Frontiers in Chemistry

Autophagic clearance of hyperphosphorylated tau is impaired in tauopathies, leading to the progressive accumulation of toxic tau species. Fungal metabolites provide a rich yet largely untapped source of neuroactive molecules with therapeutic potential. Here, we investigated metabolites from Lions Mane (Hericium erinaceus), Magic Mushrooms (Psilocybe spp.), and Ergot fungi (Claviceps spp.) using a computational drug-discovery workflow. We characterised their structural diversity, predicted blood-brain barrier permeability and toxicological properties, and integrated network pharmacology with protein-protein interaction and functional enrichment analyses to identify autophagy-related targets. Peroxisome proliferator-activated receptor gamma (PPARG), glycogen synthase kinase-3 beta (GSK3B), and casein kinase 2 alpha 1 (CSNK2A1) emerged as the three most promising candidates, given their complementary roles linking autophagy and tau pathology and their attractiveness as targets for multi-target drug discovery. Their interactions with fungal metabolites were evaluated by molecular docking, Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) rescoring, molecular dynamics simulations, complemented by machine-learning quantitative structure-activity relationship (QSAR) modelling as an additional, ligand-based line of evidence. Molecular dynamics and MM/GBSA analyses confirmed stable, target-specific binding for Corallocin A and Erinacerin M (PPARG), Chaetopyranin and Ergocryptine (GSK3B), and Hericioic Acid D and Isohericerin (CSNK2A1), alongside Emodin, a reference compound with previously reported activity against all three targets. QSAR predictions were informative primarily for the PPARG candidates, which fell within the models applicability domain; predictions for the remaining candidates fell outside their respective models applicability domains and were therefore not interpretable as evidence for or against their prioritisation. Reanalysis of an independent hippocampal proteomic dataset from Alzheimers disease patients showed CSNK2A1 protein levels to be significantly altered in the CA3 subfield, providing an additional, correlative line of support for this target; PPARG and GSK3B showed no significant changes in protein abundance, which does not preclude their functional involvement given their extensive post-translational regulation. Overall, these findings identify fungal metabolites as promising multi-target autophagy modulator candidates and provide a systematic computational strategy for prioritising them for experimental validation in tauopathies.

The ability of small molecules to cross the blood-brain barrier (BBB) remains a major bottleneck in neurotherapeutic development. While experimental assays and machine learning approaches provide approximate permeability estimates, they lack atomistic insight into the underlying transport mechanisms. Here, we employ all-atom molecular dynamics simulations of a compositionally realistic BBB lipid bilayer to characterize the passive permeation of two bioactive propolis-derived compounds, Caffeic Acid Phenethyl Ester (CAPE) and Artepillin-C (ARC). Using steered molecular dynamics and umbrella sampling, we computed free energy profiles, diffusion coefficients, and permeability metrics across the membrane. CAPE encounters a modest barrier at the lipid headgroup region but minimal resistance within the hydrophobic core, resulting in a low free energy barrier ([~]2-3 kcal/mol) and favorable permeability (logP_eff {approx} 0.28). In contrast, ARC exhibits a substantial energetic barrier within the membrane core, leading to high resistivity and strongly unfavorable permeability (logP_eff {approx} -10.91). The heterogeneous lipid model reproduces experimentally consistent membrane properties and reveals how lipid composition modulates transport energetics. These findings provide mechanistic insight into BBB permeability and demonstrate the utility of atomistic simulations for guiding the design of neuroactive therapeutics.

Structure-based drug discovery is known to apply computational methods in a tiered hierarchy, with each layer narrowing the candidate set and refining the binding picture before committing to the next, more expensive step. We present a four-tiered computational benchmarking study evaluating five engines against a panel of 36 compounds targeting B-secretase 1 (BACE1), a validated Alzheimer's disease target with extensive co-crystal ground truth. This study evaluates Flexible Docking and Boltz2 Cofolding as the primary tier, followed by Ensemble Docking, and then Protein-Ligand MD with MM/PBSA and MM/GBSA post-processing. This is then concluded with Relative Binding Free Energy Perturbation (RevFEP) as the terminal refinement layer. Each method was benchmarked against the experimental binding free energies derived from the co-crystal structures spanning -7.85 to -11.35 kcal/mol. Our findings revealed that Flexible Docking reproduced the co-crystal binding mode for 35 of 36 ligands (97.2% within 2.0 A RMSD) but did not rank potency at this resolution. Boltz2 CoFolding provided an orthogonal structural cross-check with a receptor backbone RMSD of 0.293 A against the experimental co-crystal structure. Ensemble Docking identified the optimal receptor conformation for downstream FEP setup. MD with MM/GBSA decomposition identified van der Waals complementarity as the primary potency driver (Pearson r = +0.855, R2 = 0.732 on a 10-compound subset). RevFEP delivered the highest affinity correlation of any method (Pearson r = +0.662, R2 = 0.438, Spearman p = +0.624, mean absolute error 1.02 kcal/mol across all 36 ligands), resolving potency differences within a narrow 3.5 kcal/mol congeneric window that no other engine could discriminate. We characterize what each engine contributes independently and where RevFEP delivers signals no other engine achieves.

Many valuable plant metabolites are synthesized by type III polyketide synthases (PKS) that have become targets for the engineering of microbial production systems of these compounds. The rhizomes of turmeric (Curcuma longa) and ginger (Zingiber officinalis) are highly regarded for medicinal and culinary purposes and are the sources of bioactive curcuminoid and gingeroid polyketides. Fast growing demand for these compounds has sparked effort to identify their biosynthetic pathways to facilitate their heterologous production. In turmeric, a collaborative diketide synthase (DCS) and PKS (CURS) pair synthesizes curcumin from feruloyl- and malonyl-CoA. Yet, bona fide genes for the biosynthesis of gingeroids in Ginger are not known. Here we report the identification of two DCS/PKS pairs in Ginger that have different activity profiles in E. coli engineered to provide feruloyl- and hexanoyl-CoA as substrates. We show that one PKS (ZoPKS2) makes 6-dehydrogingerdione (6-DHG) as its major product while the other PKS (ZoPKS1) is a curcumin synthase. We found that ZoPKS2 becomes an efficient curcumin synthase when hexanoyl-CoA is not available, making it a dual-function enzyme that can be used to easily switch heterologous productions towards either of these two valuable products. Precursor feeding studies show that the substrate promiscuity of the collaborative DCS/PKSs may be exploited to access different dehydrogingerdione derivatives, while structural models of the Ginger PKSs offer insights for future engineering of product profiles. We believe that this work will add to the type III PKS toolbox and enable the development of efficient production platforms for gingeroids.

N-acylethanolamines (NAEs), including the endocannabinoid anandamide, are bioactive fatty acid amides that are normally hydrolyzed by fatty acid amide hydrolase (FAAH) or N-acyl acid amidohydrolase (NAAA). Strikingly, when canonical NAE degradation is blocked, NAE levels do not increase indefinitely but instead reach a plateau. This apparent metabolic ceiling suggests that additional, underexplored pathways contribute to NAE homeostasis. Identifying these pathways is essential to determine whether NAEs are converted into inactive metabolites or products with distinct biological properties. Here, we identify NAE glycosylation as a metabolic pathway that links endocannabinoid-related lipid metabolism to glycosphingolipid turnover. We synthesized glycosylated NAEs and their isotope-encoded standards and developed targeted LC-MS/MS assays to monitor their enzymatic processing and quantify their abundance in mouse and human cells, tissues, and plasma. We show that non-lysosomal glucosylceramidase GBA2 transfers glucose or galactose to anandamide, N-oleoylethanolamine and N-palmitoylethanolamine, and lysosomal glucosylceramidase GCase hydrolyses {beta}-Glycosylated-NAEs ({beta}-Glyco-NAE) back to their parent NAEs. {beta}-Glyco-NAEs occur endogenously in macrophages and neuronal cells, increase when canonical NAE degradation is impaired, and accumulate in human samples with GCase deficiency, including Gaucher disease and GBA1-associated Parkinsons disease. {beta}-Glyco-NAEs do not engage the cannabinoid receptors, TRPV1, or PPAR, and potentiate inflammatory cytokine release, including IL6 and TNF, from microglia. Based on these findings, we pose that GBA2-dependent NAE glycosylation may constitute an overflow lipid-remodeling pathway that connects NAE metabolism to lysosomal dysfunction, inflammation and neurodegeneration.

Glyceraldehyde (GA) contributes to the development of various diseases, such as diabetes and Alzheimer's disease via protein glycation and the formation of advanced glycation end products (AGEs); however, effective strategies for neutralizing GA are limited. Carnosine (Car), an imidazole dipeptide (IDP) that is abundant in meat, suppresses protein glycation by scavenging reactive aldehydes. There are only a few reports on the antiglycation activity of Car against GA. For other IDPs, such as anserine, balenine (Bal), and homocarnosine, there are almost no reports on their antiglycation activity. In this study, we demonstrated the antiglycation activity of four types of IDPs and 2-oxocarnosine (2-oxo-Car), an oxidized form of Car, against GA-induced intracellular protein glycation and neuronal cytotoxicity. Car and Bal exhibited significantly higher reactivity with GA compared with other IDPs and 2-oxo-Car. An in silico analysis suggested that the difference in reactivity is dependent upon intramolecular hydrogen bond formation and the conformation of each IDP. Although there were differences in reactivity with GA, LC-MS analysis revealed that all of the IDPs and 2-oxo-Car reacted with two molecules of GA to form adducts containing pyridinium rings. Car and Bal exhibited high reactivity with GA and markedly suppressed GA-induced cytotoxicity in SH-SY5Y cells. Western blot and qPCR analyses revealed that IDPs suppressed GA-induced protein glycation and the upregulation of endoplasmic reticulum and oxidative stress response genes. Our results indicate that IDPs represent a novel preventive approach to AGE-related diseases and provide a foundation for the development of strategies to treat GA-related neurotoxicity.

Multiple-drug resistant (MDR) Staphylococcus aureus strains (like methicillin-resistant S. aureus or MRSA) uses an arsenal of antioxidant enzymes to mitigate host-induced oxidative stress. Among them the non-canonical Staphylococcal glutathione peroxidase (SaGpx) plays a crucial role in bacterial redox homeostasis by reducing peroxides via thioredoxin-dependent pathways. Thus, enabling oxidative stress mitigation during host infection. Despite its importance in S. aureus, its role in bacterial pathogenesis remains unexplored. This study aimed to elucidate the possible role of SaGpx in Staphylococcal virulence. First, we determined the high-resolution crystal structure of SaGpx (at 1.65 [A] resolution) using X-ray crystallography. Guided by the catalytic cleft architecture of SaGpx, small-molecule based inhibitors were then rationally designed and synthesized. These inhibitors exhibited good binding affinity to SaGpx and complete enzymatic blockade. These inhibitors exhibited potent anti-S. aureus activity (MICs 6.25-31.25 M) along with no cytotoxicity in L929 fibroblast wound-healing assays. Furthermore, the in vivo antibacterial ability of these inhibitors was evaluated using S. aureus-infected skin wound mouse model, where these compounds show potent antibacterial and wound healing ability supported by subsequent histological as well as immunohistochemical analysis. These findings suggest SaGpx as a possible virulence determinant in S. aureus and position these synthesized inhibitors as promising antivirulence therapeutics. HighlightsO_LIThe high-resolution crystal structure of Staphylococcal glutathione peroxidase is solved. C_LIO_LIBased on the SaGpx catalytic site, ,{beta}-unsaturated ketoesters derivatives are synthesized. C_LIO_LISynthesized ,{beta}-unsaturated ketoesters derivatives inhibit SaGpx activity and binds the protein at M range. C_LIO_LISynthesized ,{beta}-unsaturated ketoesters derivatives show in vitro antibacterial activity against S. aureus at low M range. C_LIO_LISynthesized ,{beta}-unsaturated ketoesters derivatives show in vivo antibacterial and wound healing ability S. aureus-infected skin wound mouse model. C_LI

Motivation: Human liver microsome (HLM)-based metabolic stability assays are fundamental in early drug discovery, shaping pharmacokinetic profiles and oral bioavailability. However, these experimental assays are labor-intensive and time-consuming, limiting their application in large-scale virtual screening. Computational models can prioritize compounds at scale, yet most are classification-based, leaving quantitative and interpretable prediction of HLM half-life limited. Results: In this study, we developed a quantitative machine learning model for the direct prediction of HLM half-life (T1/2) by integrating 11,790 compounds combining in-house and curated public data. Among various combinations of molecular features and learning algorithms, the XGBoost model with RDKit 2D descriptors achieved the best predictive performance, with an RMSE of 0.507 and an R2 of 0.431 on an independent test set. Shapley Additive Explanations (SHAP) analysis identified lipophilicity and known metabolic soft-spot features as the primary contributors to the predictions. These results suggest that this quantitative approach provides a practical framework for defining metabolic stability margins, thereby supporting rapid Go/No-go decisions in preclinical drug discovery. Availability: The source code, data, and trained model are available at https://github.com/joshua-416/PredHLM.

Marine macroalgae are valuable sources of bioactive compounds. In this study, we thus investigated the chemical composition and biological activity of an extract from the green seaweed Ulva lacinulata, composed of small bioactive compounds. Comprehensive compositional analyses and high-resolution mass spectrometry revealed its diverse molecular profile composed in particular of peptides/amino acid derivatives, saccharides, low-chain fatty diacids, oxylipins and minerals. Its anti-inflammatory activity was assessed after 6 h pre-treatment in LPS-stimulated cultured RAW 264.7 macrophages, showing that it significantly and dose-dependently reduced the expression and/or secretion of pro-inflammatory cytokines such as TNF-alpha; and IL-6, and targeted the NF-kB signaling cascade. It modulated the SIRT1-AMPK signaling axis and increased the LC3-II/LC3-I ratio, supporting the activation of a controlled autophagic response. This work highlighted the potential of this marine-derived extract as a safe and effective functional ingredient for the development of functional food and/or dietary supplements targeting chronic low-grade inflammation.

Excessive osteoclast activity contributes to pathological bone loss in osteoporosis, rheumatoid arthritis, and osteolytic malignancies. The effects of small catechol derivatives on receptor activator of nuclear factor-{kappa}B ligand (RANKL)-induced osteoclastogenesis remain poorly understood. This study investigated the effects of 4-methylcatechol (4-MC) on RANKL-induced NF-{kappa}B activation and osteoclast differentiation. 4-MC reduced RANKL-induced NF-{kappa}B luciferase activity in HEK-293T/RANK cells. 4-MC also suppressed RANKL-induced TRAP activity in RAW264.7 cells in a concentration-dependent manner and reduced the number of TRAP-positive multinucleated osteoclasts, without affecting cell viability. Molecular docking predicted non-covalent binding of 4-MC within the ATP-binding hinge region of IKK{beta} (PDB: 4KIK), forming a close polar contact with Glu97, predicted hydrogen bonds with Cys99, and a hydrophobic contact with Ile165, within the pocket occupied by the co-crystallized inhibitor K252a. Covalent docking predicted that the oxidized quinone form of 4-MC engages Cys179 in the IKK{beta} activation loop. Quantum chemical calculations confirmed a markedly higher electrophilicity index for the oxidized quinone than for the parent catechol, supporting this mechanism. In silico ADMET profiling indicated favorable drug-likeness and safety. These findings identify IKK{beta} as a plausible molecular target of 4-MC through both non-covalent and covalent mechanisms. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/741661v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@35a0d3org.highwire.dtl.DTLVardef@d19458org.highwire.dtl.DTLVardef@1623fadorg.highwire.dtl.DTLVardef@1429e8b_HPS_FORMAT_FIGEXP M_FIG C_FIG

Predicting blood-brain barrier (BBB) penetration remains a central challenge in CNS drug discovery. Existing computational models rely on physicochemical descriptors and are blind to active transport biology - the efflux pumps and carrier proteins that dominate drug exclusion at the BBB in vivo. We present BBB-Nuke, a modular prediction pipeline that integrates physicochemical scoring with explicit efflux transporter substrate modeling. The system computes ten molecular descriptors, predicts ionization state via a graph convolutional network, scores CNS-MPO desirability, and estimates substrate probability for seven efflux transporters (P-gp/MDR1, BCRP/ABCG2, MRP1, MRP2, MRP4, MATE1, OAT3) using Random Forest classifiers trained on curated ChEMBL bioactivity data. A gradient-boosted classifier trained on 67 features - ten physicochemical, seven efflux transporter probabilities, and fifty fingerprint-derived principal components - achieves an area under the receiver operating characteristic curve (AUROC) of 0.933 {+/-} 0.006 under five-fold cross-validation on 9,262 labeled compounds, and 0.810 on a fully held-out benchmark of 470 clinically validated compounds. In head-to-head comparisons, BBB-Nuke outperforms CNS-MPO, LightBBB, ADMETlab 2.0, and BBB-Score on both cross-validation and external test sets. We apply the pipeline to screen over one billion commercially available compounds from the Enamine REAL library and PubChem, identifying enriched regions of BBB-penetrant chemical space and characterizing the structural features that distinguish permeable from excluded molecules. BBB-Nuke is freely available as a Python package, REST API, and Model Context Protocol server.

Understanding protein structural dynamics is central to elucidating biological function and guiding therapeutic discovery. Hydrogen-deuterium exchange mass spectrometry (HDX-MS) typically offers peptide-level, and sometimes residue-level, time-dependent insights into protein structure, conformational dynamics and/or ligand binding. Yet, translating HDX-MS data into atomic-resolution insights and deriving mechanistic understanding remains a key challenge. Integrative strategies which utilize HDX-MS to inform computational modeling or simulations, traditionally leverage HDX-MS data with physics-based approaches through the calculation of protection factors models. Here, we developed MInt-HDX, a hybrid physics-based, machine- learning framework trained on differential HDX-MS signatures across 11 protein-ligand systems or 1032 individual peptides, using eXtreme Gradient Boosting (XGBoost) to guide small-molecule ligand docking and pose selection. By leveraging XGBoost-predicted interacting residues with three-dimensional clustering and convex-hull geometric algorithms, MInt-HDX first generates HDX-guided candidate docking sites in 3D for physics-based molecular docking and then, following docking, employs HDX-MS-informed XGBoost filtering and scoring functions for ligand- pose ranking. MInt-HDX was validated across 3 protein-ligand systems, consistently resulting in Ligand-RMSD within 3 [A] of the crystallographic ligand conformation, individual steps of MInt- HDX were optimized and its overall performance was assessed against HDX-MS data quality factors and benchmarked against common physics-based and machine learning based docking approaches. Together, this work highlights how machine learning, informed by HDX-MS and aided by physics-based approaches, can bridge the gap between solution-phase HDX-MS data and structural modeling to accelerate protein-ligand discovery pipelines. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/738285v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1f7d1dorg.highwire.dtl.DTLVardef@14f074aorg.highwire.dtl.DTLVardef@167a9b1org.highwire.dtl.DTLVardef@b5c502_HPS_FORMAT_FIGEXP M_FIG C_FIG

Natural product (NPs) and their derivatives are a major source of small-molecule drugs, and the building blocks of these NPs are often amino acids. These include both proteinogenic and nonproteinogenic amino acids (NPAAs), the latter of which expand the structural diversity of NPs. Homologation, or the addition of a methylene group to the amino acid side chain, is one modification that generates NPAAs. If the natural homologation pathway can be characterized and engineered, it could be used to diversify NPs. In this study, we investigated the terminal enzyme of this pathway, HphB, to determine its substrate scope. HphB was tested with various substrates that differed in backbone and/or side chain structures relative to its natural substrate. The results showed that HphB exhibits high promiscuity toward substrates with different side chains while maintaining strict specificity for the substrate backbone. Comparative analysis with two homologous enzymes from primary metabolic pathways revealed that HphB displays markedly higher substrate promiscuity. Bioinformatics analysis and structural modeling suggest that this promiscuity arises from the absence of a "lid" over the active site, resulting in increased solvent exposure of the substrate side chain. This study highlights the unique substrate flexibility of HphB and is a step toward engineering the homologation pathway to generate amino acid derivatives.

Cassava and cocoyam are major dietary staples in sub-Saharan Africa, commonly processed by natural fermentation before consumption. Although fermentation reduces antinutritional compounds and improves food quality, its molecular effects remain poorly characterized. We used untargeted mass spectrometry-based metabolomics with a computational annotation pipeline to compare fermentation-induced molecular remodeling in the two tubers, which showed distinct responses. In cassava, 718 of 773 significant features (92.9%) were depleted, indicating a predominantly catabolic process. In cocoyam, the response was more balanced, with 385 of 1,013 features (38.0%) enriched, including di- and tripeptides consistent with proteolytic processing. Class analysis, molecular networking, and pathway enrichment revealed tuber-specific signatures: cassava was dominated by purine metabolism, whereas cocoyam showed stronger enrichment of amino acid pathways. Cyanogenic glycoside-related features were depleted, consistent with detoxification. Biotransformation prediction also suggested putative fermentation products absent from current databases, highlighting the under-characterized chemistry of these tubers.

Metabolic dysfunction-associated steatotic liver disease (MASLD) and Clostridioides difficile (C. difficile) infection (CDI) are clinically associated, yet there is limited effective treatment for both diseases. Bile salt analogs (BSAs) have demonstrated potential in treating either MASLD or CDI. We screened a library of BSAs (n=112) previously synthesized as potential inhibitors of C. difficile spore germination, for their therapeutic potential in reducing intracellular accumulation of fatty acids in HepG2 cells as candidates for prevention and treatment of both MASLD and CDI. The screening was based on an in vitro model established by incubating HepG2 cells with free fatty acids, with obeticholic acid (OCA), a known BSA with anti-MASLD activity as a control. Gene and protein expressions were quantified to validate the treatment effect. We found that compounds C13, C24, C25, C74, C98, and C101 demonstrated significant effectiveness in both preventing the intracellular accumulation of lipids and removing pre-loaded cellular lipids. Gene expression analysis showed that C24, C25, and C74 produced a similar pattern characterized by a robust induction of FGF21 expression, while C13, C98, and C101 produced a transcription pattern that mirrors the effect of OCA. Structurally, while C13, C24, and C25 do not display drug-like properties, C74, C98, and C101 are drug-like and share a similar structure. Interestingly, C101 is a potent inhibitor of C. difficile spore germination. OCA shows a weak anti-gemination effect. Our study identified lead compound candidates for the development of novel therapeutics capable of treating both MASLD and CDI. Significance statementThe clinical association between MASLD and CDI remains an unmet need for dual acting therapeutic strategies. Given the reported potential of BSA, we screened 112 previously synthesized as potential inhibitors of C. difficile spore germination, for their therapeutic potential in reducing intracellular accumulation of fatty acids in HepG2 cells. Our study identified compounds that effectively reduce intracellular lipid accumulation and inhibit C. difficile spore germination. These results nominate lead candidates for developing dual-acting therapeutics targeting both MASLD and CDI.

The Na+-dependent citrate transporter NaCT (SLC13A5) is a key regulator of citrate homeostasis and has emerged as a therapeutic target for metabolic and neurological disease, including the SLC13A5 Epilepsy, a rare disease marked by severe sezures and neurodevelopmental delays. Current NaCT inhibitors are substrate-like molecules that competitively bind the substrate binding site. In this study, we identify previously unknown small molecule inhibitors of NaCT by targeting a putative allosteric site located at the dimer interface. We performed a virtual screen of 3.5 million compounds from the ZINC20 database against this site and selected 54 candidates for experimental testing using a cell-based citrate uptake assay. Through initial experiments, we identified three weak inhibitors, and subsequent evaluation of 26 structurally related analogs yielded six compounds with improved potency (IC50 = 12.78 M and 15.49 M). We then performed further analysis of the putative binding site by integrating structural data with deep mutational scanning evidence and comparisons with homolog structures. This analysis highlighted the importance of key residues (e.g., Phe362) in ligand modulation. These findings reveal a promising allosteric pocket and establish a chemically distinct series of NaCT inhibitors, providing a foundation for rational development of pharmacological modulators of NaCT function.

Endophytic fungi associated with the Himalayan yew (Taxus wallichiana) represent an underexplored source of bioactive secondary metabolites. This study investigated the extracellular metabolites of Annulohypoxylon purpureonitens isolated from Nepalese T. wallichiana using bioactivity screening combined with LCMS/MS-based metabolomics. The fungal extract exhibited broad-spectrum antibacterial activity, showing the strongest inhibition against Staphylococcus aureusand Enterococcus faecalis (MIC = 500 ug/mL). It also displayed notable antioxidant capacity(DPPH, ABTS, TPC &TFC) and cytotoxicity against HeLa and MCF-7 cancer cell lines. Metabolite profiling via GNPS molecular networking, manual MS/MS validation, and MASST reverse metabolomics putatively identified diverse compounds, including hydroquinidine, chlorogenic acid, muramic acid, and cordycepin conjugates widely distributed across public microbial datasets. Overall, A. purpureonitens is a promising source of multifunctional metabolites, laying a foundation for future compound isolation and functional characterization.

The binding affinities of a chimeric analog of a riboflavin derivative linked to biotin, (6- (7,8-dimethyl-2,4-dioxo-3,4-dihydrobenzo[g]pteridin-10(2H)-yl)hexyl 5-((3aS,4S,6aR)-2- oxohexahydro-1H-thieno[3,4-d]imidazol-4-yl)pentanoate), referred to as C6-Rf-biotin-tag, to the riboflavin binding retain or streptavidin are in the M range, 1.29 {+/-} 0.277 and 3.00 {+/-} 0.459, respectively. These values suggest that C6-Rf-biotin-tag has potential applications in diagnostic assay and labelling target flavin binding proteins. The C6-Rf-biotin-tag which was characterized with respect to physical and biochemical properties retains UV/Vis spectroscopic and fluorescence behavior similar to riboflavin.

Targeted covalent inhibitors are a powerful, yet underexplored, class of therapeutics, and current computational covalent screeners are constrained in early drug discovery due to the need for prior knowledge of the target site and limited throughput. We present CovSite, a blind covalent screening tool that identifies candidate reactive residues across the entire protein surface, utilizing only the protein structure and electrophile SMILES. CovSite applies a pipeline of four orthogonal physicochemical filters (nucleophile identification, solvent accessibility, environment-dependent deprotonation prediction, and semi-quantum-mechanical reactivity ranking) to identify potential small-molecule candidate inhibitors. Validated against 2,062 diverse covalent protein-ligand complexes spanning six nucleophilic residue types, CovSite achieves a 98.5% blind target site hit on a held-out benchmark set of 207 cysteine-targeted complexes while reducing the search space by 97.8%. The target-site hit detection exceeds the 53-62% accuracy of popular covalent screening tools operating under non-blind conditions on the same benchmark set. By extending nucleophilic coverage beyond cysteine to include serine, threonine, lysine, histidine, and tyrosine, and completing a screening of a 200-residue protein in two to three minutes on standard hardware, CovSite serves as a platform technology with the potential to address critical gaps in throughput, generalizability, and accuracy in this field of covalent screening. We demonstrate this capability by using CovSite as a blind, ligand-specific approach that enables iterative, machine-learning-driven covalent inhibitor generation that is impractical with existing tools, establishing a foundation for computationally guided covalent drug discovery for novel and understudied targets. TOC Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/739288v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@17abeeforg.highwire.dtl.DTLVardef@18d6299org.highwire.dtl.DTLVardef@14437eaorg.highwire.dtl.DTLVardef@1b2ec81_HPS_FORMAT_FIGEXP M_FIG C_FIG

Vodobatinib (VBN) is a weakly basic (pKa {approx} 2.3), anticancer treatment with poor enteric solubility and low oral bioavailability. This study demonstrates how an emerging polymeric amorphization technique, slurry conversion, can yield amorphous drug-polymer salts with enhanced dissolution rates. The technique had not previously been applied to a weakly basic drug, so design rules for this class of active were unknown. Two acidic polymers, poly(styrene sulfonic acid) (PSSA) and poly(acrylic acid) (PAA), were individually evaluated for salt formation with VBN. Formulation involved blending the drug and polymer in a 1:2 (v/v) ratio of a protic liquid to solvent and a 1:9 (w/w) ratio of solid to solvent. Design rules for effective combinations of solvents and protic liquids were developed and optimized to thread the needle between dissolution of all species and acid-base interactions, both of which were required to form amorphous salts. Drug loadings of 10%, 20%, and 40% by mass were tested. X-ray photoelectron spectroscopy was employed to evaluate protonation of the quinoline nitrogen atoms on VBN, a key indicator of successful salt formation. Powder X-ray diffraction was used to confirm that the resulting slurry contained amorphous VBN, and 1H NMR spectroscopy indicated residual solvent remained after drying, which remains an area for improvement. In dissolution kinetics tests in FeSSIF, the lead drug-polymer salt formulation achieved a concentration of dissolved VBN up to 140 {micro}g/mL, an improvement of >35-fold compared to <4 {micro}g/mL (LLD) for crystalline VBN. These results demonstrate that slurry conversion is a viable polymeric amorphization technique even for weakly basic drugs. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=148 SRC="FIGDIR/small/734800v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1812ceforg.highwire.dtl.DTLVardef@1ad06dcorg.highwire.dtl.DTLVardef@9d8bb7org.highwire.dtl.DTLVardef@13fcbe8_HPS_FORMAT_FIGEXP M_FIG C_FIG