Search Results (1,422)

Background: Obesity is a highly prevalent chronic disease characterized by excessive weight gain and fat accumulation. There is growing evidence that Lactiplantibacillus plantarum strains with bile salt hydrolase (BSH) activity are effective in preventing and alleviating obesity. Methods: Initially, we screened bacterial strains with high hydrolytic activity against glycochenodeoxycholic acid (GDCA), and constructed an isogenic bsh1 knockout mutant. Subsequently, male C57BL/6J mice fed a high-fat diet (HFD) were randomly assigned to receive daily gavage of either the wild-type Lp20 (Lp20-WT) or the bsh1-deficient mutant (Lp20-Δbsh1) for 8 weeks. Serum cholesterol levels and histopathological changes in liver sections were monitored. Hepatic gene expression was quantified by RT-qPCR, and fecal bacterial communities were analyzed via 16S rRNA gene sequencing. These comprehensive assessments aimed to evaluate metabolic improvements and uncover the potential mechanisms behind the observed effects. Results:L. plantarum Lp20 hydrolyzed 91.62% of GDCA, exhibiting the highest bile-salt hydrolase (BSH) activity among tested isolates. Whole-genome sequencing and in-silico analyses mapped this activity to bsh1; gene deletion of bsh1 confirmed the role of bsh1 in GDCA hydrolysis. Daily gavage of the wild-type strain (Lp20-WT) to diet-induced obese mice markedly attenuated weight gain, reduced inguinal white adipose tissue and mesenteric fat mass, and lowered serum TC and LDL-C by 20.8% and 33.3%, respectively, while decreasing ALT and AST levels and reversing hepatic steatosis. In contrast, the bsh1-null mutant (Lp20-Δbsh1) failed to elicit any measurable metabolic benefit. Mechanistically, Lp20-WT upregulated rate-limiting bile-acid synthetic enzymes CYP7A1 and CYP27A1, thereby accelerating the catabolism of cholesterol into bile acids. Concurrently, it activated hepatic TGR5 and FXR signaling axes to modulate hepatic metabolism. Moreover, Lp20-WT restructured the gut microbiota by notably enhancing the abundance of beneficial bacteria such as norank_f__Muribaculaceae, Akkermansia, and Alistipes, while reducing the abundance of potentially harmful taxa, including norank_f__Desulfovibrionaceae, Dubosiella, and Mucispirillum. Conclusions: This study provides direct evidence of BSH’s anti-obesity effects through gene deletion. Specifically, BSH lowers cholesterol by modulating hepatic bile-acid metabolism-related gene expression and altering the gut microbiota composition. However, the study is limited by a small sample size (n = 6), the use of male mice only, and its preclinical stage, indicating a need for further validation across diverse strains and human populations. Full article

Extracellular glutathione (GSH) is degraded on the cell surface, in which the γ-glutamyl residue is removed to generate cysteine–glycine (Cys–Gly) dipeptides that are subsequently transported to the cytoplasm. Carnosine dipeptidase 2 (CNDP2) is a cytoplasmic enzyme that hydrolyzes Cys–Gly and plays an important role in maintaining intracellular cysteine (Cys) homeostasis. CNDP2-mediated hydrolysis of Cys–Gly promotes Cys mobilization and contributes to the replenishment of intracellular GSH levels. CNDP2 is frequently overexpressed in various cancers and has been implicated in tumor cell proliferation and progression. This mechanism may enhance cancer cell survival by causing resistance to oxidative stress, which indicates that CNDP2 is a potential therapeutic target for cancer treatment. Although bestatin (BES) has been identified as a CNDP2 inhibitor, its limited specificity and suboptimal drug-like properties have limited its therapeutic potential. In this study, we performed an in silico screen of a small-molecule compound library and identified KKL-35 as a novel CNDP2-binding molecule. Molecular dynamics (MD) simulations suggested that KKL-35 interacts within the catalytic pocket. Biochemical assays confirmed that it inhibits CNDP2 enzymatic activity, albeit with lower potency compared with BES. Despite its modest intrinsic activity, KKL-35 exhibits favorable physicochemical and pharmacokinetic properties, which are characterized by a low topological polar surface area (TPSA), reduced molecular flexibility, and well-balanced lipophilicity. This positions it as an attractive and tractable starting point for lead optimization. Taken together, these findings establish KKL-35 as a validated CNDP2 inhibitor and a promising lead compound for the development of more selective therapeutics targeting CNDP2-mediated cancer cell metabolism. Full article

Nowadays, the explosive growth of knowledge in the epigenetics field has highlighted DNA methyltransferase 1 (DNMT1) as a key regulator of genomic methylation patterns and a promising therapeutic target in several diseases. In light of the increasing clinical interest in epigenetic enzymes, the present study aimed to develop a robust computational framework for the discovery of novel DNMT1 inhibitors, merging both structure and data-driven strategies. Particularly, the study compiled a dataset of established DNMT1 inhibitors and calculated a series of molecular properties, thus enabling the training of a machine learning model to capture critical structure–activity relationships (SARs). When benchmarked against known active compounds, the model effectively discriminated between putative inhibitors and non-inhibitors with high accuracy. In parallel, molecular docking was conducted to screen additional uncharacterized compounds, estimating their binding affinity to human DNMT1. Their respective properties were then extracted and fed into the aforementioned model to predict their inhibitory potential. Our comparative evaluation against known human DNMT1 inhibitors demonstrated high predictive accuracy, confirming the reliability of the proposed integrated approach. By uniting molecular docking with data-driven SAR modelling, this workflow offers an expedited fast-track avenue for identifying promising human DNMT1 inhibitors while reducing experimental overhead. The results highlight the effectiveness of combining cheminformatics, machine learning, and in silico techniques to guide rational drug design, and accelerate the discovery of novel epigenetic inhibitors. Full article

The gonad is one of the major byproducts of sea cucumber. Four novel antioxidant peptides (NPWGQ, PGHPF, VPYPR and ATGPQGPAGQRGPAGPTGPTGPAG) were isolated and identified from sea cucumber gonad proteins through enzymatic hydrolysis and antioxidant activity-guided fractionation, bioinformatics approaches and in silico screening. These peptides demonstrated great free radical (2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS))-scavenging activity and notable superoxide dismutase (SOD)-activation capacity. Molecular docking and molecular dynamics simulation data suggested that these peptides could form strong binding with SOD through hydrogen bonding, electrostatic interactions, and hydrophobic interactions. Among these peptides, NPWGQ displayed the most potent antioxidant and SOD-activating effects. Through searching known databases, these peptides did not show potential toxicity and are generally considered safe. The present study provides crucial theoretical support for comprehensively utilizing sea cucumber (Holothuroidea) gonad by-products and generating high-value functional food ingredients or dietary supplements. Full article

Background/Objectives: The rise in multidrug-resistant pathogens such as Pseudomonas aeruginosa (PA), coupled with declining antibiotic development, underscores the need for innovative therapeutic strategies. Repurposing approved drugs provides advantages of safety and rapid development. Since quorum sensing (QS) controls key virulence traits in PA, targeting this pathway represents a promising antivirulence approach. This study aimed to identify and repurpose existing drugs as QS inhibitors. Methods: An in silico docking screen of 3000 FDA-approved or clinically tested compounds was performed against the C4-HSL receptor RhlR. Seventeen candidates were tested in the laboratory strain PAO1 for lactone-dependent signaling inhibition. The most active compound, MK-8245, was further evaluated for effects on growth, cytotoxicity, lactone release, biofilm formation, pyocyanin, elastase, rhamnolipids, and swarming motility. Its activity was also assessed in 20 clinical PA isolates. Results: MK-8245 (40 µM) reduced QS-regulated gene expression by ~60% without affecting viability. In PAO1, it inhibited rhamnolipids (60%), pyocyanin (40%), elastase (25%), biofilm formation, and swarming motility (25%). MK-8245 also enhanced the efficacy of imipenem against biofilms. In clinical isolates, it consistently decreased lactone release (~60%), pyocyanin (~50%), rhamnolipids (~40%), biofilm formation (~30%), and swarming motility (~25%). Conclusions: MK-8245 emerges as a promising antivirulence candidate against P. aeruginosa. By disrupting QS signaling and impairing multiple virulence factors, it attenuates pathogenicity without bactericidal pressure. Its synergy with standard antibiotics and consistent activity in clinical isolates highlight its translational potential and warrant further preclinical evaluation. Full article

Pattern recognition and machine learning algorithms have become integral to modern drug discovery, offering powerful tools to uncover complex patterns in biomedical data. This article provides a comprehensive review of state-of-the-art pattern recognition techniques—including traditional machine learning (e.g., support vector machines), deep learning approaches, genome-wide association studies (GWAS), and biomarker discovery methods—as applied in pharmacogenomics and computational drug repurposing. We discuss how these methods facilitate the identification of genetic factors that influence drug response, as well as the in silico screening of existing drugs for new therapeutic uses. Two antiviral agents, ribavirin and lopinavir, are examined as extended case studies in the context of COVID-19, illustrating practical applications of pattern recognition algorithms in analyzing pharmacogenomic data and guiding drug repurposing efforts during a pandemic. We highlight successful approaches such as the machine learning-driven prediction of responders and the AI-assisted identification of repurposed drugs (exemplified by the case of baricitinib for COVID-19), alongside current limitations, including data scarcity, model interpretability, and translational gaps. Finally, we outline future directions for integrating multi-omics data, improving algorithmic interpretability, and enhancing the synergy between computational predictions and experimental validation. The insights presented highlight the promising role of pattern recognition algorithms in advancing precision medicine and accelerating drug discovery, while recognizing the challenges that must be addressed to fully realize their potential. Full article

Oropouche virus (OROV), an emerging orthobunyavirus of increasing public health concern in the Americas, currently lacks approved antiviral therapies. In this study, we employed a structure-based in silico approach to identify natural antiviral scaffolds capable of targeting the Gc glycoprotein, a class II fusion protein essential for host membrane fusion and viral entry. A library of 537 plant-derived compounds was screened against the Gc head domain (PDB ID: 6H3X) through molecular docking and redocking, followed by 100-nanosecond molecular dynamics simulations, MM-PBSA free energy calculations, and ADMET profiling. Curcumin and Berberine emerged as standout candidates. Curcumin demonstrated a balanced profile, with stable binding (−38.14 kcal/mol), low backbone RMSD (1.82 Å), and consistent radius of gyration (Rg ~ 18.8 Å), suggesting strong conformational stability and compactness of the protein–ligand complex. Berberine exhibited the most favorable binding energy (−13.10 kcal/mol) and retained dynamic stability (RMSD 1.86 Å; Rg ~ 19.0 Å), though accompanied by predicted cytotoxicity that may require structural refinement. Both compounds induced reduced residue-level fluctuations (RMSF < 2.5 Å) in functionally critical regions of the Gc protein, consistent with a mechanism of action that involves stabilization of the prefusion conformation and interference with the structural transitions required for viral entry. These findings identify curcumin and berberine as promising scaffolds for anti-OROV drug development and offer a rational foundation for future experimental validation targeting viral fusion mechanisms. Full article

Background: Telomere shortening and chronic inflammation are well-established hallmarks of aging and age-related diseases, often resulting in impaired cellular function. Identifying compounds with anti-aging potential is therefore crucial to promote healthy aging and extend lifespan. Virtual screening has emerged as a rapid and cost-effective strategy to assess the biological activity of large compound libraries. In parallel, the zebrafish (Danio rerio) model offers unique advantages for in vivo aging research and phenotypic screening. The integration of in silico and in vivo approaches has proven to enhance the efficiency and precision of therapeutic discovery. Methods: In this study, we combined ligand- and structure-based virtual screening to identify resveratrol-like polyphenols from the DrugBank database and evaluated their anti-aging effects in zebrafish models. Results: Among the top eight candidates, resveratrol and sakuranetin significantly improved telomerase-related parameters, while apigenin, genistein, and hesperetin exhibited notable anti-inflammatory activity. Conclusions: These findings underscore the value of combining computational and experimental models to accelerate the discovery of therapeutic agents targeting aging-related processes. The dual computational approach (pharmacophore similarity plus consensus docking) provided a robust prioritization pipeline directly validated in zebrafish assays. Full article

Background: Diarrheagenic Escherichia coli (DEC) remains relevant to public health and agri-food chains. The context in Brazil, as a major food producer and exporter, reinforces the need for genomic surveillance. Objective: We aimed to characterize Brazilian diffusely adhering (DAEC), enteropathogenic (EPEC), and Shiga toxin-producing E. coli (STEC) sequences in silico across O-serogroups, in addition to sequence-type (ST), virulence, resistome, and phylogenomic relationships. Methodology: We retrieved 973 genomes assigned to Brazil from NCBI Pathogen Detection Database and performed virtual-PCR screening for key DEC-genes. We then typed O-serogroups (ABRicate/EcOH), Multi-Locus Sequencing Type (MLST), virulome (Ecoli_VF), resistome (ResFinder), and characterized stx genes. Results: DEC represented 18.7% of genomes, driven primarily by EPEC. In EPEC, the eae β-1 subtype was most common; we detected, for the first time in Brazilian sequences, ξ-eae subtype and ST583/ST301. Seventy-eight percent of DAEC isolates were multidrug-resistant (MDR), and two ST were newly reported in the country (ST2141/ST500). In STEC, O157 formed a largely susceptible clade with uniform eae γ-1, whereas 57% of non-O157 were MDR. New STs (ST32/ST1804) were observed, and three genomes were closely related to international isolates. Conclusions: Despite the low DEC representation in the dataset, new STs and eae subtypes were detected in Brazil. Also, MDR in DAEC and non-O157 STEC reinforces the need for antimicrobial-resistance genomic surveillance. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a multifactorial condition requiring multi-target therapeutic strategies beyond traditional single-marker approaches. In this work, we present a fully in silico nutraceutical screening pipeline that integrates molecular prediction, systemic aggregation, and technological design. A curated panel of ten MASLD-relevant targets, spanning nuclear receptors (FXR, PPAR-α/γ, THR-β), lipogenic and cholesterogenic enzymes (ACC1, FASN, DGAT2, HMGCR), and transport/regulatory proteins (LIPG, FABP4), was assembled from proteomic evidence. Bioactivity records were extracted from ChEMBL, structurally standardized, and converted into RDKit descriptors. Predictive modeling employed a stacked ensemble of Random Forest, XGBoost, and CatBoost with isotonic calibration, yielding robust performance (mean cross-validated ROC-AUC 0.834; independent test ROC-AUC 0.840). Calibrated probabilities were aggregated into total activity (TA) and weighted TA metrics, combined with structural clustering (six structural clusters, twelve MOA clusters) to ensure chemical diversity. We used physiologically based pharmacokinetic (PBPK) modeling to translate probabilistic profiles into minimum simulated doses (MSDs) and chrono-specific exposure (%T>IC50) for three prototype concepts: HepatoBlend (morning powder), LiverGuard Tea (evening aqueous form), and HDL-Chews (postprandial chew). Integration of physicochemical descriptors (MW, logP, TPSA) guided carrier and encapsulation choices, addressing stability and sensory constraints. The results demonstrate that a computationally integrated pipeline can rationally generate multi-target nutraceutical formulations, linking molecular predictions with systemic coverage and practical formulation specifications, and thus provides a transferable framework for MASLD and related metabolic conditions. Full article

An effective method for synthesizing dihalogenated derivatives of condensed benzimidazole diones with a nodal nitrogen atom has been developed. As a result, five new heterocyclic quinones were obtained, which differed in the structure of the heterocycle annelated to imidazole, as well as the nature and arrangement of halogen atoms. A comprehensive analysis of the anticancer potential of new heterocyclic quinones revealed pronounced cytotoxic activity of the molecules against tumor cells. Using in silico methods for predicting activity spectra, it was found that the synthesized compounds are capable of interacting with a number of key targets that play an important role in oncogenesis, with the highest probability of binding to STAT3, the central regulator of cell growth, proliferation and metabolism. Experimental studies have shown that, despite the lack of pronounced ability to induce apoptosis, these substances effectively inhibit the activity of allosteric glycolytic enzymes, disrupting metabolic adaptation and energy balance of tumor cells. The obtained results expand the understanding of the molecular basis of the antitumor action of heterocyclic compounds and lay a solid foundation for their use as promising modulators of tumor cell metabolism. Full article

Background: In this research work, novel pyrazolone-derived oxadiazole-based benzamide derivatives (1–10) were synthesized through unique and facile synthetic routes. Introduction: These scaffolds were designed to be therapeutically more effective and have fewer side effects. Methods: To confirm the structure of analogs in detail, we employed ¹HNMR, ¹³CNMR, and HREI-MS spectroscopy. The potential of all derivatives was tested by screening them against alpha-amylase and alpha-glucosidase in comparison with reference anti-diabetic drug acarbose (4.50 ± 0.20 µM and 4.90 ± 0.30 µM). Results & Discussion: Among all tested analogs and standard drugs, derivative 3 proved to be the most promising candidate. It exhibited the most powerful inhibitory effect (IC₅₀ = 3.20 ± 0.20 µM and 3.60 ± 0.10 µM). To further investigate its activity, the experimental results were supported by in silico investigations. Molecular docking demonstrated strong and viable interactions between enzymes and the most potent compound. DFT calculations validated the electronic configuration, stability, and reactivity of lead molecules. Furthermore, the ADMET profile predicted the favorable drug likeness properties and low toxicity. The results of docking were further confirmed via molecular dynamics analysis, whereas the pharmacophore model of analog 3 supports the formation of a stable hydrogen bond network of derivatives with the receptor site of the enzyme. Conclusions: Collectively in silico and in vitro results underscore the therapeutic potential of these derivatives for the effective treatment of diabetes in the future. Full article

Toxicological risk assessment of chemicals without experimental toxicity data often relies on in silico predictions. However, models designed to predict inhalation toxicity associated with exposure to volatile chemicals in solution are unavailable. The aim of this research was to develop an approach to estimate toxicology-based maximum solution concentrations for novel odorants using in silico structure-based predictions. The decision trees were adapted from established open-source models for assessing mutagenicity (rule-based, ISS in vitro mutagenicity decision tree) and systemic toxicity (revised Cramer decision tree). These were implemented using Toxtree (v3.1.0), a freely available program. Thresholds of toxicologic concern (TTC) were then assigned based on the predicted hazard classification. We then used predicted vapor pressure derived from MPBPWIN™ using US EPA EPI Suite to calculate a solution concentration where inhalation exposure to a defined headspace volume would not exceed the TTC. The approach was evaluated using a published dataset of 143 chemicals with repeat exposure inhalation toxicity data, yielding health-protective predictions for 98.6% of the test set. This demonstrates that the proposed in silico approach enables the estimation of safe toxicology-based maximum solution concentrations for chemicals using open-source models and software. Full article

Background/Objective: Oncogenic tyrosine kinases (TKs) such as ALK and SRC promote cancer progression, but their effects on metabolic enzymes are still not well understood. This study examines how TK signaling regulates inosine monophosphate dehydrogenase 2 (IMPDH2), a rate-limiting enzyme in purine biosynthesis, and assesses its potential as a therapeutic target. Methods: Phosphoproteomic screening and in vitro kinase assays were used to identify phosphorylation sites on IMPDH2. Lipid-binding assays explored the role of phosphatidylinositol 3-phosphate (PI3P) in IMPDH2 regulation. Structure-based virtual screening discovered small-molecule allosteric inhibitors, which were tested in lymphoma cell models, including ALK and BTK-inhibitor resistant lines. Results: Here, we identify Inosine monophosphate dehydrogenase-2 (IMPDH2), a rate-limiting enzyme in purine biosynthesis, as a novel substrate of ALK and SRC. We show that phosphorylation at the conserved Y233 residue within the allosteric domain enhances IMPDH2 activity, linking TK signaling to metabolic reprogramming in cancer cells. We further identify PI3P as a natural lipid inhibitor that binds IMPDH2 and suppresses its enzymatic function. Using structure-based virtual screening, we developed Comp-10, a first-in-class allosteric IMPDH inhibitor. Unlike classical active-site inhibitors such as mycophenolic acid (MPA), Comp-10 decreases IMPDH1/2 protein levels, blocks filament (rod/ring) formation, and inhibits the growth of ALK and BTK inhibitor-resistant lymphoma cells. Comp-10 acts post-transcriptionally and avoids compensatory IMPDH upregulation observed with MPA (rod/ring) formation, and inhibited growth in TKI-resistant lymphoma cells. Notably, Comp-10 avoided the compensatory IMPDH upregulation observed with MPA. Conclusion: These findings uncover a novel TK–IMPDH2 signaling axis and provide mechanistic and therapeutic insight into the allosteric regulation of IMPDH2. Comp-10 represents a promising therapeutic candidate for targeting metabolic vulnerabilities in tyrosine kinase driven cancers. Full article

The urgent need to reduce the cost of new drug discovery has led us to create a new, more selective screening method using free chemoinformatics tools to restrict the high failure rates of lead compounds (>90%) during the development process because of the lack of clinical efficacy (40–50%), unmanageable toxicity (30%), and poor drug-like properties (10–15%). Our efforts focused on new molecular entities (NMEs) with reported activity as tyrosine kinase inhibitors (small molecules) as a class of great potential. The criteria for the new method are acceptable Druglikeness, desirable ADME (absorption, distribution, metabolism, and excretion), and low toxicity. After a bibliographic review, we first selected the 29 most promising compounds, always according to the literature, then collected the in silico calculated data from different platforms, and finally processed them together to conclude at 14 compounds meeting the aforementioned criteria. The novelty of the present screening method is that for the evaluation of the compounds for Druglikeness, and ADMET properties (absorption, distribution, metabolism, excretion, and toxicity), the data of the different platforms were used as a whole, rather than the results of each platform individually. Additionally, we validated our new consensus-based method by comparing the final in silico results with the experimental values of FDA (Food and Drug Administration)-approved tyrosine kinase drugs. Using inferential statistics of 39 FDA-approved tyrosine kinase drugs obtained after applying our method, we delineated the intervals of the desired values of the physicochemical properties of future active compounds. Finally, molecular docking studies enhance the credibility of the applied method as an identification tool of Druglikeness. Full article