Search Results (13)

Glycosylphosphatidylinositol (GPI) anchored wall transfer protein 1 (GWT1), a fungal-specific inositol acyltransferase, catalyzes the palmitoylation of GlcN-PI in GPI-anchor biosynthesis, crucial for mannoprotein trafficking and attachment, which are vital for cell wall integrity, biofilm formation, and virulence. More than 60,000 AI-generated molecules produced using REINVENT4 were screened using ADMET-AI and GNINA. DeepSA and PharmacoNet were used to select synthesizable and pharmacophorically rich molecules. The dynamic behaviour was explored using molecular dynamics (MD). Finally, molecular reactivity was assessed using density functional theory (DFT). After ADMET filtering, 6190 compounds were docked against GWT1, of which 315 showed better predicted binding energies than the co-crystal ligand. DeepSA identified 105 readily synthesizable candidates, and PharmacoNet retained 32 compounds with favourable pharmacophoric features, from which four final candidates (AF_M1, AF_M2, AF_M3, and AF_M4) were prioritized for further analysis. MD simulation suggested stable binding behavior towards GWT1. DFT analysis indicated favourable electronic properties, low HOMO-LUMO energy gaps, and stable optimized geometries. These molecules could serve as promising lead candidates and potential new therapeutic agents for invasive fungal infections, pending validation. Full article

δ-opioid receptor (DOR) is a promising therapeutic target for developing safer treatments for pain and neuroprotection. In this study, we applied a structure-guided drug-repurposing workflow to identify FDA-approved drugs with predicted DOR-binding and agonist-like structural features. Using a validated GNINA-based docking protocol with an active-state DOR model (PDB ID: 6PT3), we screened 2342 approved compounds and identified 39 candidates with predicted submicromolar binding affinities. These hits were further evaluated through molecular dynamics simulations, binding pocket volume analysis, and principal component analysis, which enabled the prioritization of two leading candidates, aprepitant and mavorixafor. Both compounds formed stable receptor-ligand complexes, maintained persistent interactions with Asp128, promoted contraction of the orthosteric pocket, and retained favorable redocking scores on the MD-refined receptor conformations. Overall, these results identify aprepitant and mavorixafor as promising putative DOR agonists and provide a rational foundation for their experimental validation through binding, functional, and in vivo pain studies in the future. Full article

Chrome tanning of fish skins generates hazardous effluents and carcinogenic Cr(VI) residues; chromium-free routes to valorize collagen-rich by-products from aquaculture and coastal fisheries are therefore needed. We report a 12-stage ecological protocol employing acetic acid/NaCl pickling, Acacia mearnsii tannin, A. podalyriifolia retanning, mashed-papaya enzymatic bating, and cinnamon as antimicrobial/odor adjunct, scaled from bench to pilot using exclusively locally sourced inputs, for Nile tilapia (Oreochromis niloticus) and Patagonian flounder (Paralichthys patagonicus). Three trained operators evaluated macroscopic quality against five predefined criteria adapted from SATRA and ISO 3376 grading conventions, providing a structured feasibility baseline that does not substitute for the standardized instrumental testing designated as priority future work. Both species achieved satisfactory grain stability, complete tannin penetration, pliable handle, and cinnamon-dominant odor without residual amines; dark-brown coloration is a recognized practical limitation for fashion applications. In silico molecular docking (GNINA v1.0) was used to explore the mechanistic plausibility of each ecological substitution, generating testable hypotheses rather than definitive mechanistic conclusions: the multidentate polyphenol proxy (PGG) exhibited consistently superior collagen engagement over the flavanol monomer across both collagen constructs and all three scoring metrics (1CAG: Vina affinity −5.51 ± 0.13 vs. −3.54 ± 0.35 kcal/mol; CNNscore 0.874 ± 0.009 vs. 0.771 ± 0.010; 7CWK: Vina affinity −6.98 ± 1.43 vs. −4.37 ± 0.16 kcal/mol; CNNscore 0.858 ± 0.024 vs. 0.635 ± 0.094). Dipeptide probes were reproducibly accommodated in the papain catalytic cleft, with the closest configuration reaching 3.997 Å from the catalytic nucleophile (OCS25-SG). Trans-cinnamaldehyde occupied the quorum-sensing pocket with reproducible placement (CNNscore 0.718 ± 0.034) but without score-based selectivity over structural decoys, a result interpreted as hypothesis-generating for future microbiological validation. The protocol is reproducible from bench to pilot and generalizable across two species with distinct dermal architectures. Quantitative physical-mechanical testing (shrinkage temperature, tensile strength, elongation, tear load), CIELab colorimetric analysis, and effluent characterization (COD, BOD₅, total phenolics) are designated as priorities for future validation. Full article

Background/Objectives: γ-Aminobutyric acid aminotransferase (GABA-AT) is a key enzyme responsible for GABA catabolism and represents a validated therapeutic target for epilepsy. Although existing GABA-AT inhibitors such as vigabatrin are clinically effective, their long-term use is limited by safety concerns, highlighting the need for alternative inhibitors with improved profiles. In this study, we employed an integrated natural product-oriented discovery strategy to identify novel GABA-AT inhibitors from plant-derived compounds. Methods: A library of 1006 plant-derived compounds collected from seven medicinal plants traditionally associated with sedative or anxiolytic effects was subjected to primary virtual screening using GNINA. Top-ranked candidates were further refined through secondary precision docking using aglycone forms to account for biologically relevant metabolic conversion. Detailed interaction analyses and molecular dynamics simulations were performed to assess binding stability and energetic favorability. Results: Based on computational prioritization, quercetin, salvianolic acid A, and scutellarein were selected for experimental validation. Cell-based GABA-AT activity assays in HepG2 cells demonstrated that quercetin and salvianolic acid A significantly inhibited intracellular GABA-AT activity, exhibiting comparable or greater efficacy than vigabatrin, while scutellarein showed moderate inhibition. The observed cellular inhibitory effects were consistent with predicted binding modes and dynamic stability observed in in silico analyses. Conclusions: Collectively, this study highlights the utility of an aglycone-focused, structure-based screening strategy for natural product drug discovery and identifies plant-derived aglycones as promising GABA-AT inhibitor candidates for further pharmacological development. Full article

The development of KRAS G12D inhibitors represents an effective therapeutic strategy for treating oncological pathologies. Existing quantitative structure-activity relationship (QSAR) models for KRAS G12D inhibitors have several limitations, primarily the lack of applicability domain determination and virtual screening implementation. In this study, we propose a set of regression QSAR models for KRAS G12D inhibitors by employing various molecular descriptors and machine learning methods. Our consensus model achieved a Q² test value of 0.70 on an external test set, covering 78% of the data within the applicability domain. We integrated this consensus model into our Python-based framework KRASAVA. The platform predicts inhibitory activity while considering the applicability domain, assesses compounds for compliance with Muegge’s bioavailability rules, and identifies PAINS, toxicophores, and Brenk filters. Furthermore, we structurally interpreted the QSAR models to propose several promising inhibitors and performed molecular docking on these candidates using GNINA. For the reference inhibitor MRTX1133, we reproduced the crystal structure pose with an RMSD of 0.76 Å (PDB ID: 7T47). The key interactions with amino acid residues Asp12, Asp69, His95, Arg68, and Gly60, identified for both MRTX1133 and our proposed compounds, demonstrate a strong consistency between the molecular docking and QSAR results. Full article

The search for novel low-molecular regulators using molecular docking continues to be crucial for addressing challenges in modern biomedical science. However, the current literature lacks examples of modeling covalent interactions between the ligands being docked and those already present within the proteins, such as enzyme cofactors. This study aims to improve the existing algorithms for modeling such interactions, exemplified by those in thiamine diphosphate (ThDP)-dependent enzymes. Structures containing adducts of ThDP with enzyme substrates or inhibitors are used as protein templates; the putative ligand models are prepared as (R)- or (S)-hydroxy derivatives. The Gnina framework with AD4 or Vinardo favors ligand conformations resembling those found in the protein templates and consistent with their relative inhibitory potentials in experiments in vitro. For example, local hydrophobic regions within pyruvate and branched-chain 2-oxo acid dehydrogenase structures favor the binding of esterified substrate analogs compared to their de-esterified counterparts. The preferred binding of esterified vs. de-esterified ligands is absent or even reversed for 2-oxoglutarate dehydrogenase. As a result, covalent docking of 2-oxo acid analogs to enzyme structures containing ThDP coenzyme offers a predictive capability for protein–ligand complex formation and should be used when inhibitors mimic transition states in enzymatic reactions, as observed with ThDP-dependent catalysis. Full article

Odorant receptors (ORs) in Drosophila melanogaster represent important proteins of the insect’s olfactory system, enabling the detection of environmental cues such as food sources, host plants, and mating signals. Their modulation by natural ligands offers a sustainable strategy for pest management, particularly through the use of bioactive compounds obtained from agricultural crop and food production residues (ACFPR). In this study, as a model we employed the AlphaFold-predicted structure of the odorant receptor Q9W1P8 for structure-based virtual screening. Molecular docking was carried out using GNINA, a deep learning-enhanced docking tool. Screening of 164 ACFPR-derived compounds from different sources revealed several strong binders, including α-tomatine, peonidin 3-rutinoside, and cinnamtannin B1. Predicted binding modes support the role of plant-derived molecules as candidate modulators of insect olfactory receptors. These findings highlight the utility of integrating AlphaFold models with advanced docking platforms to support the development of sustainable pest management strategies. Full article

Vorinostat, an FDA-approved histone deacetylase inhibitor, was evaluated for its potential anti-inflammatory activity through modulation of TACE (ADAM17)-mediated TNF-α signaling. The study was conducted using LPS-stimulated RAW264.7 macrophages. TACE enzymatic activity was assessed by a fluorogenic assay, TNF-α release was measured by ELISA, and phosphorylation of MAPKs and NFκB signaling proteins was examined by a western blot. Molecular docking was performed using GNINA to evaluate binding affinity to ERK. Vorinostat was found to modestly inhibit TACE enzymatic activity in vitro, while significantly suppressing TNF-α secretion in cells, comparable to the selective TACE inhibitor BMS-561392. A concentration-dependent reduction in phosphorylated IκB and NFκB was observed, along with selective inhibition of ERK phosphorylation. Docking studies indicated a stable, albeit weaker, binding of vorinostat to ERK compared to reference ERK inhibitors. These findings suggest that vorinostat suppresses TNF-α production primarily through indirect mechanisms involving ERK and NF-κB signaling pathways, rather than by direct TACE inhibition. The repositioning of vorinostat as a modulator of inflammatory signaling is supported, offering potential therapeutic value in inflammatory disorders. 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

P21-activated kinase 4 (PAK4) plays a crucial role in the proliferation and metastasis of various cancers. However, developing selective PAK4 inhibitors remains challenging due to the high homology within the PAK family. Therefore, developing highly selective PAK4 inhibitors is critical to overcoming the limitations of existing inhibitors. We analyzed the structural differences in the binding pockets of PAK1 and PAK4 by combining cross-docking and molecular dynamics simulations to identify key binding regions and unique structural features of PAK4. We then performed screening using shape and protein conformation ensembles, followed by a re-evaluation of the docking results with deep-learning-driven GNINA to identify the candidate molecule, STOCK7S-56165. Based on this, we applied a fragment-replacement strategy under electrostatic-surface-matching conditions to obtain Compd 26. This optimization significantly improved electrostatic interactions and reduced binding energy, highlighting its potential for selectivity. Our findings provide a novel approach for developing selective PAK4 inhibitors and lay the theoretical foundation for future anticancer drug design. Full article

The primary cause of atherosclerotic cardiovascular disease (ASCVD) is elevated levels of low-density lipoprotein cholesterol (LDL-C). Proprotein convertase subtilisin/kexin type 9 (PCSK9) plays a crucial role in this process by binding to the LDL receptor (LDL-R) domain, leading to reduced influx of LDL-C and decreased LDL-R cell surface presentation on hepatocytes, resulting higher circulating levels of LDL-C. As a consequence, PCSK9 has been identified as a crucial target for drug development against dyslipidemia and hypercholesterolemia, aiming to lower plasma LDL-C levels. This research endeavors to identify promising inhibitory candidates that target the allosteric site of PCSK9 through an in silico approach. To start with, the FDA-approved Drug Library from Selleckchem was selected and virtually screened by docking studies using Glide extra-precision (XP) docking mode and Smina software (Version 1.1.2). Subsequently, rescoring of 100 drug compounds showing good average docking scores were performed using Gnina software (Version 1.0) to generate CNN Score and CNN binding affinity. Among the drug compounds, amikacin, bestatin, and natamycin were found to exhibit higher docking scores and CNN affinities against the PCSK9 enzyme. Molecular dynamics simulations further confirmed that these drug molecules established the stable protein–ligand complexes when compared to the apo structure of PCSK9 and the complex with the co-crystallized ligand structure. Moreover, the MM-GBSA calculations revealed binding free energy values ranging from −84.22 to −76.39 kcal/mol, which were found comparable to those obtained for the co-crystallized ligand structure. In conclusion, these identified drug molecules have the potential to serve as inhibitors PCSK9 enzyme and these finding could pave the way for the development of new PCSK9 inhibitory drugs in future in vitro research. Full article

G-quadruplexes (G4s) have been identified as a potential alternative chemotherapy target. A series of eight β-amino acid derived naphthalenediimides (NDI) were screened against a series of oncogenic G4 sequences: c-KIT1, h-TELO, and TBA. Three sets of enantiomers were investigated to further our understanding of the effect of point chirality on G4 stabilisation. Enantioselective binding behaviour was observed with both c-KIT1 and h-TELO. Docking studies using GNINA and UV-vis titrations were employed to better understand this selective binding behaviour. Full article

Virtual screening—predicting which compounds within a specified compound library bind to a target molecule, typically a protein—is a fundamental task in the field of drug discovery. Doing virtual screening well provides tangible practical benefits, including reduced drug development costs, faster time to therapeutic viability, and fewer unforeseen side effects. As with most applied computational tasks, the algorithms currently used to perform virtual screening feature inherent tradeoffs between speed and accuracy. Furthermore, even theoretically rigorous, computationally intensive methods may fail to account for important effects relevant to whether a given compound will ultimately be usable as a drug. Here we investigate the virtual screening performance of the recently released Gnina molecular docking software, which uses deep convolutional networks to score protein-ligand structures. We find, on average, that Gnina outperforms conventional empirical scoring. The default scoring in Gnina outperforms the empirical AutoDock Vina scoring function on 89 of the 117 targets of the DUD-E and LIT-PCBA virtual screening benchmarks with a median 1% early enrichment factor that is more than twice that of Vina. However, we also find that issues of bias linger in these sets, even when not used directly to train models, and this bias obfuscates to what extent machine learning models are achieving their performance through a sophisticated interpretation of molecular interactions versus fitting to non-informative simplistic property distributions. Full article