Search Results (676)

Tyrosyl-DNA phosphodiesterase 1 (TDP1) is a key enzyme for the repair of stalled topoi-somerase 1 (TOP1)-DNA complexes. We have previously developed a TDP1 inhibitor, compound OL9-116, which is capable of enhancing the action of the anticancer drug topotecan (TPC), a TOP1 poison, in vitro and in vivo. In this study, the inhibition mode of OL9-116 (uncompetitive) was investigated. We have shown that N-terminal domain of TDP1, which is important for the cell function of TDP1 but is not involved in catalysis directly, reduced the inhibitory potency of OL9-116 probably by influencing the conformation of the enzyme. OL9-116 did not reduce cell viability and did not affect mitochondrial membrane potential. OL9-116 enhanced the cytotoxic/antiproliferative effect of TPC on the panel of tumor cells. This effect was not observed on nontumor cells or TDP1-deficient cells. OL9-116 and TPC had different effects on TDP1 and TOP1 gene expression detected by PCR depending on the cell type and the presence of functional TDP1. The direct relation between the effects of the compounds on the gene expression and cell survival was not found. The obtained data indicated a synergistic effect of OL9-116 and TPC, which appeared to be mediated by TDP1 inhibition rather than by an effect on TDP1 gene expression. Full article

Two phenylacetaldehyde dehydrogenases, originating from Escherichia coli K-12 (FeaB-K-12) and Sphingopyxis fribergensis Kp5.2 (FeaB-Kp5.2), were immobilized on powdery silica carrier with various functionalization. First, the suitability of these carriers for application in combination with phenylacetaldehydes and phenylacetic acids was studied. Out of two carriers functionalized differently, mesoporous cellular foam, whose surface was modified with 3-glycidyloxypropyl groups (MCF-G), showed promising results. Hence, this carrier was further tested at 17 different immobilization conditions. Despite both enzymes showing high immobilization efficiency, the initial activities were relatively low compared to the free enzymes. Interestingly, the immobilized FeaB-Kp5.2 on MCF-G-Kw showed about 80% of retained activity after two months of incubation at 0 °C, indicating that the immobilization enhances the stability of this enzyme. In contrast, no changes in the temperature stability of FeaB-Kp5.2 due to immobilization could be noted. However, relative enzyme activities towards all three substituted phenylacetaldehydes could be increased by the immobilization to approximately 130%. The most active and stable powdery immobilizate was MCF-G-Kw-FeaB-Kp5.2 at pH 8. In addition, FeaB-Kp5.2 was also immobilized and tested on monolith silica carrier for continuous catalysis to produce phenylacetic acids. Full article

Nano–bio hybrid catalysts have emerged as a promising platform for sustainable energy conversion by integrating the high selectivity of enzymes with the structural robustness and conductivity of nanomaterials. In recent years, the growing demand for clean energy technologies has driven the development of biohybrid systems capable of efficient electron transfer, enhanced catalytic activity, and improved operational stability. This review comprehensively discusses the design principles, mechanistic foundations, and performance metrics of enzyme–nanomaterial interfaces for energy-related applications. We first outline the fundamentals of enzymatic redox catalysis and the limitations of free enzymes in practical systems. Subsequently, we examine the functional roles of nanomaterials including carbon-based materials, metal and metal oxide nanoparticles, and two-dimensional platforms such as MXenes in facilitating enzyme immobilization and promoting direct or mediated electron transfer. Special emphasis is placed on engineering strategies at the bio–nano interface, including immobilization techniques, surface functionalization, and structural tuning to optimize catalytic efficiency. The review further highlights representative hybrid systems based on laccase, glucose oxidase, peroxidase, and hydrogenase enzymes, and evaluates their applications in biofuel cells, solar–bio hybrid systems, green oxidation reactions, and self-powered biosystems. Stability challenges, deactivation mechanisms, and enhancement strategies such as polymer coatings, cross-linking, and nanoconfinement are critically analyzed. Finally, emerging directions including artificial enzymes, AI-guided catalyst design, and self-healing bioelectrodes are discussed to provide a forward-looking perspective on next-generation sustainable bioelectrocatalytic systems. Full article

Organoselenium chemistry has undergone remarkable development over the past five decades, evolving from its initial association with high toxicity into a field with pivotal contributions to materials science, organic synthesis, catalysis, and Medicinal Chemistry. Among the diverse biological activities displayed by organoselenium compounds, their redox behaviour is particularly compelling, as many of these molecules act as efficient mimetics of the antioxidant enzyme glutathione peroxidase (GPx). In this work, we investigated the GPx-like activity of a series of N,N′-diaryl selenoureas toward the depletion of H₂O₂ and cumene hydroperoxide (CumOOH) as model ROS. Their reactivity was correlated with the electronic nature of the aryl substituents using a Hammett-type analysis, revealing a strong dependence of the reaction rate on remote electronic perturbations within the aromatic ring. Combined UV and NMR studies provided mechanistic evidence supporting a catalytic cycle in which selenoureas, operating at sub-stoichiometric loadings (1 mol%) and using a thiol as a cofactor-like molecule, can be used to efficiently scavenge ROS with half-lives of only a few minutes (~10–60 min). Furthermore, these selenoureas exhibited potent antiproliferative activity across several human solid tumour cell lines. Overall, these results offer mechanistic insight into the ROS-eliminating pathways of selenoureas and highlight their potential as chemopreventive or anticancer agents. Full article

Isomaltulose is produced via sucrose isomerase catalysis by Serratia plymuthica A30. The enzyme was expressed in Bacillus subtilis using surface display, in combination with Bacillus subtilis spore coat CotC. The promoter was further selected and optimized to determine P_amyE as the most suitable promoter, while the spore coat protein assay reveals CotC as optimal. By scanning and analyzing the catalyst motifs with single-point mutation construction, a maximum isomaltulose yield of 27.21 mg/mL was recorded in the F181I-mutant enzyme. Another strain encoding the H363P-mutant reached a maximum yield of 20.84 mg/mL, while the k_cat value also increased from 17.64 to 24.80. Structural analysis showed that the F181I-mutant had higher thermostability, whereas the H363P-mutant had increased k_cat. Both mutants displayed a 5-fold increase in isomaltulose yield with relatively simple construction procedures, making them suitable for high-level isomaltulose production. Full article

Adenosine triphosphate (ATP) serves as a central energy currency and signaling molecule in cellular processes, with ATP-binding sites in proteins playing critical roles in enzymatic catalysis, signal transduction, and gene regulation. The accurate identification of ATP-binding sites is essential for understanding protein function mechanisms and facilitating drug discovery, enzyme engineering, and disease pathway analysis. In this study, we present a novel hybrid deep learning framework that synergizes heterogeneous learning paradigms based on protein sequence information for accurate ATP-binding site prediction. Our approach integrates two complementary base classifiers. One is a Transformer-based model, which leverages high-level contextual embeddings generated by Evolutionary Scale Modeling 2 (ESM-2), a state-of-the-art protein language model, combined with a local–global dual-attention mechanism that enables the model to simultaneously characterize short-segment and long-range contextual dependencies across the entire protein sequence. The other is a deep Q-network (DQN)-inspired classifier that achieves residue-level prediction as a sequential decision-making process. The final predictions are generated using a weighted ensemble strategy, where optimal weights are determined via cross-validations to leverage the strengths of both models. The prediction results on benchmark independent testing sets indicate that our method achieves satisfactory performance on key metrics. Beyond predictive efficacy, this work uncovers the intrinsic biological mechanisms underlying protein–ATP interactions, including the synergistic roles of local structural motifs and global conformational constraints, as well as family-specific binding patterns, endowing the research with substantial biological significance. The research in this work offers a deeper understanding of the protein–ligand recognition mechanisms and supportive efforts on large-scale functional annotations that are critical for system biology and drug target discovery. Full article

Charge transport underpins essential biological processes, including cellular respiration, photosynthesis, and enzymatic catalysis. Advances in molecular electronics have enabled single-molecule measurements that unequivocally establish redox-active proteins as efficient electron conductors, with their metal cofactors serving as intrinsic redox relays. By contrast, ubiquitous non-redox proteins lacking such redox centers have long been considered poor conductors. However, recent research has challenged this view, demonstrating that efficient charge transport in non-redox proteins can be mediated through polypeptide backbones, aromatic side-chain arrays, and hydrogen bond networks. This review surveys progress in understanding the single-molecule conductance of non-redox proteins. Firstly, we elucidate the fundamental transport mechanisms, highlighting the interplay between coherent tunneling and thermally activated hopping. We then provide an overview of state-of-the-art experimental techniques for single-molecule characterization. Through analysis of diverse systems spanning short peptides to large enzymes, we illustrate how aromatic amino acid networks and dynamic conformational fluctuations govern conductance, enabling emerging applications in label-free biosensing and single-molecule protein/DNA sequencing. Finally, we discuss persistent challenges and outline future opportunities for integrating protein-based conductors into bioelectronic devices. This review aims to stimulate further research and pave the way for novel applications harnessing protein conductance. Full article

D-allulose is a rare low-calorie sugar with considerable health benefits and industrial potential. Compared with chemical synthesis and free enzyme catalysis, microbial production using engineered cells offers a low-cost and highly stable solution. Therefore, we investigated the reaction pathway underlying the synthesis of D-allulose from D-glucose. Specifically, the enhancement of glucose isomerase-catalyzed reactions and their role in D-allulose synthesis were evaluated. First, a mutant strain with significantly increased glucose isomerase from Anoxybacillus kamchatkensis G10 (AGGI) expression was obtained through ultraviolet mutagenesis combined with high-throughput flow cytometry. A 4.55-fold increase in AGGI activity and a D-fructose conversion yield of 51.2% were obtained. A dual-enzyme pathway was subsequently constructed by co-expressing AGGI and D-allulose 3-epimerase (DAEase) in the optimized host. After balancing the catalytic requirements of both enzymes through optimization of reaction conditions, CRISPR-associated transposase was employed to efficiently integrate the glucose transporter gene galP into the genome, further enhancing substrate supply. The final engineered Escherichia coli strain achieved a D-allulose conversion rate of 15% from 20 g/L D-glucose. This demonstrates the crucial role of glucose isomerase in microbial D-allulose production and advances the optimization and development of D-allulose synthesis strategies using D-glucose as a substrate. Full article

In this paper, batch stirred-tank alcoholysis reactions of used and refined sunflower oils were performed with n-octyl, myristyl, cetyl, oleyl, and stearyl alcohols using immobilized lipases Novozym 435 and Lipozyme IM as catalysts. Alcohol conversions ranged from 74% to 94%, with slight differences between used frying sunflower oil and refined sunflower oil. The resulting wax esters were purified via stepwise column chromatography. The different regioselectivity of the biocatalysts led to distinct reaction pathways, and Novozym 435 proved to be the most effective enzyme, providing higher conversions and no detectable by-products. This study demonstrates the valorization of waste frying oils into high-value wax esters through enzymatic alcoholysis, comparing two industrially relevant immobilized lipases and achieving high conversion across multiple long-chain alcohols. The results highlight a sustainable alternative to conventional chemical catalysis and extend biocatalytic applications beyond traditional biodiesel production. By incorporating waste lipids into value-added products, the overall sustainability and circularity of the system are improved, contributing to green and sustainable chemistry. Full article

As an isoflavone metabolite with diverse physiological activities, the development of efficient and sustainable manufacturing technologies for (S)-equol holds significant importance. This study focuses on the semi-rational design of daidzein reductase (DZNR), the first key enzyme in the (S)-equol biotransformation pathway. Through multiple sequence alignment and three-dimensional structural analysis, two critical residues, Gly30 and Ala105, were identified in DZNR. A library of single and combinatorial mutants was constructed and screened, yielding the double variant DZNR30S+105S with substantially enhanced catalytic performance. In a whole-cell biocatalytic system, the recombinant E. coli (Escherichia coli) strain harboring this combinatorial mutant achieved a yield of 238.3 mg/L (S)-equol at a substrate concentration of 1 mM daidzein, demonstrating markedly improved catalytic efficiency. Upon increasing the daidzein concentration to 2 mM, the reaction reached equilibrium within 5 h, producing 384.6 mg/L (S)-equol, which highlights the mutant’s excellent potential for high-substrate-concentration applications. This study not only provides novel mechanistic insights into DZNR catalysis but also successfully establishes a DZNR variant with enhanced activity, offering an efficient biocatalytic component for the industrial-scale biomanufacturing of (S)-equol and thereby advancing the development of green biosynthesis technologies for this valuable compound. Full article

The ability of enzymes to recognize and process structurally diverse substrates is fundamental to metabolic flexibility and biological regulation. In melanin biosynthesis, human tyrosinase (Tyr) catalyzes the oxidation of several chemically distinct intermediates, including L-tyrosine, L-DOPA, DHICA, and DHI. Although its catalytic chemistry is well established, the structural basis of substrate selectivity and how it is altered by disease-associated mutations remains unclear. Using molecular docking and molecular dynamics simulations, we mapped the Tyr active site and identified 23 evolutionarily conserved residues that mediate multi-substrate recognition and binding. Across all substrates, binding induces coordinated conformational responses, particularly within an anchoring region (334–347) that provides electrostatic and hydrophobic steering, and a flexible gating loop (374–386) that modulates access and stabilizes bound intermediates. The OCA1B-associated P406L mutation, although distant from the catalytic core, disrupts long-range dynamic coupling and impairs loop flexibility, while 25 ClinVar-listed genetic variants at substrate-interacting residues weaken active-site organization, underscoring the sensitivity of Tyr’s dynamic network to perturbation. Integrating these findings, we propose an ordered multi-substrate binding mechanism in which substrates are first guided by the anchoring region, then aligned by the universal triad, and finally refined through loop-mediated, substrate-specific contacts. Our work suggests a dynamic framework that could be useful for understanding human tyrosinase catalysis, genetic mutation impact, and future engineering strategies. Full article

Hot springs represent unique geothermal ecosystems where extreme physicochemical conditions intersect with remarkable microbial diversity and metabolic innovation. These natural laboratories harbor specialized communities of thermophilic and hyperthermophilic microorganisms that have evolved exceptional adaptations to elevated temperatures, extreme pH, and high salinity. This review synthesizes current understanding of hot spring systems as multifunctional natural resources, examining their roles in fundamental microbiology, biotechnology, and sustainable development. We explore the ecological principles governing microbial community assembly, the taxonomic and functional diversity of prokaryotic and eukaryotic microorganisms, and the genomic mechanisms underlying thermophilic adaptation. Hot springs yield enzymes revolutionizing molecular biology and industrial catalysis, bioactive metabolites with pharmaceutical potential, and novel bioremediation capabilities including plastic degradation. Beyond biological significance, these systems contain valuable minerals and rare earth elements, supporting an emerging bioeconomy integrating wellness tourism, bioprospecting, and sustainable resource extraction. However, critical knowledge gaps remain regarding viral ecology, horizontal gene transfer, eukaryotic diversity, and climate change impacts. We emphasize that hot springs merit renewed interdisciplinary attention as model systems for understanding extremophile physiology, early life evolution, and the development of nature-based biotechnological solutions. Realizing their full potential requires balanced management strategies that preserve ecosystem integrity while enabling responsible utilization of these irreplaceable geobiological resources. Full article

Enzyme kinetic parameters $(k_{\mathit{cat}}, K_m, \mathrm{and} k_{\mathit{cat}}/K_m)$ are fundamental for quantifying catalytic efficiency and substrate specificity in biochemistry and drug discovery. However, experimental determination is resource intensive, and accurate prediction remains a persistent challenge due to the complex spatial nature of catalysis. In this paper, we present UniKineG, a novel deep learning framework that redefines kinetic prediction by modeling the explicit spatial state of enzyme–substrate complexes. Unlike conventional methods that treat proteins and ligands as isolated modalities, UniKineG employs molecular docking to embed both entities into a unified 3D coordinate system. Within this shared geometric context, we utilize a heterogeneous graph neural network integrated with geometric vector perceptrons (GVPs) to capture intricate vector-based interactions, such as directional hydrogen bonds, hydrophobic contacts, and electrostatic complementarity. This structure-based approach confers exceptional robustness: UniKineG effectively overcomes the dependency on high-sequence homology, demonstrating superior generalization on out-of-distribution (OOD) datasets encompassing both unseen enzyme sequences and diverse substrate scaffolds. Consistently outperforming state-of-the-art predictors, UniKineG achieves high-precision predictions. This work establishes a solid foundation for understanding enzyme–small molecule interactions in 3D space and offers a transformative tool for computational enzymology. Full article

Fructooligosaccharides (FOS) represent a major source of prebiotic compounds. They are widely used in functional foods for their ability to modify intestinal microbiota in animals and humans. To address the significant issue of fructooligosaccharide production being influenced by glucose concentration, this study designed a dual-enzymatic co-catalysis system for glucose isomerase (GI) and a mutant FTase (FTase^142P-242K). This system successfully increased the FOS synthesis rate (42.31 to 55.51%, w/w). Glucose isomerase catalyzes the isomerization of glucose to fructose, and the subsequent release of fructose from the active site permits the enzyme to re-enter its catalytic cycle. The optimal conditions for catalysis were found at 45 °C, pH 5.5, and 1 mM Ba²⁺. In contrast, the optimal fermentation process was established at 25 °C and induction with 1 mM IPTG. Finally, the efficient production of FOS using low-cost byproduct molasses was achieved. Fermentation optimization of the dual-enzyme system resulted in FOS yield of 53.92% (w/w), a significant increase (44.54%, w/w) from the yield obtained using single-enzyme catalysis. Based on the research, a novel and sustainable approach for high-yield synthesis of Fructooligosaccharides involves minimizing the inhibitory effect of glucose produced during sucrose transformation. Full article