Search Results (76)

Background: The ability of synthetic plastics to persist in the environment and the accumulation of microplastics has intensified the need to explore biological mechanisms capable of interacting with, and possibly degrading, polymeric materials. Microbial enzymes that have extensive catalytic flexibility represent promising candidates in this context. Aim: This study set out to examine the molecular interaction patterns and dynamical stability of Pseudomonas aeruginosa elastase (LasB) with representative structural fragments of typical synthetic plastics to assess the suitability of the enzyme to polymer-derived substrates. Methods: The crystallographic structure of LasB (PDB ID: 1EZM) was retrieved from the Protein Data Bank and pre-prepared with the help of AutoDock4.2.6 Tools. Those polymer-derived ligands that were associated with the major industrial plastics such as polyamide (PA), polyvinyl chloride (PVC), polycarbonate (PC), poly-ethylene terephthalate (PET), polymethyl methacrylate (PMMA), and polyurethane (PUR) were retrieved in the PubChem database and geometrically optimized with the help of the MMFF94 force field. AutoDock Vina, with a specific grid box around the catalytic pocket, including Zn²⁺ ion, was used to perform molecular docking simulations. PyMOL and BIOVIA Discovery Studio software were used to analyze binding conformations, interaction residues and types of intermolecular contacts. Phosphoramidon, a known metalloprotease inhibitor, served as a positive control to confirm the docking protocol. Additional assessment of the structural stability and conformational behavior of the enzyme–ligand complexes was conducted by molecular dynamics (MD) simulations with the Desmond engine and explicit solvent model in a 50 ns trajectory using the OPLS4 force field. RMSD, RMSF, radius of gyration, hydrogen bonding analysis and solvent accessibility parameters were used to measure structural stability. Results: The docking experiment showed varying binding affinities with the test polymers. Polycarbonate (−5.774 kcal/mol) and polyurethane (−5.707 kcal/mol) had the highest in-teractions with the LasB catalytic pocket, polyamide (−5.277 kcal/mol) and PET (−4.483 kcal/mol) followed PMMA and PVC, which had weaker affinities. The following were the important residues involved in interaction networks: Glu141, His140, Val137, Arg198, Tyr114, and Trp115 that were implicated in interaction networks with hydrophobic interactions, π-cation interactions and van der Waals forces that were the major stabilization forces. MD simulations had stabilized complexes, and RMSD values were found to be within acceptable ranges of stability, and ligand-specific changes (around 1.0-3.2 A), which is also in line with stable protein-ligand systems. Phosphoramidon used as a positive control had an RMSD of 1.205 A which is within this stability range. PCA determined various ligand-bound conformational states of LasB with PA in com-pact state, PC and PVC in intermediate states and PUR, PMMA and PET in ex-panded conformations, indicating structur-al stability and adaptability of the binding pocket. Conclusion: These findings show that LasB has a structurally flexible catalytic pocket that can accommodate a wide range of polymer-derived ligands. These results offer an insight into the recognition of enzymes with polymers at the molecular level and also indicate that LasB might help in the interaction of microorganisms with synthetic plastics in environmental systems. Full article

Azobenzene derivatives constitute a prototypical class of photoresponsive molecular switches with broad utility in synthetic chemistry and biomedical research, owing to their distinctive physicochemical properties. Recent molecular engineering has enabled red-shifted photoisomerization into the visible biological window, thereby enhancing tissue penetration and reducing phototoxicity. This review systematically surveys contemporary advances in azobenzene-based photoswitchable systems with a specific focus on medicinal chemistry and photopharmacology. Emphasis is placed on rational design strategies—including ortho-functionalization, heteroaryl substitution, and bridged diazocine scaffolds—that improve photophysical properties, thermal stability, and photostationary state distributions. Particular attention is devoted to the integration of these novel azobenzene motifs as privileged pharmacophores, highlighting their emerging therapeutic applications in neurological modulation, enzyme inhibition, receptor targeting, and oncology, as well as their translational potential in drug discovery and photodynamic therapy. 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

Imine reductases (IREDs) have emerged as valuable biocatalysts for the asymmetric synthesis of chiral amines, key intermediates in numerous active pharmaceutical ingredients. Their ability to operate under mild reaction conditions with high chemo- and stereoselectivity provides an attractive alternative to conventional metal-catalyzed or chemical reduction processes. However, the broader industrial application of wild-type IREDs is often constrained by their limited substrate scope and moderate catalytic efficiency. Recent advances in biocatalysis have demonstrated that engineered IREDs can catalyze the reduction of a wide range of natural and non-natural imines, significantly expanding their applicability in pharmaceutical and fine chemical synthesis. In parallel, enzyme immobilization strategies have proven highly effective for improving operational stability, facilitating enzyme reuse, and enabling continuous flow biocatalytic processes. Efficient cofactor regeneration systems have further enhanced the practical implementation of IRED-based transformations. Advances in protein engineering, including structure-guided design, semi-rational mutagenesis, and directed evolution, have generated enzyme variants with improved catalytic activity, stereoselectivity, and substrate tolerance. The integration of high-throughput screening technologies and machine-learning-assisted enzyme design has further accelerated the discovery and optimization of efficient IRED biocatalysts. This review summarizes recent progress in the protein engineering and immobilization of IREDs and discusses future perspectives for their industrial application. Full article

Antimicrobial resistance (AMR) remains a major clinical challenge, with Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species (ESKAPE) accounting for a substantial share of multidrug-resistant (MDR) infections worldwide. These organisms undermine antibiotic efficacy through reduced permeability, surface shielding, biofilm formation, and rapid genetic adaptation, mechanisms that primarily restrict effective exposure at infection sites. Bacteriophages, phage-derived enzymes, and Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based antimicrobials provide selective and mechanistically distinct alternatives to conventional antibiotics, but their performance in vivo is often limited by instability in physiological environments, immune neutralization, uneven tissue distribution, and insufficient access to bacteria protected by biofilms or surface-associated barriers. This narrative review examines how nanotechnology-based delivery systems can address these constraints. We first outline the delivery-relevant biological barrier characteristic of ESKAPE pathogens, then summarize the therapeutic potential and inherent limitations of whole phages, phage-derived enzymes, and CRISPR-based antimicrobials when used without formulation. Major nanotechnology platforms for antibacterial delivery are reviewed, followed by analysis of how nano-enabled systems can improve stability, localization, and persistence of these biological agents. A pathogen-aware integration framework is presented that links dominant barriers in each ESKAPE pathogen to the biological modality and nano-enabled delivery strategy most likely to enhance exposure at infection sites. Translational challenges, regulatory considerations, and emerging directions, including responsive delivery systems and personalized approaches, are also discussed. Overall, nano-enabled phage-based therapeutics represent a realistic and adaptable strategy for managing MDR ESKAPE infections. Therapeutic success depends on both continued discovery and engineering of antibacterial agents and effective delivery design. Full article

Mycolic acids (MAs) are unique and essential components of the Mycobacterium cell envelope, pivotal for its structural integrity, impermeability, and intrinsic antibiotic resistance. These properties that underpin mycobacterial pathogenicity also render the MA biosynthetic pathway a rich resource of targets for anti-tuberculosis drug discovery. Concurrently, in the realm of industrial biotechnology, engineered non-pathogenic mycobacteria are being optimized for steroid drug bioproduction through strategic modulation of the MA pathway to enhance cell permeability and boost the yield of desired products. This review systematically delineates the MA biosynthetic pathway and its critical enzymes. It further summarizes recent progress in developing anti-tuberculosis therapeutics that inhibit these enzymes and discusses innovative engineering strategies that harness the same pathway of non-pathogenic mycobacteria for green steroid drug manufacturing. By bridging these two distinct fields, the review provides a holistic perspective and novel insights for advancing both infectious disease control and sustainable pharmaceutical production. Full article

Enzyme engineering drives innovation in biotechnology, medicine, and industry, yet conventional approaches remain limited by labour-intensive workflows, high costs, and narrow sequence diversity. Artificial intelligence (AI) is revolutionising this field by enabling rapid, precise, and data-driven enzyme design. Machine learning and deep learning models such as AlphaFold2, RoseTTAFold, ProGen, and ESM-2 accurately predict enzyme structure, stability, and catalytic function, facilitating rational mutagenesis and optimisation. Generative models, including ProteinGAN and variational autoencoders, enable de novo sequence creation with customised activity, while reinforcement learning enhances mutation selection and functional prediction. Hybrid AI–experimental workflows combine predictive modelling with high-throughput screening, accelerating discovery and reducing experimental demand. These strategies have led to the development of synthetic “synzymes” capable of catalysing non-natural reactions, broadening applications in pharmaceuticals, biofuels, and environmental remediation. The integration of AI-based retrosynthesis and pathway modelling further advances metabolic and process optimisation. Together, these innovations signify a shift from empirical, trial-and-error methods to predictive, computationally guided design. The novelty of this work lies in presenting a unified synthesis of emerging AI methodologies that collectively define the next generation of enzyme engineering, enabling the creation of sustainable, efficient, and functionally versatile biocatalysts. Full article

This review surveys literature from 2010 to 2025 on Aspergillus-derived enzymes for kinetic resolution (KR), using conventional databases and AI-assisted platforms. Among over 340 species, A. niger, A. oryzae, and A. terreus are widely recognized as safe and industrially relevant. Lipases from these fungi exhibit high stability, broad substrate specificity, and enantioselectivity, enabling efficient resolution of racemic mixtures. Advances in enzyme immobilization, protein engineering, and reaction medium optimization have enhanced catalytic performance under diverse conditions. Complementary enzymes, including esterases and epoxide hydrolases, further expand biocatalytic applications. Despite increasing demand for enantiopure compounds, challenges in yield, scalability, and enzyme discovery call for integrated molecular and process strategies. Aspergillus spp. emerge as a promising system for high-level enzyme expression, offering robust secretion capacity, efficient post-translational processing, and strong adaptability for industrial biocatalysis. Full article

Protein engineering is a rapidly evolving field that plays a critical role in transforming drug discovery and development. This innovative field harnesses the unique structural and functional properties of engineered proteins, such as monoclonal antibodies, nanobodies, therapeutic enzymes, and cytokines, to address complex diseases more effectively than traditional small-molecule drugs. These biologics not only enhance therapeutic specificity but also minimize adverse effects, marking a significant advancement in patient care. However, the journey of protein engineering is not without challenges. Issues related to protein folding, stability, and potential immunogenicity pose significant complications. Additionally, navigating the complex regulatory landscape can delay the transition from laboratory to clinical application. Addressing these hurdles requires the integration of cutting-edge technologies, including phage and yeast display technology, CRISPR, and advanced computational modeling, which enhance the predictability and efficiency of protein design. In this review, we explore the multifaceted impact of protein engineering on modern medicine, highlighting its potential to transform treatment paradigms, methodologies, challenges, and the successful development and approval of recombinant protein-based therapies. By navigating the complexities and leveraging technological advancements, the field is poised to unlock new therapeutic possibilities, ultimately improving patient outcomes and transforming healthcare. Full article

Escherichia coli LS5218 is an attractive host for producing polyhydroxybutyrate. The strain, however, strongly requires heterologous gene expressions like phaC for efficient production. For enhancing the production, the whole gene expressions relating to end product-producing flow should be optimized so that not only heterologous induced-genes but also other relating genes are comprehensively analyzed on the transcription levels, resulting in normally time-consuming mutant-creation. Additionally, the explanation for each transcriptional relationship is likely to follow the relationships on known metabolic pathway map to limit the consideration. This study aimed to infer gene regulatory networks within glycolysis, a central metabolic pathway in LS5218, using machine learning-based causal discovery methods. To construct a directed acyclic graph representing the gene regulatory network, we employed the NOTEARS algorithm (Non-combinatorial Optimization via Trace Exponential and Augmented lagRangian for Structure learning). Using transcription data of 264 time-resolved sampling points, we inferred the gene regulatory network and identified several distal regulatory relationships. Notably, gapA, a key enzyme controlling the transition between the preparatory and rewarding phases in glycolysis, was found to influence pgi, the enzyme at the pathway’s entry point. These findings suggest that inferring such nonlocal regulatory interactions can provide valuable insights for guiding genetic engineering strategies. Full article

Nitrile-containing compounds are integral to pharmaceuticals, agrochemicals and polymer industries, yet their environmental persistence and toxicity pose major challenges. Biocatalytic approaches using nitrile-converting enzymes—particularly nitrilases and nitrile hydratases—offer sustainable alternatives to conventional hydrolysis, enabling the selective transformation of nitriles into amides and acids under mild conditions. This review presents an industrial perspective on nitrile-converting enzymes, summarising their catalytic potential, current limitations, and emerging strategies for stability, activity and performance enhancement. Advances in protein engineering, metagenomic discovery and biocatalytic optimisation have already expanded their wider applicability, while synthetic biology and protein design tools are accelerating the development of tailored biocatalysts. The integration of these enzymes into cascades and chemoenzymatic processes supports scalable and innovative solutions to green manufacturing. Collectively, these emerging strategies position nitrile-converting enzymes as versatile tools for sustainable catalysis, with growing relevance in fine chemical synthesis, waste remediation, and bio-based synthetic platforms. Full article

RNA polymerases are essential enzymes that catalyze DNA transcription into RNA, vital for protein synthesis, gene expression regulation, and cellular responses. Non-template-dependent RNA polymerases, which synthesize RNA without a template, are valuable in biological research due to their flexibility in producing RNA without predefined sequences. However, their substrate polymerization mechanisms are not well understood. This study examines Poly (A) polymerase (PAP), a nucleotide transferase superfamily member, to explore its substrate selectivity using computational methods. Previous research shows PAP’s polymerization efficiency for nucleoside triphosphates (NTPs) ranks ATP > GTP > CTP > UTP, though the reasons remain unclear. Using 500 ns Gaussian accelerated molecular dynamics simulations, stability analysis, secondary structure analysis, MM-PBSA calculations, and Markov state modeling, we investigate PAP’s differential polymerization efficiencies. Results show that ATP binding enhances PAP’s structural flexibility and increases solvent-accessible surface area, likely strengthening protein–substrate or protein–solvent interactions and affinity. In contrast, polymerization of other NTPs leads to a more open conformation of PAP’s two domains, facilitating substrate dissociation from the active site. Additionally, ATP binding induces a conformational shift in residues 225–230 of the active site from a loop to an α-helix, enhancing regional rigidity and protein stability. Both ATP and GTP form additional π–π stacking interactions with PAP, further stabilizing the protein structure. This theoretical study of PAP polymerase’s substrate selectivity mechanisms aims to clarify the molecular basis of substrate recognition and selectivity in its catalytic reactions. These findings offer valuable insights for the targeted engineering and optimization of polymerases and provide robust theoretical support for developing novel polymerases for applications in drug discovery and related fields. Full article

Laccases, a class of multicopper oxidases found in diverse biological sources, have emerged as key green biocatalysts with significant potential for eco-friendly pollutant degradation. Their ability to drive electron transfer reactions using oxygen, converting pollutants into less harmful products, positions laccases as promising tools for scalable and sustainable treatment of wastewater, soil, and air pollution. This review explores laccase from a translational perspective, tracing its journey from laboratory discovery to real-world applications. Emphasis is placed on recent advances in production optimization, immobilization strategies, and nanotechnology-enabled enhancements that have improved enzyme stability, reusability, and catalytic efficiency under complex field conditions. Applications are critically discussed for both traditional pollutants such as synthetic dyes, phenolics, and pesticides and emerging contaminants, including endocrine-disrupting chemicals, pharmaceuticals, personal care products, microplastic additives, and PFAS. Special attention is given to hybrid systems integrating laccase with advanced oxidation processes, bioelectrochemical systems, and renewable energy-driven reactors to achieve near-complete pollutant mineralization. Challenges such as cost–benefit limitations, limited substrate range without mediators, and regulatory hurdles are evaluated alongside solutions including protein engineering, mediator-free laccase variants, and continuous-flow bioreactors. By consolidating recent mechanistic insights, this study underscores the translational pathways of laccase, highlighting its potential as a cornerstone of next-generation, scalable, and eco-friendly remediation technologies aligned with circular bioeconomy and low-carbon initiatives. Full article

The development of effective tools to combat viral diseases remains a major challenge for the aquaculture industry. Infectious pancreatic necrosis virus (IPNv) is one of the most devastating pathogens affecting salmonids, leading to high mortality rates and substantial economic losses worldwide. Here, we present a novel nanobody discovery pipeline based on a Type IIS restriction enzyme-driven library assembly method that enables the rapid generation of highly diverse nanobody repertoires. This streamlined approach not only shortens the time required for nanobody identification but also offers remarkable adaptability, allowing its application to virtually any protein target, including antigens from aquaculture pathogens and beyond. By integrating this strategy with density gradient–based enrichment and high-throughput screening, we successfully identified and validated a nanobody against the VP2 protein of IPNv, a key structural component essential for viral infectivity. These findings highlight the potential of this platform both as a versatile methodological advance in antibody engineering and as a practical foundation for developing innovative diagnostic and therapeutic tools. Ultimately, nanobodies generated through this pipeline could play a pivotal role in improving disease management and enhancing sustainability in aquaculture. Full article

Enzymes are highly selective and efficient biological catalysts that play a critical role in modern industrial biocatalysis. Their ability to operate under mild conditions and reduce environmental impact makes them ideal alternatives to conventional chemical catalysts. This review provides a comprehensive overview of advances in enzyme-based catalysis, focusing on enzyme classification, engineering strategies, and industrial applications. The six major enzyme classes—hydrolases, oxidoreductases, transferases, lyases, isomerases, and ligases—are discussed in the context of their catalytic roles across sectors such as pharmaceuticals, food processing, textiles, biofuels, and environmental remediation. Recent developments in protein engineering, including directed evolution, rational design, and computational modeling, have significantly enhanced enzyme performance, stability, and substrate specificity. Emerging tools such as machine learning and synthetic biology are accelerating the discovery and optimization of novel enzymes. Progress in enzyme immobilization techniques and reactor design has further improved process scalability, reusability, and operational robustness. Enzyme sourcing has expanded from traditional microbial and plant origins to extremophiles, metagenomic libraries, and recombinant systems. These advances support the integration of enzymes into green chemistry and circular economy frameworks. Despite challenges such as enzyme deactivation and cost barriers, innovative solutions continue to emerge. Enzymes are increasingly enabling cleaner, safer, and more efficient production pathways across industries, supporting the global shift toward sustainable and circular manufacturing. Full article