Search Results (164)

Chloroplasts are key organelles in plants that carry out photosynthesis, convert light energy into chemical energy, and synthesize organic compounds. In this study, a stably heritable chlorophyll-deficient mutant was screened from the ethyl methanesulfonate-induced mutation library of Wuyunjing 21 (WYJ21). This mutant was designated as chlorophyll deficient 6 (cde6). The cde6 mutant exhibits a low chlorophyll content, photosynthetic defects, an impaired chloroplast structure, a significant reduction in the number of stacked thylakoid layers, and a yellow-green leaf phenotype in the early tillering stage. Through MutMap analysis, it was found that the cde6 mutant harbors a single-base mutation (T→A) in the LOC_Os07g38300 gene. This mutation results in an amino acid substitution from valine (Val) to aspartic acid (Asp) in the encoded protein, thereby affecting the protein’s structure and function. The mutation of CDE6 leads to decreased expression of genes related to chloroplast development and chlorophyll biosynthesis. Further studies revealed that the CDE6, a potential chloroplast ribosome recycle factor, leads to high temperature sensitivity in rice when mutated. As high-temperature stress is a primary constraint to global rice productivity, the identification of CDE6 provides a genetic target for improving thermotolerance. In conclusion, these findings demonstrate that CDE6 plays a crucial role in chloroplast biogenesis and provide new insights into its regulatory function in high-temperature tolerance. Full article

Expanding the chemical diversity of DNA–encoded libraries (DELs) is crucial for identifying binders to emerging drug targets using DEL technology. In the present study, as part of our ongoing efforts to develop on–DNA diazide platforms (D–DAPs)—platform molecules possessing both aromatic and aliphatic azide groups on a single core reactive scaffold—we designed and synthesized a new compact diazide platform, designated as a compact D–DAP (C–D–DAP). This molecule is based on a low–molecular–weight reactive scaffold, 3–azido–5–(azidomethyl)benzoic acid, to facilitate small–molecule drug discovery targeting enzymes and G protein–coupled receptors (GPCRs). Furthermore, we established two distinct stepwise warhead construction strategies that exploit the chemoselective transformations of the azide groups in the C–D–DAP, which exhibit different reactivities. In addition, four virtual DELs were generated based on stepwise warhead elaboration from the C–D–DAP scaffold. Comparative chemical diversity analysis against bioactive compounds from ChEMBL revealed that these virtual libraries populate structural regions that are sparsely represented among known molecules. Each virtual library also occupies a distinct region of structural space relative to the others and displays intermediate quantitative estimate of drug–likeness (QED) values. Full article

Mimotope-based immunoassays offer an eco-friendly alternative to chemically synthesized antigens for the quantitative analysis of small molecules, but their use for practical on-site and high-throughput residue monitoring remains limited. Herein, we report the selection, production, and application of a phage display–derived mimotope targeting an anti-uniconazole monoclonal antibody (UCZ-mAb), with the aim of developing two complementary immunoassays that enable sensitive, eco-friendly detection of UCZ residues in agricultural samples. A 12-mer phage-displayed peptide library was screened to identify UCZ-specific mimotopes, and a selected sequence was genetically fused to SpyTag and expressed in Escherichia coli to generate a SpyTagged mimotope. Leveraging the SpyCatcher/SpyTag self-assembly system, the SpyTagged mimotope was directionally conjugated onto SpyCatcher-functionalized time-resolved fluorescence beads (TRFBs) and subsequently used as a signal-labeled competitive antigen in a lateral flow immunoassay (LFIA) designed for rapid on-site screening. In parallel, a wash-free magnetic separation immunoassay (MSIA) suitable for green, high-throughput screening in routine laboratories was established using self-assembled mimotope-TRFB probes. The LFIA and MSIA exhibited half-maximal inhibitory concentrations (IC₅₀) of 3.70–6.72 μg/kg and 16.4–18.3 μg/kg, respectively, in real samples. Spiked-sample recoveries ranged from 91.1 to 107.8% for LFIA and 92.6–115.7% for MSIA, demonstrating acceptable accuracy and precision. These results indicate that the SpyTagged mimotope–based LFIA and MSIA provide complementary, reliable, and sensitive platforms for on-site screening and high-throughput monitoring of UCZ residues in agricultural samples, while avoiding the drawbacks associated with traditional chemical antigen synthesis. Full article

Four benzyltrimethylammonium (BTMA) salts were successfully prepared: bis(benzyltrimethylammonium) hexachloroplatinate (1), benzyltrimethylammonium tetrachloroaurate (2), bis(benzyltrimethylammonium) tetrachlorocuprate (3), and bis(benzyltrimethylammonium) tetrabromocuprate (4) from benzyltrimethylammonium hydroxide (Triton B). Their crystal structures were determined by single-crystal X-ray diffraction, and the supramolecular architectures were characterized hierarchically. Extended Hirshfeld surface analysis, including enrichment ratio calculations, was performed to evaluate intermolecular interactions. Nonclassical hydrogen bonds, such as C–H^…Cl(Br), involving the anions, contribute to the formation of self-assembled architectures. Additional stabilization arises from π^…π and Cu–Br^…π interactions, particularly in crystals 2 and 4, respectively. Hirshfeld surface analysis showed that H^…H and C^…H/H^…C interactions are the dominant contributors in all crystals. According to enrichment ratio calculations, C^…H/H^…C interactions in 1, 3, and 4; Cl^…H/H^…Cl in 1 and 3; Cu^…H/H^…Cu in 3 and 4; and Br^…H/H^…Br and Br^…C/C^…Br in 4 are statistically favored in the crystal packing. Halogen bonding Cl^…Cl was observed in 1 but does not significantly influence packing. Energy framework calculations indicated that dispersive interactions are favorable in the analyzed crystals. A library of H-bonding supramolecular patterns, including interchangeable synthons, is provided and may guide the rational design of new derivatives with controllable features. Finally, the topology of intermolecular connections and the electronic structure of the benzyltrimethylammonium cation, investigated by quantum-chemical calculations, provide insights into its reactivity. Full article

Background/Objectives: GuaB, which is known as inosine 5′-phosphate dehydrogenase (IMPDH), is an enzymatic target involved in the de novo guanine biosynthetic pathway of the multidrug-resistant (MDR) Acinetobacter baumannii. GuaB has emerged as a potential therapeutic target to cope with increasing antibiotic resistance. Here, we used machine learning-based virtual screening as a verification technique to find potential inhibitors possessing different chemical scaffolds, using structure-based drug design as a discovery platform. Methods: Four machine learning models, built based on chemical fingerprint data, were trained, and the best models were used for virtual screening of the ChEMBL library, which covers 153 active molecules. Molecular dynamics (MD) simulations of 200 ns were carried out for all three compounds in order to explain conformational changes, evaluate stability, and provide validation of the docking results. Post-simulation analyses include principal component analysis (PCA), bond analysis, free-energy landscape (FEL), dynamic cross-correlation matrix (DCCM), radial distribution function (RDF), salt-bridge identification, and secondary-structure profiling, etc. Results: For molecular docking, the screened compounds were used against the GuaB protein to achieve proper docked conformation. Upon visual examination of the best-docked compounds, three leads (lead-1, lead-2, and lead-3) were found to have better interaction with the GuaB protein in comparison to the control. The mean RMSD scores between the three leads and the control were between 2.54 and 2.89 Å. In addition, the three leads as well as the control were characterized for pharmacokinetic features. All three leads met Lipinski’s Rule 5 and were thus drug-like. PCA and FEL analyses showed that lead-2 exhibited improved conformational stability, identified as deeper energy minima, whereas RDF and DCCM analyses revealed that lead-2 and lead-3 exhibited strong local structuring and concerted dynamics. In addition, lead-2 displayed a very rich hydrogen-bonding network with a total of 460 frames possessing such interactions, which is the highest among the complexes investigated here. Based on entropy calculations and the maximum entropy method of gamma–gram, lead-1 proved to be the most stable one with the lowest binding free-energy. Conclusions: This study provides an integrated machine learning-based virtual screening pipeline for the identification of new scaffolds to moderate infections associated with AMR; however, in vitro validation is still required to assess the efficacy of such compounds. Full article

Neuraminidase (NA) decorates the surface of the influenza virus, exerting a sialidase activity that enables the viral particle to be released in the host cell. Numerous sialic-based antiviral agents competitively bind to the NA cavity and are marketed worldwide for the treatment of Influenza A infection. We designed and validated a structure-based pharmacophore model for influenza neuraminidase (NA), which guided a virtual screening campaign against an in-house library of compounds already available for testing. This fast and cost-effective in silico strategy resulted in the identification of seven candidates possessing indole or isoquinoline chemical core. In vitro assays confirmed their favorable cytotoxicity profiles and identified only one, the 1-(1H-indol-3-ylcarbonyl)-3-piperidinecarboxylic acid (1), with reproducible inhibitory activity toward NA at non-cytotoxic concentrations. This work suggested a validated workflow for the discovery of novel NA inhibitors and highlighted an indole-based hit compound as a starting point for further optimization. Full article

Background: Clear aligners have become a common alternative to fixed appliances for tooth movement, and thermoplastic retainers hold the outcome. The prolonged intraoral contact of these devices has made the materials a focus of biocompatibility research. Objectives: This paper aims to summarize laboratory evidence on the biocompatibility of clear aligners and thermoplastic retainers. Materials included thermoformed polyethylene terephthalate glycol-modified (PETG), multilayer polyurethane, and directly printed resins. Primary outcomes were cytotoxicity, endocrine activity, and chemical or particle release. Methods: We systematically searched PubMed, the Cochrane Library, and Google Scholar through 31 May 2025, and we followed the PRISMA 2020 statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). We applied predefined eligibility criteria. Two reviewers screened records and extracted data in duplicate, including study design, extraction conditions, surface-area-to-volume ratio (SA/V), cell models, endpoints, and analytical sensitivity as the limit of detection (LOD) and limit of quantification (LOQ). We assessed the risk of bias across seven domains and graded certainty by outcome. We did not register a protocol prospectively. Results: Seventeen studies met the inclusion criteria. Materials spanned multilayer polyurethanes (SmartTrack, Clarity), PETG sheets (Essix ACE, Duran), and directly printed resins (Graphy TC-85DAC); a subset tested zinc-oxide (ZnO) nanoparticle coatings. Typical extractions immersed 0.1–1 g of material in cell-culture medium or artificial saliva at 37 °C for 24 h to 30 days. Cell viability usually remained ≥80%. Mild cytotoxicity (about 60–70% viability) appeared with harsher extractions, extended soaks, or an inadequate post-curing of printed parts. The estrogen-sensitive proliferation assay (E-Screen) returned negative results. In saliva-like media, bisphenol A (BPA) and related leachables were undetectable or in the low ng/mL range. In printed resins, urethane dimethacrylate (UDMA) sometimes appeared in water extracts, and amounts varied with curing quality. Evidence for chemical leaching and endocrine outcomes is sparse. We found no eligible in vitro study that quantified particle or microplastic release while also measuring a biological endpoint; we discuss particle findings from mechanical wear simulations only as the external context. Limitations: The evidence base is limited to in vitro studies. Many reports incompletely described extraction ratios and processing parameters. Risk of bias and certainty: Most studies used appropriate cell models and controls, but the reporting of surface-area-to-volume ratios, LOD/LOQ, and detailed post-processing parameters was often incomplete. Sample sizes were small, and dynamic wear or enzymatic conditions were uncommon. The overall risk of bias was moderate, and the certainty of evidence was low to moderate due to heterogeneity and in vitro indirectness. Conclusions: Under standard laboratory conditions, clear aligners and thermoplastic retainers show a favorable biocompatibility profile. For printed resins, outcomes depend mainly on processing quality, especially thorough washing and appropriate light-curing parameters. To improve comparability and support clinical translation, we recommend harmonized test protocols, transparent reporting, interlaboratory ring trials, and targeted clinical biomonitoring. Full article

Computational and machine learning approaches play a pivotal role in identifying, characterizing, and targeting noncanonical DNA structures, including G-quadruplexes, Z-DNA, hairpins, and triplexes. These configurations play critical roles in maintaining genomic stability, facilitating DNA repair, and regulating chromatin organization. Although the human genome predominantly adopts the B DNA conformation, evidence indicates that non-B DNA forms exert significant influence on gene expression and disease development. This highlights the need for dedicated computational frameworks to systematically investigate these alternative structures. Machine learning model, encompassing supervised and unsupervised algorithms such as K Nearest Neighbors, Support Vector Machines, and deep learning architectures including Convolutional Neural Networks, have shown considerable potential in predicting sequence motifs predisposed to forming non-B DNA conformations. These predictive tools contribute to identifying genomic regions associated with disease susceptibility. Complementary bioinformatics platforms and molecular docking tools, notably Auto Dock, along with chemical libraries like ZINC, facilitate the virtual screening of small molecules targeting specific DNA structures. Stabilizers of G quadruplexes, exemplified by CX 5461, have demonstrated therapeutic promise in BRCA-deficient cancers, highlighting the translational impact of computational methods on drug discovery. Anticipating DNA structural shifts opens new avenues in personalized medicine for complex diseases, with computational chemistry and machine learning deepening our understanding of DNA topology and guiding smarter ligand design. The integrated approach proposed in this review addresses the previous studies performed in this field and highlights the current limitations in structural genomics and advances the development of precision therapeutics aligned with individual genomic profiles. Full article

The primary objective of this study is to develop a machine learning-based predictive model using corrosion rate data for magnesium alloys compiled from the literature. Corrosion rates measured under different deformation rates and heat treatment parameters were analyzed using artificial intelligence algorithms. Variables such as chemical composition, heat treatment temperature and time, deformation state, pH, test method, and test duration were used as inputs in the dataset. Various regression algorithms were compared with the PyCaret AutoML library, and the models with the highest accuracy scores were analyzed with Gradient Extra Trees and AdaBoost regression methods. The findings of this study demonstrate that modelling corrosion behaviour by integrating chemical composition with experimental conditions and processing parameters substantially enhances predictive accuracy. The regression models, developed using the PyCaret library, achieved high accuracy scores, producing corrosion rate predictions that are remarkably consistent with experimental values reported in the literature. Detailed tables and figures confirm that the most influential factors governing corrosion were successfully identified, providing valuable insights into the underlying mechanisms. These results highlight the potential of AI-assisted decision systems as powerful tools for material selection and experimental design, and, when supported by larger databases, for predicting the corrosion life of magnesium alloys and guiding the development of new alloys. Full article

Wetting behavior of molten metals on solid substrates is a critical phenomenon influencing numerous industrial applications, including welding, anti-corrosion coatings, and metal additive manufacturing (AM). In particular, molten metal jetting (MMJ), an emerging AM technology, requires that the molten metal remain pinned at the nozzle exit. Thus, each new metal requires a specific nozzle material to ensure consistent droplet ejection and deposition, making it important to rapidly identify the appropriate wetting combinations. However, traditional measurements of wetting angles require expensive equipment and only allow one combination of materials to be investigated at a time which can be time consuming. This work introduces a rapid screening method based on sessile droplet experiments to evaluate wetting profiles across multiple metal–substrate combinations simultaneously. This study investigates the wetting interactions of molten Al alloy (Al4008), Cu, and Sn on various ceramic and metal substrates to identify optimal material combinations for MMJ nozzle designs. Results demonstrate that Al4008 achieves wetting on ceramic substrates such as AlN, TiO₂, and SiC, with varying mechanisms including chemical reactions and weak surface interactions. Additionally, theoretical predictions regarding miscibility gaps and melting point differences were verified for Cu and Sn on refractory metals like Mo and W. Findings from this study contribute to the establishment of a materials compatibility library, enabling the selection of wetting/non-wetting combinations for stable MMJ operation. This resource not only advances MMJ technologies but also provides valuable insights for broader applications such as welding, coating, and printed electronics. Full article

Expanding the chemical space of DNA-encoded libraries (DELs) is desirable for identifying novel bioactive compounds and enhancing hit quality in affinity-based screening. In this study, we designed and synthesized a new on-DNA diazide platform (DAP) molecule that incorporates both aromatic and aliphatic azido groups within a single scaffold. These orthogonal azides exhibit distinct reactivity profiles, enabling a stepwise warhead construction strategy through chemoselective transformations. This approach facilitates greater structural diversity and efficient incorporation of diverse building blocks. A virtual DEL was generated based on this DAP scaffold, and its chemical space was compared with that of bioactive compounds in the ChEMBL database. The analysis revealed that this virtual library occupied a distinct and previously unexplored region of chemical space, highlighting the potential of this DAP-based strategy for discovering structurally novel DEL members with biological relevance. 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

Phage display has advanced the discovery of peptides that selectively bind to a wide variety of cell surface molecules, offering new modalities to modulate disease-related protein–protein interactions (PPIs). These cell-binding peptides occupy a unique pharmaceutical space between small molecules and large biologics, and their growing popularity has opened up new avenues for targeting cell surface proteins that were previously considered undruggable. This work provides an overview of methods for identifying cell-selective peptides using phage display combinatorial libraries, covering in vitro, ex vivo, and in vivo biopanning approaches. It addresses key considerations in library design, including the peptide conformation (linear vs. cyclic) and length, and highlights examples of clinically approved peptides developed through phage display. It also discusses the on-phage chemical cyclization of peptides to overcome the limitations of genetically encoded disulfide bridges and emphasizes advances in combining next-generation sequencing (NGS) with phage display to improve peptide selection and analysis workflows. Furthermore, due to the often suboptimal binding affinity of peptides identified in phage display selections, this article discusses affinity maturation techniques, including random mutagenesis and rational design through structure–activity relationship (SAR) studies to optimize initial peptide candidates. By integrating these developments, this review outlines practical strategies and future directions for harnessing phage display in targeting challenging cell surface proteins. Full article

Background: The Er:YAG laser has gained attention in dentistry for its potential to enhance microbial disinfection through targeted photothermal and photoacoustic mechanisms. Objective: This systematic review aimed to evaluate the antibacterial and bactericidal efficacy of Er:YAG laser therapy across clinically relevant oral pathogens in in vitro models. Methods: Following the PRISMA 2020 guidelines, a systematic search of PubMed, Embase, Scopus, and the Cochrane Library was conducted for studies published between 2015 and 2025. The review protocol was registered with PROSPERO (CRD420251031368). Eligibility criteria included in vitro or animal studies assessing the bactericidal effects of the Er:YAG laser on oral bacteria or fungi, either alone or in combination with chemical disinfectants. Study selection, data extraction, and quality assessment were conducted independently by multiple reviewers. Results: Ten in vitro studies met inclusion criteria. The Er:YAG laser demonstrated significant antibacterial effects against Enterococcus faecalis, Streptococcus mutans, Porphyromonas gingivalis, Candida albicans, and other species. Greater bacterial reduction was consistently observed when the laser was combined with adjunctive irrigants such as sodium hypochlorite or hydrogen peroxide. The laser was effective in reducing biofilm biomass and viable counts, particularly in complex anatomical settings. Most studies were rated as low risk of bias. Conclusions: Er:YAG laser therapy is a promising adjunctive tool for microbial disinfection in dentistry, particularly in challenging anatomical sites. Further well-designed in vivo and clinical studies are needed to confirm its efficacy and determine optimal treatment parameters. Full article

Clusterin is one of the many known proteins implicated in cancer chemoresistance, which hinders the effectiveness of chemotherapy. This study aimed to design novel inhibitors targeting clusterin using fragment-based drug discovery (FBDD). This approach aims to develop new medicines by identifying small, simple molecules known as “fragments” that can bind to a specific target, such as a disease-causing protein. In this study, a primary ligand-binding site and an allosteric site on the clusterin molecule were identified through hotspot analysis. We screened commercially available fragment libraries for anti-cancer activity and applied the “rule of three” to ensure drug-like properties. The highest-affinity fragment underwent “fragment-growing” to develop potential drug candidates. After docking and toxicity screening, 194 candidate drugs were identified. Quantitative structure-activity relationship (QSAR) analysis revealed that the chemical size and complexity of the fragments significantly contributed to their binding affinity. Pharmacokinetic analyses of candidate drugs from FBDD followed by molecular dynamics simulation of the top 1 final candidate drug precursor demonstrated comparatively better affinity (average = −34.01 kcal/mol) than the reference compound (average = −6.15 kcal/mol) and significant ligand flexibility. This study offers a potential strategy to identify fragments or molecules that may serve as drugs against clusterin-related chemoresistance. Full article