Search Results (505)

Studies of natural products and their semisynthetic derivatives are a valuable source of therapeutic agents. The aim of this work was to obtain new 30-phosphoramidate derivatives of betulin and determine their biological potential. The synthetic approach utilized the Staudinger reaction (the introduction of a phosphoramidate group), the Steglich reaction (the introduction of an alkynyl group), and the Jones reaction (the introduction of a carboxyl group). The structures of the target compounds were determined using spectroscopic methods (¹H NMR, ¹³C NMR, ³¹P NMR, and HRMS). The new derivatives were tested for antiproliferative activity against MV4-11, A549, MCF-7, PC-3, and HCT116 cancer cells and against normal MCF-10A cells using the MTT and SRB methods. Apoptosis studies were performed for the most active compounds (6B and 7A), potential molecular targets (AutoDock software) were identified, and lipophilicity parameters (RP-TLC method, SwissADME website) were determined. The greatest effect on apoptosis and caspase 3/7 activation was observed for the diester derivative 7A. Compound 7A showed a high lipophilicity parameter in the study group. Full article

The indole scaffold is common in natural products and bioactive compounds, including anti-cancer and anti-inflammatory medicines. In this work, a series of indole-acrylamide derivatives was synthesized, and their antiproliferative and anti-inflammatory effects were evaluated on COX enzymes and against a panel of cancer cell lines. All the final compounds were characterized via HRMS and (¹H & ¹³C)-NMR. Anticancer and anti-inflammatory activities were evaluated using standard biomedical techniques by SRB, MTS, and COX kit assays. Additionally, the molecular docking analysis was conducted using the AutoDock Vina tool. The results demonstrated that the produced compounds displayed significant inhibitory effects on the COX-2 enzyme, with IC₅₀ values of 128 nM to 1.04 µM. 6a demonstrated significant COX-2 selectivity with an IC₅₀ of 128 nM and an SI of 352, highlighting its preference for COX-2 over COX-1. 6c exhibited potent COX-2 inhibition with an IC₅₀ of 0.215 µM and an SI of 10.6. The assessed compounds exhibited substantial cytotoxic effects on cancer cells, especially against liver cancer cell lines (Huh7, HepG2, Mahlavu, and SNU475), and breast cancer (MCF-7). 6d compound was the most COX-1 selective inhibitor, which observed potent activity against hepatocellular carcinoma, with IC₅₀ values as low as 3.5 µM, and was highly effective against MCF-7. Additionally, COX-2 selective inhibitors, 6a and 6b, exhibited strong antiproliferative effects against both breast cancer (MCF-7) and melanoma (B16F1), with IC₅₀ values ranging from 4.75 to 15.4 µM. Furthermore, the molecular docking of 6a demonstrated a strong affinity for the COX-2 enzyme, with energy scores (S) of −8.392 kcal/mol, comparable to celecoxib’s score of −10.96 kcal/mol. The findings suggest a possible correlation between COX-2 inhibition and anticancer efficacy, especially for compounds 6a and 6c, which demonstrate excellent COX-2 selectivity and notable antiproliferative effects, positioning them as prospective candidates for further advancement in cancer treatment. Full article

Rapid antigenic drift in the coronavirus spike protein motivates alternative antiviral strategies. We target the conserved nucleocapsid (N) protein—central to RNA binding, genome packaging, and replication—and perform a comparative, cross-species 3D structure-based in silico evaluation. A library of 494 compounds (natural, phytochemical, synthetic) was docked with AutoDock Vina against the MERS-CoV N–terminal RNA–binding domain (NTD; PDB 7DYD) and the C–terminal dimerization domains (CTD) of SARS-CoV (2CJR) and SARS-CoV-2 (8R6E), reflecting the availability of high-resolution, functionally relevant domain structures for each virus. Top-ranked poses underwent ADME profiling and 100 ns GROMACS molecular-dynamics (MD) simulations. Myricetin 3-O-β-D-Galactopyranoside (myricetin) showed the most favorable predicted docking scores across targets (−8.9 kcal/mol, MERS–NTD; −10.1, SARS–CTD; −9.8, SARS-CoV-2 CTD). Curcumin showed moderate predicted affinity (−7.1 to −8.1), while MCC950 achieved consistently favorable docking score (−7.9 to −9.0). ADME results highlighted a trade-off: glycosylated flavonoids offered rich interaction networks but violated oral drug-likeness criteria (e.g., high TPSA), whereas MCC950 met Lipinski/Veber guidelines, supporting translational potential. MD analyses revealed ligand- and target-specific stability: myricetin maintained persistent binding over 100 ns in the SARS-CoV-2 CTD with lower RMSD than comparators; curcumin exhibited transient stability (~30 ns) in MERS- and SARS-bound complexes; MCC950 showed intermittent interactions. Collectively, these findings suggest that the conserved N protein RNA-binding groove represents a resistance-resilient target for broad-spectrum antiviral discovery. Natural flavonoids provide promising scaffolds for optimization, and MCC950 warrants further exploration given its drug-like profile. As this study is purely computational, the results are hypothesis-generating and should be validated via RNA-binding disruption assays, antiviral cell studies, and in vivo models. Full article

Background: The extracellular-signal-regulated kinase (ERK) cascade regulates cell proliferation, differentiation, and survival, and ERK2 mediates substrate phosphorylation, influencing gene expression and cellular functions. Methods: In the current study, a pool of new molecules was generated using the DeLA-Drug, a machine learning (ML)-assisted de novo design tool. The chemical space was reduced through a similarity search against active ERK2 inhibitors and molecular docking with AutoDock vina, followed by pharmacokinetic assessment in DeepPK. Poses of the final selected molecules were refined in DiffDock, and dynamicity was assessed through molecular dynamics (MD) simulation. Finally, the free-energy perturbation (FEP)-based binding affinity was explored in Gromacs2023.4. Results: From the above approaches, four molecules (Ek1, Ek2, Ek3, and Ek4) were identified as promising candidates with favorable binding interactions. Molecular docking revealed that the selected molecules exhibited higher binding affinity for ERK2, ranging from −9.50 to −10.50 kcal/mol. The dynamics assessment via MD simulation clearly revealed their strong association with ERK2, corroborated by the lower deviation of the ERK2 backbone in dynamic states. All four screened molecules have satisfactory pharmacokinetic properties, medicinal chemistry properties, and good synthetic accessibility scores, indicating their potential as drug-like compounds under Lipinski’s rule of five to inhibit or modulate ERK2 activity. The FEP energy of Ek1 was found to be −26.85 kJ/mol, which is higher than the standard molecule (−22.77 kJ/mol) and indicates its strong affinity toward ERK2. Conclusions: These results suggest that all proposed ERK2 modulators are potential avenues for future drug discovery targeting ERK2, subject to experimental validation. Full article

Background/Objectives: Staphylococcus aureus virulence is tightly regulated by the agr (accessory gene regulator) quorum-sensing system. Targeting AgrC, the histidine kinase receptor that serves as a core regulator of agr signaling, represents a promising antivirulence strategy that circumvents conventional bactericidal pressure. Methods: In this study, structure-based virtual screening using AutoDock Vina was performed, followed by molecular dynamics simulations, to identify potent analogs of known AgrC inhibitors. Results: A cyclo[Ala-Phe-OLeu-Phe-D-Leu] exhibiting high binding affinity and stable receptor interaction was selected for further evaluation. Antimicrobial susceptibility testing confirmed that the compound did not inhibit bacterial growth. However, at a concentration of 16 µg/mL, it significantly inhibited hemolytic activity with high reproducibility, and the inhibition rate reached 77.60%. Quantitative reverse transcription PCR (RT-qPCR) demonstrated that the compound decreased some key AgrC-mediated genes, including agrC, agrA, saeS, hla, spa, fnbA, and lukS. Conclusions: These findings identify a promising cyclic pentapeptide inhibitor of AgrC that effectively attenuates S. aureus virulence without exerting bactericidal pressure. This work provides a valuable lead compound and offers novel insights for the development of advanced, safe, and effective antivirulence therapeutics. Full article

Cancer therapy development increasingly focuses on multi-target approaches to inhibit key proteins involved in tumor growth and angiogenesis. This study explored the potential inhibitory interactions of 110 cannabinoid derivatives using molecular docking simulations against epidermal growth factor receptor (EGFR), vascular endothelial growth factor receptor-1 (VEGFR-1), and VEGFR-2. Blind docking with AutoDock Vina identified eight recurrent hits across all three targets, including polar THC glucuronides and more drug-like cannabinoid scaffolds. Among these, 2′-Hydroxy-Delta (9)-THC and Ajulemic Acid combined favorable multi-target binding with superior predicted pharmacokinetic properties compared with other cannabinoids and reference inhibitors (lapatinib, motesanib, and sorafenib). ADME predictions highlighted Ajulemic Acid as the most promising oral candidate, showing optimal molecular weight, high oral bioavailability, and good gastrointestinal absorption, while 2′-Hydroxy-Delta (9)-THC exhibited potential for central nervous system exposure due to predicted blood–brain barrier permeability. In contrast, glucuronidated THC metabolites and highly lipophilic cannabinol esters displayed strong docking scores but suboptimal drug-likeness, suggesting prodrug- or metabolite-like behavior rather than suitability as primary oral leads. Toxicity predictions classified all compounds as moderately toxic, with Ajulemic Acid showing a comparatively more favorable safety profile. These findings do not demonstrate biological inhibition and should be interpreted strictly as hypothesis-generating computational evidence, providing a rational framework for future in vivo and in vitro validations. Full article

Modern advances in artificial intelligence have accelerated the development of computational tools for protein–ligand structure prediction, yet their real-world performance remains uneven across receptor classes and ligand chemotypes. Recently published cryo-EM structures of several different psychedelics bound to the serotonin 5HT_2A receptor provide a unique opportunity to explore how modern AI-based modeling performs in a pharmacologically important GPCR system. Here, we compare three major approaches: AI-based protein–ligand cofolding (Boltz-2), a leading AI-driven docking module (Uni-Mol Docking v2), and a widely used classical physics-based docking pipeline (AutoDock Vina) across a series of tryptamine and phenethylamine psychedelics. Predicted binding poses were comparatively assessed through structural alignment with these newly available cryo-EM complexes. Additionally, calcium-mobilization assays were performed to provide a coarse functional readout for comparison with computationally predicted binding affinities. This study integrates methodological review with exploratory benchmarking to illustrate how different modeling paradigms behave on a shared receptor–ligand test set. Our results highlight substantial variation between modeling strategies, with AI-based cofolding often producing global binding orientations more closely resembling experimental structures, and classical docking showing greater variability across ligands, while still outperforming AI-driven docking on average. These observations underscore both the growing utility and current limitations of AI-assisted structure prediction in serotonergic drug discovery, and emphasize the importance of careful, experimentally anchored evaluation as such tools continue to advance. Full article

Leishmaniasis remains a neglected tropical disease with treatment limitations driven by toxicity, cost, and emerging resistance. Trypanothione reductase (TryR) and glycerol-3-phosphate dehydrogenase (GPDH) are essential Leishmania enzymes supporting redox homeostasis and energy/redox-linked metabolism, providing a biologically grounded rationale for dual-target inhibition. We applied an integrated in silico workflow to prioritize candidate inhibitors using ADMET prediction (SwissADME/pkCSM), molecular docking (AutoDock Vina), and 100 ns molecular dynamics (MD) simulations; human GPDH was included as a negative control to probe potential off-target liability. ADMET screening identified 41 drug-like candidates, with most predicted to have high GI absorption and low toxicity flags across assessed endpoints (computational predictions interpreted cautiously). Docking highlighted two leading compounds. CID 6529858 showed the most favorable predicted binding to Leishmania GPDH (−8.9 kcal/mol) with a modest parasite-favored score difference versus human GPDH (−7.2 kcal/mol; Δ = −1.7 kcal/mol), while eupatorin (CID: 97214) displayed dual-target potential (TryR −7.5 kcal/mol; Leishmania GPDH −8.2 kcal/mol; human GPDH −7.2 kcal/mol; Δ = −1.0 kcal/mol). In MD, key complexes remained stable: CID 6529858 exhibited low GPDH backbone deviation (~0.25–0.40 nm), and eupatorin showed the most stable TryR trajectory (average RMSD ~0.45 nm), supported by generally low residue fluctuations across complexes. PCA further suggested ligand-associated restriction of large-scale motions (e.g., GPDH PC1 = 27.38%; TryR PC1 = 18.1%). Overall, these results nominate eupatorin as a promising dual-target lead and CID 6529858 as a strong GPDH-focused scaffold, warranting experimental enzyme inhibition, antiparasitic efficacy, and host–cell cytotoxicity testing to confirm potency and selectivity. Full article

Background/Objectives: Software is now core infrastructure in biomedical science, yet fragmented workflows across subfields hinder reproducibility and delay the translation of data into actionable decisions. There is a critical need for a cross-disciplinary synthesis to bridge these silos and establish a unified framework for software maturity. This narrative review addresses this gap by synthesizing representative software ecosystems across three major pillars: bioinformatics, molecular modeling/simulations, and epidemiology/public health. Methods: A narrative review of articles indexed in PubMed/NCBI, Web of Science, and Scopus between 2000 and 2025 was conducted. Domain-specific terms related to bioinformatics, molecular modeling, docking, molecular dynamics, epidemiology, public health, and workflow management were combined with software- and algorithm-focused keywords. Studies describing, validating, or applying documented tools with biomedical relevance were included. Results: Across domains, mature data standards and reference resources (e.g., FASTQ, BAM/CRAM, VCF, mzML), widely adopted platforms (e.g., BLAST+ (v2.16.0, NCBI, Bethesda, MD, USA), Bioconductor (v3.20, Bioconductor Foundation, Seattle, WA, USA), AutoDock Vina (v1.2.5, Scripps Research, La Jolla, CA, USA), GROMACS (v2024.3, GROMACS Team, Stockholm, Sweden), Epi Info (v7.2.6, CDC, Atlanta, GA, USA), QGIS (v3.40, QGIS.org, Gossau, Switzerland), and increasing use of workflow engines were identified. Software pipelines routinely transform molecular and surveillance data into interpretable features supporting hypothesis generation. Conclusions: Integrated, standards-based, and validated software pipelines can shorten the path from measurement to decision in biomedicine and public health. Future progress depends on reproducibility practices, benchmarking, user-centered design, portable implementations, and responsible deployment of machine learning. Full article

This study compares the chemical composition, antimicrobial effects, and antibiotic-potentiating capacity of three Lauraceae essential oils (EO): Cryptocarya agathophylla (CAEO), Litsea cubeba (LCEO), and Laurus nobilis (LNEO). Gas chromatography–mass spectrometry (GC–MS) analysis revealed distinct chemotypes: CAEO and LCEO were dominated by oxygenated monoterpenes, while LNEO contained the highest levels of monoterpene hydrocarbons. Antibacterial testing against nine bacterial strains showed strain-dependent growth suppression trends, while true minimum inhibitory concentrations (MICs) were reached only in selected cases. EO–ampicillin interactions were evaluated using MIC-based checkerboard criteria, whereas OD-derived inhibition parameters were used exclusively to describe sub-MIC potentiation trends. In combination assays, LNEO exhibited the most pronounced potentiating effects against Streptococcus pyogenes, Shigella flexneri, and Haemophilus influenzae, while CAEO and LCEO showed moderate or strain-dependent enhancement. Hierarchical clustering highlighted distinct oil- and strain-specific interaction profiles. Overall, although CAEO displayed stronger intrinsic antibacterial effects when tested alone, LNEO emerged as the most effective potentiator of ampicillin activity in a strain-dependent manner. The effects of the major compounds identified in the Lauraceae EO were assessed in silico against protein targets of some microorganisms using the AutoDock software version 4.2.6. The docking scores revealed binding affinities of the bioactive compounds towards Dpr protein (4.3–5.8 kcal/mol), DNA gyrase (4.7–7.1 kcal/mol), mono- diacylglycerol lipase (4.4–6.2 kcal/mol), CYP51 (5.8–8.0 kcal/mol), phage-encoded quorum sensing anti-activator (5.8–8.0 kcal/mol) and Chondroitin ABC lyase I (4.8–6.3 kcal/mol). Two (2) hit compounds (α-Citral, β-Citral) were finely defined by strong hydrophobic and hydrophilic interactions with the bacterial and fungal protein targets, respectively. Full article

Ground-based full-physical experiments for space rendezvous and docking serve as a critical step in verifying the reliability of docking technology. The high-precision active attitude setting of spacecraft simulators represents a key technology for ground-based full-physical experiments. In order to satisfy the requirement for high-precision attitude control in these experiments, this paper proposes an enhanced method based on auto disturbance rejection control (ADRC). This paper addresses the limitations of traditional deadband–hysteresis relay controllers, which exhibit low steady-state accuracy and insufficient disturbance rejection capability. This approach employs a nonlinear extended state observer (NESO) to estimate and compensate for total system disturbances in real time. Concurrently, it incorporates an adaptive mechanism for deadband and hysteresis parameters, dynamically adjusting controller parameters based on disturbance estimates and attitude errors. This overcomes the trade-off between accuracy and power consumption that is inherent in fixed-parameter controllers. Furthermore, the method incorporates a nonlinear tracking differentiator (NTD) to schedule transitions, enabling rapid attitude settling without overshoot. The stability analysis demonstrates that the proposed controller achieves local asymptotic stability and global uniformly bounded convergence. The simulation results demonstrate that under three typical operating conditions (conventional attitude setting, pre-separation connector stabilisation, and docking initial condition establishment), the steady-state attitude error remains within ±0.01°, with convergence times under 3 s and no overshoot. These results closely match ground test data. This approach has been demonstrated to enhance the engineering applicability of the control system while ensuring high precision and robust performance. Full article

Background/Objectives: Obstructive sleep apnea (OSA) affects approximately 1 billion adults worldwide with extensive comorbidities, including cardiovascular disease, metabolic disorders, and cognitive decline, yet pharmacological therapies remain limited. Conventional bottom-up omics approaches identify numerous genes overlapping with other diseases, hindering therapeutic translation. This study introduces a top-down, comorbidity-driven approach to identify actionable molecular targets and develop rational dual kinase inhibitors for OSA management. Methods: We implemented a five-tier modeling workflow: (1) comorbidity network analysis, (2) disease module identification through NetworkAnalyst, (3) mechanistic pathway reconstruction of the CK1δ-(HIF1A)-PINK1 signaling cascade, (4) molecular docking analysis of Nigella sativa alkaloids and reference inhibitors (IC261, PF-670462) against CK1δ (PDB: 3UYS) and PINK1 (PDB: 5OAT) using AutoDock Vina, and (5) rational design and computational validation of novel dual inhibitors (ICL, PFL) integrating pharmacophoric features from natural alkaloids and established kinase inhibitors. Results: Extensive network analysis revealed a discrete OSA disease module centered on two interconnected protein kinases—CK1δ and PINK1—that mechanistically bridge circadian disruption and neurodegeneration. Among natural alkaloids, Nigellidine showed strongest CK1δ binding (−8.0 kcal/mol) and Nigellicine strongest PINK1 binding (−8.6 kcal/mol). Rationally designed dual inhibitors demonstrated superior binding: ICL (−7.2 kcal/mol PINK1, −8.9 kcal/mol CK1δ) and PFL (−10.8 kcal/mol CK1δ, −11.2 kcal/mol PINK1), representing −2.6–2.8 kcal/mol improvements over reference compounds. Conclusions: This study establishes a comorbidity-driven translational framework identifying the CK1δ-PINK1 axis as a therapeutic target in OSA. The rationally designed dual inhibitors represent third-generation precision therapeutics addressing OSA’s multi-dimensional pathophysiology, while the five-tier workflow provides a generalizable template for drug discovery in complex multimorbid diseases. Full article

Background: Haemonchus contortus is a gastrointestinal nematode that affects small ruminants and exhibits widespread resistance to commercial anthelmintics. This has driven interest in natural compounds such as fatty acids and sterols; however, their biological relevance against resistant parasite strains remains insufficiently understood. Methods: The nematicidal potential of four fatty acids (palmitic, linoleic, pentadecanoic, and stearic acids) and two sterols (β-sitosterol and ergosterol), all of them commercially available in Mexico, was evaluated against infective L3 larvae of a benzimidazole-resistant H. contortus strain. In vitro larval mortality and migration inhibition assays were performed, and molecular docking analyses were conducted to explore interactions with the glutamate-gated chloride channel (GluCl) using AutoDock4. Statistical analyses were performed using ANOVA followed by Tukey’s post hoc test (p < 0.05). Results: Molecular docking indicated strong binding affinities of ergosterol and β-sitosterol to GluCl, comparable to that of ivermectin. In vitro assays showed that fatty acids, particularly linoleic acid, produced more pronounced effects on larval motility, suggesting predominantly nematostatic activity. No clear dose–response relationship was observed in migration assays, and in vitro mortality remained limited across treatments. Conclusions: The results highlight a disconnect between in silico binding affinity and in vitro biological activity, particularly in a drug-resistant H. contortus strain. Integrating in vitro bioassays with computational approaches provides valuable mechanistic insight but also underscores the limitations of affinity-based predictions for assessing anthelmintic efficacy. Full article

Background: β-site amyloid precursor protein cleaving enzyme 1 (BACE1) inhibitors demonstrated amyloid-lowering efficacy but failed in phase II/III clinical trials due to adverse effects and limited disease-modifying outcomes. This study employed an integrated network pharmacology and molecular docking approach to quantitatively elucidate the multitarget mechanisms of 4 (phase II/III) discontinued BACE1 inhibitors (Verubecestat, Lanabecestat, Elenbecestat, and Umibecestat) and the preclinical compound AM-6494 in Alzheimer’s disease (AD). Methods: Drug-associated targets were intersected with AD-related genes to construct a protein–protein interaction (PPI) network, followed by topological analysis to identify hub proteins. Gene Ontology (GO) and KEGG pathway enrichment analyses were performed using statistically significant thresholds (p < 0.05, FDR-adjusted). Molecular docking was conducted using AutoDock Vina to quantify binding affinities and interaction modes between the selected compounds and the identified hub proteins. Results: Network analysis identified 10 hub proteins (CASP3, STAT3, BCL2, AKT1, MTOR, BCL2L1, HSP90AA1, HSP90AB1, TNF, and MDM2). GO enrichment highlighted key biological processes, including the negative regulation of autophagy, regulation of apoptotic signalling, protein folding, and inflammatory responses. KEGG pathway analysis revealed significant enrichment in the PI3K–AKT–MTOR signalling, apoptosis, and TNF signalling pathways. Molecular docking demonstrated strong multitarget binding, with binding affinities ranging from approximately −6.6 to −11.4 kcal/mol across the hub proteins. Umibecestat exhibited the strongest binding toward AKT1 (−11.4 kcal/mol), HSP90AB1 (−9.5 kcal/mol), STAT3 (−8.9 kcal/mol), HSP90AA1 (−8.5 kcal/mol), and MTOR (−8.3 kcal/mol), while Lanabecestat showed high affinity for AKT1 (−10.6 kcal/mol), HSP90AA1 (−9.9 kcal/mol), BCL2L1 (−9.2 kcal/mol), and CASP3 (−8.5 kcal/mol), respectively. These interactions were stabilized by conserved hydrogen bonding, hydrophobic contacts, and π–alkyl interactions within key regulatory domains of the target proteins, supporting their multitarget engagement beyond BACE1 inhibition. Conclusions: This study demonstrates that clinically failed BACE1 inhibitors engage multiple non-structural regulatory proteins that are central to AD pathogenesis, particularly those governing autophagy, apoptosis, proteostasis, and neuroinflammation. The identified ligand–hub protein complexes provide a mechanistic rationale for repurposing and optimization strategies targeting network-level dysregulation in Alzheimer’s disease, warranting further in silico refinement and experimental validation. Full article