enriching and capturing useful information. Based on the accurate and preemptive prediction of CYP2C9 variants, we performed the saturation mutation prediction on several sites of CYP2C9 to identify new variants with the expected function. Furthermore, the drug metabolic activities of these newly identified variants were investigated with two probe drugs by in vitro metabolic activity assessments. Results An accuracy of up to 93.5% for the classification of activity levels was yielded in the testing dataset. In addition, the tasks for recognizing the high-activity and low-activity variants both achieved 93.5% accuracies, demonstrating that vERnet-P truly learned the features related to enzyme activity, rather than the selection bias to one kind of samples. Strikingly, the AUC values of the ROC and the PR reached 0.971 and 0.966 respectively in the testing cohort for vERnet-P. vERnet-P achieved the strongest agreement with massively parallel assessments relative to the other 8 computational variant effect predictors in the task of predicting CYP2C9 variant enzyme activity. We explored 6 mutant sites in CYP2C9 by using vERnet-P to perform saturation mutation prediction and found 12 novel variants with a high possibility of activity changing. We identified 6 variants with most likely increased activity by using the prediction of wild-type as a cutoff, consequently, 6 variants with most likely decreased activity were selected. According to these 12 discovered variants, 10 were strongly confirmed by the in vitro metabolic activity assays of both probe drugs. Conclusions vERnet-P achieved state-of-the-art performance in predicting the CYP2C9 variant enzyme activity. The preemptive prediction for CYP2C9 variants can efficiently provide the clinical drug dosing guideline. Furthermore, by saturating mutation prediction on several sites, our results indicated the evolutionary direction of CYP2C9 enzyme activity and discovered some brand-new CYP2C9 variants with ultra-strong functional alterations, which have not previously been reported. The results of in vitro metabolic activity assessment were highly consistent with our in silico prediction, indicating the potential of AI models for inferring evolutionary direction and designing novel variants with the expected function.

Enzymatic oxidation of polyethylene by Galleria mellonella intestinal cytochrome P450s

Polyethylene is widely used but highly resistant to biodegradation, owing to its composition of only a hydrocarbon backbone. For biodegradation to occur, oxidation within the polymer needs to be initiated. Galleria mellonella was the first insect discovered to autonomously oxidize polyethylene without the aid of gut microbes. However, the specific enzyme remains unidentified. Here, we identified for the first time two polyethylene oxidation enzyme candidates of cytochrome P450 (CYP), 6B2-GP04 and CYP6B2-13G08, from the G. mellonella midgut. Both candidate clones oxidized polyethylene efficiently, generating short-chain aliphatic compounds, with CYP6B2-GP04 exhibiting higher activity than CYP6B2-13G08 in yeast and insect cells. In silico structural modeling approaches revealed that the CYP6B2-GP04 Phe118 was essential for interacting with hydrocarbons, which was further validated by mutating it to glycine. Furthermore, directed enzyme evolution led to the identification of an enzyme variant with significantly increased oxidation efficiency. Our findings offer promising enzyme-based solutions for polyethylene biodegradation, potentially mitigating polyethylene-driven plastic pollution.

Enzyme structure correlates with variant effect predictability

Protein engineering increasingly relies on machine learning models to computationally pre-screen promising novel candidates. Although machine learning approaches have proven effective, their performance on prospective screening data leaves room for improvement; prediction accuracy can vary greatly from one protein variant to the next. So far, it is unclear what characterizes variants that are associated with large prediction error. In order to establish whether structural characteristics influence predictability, we created a novel high-order combinatorial dataset for an enzyme spanning 3,706 variants, that can be partitioned into subsets of variants with mutations at positions exclusively belonging to a particular structural class. By training four different supervised variant effect prediction (VEP) models on structurally partitioned subsets of our data, we found that predictability strongly depended on all four structural characteristics we tested; buriedness, number of contact residues, proximity to the active site and presence of secondary structure elements. These dependencies were also found in several single mutation enzyme variant datasets, albeit with dataset specific directions. Most importantly, we found that these dependencies were similar for all four models we tested, indicating that there are specific structure and function determinants that are insufficiently accounted for by current machine learning algorithms. Overall, our findings suggest that improvements can be made to VEP models by exploring new inductive biases and by leveraging different data modalities of protein variants, and that stratified dataset design can highlight areas of improvement for machine learning guided protein engineering.

Harnessing Pharmacogenomics in Clinical Research on Psychedelic-Assisted Therapy.

Psychedelics have recently re-emerged as potential treatments for various psychiatric conditions that impose major public health costs and for which current treatment options have limited efficacy. At the same time, personalized medicine is increasingly being implemented in psychiatry to provide individualized drug dosing recommendations based on genetics. This review brings together these topics to explore the utility of pharmacogenomics (a key component of personalized medicine) in psychedelic-assisted therapies. We summarized the literature and explored the potential implications of genetic variability on the pharmacodynamics and pharmacokinetics of psychedelic drugs including lysergic acid diethylamide (LSD), psilocybin, N,N-dimethyltryptamine (DMT), 5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT), ibogaine and 3,4-methylenedioxymethamphetamine (MDMA). Although existing evidence is limited, particularly concerning pharmacodynamics, studies investigating pharmacokinetics indicate that genetic variants in drug-metabolizing enzymes, such as cytochrome P450, impact the intensity of acute psychedelic effects for LSD and ibogaine, and that a dose reduction for CYP2D6 poor metabolizers may be appropriate. Furthermore, based on the preclinical evidence, it can be hypothesized that CYP2D6 metabolizer status might contribute to altered acute psychedelic experiences with 5-MeO-DMT and psilocybin when combined with monoamine oxidase inhibitors. In conclusion, considering early evidence that genetic factors can influence the effects of certain psychedelics, we suggest that pharmacogenomic testing should be further investigated in clinical research. This is necessary to evaluate its utility in improving the safety and therapeutic profile of psychedelic therapies and a potential future role in personalizing psychedelic-assisted therapies, should these treatments become available.

Directed evolution of an orthogonal transcription engine for programmable gene expression in eukaryotes.

T7 RNA polymerase has enabled orthogonal control of gene expression and recombinant protein production across diverse prokaryotic host chassis organisms for decades. However, the absence of 5' methyl guanosine caps on T7 RNAP derived transcripts has severely limited its utility and widespread adoption in eukaryotic systems. To address this shortcoming, we evolved a fusion enzyme combining T7 RNAP with the single subunit capping enzyme from African swine fever virus using Saccharomyces cerevisiae. We isolated highly active variants of this fusion enzyme, which exhibited roughly two orders of magnitude higher protein expression compared to the wild-type enzyme. We demonstrate the programmable control of gene expression using T7 RNAP-based genetic circuits in yeast and validate enhanced performance of these engineered variants in mammalian cells. This study presents a robust, orthogonal gene regulatory system applicable across diverse eukaryotic hosts, enhancing the versatility and efficiency of synthetic biology applications.

Comparative study of stability and activity of wild-type and mutant human carbonic anhydrase II enzymes using molecular dynamics and docking simulations

The human carbonic anhydrase II (HCA II) enzyme is a cytosolic protein that catalyzes the reversible hydration of carbon dioxide (CO2). Considering the critical role of the HCA II and the effects of some mutations on the activity and stability of the enzyme in humans, several computational methods are used to study the structure and dynamics of the wild-type and the mutant enzymes with three ligands, CO2, 4-nitrophenyl acetate and acetazolamide. Our results of MD simulation of a wild-type enzyme with 4-nitrophenyl acetate show that it created essential effects on the fluctuation of this enzyme and made it more unstable and less compact than the same enzyme without ligand. In the MD of the mutant enzyme with 4-nitrophenyl acetate ligand, no significant difference is observed between with and without ligand. The affinity of the wild-type enzyme to the 4-nitrophenyl acetate is notably higher than the mutant enzyme with the same ligand. Furthermore, results showed that wild-type and mutant enzymes with CO2 are more favorable in stability and flexibility than the same enzymes without ligand. The MD results of wild-type with acetazolamide indicate instability compare without ligand, but in MD of mutant enzyme with acetazolamide show that it more stable and compact than the same enzyme without ligand. Finally, Comparing protein trajectories to assess the impact of ligands on the stability and activity of HCA II enzymes can have medical applications and can in the engineering and design of new variants of carbonic anhydrase enzyme.

A growth-based screening strategy for engineering the catalytic activity of an oxygen-sensitive formate dehydrogenase.

Enzyme engineering is a powerful tool for improving or altering the properties of biocatalysts for industrial, research, and therapeutic applications. Fast and accurate screening of variant libraries is often the bottleneck of enzyme engineering and may be overcome by growth-based screening strategies with simple processes to enable high throughput. The currently available growth-based screening strategies have been widely employed for enzymes but not yet for catalytically potent and oxygen-sensitive metalloenzymes. Here, we present a screening system that couples the activity of an oxygen-sensitive formate dehydrogenase to the growth of Escherichia coli. This system relies on the complementation of the E. coli formate hydrogenlyase (FHL) complex by Mo-dependent formate dehydrogenase H (EcFDH-H). Using an EcFDH-H-deficient strain, we demonstrate that growth inhibition by acidic glucose fermentation products can be alleviated by FHL complementation. This allows the identification of catalytically active EcFDH-H variants at a readily measurable cell density readout, reduced handling efforts, and a low risk of oxygen contamination. Furthermore, a good correlation between cell density and formate oxidation activity was established using EcFDH-H variants with variable catalytic activities. As proof of concept, the growth assay was employed to screen a library of 1,032 EcFDH-H variants and reduced the library size to 96 clones. During the subsequent colorimetric screening of these clones, the variant A12G exhibiting an 82.4% enhanced formate oxidation rate was identified. Since many metal-dependent formate dehydrogenases and hydrogenases form functional complexes resembling E. coli FHL, the demonstrated growth-based screening strategy may be adapted to components of such electron-transferring complexes.IMPORTANCEOxygen-sensitive metalloenzymes are highly potent catalysts that allow the reduction of chemically inert substrates such as CO2 and N2 at ambient pressure and temperature and have, therefore, been considered for the sustainable production of biofuels and commodity chemicals such as ammonia, formic acid, and glycine. A proven method to optimize natural enzymes for such applications is enzyme engineering using high-throughput variant library screening. However, most screening methods are incompatible with the oxygen sensitivity of these metalloenzymes and thereby limit their relevance for the development of biosynthetic production processes. A microtiter plate-based assay was developed for the screening of metal-dependent formate dehydrogenase that links the activity of the tested enzyme variant to the growth of the anaerobically grown host cell. The presented work extends the application range of growth-based screening to metalloenzymes and is thereby expected to advance their adoption to biosynthesis applications.

In silico Investigation of the Interactions of Thymol and Carvacrol on the Spike Protein of Omicron Variant and MPro Enzyme of Coronavirus

Many drug studies have been conducted against the coronavirus disease, which has affected the whole world since December 2019, and some studies have been carried out on natural treatment methods. Many ideas for curing coronavirus disease of T. vulgaris known as thyme plant have been presented, although there are gaps in the literature on the subject. In this work, the anti-severe acute respiratory syndrome coronavirus 2 potential of the major compounds of the T. vulgaris plant’s essential oil was investigated in silico. The major components of the T. vulgaris plant's essential oil are thymol and carvacrol. Using molecular docking experiments, we evaluated the effects of thymol and carvacrol in thyme essential oil on Omicron variant spike protein and main protease enzyme (Mpro) of severe acute respiratory syndrome coronavirus 2. We also used online databases to investigate the adsorption, distribution, metabolism, absorption, and toxic (ADMET) aspects of these two compounds. It was determined that thymol and carvacrol have strong binding affinity to the spike protein of the Omicron variant and the main protease enzyme. The compounds interact with target proteins through electrostatic, hydrogen bonds, and hydrophobic interactions. More promising findings are obtained when the contacts of carvacrol with target proteins are assessed in terms of the structure-activity relationship.

Engineering of (−)‐Limonene‐3‐Hydroxylase to Enhance Its Regioselectivity for Biosynthesis of (−)‐Menthol

AbstractThere is high market demand for (−)‐menthol because of its unique aroma and cooling properties. The first bottleneck encountered in the biosynthesis of (−)‐menthol in Escherichia coli is the low hydroxylation efficiency of (−)‐limonene‐3‐hydroxylase. Few known enzymes can hydroxylate the third carbon atom of (−)‐limonene. In this work, initially, we searched for new enzymes and screened a library of cytochrome P450BM3 variants aiming to improve catalysis of this reaction, but did not identify any variants with improved activity or regioselectivity. Then, through protein engineering of a cytochrome P450 monooxygenase variant from Pseudomonas putida (PpcamY96F/V247L), we obtained variant PpcamF87W/Y96F/L244I/V247L/V396A with regioselectivity of 93% (starting from 70%) toward (−)‐limonene without losing activity. Because of the improvement in regioselectivity, its turnover number was 1.5 times that of the starting enzyme. We analyzed the reasons for the improvement through molecular dynamics simulations. The new variant also exhibited 95% regioselectivity toward (+)‐limonene. This enzyme variant can be applied in biosynthesis of (−)‐menthol.

Mutant β-fructofuranosidase synthesizing blastose [β-d-Fruf-(2→6)-d-Glcp]

Fructooligosaccharides (FOS) are leading prebiotics that help keep the gut healthy and aid wellness by stimulating the growth and activity of beneficial intestinal bacteria. The best-studied FOS are inulin-type FOS, mainly oligosaccharides with β-Fruf-(2→1)-Fruf linkages, including 1-kestose [β-Fruf-(2→1)-β-Fruf-(2↔1)-α-Glcp] and nystose [β-Fruf-(2→1)-β-Fruf-(2→1)-β-Fruf-(2↔1)-α-Glcp]. However, the properties of other types of FOS—levan-type FOS with β-Fruf-(2→6)-Fruf linkages and neo-type FOS with β-Fruf-(2→6)-Glcp linkages—remain ambiguous because efficient methods have not been established for their synthesis. Here, using site-saturation mutation of residue His79 of β-fructofuranosidase from Zymomonas mobilis NBRC13756, we successfully obtained a mutant β-fructofuranosidase that specifically produces neo-type FOS. The H79G enzyme variant loses the native β-Fruf-(2→1)-Fru-transfer ability (which produces 1-kestose), and instead has β-Fruf-(2→6)-Glc-transfer ability and produces neokestose. Its hydrolytic activity specific to the β-Fruf-(2↔1)-α-Glcp bond of neokestose then yields blastose [β-Fruf-(2→6)-Glcp]. The enzyme produces 0.4 M blastose from 1.0 M sucrose (80 % of the theoretical yield). The production system for blastose established here will contribute to the elucidation of the physiological functions of this disaccharide.

Generation and characterization of two acid-resistant macrocin O-methyltransferase variants with a higher enzyme activity at 30 °C from Streptomyces fradiae

Tylosin is an important macrolide antibiotic produced by Streptomyces fradiae. In the biosynthesis of tylosin, macrocin O-methyltransferase TylF catalyzes the conversion of the side-product tylosin C (macrocin) to the primary component tylosin A (C/A conversion). This conversion is the rate-limiting step in the biosynthesis of tylosin, and affects the quality of the end product. To find a high activity and environment-adapted TylF enzyme, a TylF variant pool has been constructed via protein evolution approach in our previous study (Fan et al., 2023 [41]). In this study, the TylF variants with higher C/A conversion rates were expressed in E. coli and purified. The variants TylFY139F, TylFQ138H, F232Y and TylFT36S, V54A were shown to have a higher C/A conversion rate at 30 °C than that of TylF at 38 °C. Moreover, they had a greater acid resistance and showed more adaptable to the pH change during fermentation. Further protein structural and substrate-binding affinity analyses revealed that the T36S, V54A, Q138H, Y139F, and F232Y mutations enlarged the volume of the substrate-binding pocket, thereby increasing the affinity of enzyme variants for their substrates of SAM and macrocin, and decreasing the inhibition of SAH. Three of the TylF variants were overexpressed in the industrial tylosin-producing S. fradiae strain, and the recombinant strains showed the highest C/A conversion at 30 °C without heating up to 38 °C during the last 24 h of fermentation. This is of great energy-saving significance for tylosin industrial production.

Comprehensive Analysis of Allulose Production: A Review and Update.

Advancements in D-allulose production have seen significant strides in recent years, focusing on enzymatic conversion methods. Key developments include traditional immobilization techniques, the discovery of novel enzymes, directed evolution studies, and biosynthesis through metabolic pathway modification. Enzymatic conversion, particularly utilizing D-allulose 3-epimerase, remains fundamental for industrial-scale production. Innovative immobilization strategies, such as functionalized nano-beads and magnetic MOF nanoparticles, have significantly enhanced enzyme stability and reusability. Directed evolution has led to improved enzyme thermostability and catalytic efficiency, while synthetic biology methods, including phosphorylation-driven and thermodynamics-driven pathways, have optimized production processes. High-throughput screening methods have been crucial in identifying and refining enzyme variants for industrial applications. Collectively, these advancements not only enhance production efficiency and cost-effectiveness but also adhere to sustainable and economically viable manufacturing practices. The past five years have witnessed critical developments with significant potential impact on the commercial viability and global demand for allulose.

Optimization of vanillin biosynthesis in Escherichia coli K12 MG1655 through metabolic engineering

Vanillin is an important flavouring agent applied in food, spices, pharmaceutical industries and other fields. Microbial biosynthesis of vanillin is considered a sustainable and economically feasible alternative to traditional chemical synthesis. In this study, Escherichia coli K12 MG1655 was used for the de novo synthesis of VAN by screening highly active carboxylic acid reductases and catechol O-methyltransferases, optimising the protocatechuic acid pathway, and regulating competitive metabolic pathways. Additionally, major alcohol by-products were identified and decreased by deleting three endogenous aldo–keto reductases and three alcohol dehydrogenases. Finally, a highest VAN titer was achieved to 481.2 mg/L in a 5 L fermenter from glucose. This work provides a valuable example of pathway engineering and screens several enzyme variants for the first time in E. coli.

Tracking of Ubiquitin Signaling through 3.5 Billion Years of Combinatorial Conjugation.

Ubiquitination is an evolutionary, ancient system of post-translational modification of proteins that occurs through a cascade involving ubiquitin activation, transfer, and conjugation. The maturation of this system has followed two main pathways. The first is the conservation of a universal structural fold of ubiquitin and ubiquitin-like proteins, which are present in both Archaea and Bacteria, as well as in multicellular Eukaryotes. The second is the rise of the complexity of the superfamily of ligases, which conjugate ubiquitin-like proteins to substrates, in terms of an increase in the number of enzyme variants, greater variation in structural organization, and the diversification of their catalytic domains. Here, we examine the diversity of the ubiquitination system among different organisms, assessing the variety and conservation of the key domains of the ubiquitination enzymes and ubiquitin itself. Our data show that E2 ubiquitin-conjugating enzymes of metazoan phyla are highly conservative, whereas the homology of E3 ubiquitin ligases with human orthologues gradually decreases depending on "molecular clock" timing and evolutionary distance. Surprisingly, Chordata and Echinodermata, which diverged over 0.5 billion years ago during the Cambrian explosion, share almost the same homology with humans in the amino acid sequences of E3 ligases but not in their adaptor proteins. These observations may suggest that, firstly, the E2 superfamily already existed in its current form in the last common metazoan ancestor and was generally not affected by purifying selection in metazoans. Secondly, it may indicate convergent evolution of the ubiquitination system and highlight E3 adaptor proteins as the "upper deck" of the ubiquitination system, which plays a crucial role in chordate evolution.

When less is more: The association between the expression of polymorphic CYPs and AFB1-induced HCC.

An individual's genetic fingerprint is emerging as a pivotal predictor of numerous disease- and treatment-related factors. Single nucleotide polymorphisms (SNPs) in drug-metabolizing enzymes play key roles in an individual's exposure to a malignancy-associated risk, such as Aflatoxin B1 (AFB1)-induced hepatocellular carcinoma (HCC). This study aimed at reviewing literature on the polymorphisms that exist in CYP enzymes and their possible link with susceptibility to AFB1-induced HCC. A set of keywords associated with the study subject of interest was used to search the Google Scholar and the PubMed database. The last ten years' worth of research projects were included in the results filter. The research involved HCC patients and any connection between polymorphic forms of CYP enzymes and their susceptibility to AFB1-induced HCC, including older but significant data. Variations in CYP1A2 and CYP3A4 were reported to impact the rate and magnitude of AFB1 bio-activation, thus influencing an individual's vulnerability to develop HCC. In HCC patients, the activity of CYP isoforms varies, where increased activity has been reported with CYP2C9, CYP2D6, and CYP2E1, while CYP1A2, CYP2C8, and CYP2C19 exhibit decreased activity. CYP2D6*10 frequency has been discovered to differ considerably in HCC patients. Rs2740574 (an upstream polymorphism in CYP3A4 as detected in CYP3A4*1B) and rs776746 (which affects CYP3A5 RNA splicing), both of which influence CYP3A expression, thus impacting the variability of AFB1-epoxide adducts in HCC patients. CYP1A2 is the primary enzyme accountable for the formation of harmful AFBO globally. CYP3A4, CYP3A5, CYP3A7, CYP2B7, and CYP3A3 are also implicated in the bio-activation of AFB1 to mutagenic metabolites. It is thought that CYP3A4 is the protein that interacts with AFB1 metabolism the most. Polymorphic variants of CYP enzymes have a functional impact on the susceptibility to AFB1-induced HCC. Outlining such variation and their implications may provide deeper insights into approaching HCC in a more personalized manner for guiding future risk-assessment, diagnosis, and treatment.

Expanding the substrate selectivity of the fimsbactin biosynthetic adenylation domain, FbsH.

Nonribosomal peptide synthetases (NRPSs) produce diverse natural products including siderophores, chelating agents that many pathogenic bacteria produce to survive in low iron conditions. Engineering NRPSs to produce diverse siderophore analogs could lead to the generation of novel antibiotics and imaging agents that take advantage of this unique iron uptake system in bacteria. The highly pathogenic and antibiotic-resistant bacteria Acinetobacter baumannii produces fimsbactin, an unusual branched siderophore with iron-binding catechol groups bound to a serine or threonine side chain. To explore the substrate promiscuity of the assembly line enzymes, we report a structure-guided investigation of the stand-alone aryl adenylation enzyme FbsH. We report on structures bound to its native substrate 2,3-dihydroxybenzoic acid (DHB) as well as an inhibitor that mimics the adenylate intermediate. We produced enzyme variants with an expanded binding pocket that are more tolerant for analogs containing a DHB C4 modification. Wild-type and mutant enzymes were then used in an in vitro reconstitution analysis to assess the production of analogs of the final product as well as several early-stage intermediates. This analysis shows that some altered substrates progress down the fimsbactin assembly line to the downstream domains. However, analogs from alternate building blocks are produced at lower levels, indicating that selectivity exists in the downstream catalytic domains. These findings expand the substrate scope of producing condensation products between serine and aryl acids and identify the bottlenecks for chemoenzymatic production of fimsbactin analogs.