Search Results (1,971)

Background: Anaplastic lymphoma kinase (ALK) is a key receptor tyrosine kinase involved in regulating signaling pathways critical for cell proliferation, differentiation, and survival. Mutations or rearrangements of the ALK gene lead to aberrant kinase activation, driving tumorigenesis in various cancers. Although ALK inhibitors have shown clinical benefits, drug resistance remains a significant barrier to long-term efficacy. Developing novel ALK inhibitors capable of overcoming resistance is therefore essential. Methods: A structure-based pharmacophore model was constructed using the 3D structures of five approved ALK inhibitors. Systematic virtual screening of the Topscience drug-like database was performed incorporating PAINS filtering, ADMET prediction, and molecular docking to identify promising candidates. In vitro antiproliferative assays, molecular docking, molecular dynamics simulations, and MM/GBSA binding free energy calculations were used to evaluate biological activity and elucidate binding mechanisms. Results: Two candidates, F1739-0081 and F2571-0016, were identified. F1739-0081 exhibited moderate antiproliferative activity against the A549 cell line, suggesting potential for further optimization. Computational analyses revealed its probable binding modes and interactions with ALK, supporting the observed activity. Conclusions: This study successfully identified novel ALK inhibitor candidates with promising biological activity. The integrated computational and experimental approach provides valuable insights for the rational design of optimized ALK inhibitors to address drug resistance in cancer therapy. Full article

Background: Third-generation aromatase inhibitors (CYP19A1) are the mainstay of treatment for estrogen-receptor-positive breast cancer. This is because estrogen is required for cancer growth in approximately 70% of patients with this condition. Although potent and effective, aromatase inhibitors induce resistance and secondary effects, requiring treatment to be discontinued. This clinical limitation highlights the need to search for new molecules. Previous studies have led to the identification of a set of indole sulfonamide molecules that exhibit interesting activity against aromatase. Methods: Phenyl and benzyl sulfonamide derivatives with alkylated heterocycles linked by short methylene bridges were designed and synthesized. The aromatase inhibition and cytotoxicity were tested through in vitro assays. Molecular docking and dynamic simulations evaluated the interactions with the aromatase enzyme, while a target fishing strategy linked to gene associations relevant to breast cancer helped to uncover other targets. Results: All of the non-steroidal inhibitors synthesized showed significant activity. Compounds 3 and 9 demonstrated IC₅₀ values in the low micromolar range and selective action against MCF7 breast cancer cells over healthy lines. Computational studies confirmed stable and favorable aromatase binding. Target fishing identified EGFR and PTK2B as additional potential targets for a multi-target therapeutic strategy. Conclusions: Compounds 3 and 9 outperform indole-based inhibitors in their potency and selectivity, revealing strong therapeutic potential. Their binding affinity and specificity support further development. EGFR and PTK2B may enable a broader, multi-target approach. Full article

Men have limited options for contraception, despite the widely accepted public health benefits of it, placing the contraceptive burden solely on women. The current study focuses on inhibiting the PP1γ2 enzyme, which plays a role in sperm maturation and motility. The study considered three top compounds based on the findings of molecular docking. The three compounds exhibited a good interaction profile with a binding affinity score of D751-0223 (−8.7 kcal/mol), D751-014 (−8.1 kcal/mol), and N117-0087 (−8 kcal/mol) measured in kcal/mol. Molecular dynamics simulation (MDS) were performed on the PP1γ2–ligand complexes along with the Apo form. The results suggested that all the complexes were stable with no major deviations observed compared to Apo. The average RMSDs for PP1γ2-D751-0223, D751-014, and Apo were 1.27 Å, 1.73 Å, 1.39 Å, and 1.69 Å, respectively. The PP1γ2–ligand complexes were observed with unique salt bridge interactions such as Glu133-Arg137, Asp4-Lys107, Asp188-Arg116, and Glu120-Arg90. The principal component analysis (PCA) findings indicated that every complex had a distinctive motion state. Furthermore, the net MM/PBSA scores for D751-0223, D751-0143, and N117-0087 were −80.01 kcal/mol, −72.18 kcal/mol, and −64.26 kcal/mol, respectively, while the MM/GBSA and MM/PBSA values were −82, −73.07,−67.26 and −80.01, −72.18, −64.26, measured in kcal/mol, respectively. The WaterSwap energy estimation was performed to validate the former technique, and the findings demonstrated that PP1γ2-D751-0223 is a stable complex, with a value of −51.05 kcal/mol. This work provides a baseline to researchers for the identification of novel therapeutic approaches for non-hormonal male contraceptives. Full article

This study explores the synthesis, characterization, and extraction efficiency of hydrophobic natural deep eutectic solvents (NADESs), along with the allergen-modulating potential of extracted bioactive compounds. Six NADESs were synthesized using binary combinations of camphor, thymol, eugenol, and menthol (1:1 molar ratio) and characterized using Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis and differential thermal analysis (TGA/DTA), density functional theory (DFT), and molecular dynamics simulations (MD simulations). Bioactive compounds were extracted from Thujopsis dolabrata wood biomass via ultrasonic-assisted extraction and analyzed using gas chromatography–mass spectrometry (GC–MS). The total essential oil yield, estimated semiquantitatively by summing the peak areas of key terpenoid compounds, ranged from 1.91% to 7.90% across different NADES systems, indicating their varied extraction capacities. Molecular docking was performed to assess their allergen-modulating interactions with Amb a 1 and Cry j 1. All NADESs exhibited single-stage decomposition (110–125 °C) except camphor–menthol, which recrystallized. FTIR and simulations confirmed strong hydrogen bonding in eugenol-based NADESs, particularly menthol–eugenol. Extraction identified 47 bioactive compounds, with 4,5α-Epoxy-3-methoxy-17-methyl-7α-(4-phenyl-1,3-butadienyl)-6β,7β-(oxymethylene) morphinan as the most abundant (9.31–11.16%). It exhibited the highest binding affinity (Cry j 1: −8.60 kcal/mol, Amb a 1: −7.40 kcal/mol) and lowest inhibition concentration (Cry j 1: 0.49 µM, Amb a 1: 3.74 µM), suggesting strong allergen-modulating potential. Hydrophobic interactions and hydrogen bonding drove protein–ligand binding. These findings highlight NADESs as effective, sustainable solvents for extracting bioactive compounds with allergen-modulating potential. Full article

Endophytes are important sources of bioactive secondary metabolites with therapeutic and agricultural relevance. This study reports the isolation and characterization of bioactive compounds from endophytic Fusarium solani associated with Solanum surattense. The fungal strain, selected after preliminary screening for its antimicrobial potential, was identified through morphological and molecular methods. A pure compound, 4-hydroxyphenyl 8-chlorooctanoate with a molecular mass of 270, was obtained and structurally characterized using GC–MS, FTIR, and NMR spectroscopy. Its anti-microbial potential was evaluated through molecular docking against key bacterial (Staphylococcus aureus) and fungal (Aspergillus fumigatus) targets, showing notable binding affinities with ClpP protease (−7.1 kcal/mol) and 14α-demethylase (−7.4 kcal/mol), respectively. Molecular dynamics simulations further confirmed the stability of the 5FRB-compound complex, with lower RMSD and RMSF values indicating strong structural integrity. Supporting analyses (B-factor and radius of gyration) confirmed the compactness and rigidity of the complex. These findings highlight the potential of 4-hydroxyphenyl 8-chlorooctanoate as a promising antimicrobial agent and provide a strong basis for further in vitro and in vivo validation of the purified compound as an antimicrobial candidate. Full article

Background: The increasing number of resistant bacterial strains is reducing the effectiveness of antimicrobial drugs in preventing infections. It has been shown that resistant strains invade living organisms and cause a wide range of illnesses, leading to a surprisingly high death rate. Objective: The present study aimed to identify novel dihydropteroate synthase (DHPS) inhibitors from Stenotrophomonas maltophilia using structure-based computational techniques. Methodology: This in silico study used various bioinformatics and cheminformatics approaches to find new DHPS inhibitors. It began by retrieving the crystal structure via PDB ID: 7L6P, followed by energy minimization. The DHPS enzyme was virtually screened against the CHEMBL library to target S. maltophilia through enzyme inhibition. Then, absorption, distribution, metabolism, and excretion (ADME) analysis was performed to select the top hits. This process identified the top-10 hits. Additionally, imidazole (control) was used for comparative assessment. Furthermore, a 100 ns molecular dynamics simulation and post-simulation analyses were conducted. The docking results were validated through binding free energy calculations and entropy energy estimation approaches. Results: The docking results prioritized 10 compounds based on their binding scores, with a maximum threshold of −7 kcal/mol for selection. The ADME assessment shortlisted 3 out of 10 compounds: CHEMBL2322256, CHEMBL2316475, and CHEMBL2334441. These compounds satisfied Lipinski’s rule of five and were considered drug-like. The identified inhibitors demonstrated greater stability and less deviation compared to the control (imidazole). The average RMSD stayed below 2 Å, indicating overall stability without major deviations in the DHPS–ligand complexes. Post-simulation analysis assessed the stability and interaction profiles of the complexes under physiological conditions. Hydrogen bonding analysis showed the control to be more stable than the three tested complexes. Increased salt bridge interactions suggested stronger electrostatic stabilization, while less alteration of the protein’s secondary structure indicated better structural compatibility. These findings support the potential of these novel ligands as potent DHPS inhibitors. Binding energy estimates showed that CHEMBL2322256 was the most stable, with scores of −126.49 and −124.49 kcal/mol. Entropy calculations corroborated these results, indicating that CHEMBL2322256 had an estimated entropy of 8.63 kcal/mol. Conclusions: The newly identified compounds showed more promising results compared to the control. While these compounds have potential as innovative drugs, further research is needed to confirm their effectiveness as anti-DHPS agents against antibiotic resistance and S. maltophilia infections. Full article

Background: Anaplastic lymphoma kinase (ALK) is a validated oncogenic driver in non-small cell lung cancer and other malignancies, making it a clinically relevant target for small-molecule inhibition. Methods: Here, we report a computational discovery pipeline integrating structure-based virtual screening, machine learning-guided prioritization, molecular dynamics simulations, and binding free energy analysis to identify potential ALK inhibitors from a natural product-derived subset of the ZINC20 database. We trained and benchmarked eleven machine learning models, including tree-based, kernel-based, linear, and neural architectures, on curated bioactivity datasets of ALK inhibitors to capture nuanced structure-activity relationships and prioritize candidates beyond conventional docking metrics. Results: Six compounds were shortlisted based on binding affinity, solubility, bioavailability, and synthetic accessibility. Molecular dynamics simulations over 100 ns revealed stable ligand engagement, with limited conformational fluctuations and consistent retention of the protein’s structural integrity. Key catalytic residues, including GLU105, MET107, and ASP178, displayed minimal fluctuation, while hydrogen bonding and residue interaction analyses confirmed persistent engagement across all ligand-bound complexes. Binding free energy estimates identified ZINC3870414 and ZINC8214398 as top-performing candidates, with ΔG_total values of –46.02 and –46.18 kcal/mol, respectively. Principal component and dynamic network analyses indicated that these compounds restrict conformational sampling and reorganize residue communication pathways, consistent with functional inhibition. Conclusions: These results highlight ZINC3870414 and ZINC8214398 as promising scaffolds for further optimization and support the utility of integrating machine learning with dynamic and network-based metrics in early-stage kinase inhibitor discovery. Full article

Cancer is a multifaceted and life-threatening disease characterized by the unregulated proliferation of malignant cells. Developing new therapies and diagnostic methods for cancer remains a critical focus of research. Proteins involved in cancer progression are being targeted to facilitate the discovery of effective biological treatments. Among these, the ART1 protein plays a critical role in promoting cancer progression, establishing it as a key target for drug therapy. Actinomycetes, known for their anticancer activity, were explored in this study for their potential to inhibit ART1. One hundred bioactive secondary metabolites derived from actinomycetes were subjected to in silico screening to evaluate their potential anticancer activity through inhibition of ART1. The three-dimensional structure of ART1 was generated using the SWISS-MODEL tool and validated through the Save server 6.0 and ProSa web. The structural stability of the ART1 protein was evaluated through molecular dynamics analysis using the iMod server. The potential active sites within the ART1 structure were mapped using the Computed Atlas of Surface Topography of Proteins (CASTp). Molecular docking and protein–ligand interaction studies were performed using AutoDock Vina. Additionally, pharmacophore modeling was conducted using the Pharmit server to identify promising compounds. Toxicity predictions and in silico drug-likeness assessments were carried out using Swiss-ADME and ADMET Lab which evaluate Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties. Molecular dynamics simulations results for the ART1 protein demonstrated high stability over time. Additionally, resistomycin, borrelidin, tetracycline, and oxytetracycline were identified as the top-ranking ligands, exhibiting binding energies between −8.9 kcal/mol and −9.3 kcal/mol. These ligands exhibited favorable pharmacophore profiles, drug-likeness, and ADMET properties, indicating their potential safety and efficacy in humans. In conclusion, the selected actinomycete-derived ligands show promise for further research and development as potential anticancer agents targeting ART1. Full article

Objective: Visceral leishmaniasis (VL), a Neglected Tropical Disease caused by Leishmania donovani, remains insufficiently addressed by current therapies due to high toxicity, poor efficacy, and immunosuppressive complications. This study aimed to identify and characterize repurposed drugs that simultaneously target parasite-encoded and host-associated mechanisms essential for VL pathogenesis. Methods: Two complementary in silico drug repurposing strategies were employed. The first method utilized electron–ion interaction potential (EIIP) screening followed by molecular docking and molecular dynamics (MD) simulations targeting two L. donovani proteins: Rab5a and pteridine reductase 1 (PTR1). The second approach employed network-based drug repurposing using the Drugst.One platform, prioritizing candidates via STAT3-associated gene networks. Predicted drug–target complexes were validated by 100 ns MD simulations, and pharmacokinetic parameters were assessed via ADMET profiling using QikProp v7.0 and SwissADME web server. Results: Entecavir and valganciclovir showed strong binding to Rab5a and PTR1, respectively, with Glide Scores of −9.36 and −9.10 kcal/mol, and corresponding MM-GBSA ΔG_bind values of −14.00 and −13.25 kcal/mol, confirming their stable interactions and repurposing potential. Network-based analysis identified nifuroxazide as the top candidate targeting the host JAK2/TYK2–STAT3 axis, with high stability confirmed in MD simulations. Nifuroxazide also displayed the most favorable ADMET profile, including oral bioavailability, membrane permeability, and absence of PAINS alerts. Conclusions: This study highlights the potential of guanine analogs such as entecavir and valganciclovir, and the nitrofuran derivative nifuroxazide, as promising multi-target drug repurposing candidates for VL. Their mechanisms support a dual strategy targeting both parasite biology and host immunoregulation, warranting further preclinical investigation. Full article

Isoliquiritigenin (ISL) is a type of chalcone that widely exists in medicinal plants of the Leguminosae family and exhibits a remarkable anti-ischemic stroke (IS) effect. However, the anti-IS mechanisms of ISL remain to be systematically elucidated. In this study, network pharmacology was used to predict potential targets related to the anti-IS effect of ISL. The binding ability of ISL to potential core targets was further analyzed by molecular docking and molecular dynamics (MD) simulations. By establishing an oxygen–glucose deprivation/reoxygenation (OGD/R)-induced HT22 cell model, the anti-IS mechanisms of ISL were investigated via RT-qPCR and Western Blot (WB). As a result, network pharmacology analysis revealed that APP, ESR1, MAO-A, PTGS2, and EGFR may be potential core targets of ISL for anti-IS treatment. Molecular docking and molecular dynamics simulation results revealed that ISL can stably bind to the five potential core targets and form stable complex systems with them. The results of the cell experiments revealed a significant anti-IS effect of ISL. Additionally, mRNA and protein expression levels of APP, MAO-A and PTGS2 or ESR1 in the ISL treatment group were significantly lower or higher than those in the OGD/R group In conclusion, ISL may improve IS by regulating the protein expression levels of APP, ESR1, MAO-A, and PTGS2. Full article

This study was designed to systematically identify novel umami peptides in lager beer, clarify their molecular interactions with the T1R1/T1R3 receptor, and determine their specific effects on multidimensional sensory attributes. The peptides were characterized by LC-MS/MS combined with de novo sequencing, and 906 valid sequences were obtained. Machine-learning models (UMPred-FRL, Tastepeptides-Meta, and Umami-MRNN) predicted 76 potential umami peptides. These candidates were docked to T1R1/T1R3 with the CDOCKER protocol, producing 57 successful complexes. Six representative peptides—KSTEL, DELIK, DIGISSK, IEKYSGA, DEVR, and PVPL—were selected for 100 ns molecular-dynamics simulations and MM/GBSA binding-energy calculations. All six peptides stably occupied the narrow cleft at the T1R1/T1R3 interface. Their binding free energies ranked as DEVR (−44.09 ± 5.47 kcal mol⁻¹) < KSTEL (−43.21 ± 3.45) < IEKYSGA (−39.60 ± 4.37) ≈ PVPL (−39.53 ± 2.52) < DELIK (−36.14 ± 3.11) < DIGISSK (−26.45 ± 4.52). Corresponding taste thresholds were 0.121, 0.217, 0.326, 0.406, 0.589, and 0.696 mmol L⁻¹ (DEVR < KSTEL < IEKYSGA < DELIK < PVPL < DIGISSK). TDA-based sensory validation with single-factor additions showed that KSTEL, DELIK, DEVR, and PVPL increased umami scores by ≈21%, ≈22%, ≈17%, and ≈11%, respectively, while DIGISSK and IEKYSGA produced marginal changes (≤2%). The short-chain peptides thus bound with high affinity to T1R1/T1R3 and improved core taste and mouthfeel but tended to amplify certain off-flavors, and the long-chain peptides caused detrimental impacts. Future formulation optimization should balance flavor enhancement and off-flavor suppression, providing a theoretical basis for targeted brewing of umami-oriented lager beer. Full article

Saffron (Crocus sativus L.) contains bioactive compounds with potential health benefits, including modulation of protein function and gene expression. However, their ability to tune the epigenetic machine remains poorly understood. This study employs molecular docking (AutoDock Vina 1.4), dynamics simulations, and MM/PBSA calculations to investigate the interactions between four saffron-derived molecules—crocetin, beta-D-glucosyl trans-crocetin, picrocrocin and safranal—and four epigenetic enzymes—DNMT1, DNMT3a, HDAC2, and SIRT1. Our in silico screening identifies beta-D-glucosyl trans-crocetin, one of the saffron’s crocins, as a potential DNMT1 inhibitor. Along with crocetin, it also shows the ability to inhibit HDAC2 and activate SIRT1. Picrocrocin displays a resveratrol-like ability to activate SIRT1. None of the saffron-derived compounds effectively bind or inhibit DNMT3a. Among the tested molecules, safranal shows no interaction with the selected epigenetic targets. These findings highlight saffron’s nutriepigenomic potential and emphasize the need for functional validation within relevant in vitro and in vivo experimental methodologies. Full article

This study explores how aristolochic acid I (AAI) drives hepatocellular carcinoma (HCC). We first employ network toxicology and machine learning to map the key molecular target genes. Next, our research utilizes molecular docking to evaluate how AAI binds to these targets, and finally confirms the stability and dynamics of the resulting complexes through molecular dynamics simulations. We identified 193 overlapping target genes between AAI and HCC through databases such as PubChem, OMIM, and ChEMBL. Machine learning algorithms (SVM-RFE, random forest, and LASSO regression) were employed to screen 11 core genes. LASSO serves as a rapid dimension-reduction tool, SVM-RFE recursively eliminates the features with the smallest weights, and Random Forest achieves ensemble learning through decision trees. Protein–protein interaction networks were constructed using Cytoscape 3.9.1, and key genes were validated through GO and KEGG enrichment analyses, an immune infiltration analysis, a drug sensitivity analysis, and a survival analysis. Molecular-docking experiments showed that AAI binds to each of the core targets with a binding affinity stronger than −5 kcal mol⁻¹, and subsequent molecular dynamics simulations verified that these complexes remain stable over time. This study determined the potential molecular mechanisms underlying AAI-induced HCC and identified key genes (CYP1A2, ESR1, and AURKA) as potential therapeutic targets, providing valuable insights for developing targeted strategies to mitigate the health risks associated with AAI exposure. Full article

Ulcerative colitis (UC) is a chronic inflammatory bowel disease associated with disrupted lipid metabolism. This study aimed to uncover novel molecular subtypes and biomarkers by integrating sphingolipid metabolism-related genes (SMGs) with machine learning approaches. Using data from the GEO and GeneCards databases, 29 UC-related SMGs were identified. Consensus clustering was employed to define distinct molecular subtypes of UC, and a diagnostic model was developed through various machine learning algorithms. Further analyses—including functional enrichment, transcription factor prediction, single-cell localization, potential drug screening, molecular docking, and molecular dynamics simulations—were conducted to investigate the underlying mechanisms and therapeutic prospects of the identified genes in UC. The analysis revealed two molecular subtypes of UC: C1 (metabolically dysregulated) and C2 (immune-enriched). A diagnostic model based on three key genes demonstrated high accuracy in both the training and validation cohorts. Moreover, the transcription factor FOXA2 was predicted to regulate the expression of all three genes simultaneously. Notably, mebendazole and NVP-TAE226 emerged as promising therapeutic agents for UC. In conclusion, SMGs are integral to UC molecular subtyping and immune microenvironment modulation, presenting a novel framework for precision diagnosis and targeted treatment of UC. Full article

Background: Antibacterial resistance (ABR) poses a major challenge to global health, with methicillin-resistant Staphylococcus aureus (MRSA) being one of the prominent multidrug-resistant strains. MRSA has developed resistance through the expression of Penicillin-Binding Protein 2a (PBP2a), a key transpeptidase enzyme involved in bacterial cell wall biosynthesis. Objectives: The objective was to design and characterize a novel small-molecule inhibitor targeting PBP2a as a strategy to combat MRSA. Methods: We synthesized a new indole triazole conjugate (ITC) using eco-friendly and click chemistry approaches. In vitro antibacterial tests were performed against a panel of strains to evaluate the ITC antibacterial potential. Further, a series of in silico evaluations like molecular docking, MD simulations, free energy landscape (FEL), and principal component analysis (PCA) using the crystal structure of PBP2a (PDB ID: 4CJN), in order to predict the mechanism of action, binding mode, structural stability, and energetic profile of the 4CJN-ITC complex. Results: The compound ITC exhibited noteworthy antibacterial activity, which effectively inhibited the selected strains. Binding score and energy calculations demonstrated high affinity of ITC for the allosteric site of PBP2a and significant interactions responsible for complex stability during MD simulations. Further, FEL and PCA provided insights into the conformational behavior of ITC. These results gave the structural clues for the inhibitory action of ITC on the PBP2a. Conclusions: The integrated in vitro and in silico studies corroborate the potential of ITC as a promising developmental lead targeting PBP2a in MRSA. This study demonstrates the potential usage of rational drug design approaches in addressing therapeutic needs related to ABR. Full article