Search Results (132)

Kallikrein-related peptidases (KLKs) are hallmarks of higher vertebrates, in particular of mammals. While the 15 human KLKs occur in nearly all tissues and body fluids and participate in many physiological processes, they are also involved in severe diseases. Among them are prostate, ovarian and breast cancer, as well as inherited skin and neurological disorders. Thus, KLKs have become targets for inhibitory compounds in academic and commercial research. The most prominent clinical biomarker and anti-cancer target for various approaches is PSA/KLK3. Already in the distant past, natural crude extracts were the source of medicine, while purified natural compounds and their derivatives are still the basis of about 50% of all pharmaceuticals. Nevertheless, structure-based rational design and high-throughput screening of natural and synthetic compound libraries are highly effective approaches for discovering lead compounds in the development of new drugs. Recently, computer-aided virtual or in silico screening has become a rapid method for such discoveries when combined with in vitro assays using protein targets or tests in cell cultures. To date, the successful implementation of artificial intelligence (AI) in the biosciences has significantly contributed to drug discovery. Our review focuses on state-of-the-art strategies and techniques in the context of KLK targets. Full article

The emergence of multidrug-resistant Staphylococcus aureus underscores the urgent need for novel therapeutic agents targeting essential bacterial pathways. The lipoteichoic acid synthase (LtaS) is crucial for the synthesis of lipoteichoic acid in the cell wall of Gram-positive bacteria and represents a promising and vulnerable target for antimicrobial drug development. This study employed a comprehensive computational pipeline to identify potent inhibitors of the LtaS enzyme. A library of natural compounds was retrieved from the COCONUT database and screened against the crystal structure of the extracellular domain of LtaS (eLtaS) (PDB ID: 2W5R, obtained from the Protein Data Bank) through a multi-stage molecular docking strategy. This process started with High-Throughput Virtual Screening (HTVS), followed by Standard Precision (SP) docking, and culminated in Extra Precision (XP) docking to refine the selection of hits. The top-ranking compounds from XP docking were subsequently subjected to MM-GBSA binding free energy calculations for further filtration. The stability and dynamic behavior of the resulting candidate complexes were then evaluated using 100 ns molecular dynamics (MD) simulations, which confirmed the structural integrity and binding stability of the ligands. Density Functional Theory calculations revealed that screened ligands exhibit improved electronic stabilization and charge-transfer characteristics compared to a reference compound, suggesting enhanced reactivity and stability relevant for hit identification. Finally, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiling was conducted to assess the drug-likeness and pharmacokinetic safety of the lead compounds. These findings support them as promising orally active leads for further optimization. Our integrated approach shortlisted eight initial hits (A–H) that showed interesting scaffold diversity and finally identified two compounds, herein referred to as Compound A and Compound B, which demonstrated stable binding, favorable free energy, and an acceptable Absorption, Distribution, Metabolism, and Excretion, and Toxicity (ADMET) profile. These candidates emerge as promising starting points for developing novel anti-staphylococcal agents targeting the LtaS enzyme that cand be further proved by experimental validation. Full article

7-hydroxycoumarin-4-acetic acid has been synthesized via Pechman condensation. In silico evaluation 7-hydroxycoumarin-4-acetic acid as a ligand of different structures of CYPs from PDB database was completed. The results of docking reveal possibilities to use this compound as a cytochromes P450 substrate. The best energy binding amongst CYPs database was found for human CYP1A1 structure. Our results provide certain opportunities to use coumarin derivatives as inhibitors or activators of CYPs. Full article

Computer-aided docking combined with AlphaFold-based protein modeling revealed strong binding affinities of the ligand DOLA ((Z)-5-(dimethylamino)-N-(octadec-9-en-1-yl)naphthalene-1-sulfonamide) to metabolic proteins in the insect species Tenebrio molitor, Tribolium castaneum, Locusta migratoria, Lucilia cuprina, and Drosophila melanogaster. Significant interactions were observed with cytochrome P450 families CYP6 and CYP4 involved in detoxification, hormone regulation, and fatty acid metabolism, with binding energies ranging from −10.4 to −8.5 kcal/mol. DOLA also showed affinity for enzymes related to fatty acid metabolism and transport, including long-chain fatty acid-CoA ligase, fatty acyl-CoA reductases, CYP18a1, lipocalins, and fatty acid desaturases. These in silico findings highlight DOLA as a promising ligand for studying insect metabolic pathways and as a potential agent for population control, warranting further experimental validation. Full article

The γδ T cells belong to a subgroup of T cells known as non-conventional T cells due to their limited T cell receptor (TCR) repertoire and ability to recognize non-peptide antigens. They play a crucial role in combating infections and tumors. Vγ9Vδ2 T cells are typically activated by molecules containing diphosphate groups, collectively known as phosphoantigens (pAgs), through a non-canonical mechanism which involves the intracellular domain of butyrofilin (BTN)3A1 protein. However, no FDA-approved drugs have yet been shown to activate them, and the underlying cellular mechanisms remain unknown. In this study, we combined high-throughput virtual screening of an FDA-approved drug database with in vitro cellular assays to identify potential γδ T cells activators. Our findings demonstrate that Nitazoxanide (NTZ) and Tinidazole induce moderate elicited a statistically significant increase in interferon (IFN)-γ production of Vγ9Vδ2 T cells by their probably interaction with the pAg binding site of BTN3A1. Additionally, NTZ induces expression of CD107a, but only at the highest concentrations tested and promotes the upregulation of HLA-DR in total PBMCs and CD14⁺ monocytes. Blocking BTN3A with a specific antibody led to a marked reduction in all NTZ-induced activations. This work identifies NTZ as a previously unrecognized activator of γδ T cells, highlighting its immunomodulatory potential beyond its known clinical uses. These findings broaden our understanding of γδ T cells pharmacology and suggest new opportunities for drug repurposing and the design of novel chemical scaffolds. Further mechanistic studies will be essential to fully define how NTZ engages the BTN3A–γδ T cells axis. Full article

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

Drug discovery is an intricate and resource-intensive process in which computational approaches, such as molecular docking, are essential, particularly in the early stages, to identify potential hits. However, docking still has many drawbacks, including problems in managing protein flexibility and the reliability of scoring functions. In this paper, we systematically compared the performance of AutoDock Vina, one of the most widely used open-source docking tools, with GNINA. This advanced evolution integrates convolutional neural networks (CNNs) for pose scoring. The comparison was conducted on ten heterogeneous protein targets, including metalloenzymes, kinases, and G-protein-coupled receptors (GPCRs). With the ability to accurately replicate binding poses and their energy values, GNINA showed outstanding performance in both virtual screening (VS) of active ligands and re-docking steps of co-crystallized ligands. GNINA’s enhanced ability to accurately distinguish between true positives and false positives—a specificity not found with AutoDock Vina—is confirmed by ROC curves and Enrichment Factor (EF) results. Therefore, we propose an integrated GNINA-based workflow that can significantly enhance the quality and reliability of docking results, providing a valuable tool for optimizing the initial stages of drug discovery. Full article

The convergence of artificial intelligence (AI) and nanomedicine has transformed cancer vaccine development, particularly in optimizing RNA-loaded lipid nanoparticles (LNPs). Stability and targeted delivery are major obstacles to the clinical translation of promising RNA-LNP vaccines for cancer immunotherapy. This systematic review analyzes the AI’s impact on LNP engineering through machine learning-driven predictive models, generative adversarial networks (GANs) for novel lipid design, and neural network-enhanced biodistribution prediction. AI reduces the therapeutic development timeline through accelerated virtual screening of millions of lipid combinations, compared to conventional high-throughput screening. Furthermore, AI-optimized LNPs demonstrate improved tumor targeting. GAN-generated lipids show structural novelty while maintaining higher encapsulation efficiency; graph neural networks predict RNA-LNP binding affinity with high accuracy vs. experimental data; digital twins reduce lyophilization optimization from years to months; and federated learning models enable multi-institutional data sharing. We propose a framework to address key technical challenges: training data quality (min. 15,000 lipid structures), model interpretability (SHAP > 0.65), and regulatory compliance (21CFR Part 11). AI integration reduces manufacturing costs and makes personalized cancer vaccine affordable. Future directions need to prioritize quantum machine learning for stability prediction and edge computing for real-time formulation modifications. Full article

Phase-sensitive surface plasmon resonance (SPR) detection is widely employed in molecular dynamics studies and SPR imaging owing to its real-time capability, high sensitivity, and compatibility with imaging systems. A key research objective is to achieve higher measurement resolution of refractive index under optimal dynamic range conditions. We present an enhanced SPR phase imaging system combining a quad-polarization filter array for phase differential detection with a novel polarization pair, block matching, and 4D filtering (PPBM4D) algorithm to extend the dynamic range and enhance resolution. By extending the BM3D framework, PPBM4D leverages inter-polarization correlations to generate virtual measurements for each channel in the quad-polarization filter, enabling more effective noise suppression through collaborative filtering. The algorithm demonstrates 57% instrumental noise reduction and achieves 1.51 × 10⁻⁶ RIU resolution (1.333–1.393 RIU range). The system’s algorithm performance is validated through stepwise NaCl solution switching experiments (0.0025–0.08%) and protein interaction assays (0.15625–20 μg/mL). This advancement establishes a robust framework for high-resolution SPR applications across a broad dynamic range, particularly benefiting live-cell imaging and high-throughput screening. Full article

Lactate dehydrogenase (LDH) catalyzes the reversible interconversion of pyruvate and lactate, coupled with the redox cycling of NADH and NAD⁺. While LDHA has been extensively studied as a therapeutic target, particularly in cancer, due to its role in the Warburg effect, LDHB remains underexplored, despite its involvement in the metabolic reprogramming of specific cancer types, including breast and lung cancers. Most known LDH inhibitors are designed against the LDHA isoform and act competitively at the active site. In contrast, LDHB exhibits distinct kinetic properties, substrate preferences, and structural features, warranting isoform-specific screening strategies. In this study, 115 natural compounds previously reported as LDHA inhibitors were systematically evaluated for LDHB inhibition using an integrated in silico and in vitro approach. Virtual screening identified 16 lead phytochemicals, among which luteolin and quercetin exhibited uncompetitive inhibition of LDHB, as demonstrated by enzyme kinetic assays. These findings were strongly supported by molecular docking analyses, which revealed that both compounds bind at an allosteric site located at the dimer interface, closely resembling the binding mode of the established LDHB uncompetitive inhibitor AXKO-0046. In contrast, comparative docking against LDHA confirmed their active-site binding and competitive inhibition, underscoring their isoform-specific behavior. Our findings highlight the necessity of assay conditions tailored to LDHB’s physiological role and demonstrate the application of a previously validated colorimetric assay for high-throughput screening. This work lays the foundation for the rational design of selective LDHB inhibitors from natural product libraries. Full article

The Chikungunya virus (CHIKV) continues to pose a significant global health challenge due to the absence of effective antiviral treatments and limited vaccine availability. This study employed a comprehensive in silico workflow, incorporating high-throughput virtual screening, binding free-energy calculations, ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis, and 200 ns molecular dynamics (MD) simulations, to identify new inhibitors targeting the E1–E2 glycoprotein complex, crucial for CHIKV entry and membrane fusion. Four promising candidates were identified from a library of 20,000 compounds, with CID 136801451 showing the most potent binding (docking score: −10.227; ΔG_bind: −51.53 kcal/mol). The top four compounds exhibited favorable ADMET profiles, meeting nearly all criteria. MD simulations confirmed stable binding and strong interactions between CID 136801451 and the E1–E2 complex, evidenced by consistently low RMSD values. These findings highlight CID 136801451 as a promising CHIKV entry inhibitor, warranting further in vitro and in vivo evaluation to advance the development of effective anti-CHIKV therapeutics. Full article

Background: Tankyrase (TNKS1) regulates the WNT/β-catenin pathway, while CDK8 is a transcriptional regulator overexpressed in renal cell carcinoma (RCC). This study aims to identify novel dual inhibitors of tankyrase and Cyclin-dependent kinase 8 (CDK8), utilizing bioinformatics and in vitro methods and to assess their efficiency in renal cancer cells. Methods: To identify leads, the ChemBridge library was screening using high-throughput virtual screening (HTVS), which was followed by protein–ligand interaction analysis, Molecular Dynamics (MD) simulation, and Gibbs binding free energy estimation. A-498, Caki-1, and HK-2 cells were employed to validate in vitro efficacy. Results: TCS9725 was discovered by HTVS with binding affinities of −8.1 kcal/mol and −8.2 kcal/mol for TNKS1 and CDK8, respectively. TCS9725 had robust binding interactions with root mean square deviation values of 0.00 nm. The ΔG binding estimate was −27.45 for TNKS1 and −27.88 for CDK8, respectively. ADME predictions favored specific small-molecule inhibition profiles. TCS9725 reduced TNKS1 and CDK8 activities with IC50s of 243 nM and 403.6 nM, respectively. The compound efficiently inhibited the growth of A-498 and Caki-1 cells with GI50 values of 385.9 nM and 243.6 nM, respectively, with high selectivity compared to the non-cancerous kidney cells. TCS9725 decreased STAT1 and β-catenin positivity in A-498 and Caki-1 cells. The compound induced apoptosis and reduced TGFβ-stimulated trans-endothelial migration and p-smad2/3 signaling in both RCC cells. Conclusions: This work provides valuable insights into the therapeutic potential of TCS9725, a dual inhibitor of TNKS1 and CDK8. Further developments of this molecule could lead to new and effective treatments for this devastating disease. Full article

Background: Ischemic stroke (IS) is a severe condition with limited therapeutic options. Pyroptosis, a type of programmed cell death linked to inflammation, is closely associated with IS-related damage. Studies suggest inflammation aligns with the traditional Chinese medicine (TCM) concept of “fire-heat syndrome”. Huanglian Jiedu Decoction (HLJD), a TCM formula known for clearing heat and purging fire, has shown therapeutic effects on IS, potentially by regulating pyroptosis. Study design: Eight-week-old male mice were divided into six groups: sham operation, model, positive drug, and low-, medium-, and high-dose HLJD groups. After a week of adaptive feeding, mice received respective treatments for five days, followed by modeling on the sixth day, with samples collected 23 h post-perfusion. Analyses included multi-omics, physiology, histopathology, virtual drug screening, target affinity assessment, and molecular biology techniques to measure relevant indicators. Results: HLJD effectively mitigated IS-related damage, maintaining neurological function, reducing ischemic levels, protecting cellular morphology, inhibiting neuronal apoptosis, and preserving blood–brain barrier integrity. Bioinformatics of high-throughput omics data revealed significant activation of pyroptosis and related inflammatory pathways in IS. ScRNA-seq identified neutrophils, macrophages, and microglia as key pyroptotic cell types, suggesting potential therapeutic targets. Network pharmacology and molecular docking identified NLRP3 as a critical target, with 6819 ligand–receptor docking results. SPR molecular fishing, LC-MS, molecular dynamics, and affinity measurements identified small molecules with high affinity for NLRP3. Molecular biology techniques confirmed that HLJD regulates pyroptosis via the classical inflammasome signaling pathway and modulates the inflammatory microenvironment. Conclusions: Following IS, pyroptosis in myeloid cells triggers an inflammatory cascade, leading to neural damage. HLJD may inhibit NLRP3 activity, reducing pyroptosis and associated inflammation, and ultimately mitigating damage. Full article

The prostaglandin E2 receptor EP3 is emerging as a promising therapeutic target in cardiovascular diseases because of its involvement in vascular inflammation, platelet aggregation, and vasoconstriction. However, selective EP3 ligands with validated biological activities are scarce. In this study, we combined computational and experimental strategies to identify and validate novel EP3 receptor ligands with therapeutic potential. We implemented a high-throughput, structure- and ligand-based virtual screening pipeline, enabling efficient exploration of approved drugs and natural compounds from DrugBank and FooDB libraries. Top-scoring candidates were prioritised based on binding energy and pharmacophoric similarity. Selected hits were subjected to in silico ADME/Tox profiling using QikProp to identify molecules with favourable pharmacokinetic and safety parameters. TUCA, masoprocol, and pravastatin sodium have emerged as lead candidates and were validated in vitro using endothelial migration and platelet aggregation assays. TUCA exhibited the most consistent inhibitory effect on endothelial migration, whereas masoprocol and hydrocortisone significantly reduced platelet aggregation. These findings establish a multidimensional workflow for the rational identification of EP3 ligands and support their potential use in cardiovascular therapeutics. Full article

Background/Objectives: The increasing threat of antibiotic resistance and the declining efficiency of traditional drug discovery pipelines highlight the urgent need for novel drug targets and effective enzyme inhibitors against infectious diseases. Oligopeptidase B (OPB), a serine protease with trypsin-like specificity that processes low-molecular-weight peptides and oligopeptides, is present in bacteria and certain parasites but absent in mammals. This unique distribution makes OPB an attractive and selective target for antimicrobial drug development. Methods: Three-dimensional models of OPB from Serratia marcescens and Stenotrophomonas maltophilia, previously identified by our research group, were constructed via homology modeling using the best available OPB template from the RCSB Protein Data Bank. The S. marcescens OPB model was subjected to high-throughput virtual screening (HTVS) against the Natural Products Atlas (npatlas) database. Top-ranking compounds were further evaluated using Glide standard precision (SP) and extra precision (XP) docking protocols. Binding affinities were refined using molecular mechanics with generalized born and surface area (MM–GBSA) calculations. Molecular dynamics (MD) simulations assessed binding stability, while absorption distribution metabolism excretion and toxicity (ADMET) profiling evaluated drug-likeness and pharmacokinetic properties. Results: Ten natural product compounds demonstrated stronger binding affinities than antipain, a well-known oligopeptide-based protease inhibitor, as indicated by their more favorable MM–GBSA scores of −60.90 kcal/mol (S. marcescens) and −27.07 kcal/mol (S. maltophilia). Among these, dichrysobactin and validamycin E consistently exhibited favorable binding profiles across both OPB models. MD simulations confirmed the stability of their interactions with OPB active sites, maintaining favorable binding conformations throughout the simulation period. ADMET analysis suggested that while both compounds show promise, lead optimization is required to enhance their drug-like characteristics. Conclusions: This study identifies dichrysobactin and validamycin E as promising OPB inhibitors with potential antimicrobial activity. These findings support their further development as selective and potent agents against bacterial pathogens, including resistant strains, and underscore the need for experimental validation to confirm their efficacy and safety. Full article