Journal of Molecular Graphics and Modelling

Toll-like receptors (TLRs) are vital components of the innate immune system, recognizing both exogenous pathogens signals (PAMPs) and internal stress signals (DAMPs). TLR2 is unique among the human (Homo sapiens) TLR family members, as it contains a large cavity for binding hydrophobic ligands, such as lipoteichoic acid (LTA) and di/triacyl lipopeptides (Pam2/3CSK4). This study analyzed the structural phylogeny of cavity presence in the TLR2 lineage in vertebrates (vTLR) enabled by AI protein structure predictions and explored the potential convergent evolution of similar features in invertebrates (iTLRs). Analysis of AI models of TLR2s shows that this cavity is consistently present in TRL2 orthologs across jawed vertebrates (Gnathostomata). In jawless vertebrates (Cyclostomatha), these cavities were found in lamprey (Petromyzon marinus) TLR2 model, but only in some extant hagfish (Myxini), suggesting an ancestral origin in basal vertebrates followed by lineage-specific losses. TLR2 paralogs were found in several species, with a similar central cavity but potentially different ligand specificities. In silico ligand docking showed Pam2CSK4 binds to this cavity in all TLRs and paralogs consistently, demonstrating the conserved function of the ligand-binding pocket in gram-positive bacteria recognition across TLR2 branches. Changes in the TLR2 cavity size and shape in some vertebrate groups show the evolution of this DAMP recognition mechanism adapted to its respective pathogens. iTLRs form a separate phylogenetic branch with distinct structural features, but in literature some are considered to be TLR2 orthologs. Indeed, TLRs from some species of Helobdella and Ciona, contain a cavity with some similarity to that in the vTLR2 lineage. However, detailed structural comparisons of their location in the LRR domain and the structural details of the models suggest that their cavities have developed independently from that in TLR2s. Smaller cavities are present in other branches of the LRR family, but show different locations, shapes, and features, indicating that the binding of small ligands in the internal cavities within the LRR domains evolved multiple times in the LRR domain family history.

Synaptic vesicle glycoproteins 2 (SV2) are integral membrane proteins essential for neurotransmitter release and are implicated in neurological disorders including epilepsy and Parkinsons disease. In the attempt to develop a ligand selective for SV2C, and in collaboration with UCB, UCB-F was identified as a potential candidate. However, the affinity of UCB-F to SV2C was found to be temperature dependent, decreasing by about 10-fold from +4 to 37 degrees. UCB1A was subsequently identified as SV2C ligand displaying in vitro a 100-fold selectivity for SV2C compared with SV2A. In this study we investigated whether the binding of UCB-1A to SV2A and SV2C was affected by the temperature. A combination of experimental binding assay data and molecular dynamics (MD) simulations were used. The binding studies revealed that UCB1A affinity for SV2A decreased significantly at 37 {degrees}C compared with 4 {degrees}C, whereas binding to SV2C remained largely unchanged. MD simulations reproduced these observations, namely that ligand RMSD values at 310 K showed that UCB1A binding fluctuated markedly in the SV2A complex, with many trajectories exceeding the 3.0 [A] stability cutoff, whereas UCB1A remained relatively well-anchored in SV2C under the same conditions. Structural analysis showed that, while UCB1A adopts a conserved binding pose across all isoforms stabilized by {pi}- {pi} stacking and a hydrogen bond with Asp, SV2C possesses a unique stabilizing feature. In SV2C, Tyr298 is less exposed to the solvent and engages in a persistent hydrogen bond with Asparagine, a structural feature that reinforces pocket stability and limits temperature-induced destabilization. This interaction is absent in SV2A, consistent with its greater temperature sensitivity. Together, these findings provide a mechanistic explanation for the experimentally observed temperature independence of UCB1A binding to SV2C. More broadly, the results highlight the importance of incorporating physiologically relevant temperatures into SV2 ligand evaluation and demonstrate how combining experiments with simulations can uncover isoform-specific mechanisms of ligand recognition and stability.

Prasugrel is a prodrug, widely used in antiplatelet strategy for secondary prevention after acute coronary syndrome. The metabolism of prasugrel leads to the formation of the Prasugrel Active Metabolite (PAM), an irreversible P2Y12 receptor antagonist. Its mode of binding has not yet been fully established, although it is known that it binds covalently to P2Y12 by forming a disulfide bridge with cysteines and its sulfur moiety. PAM is a molecule with two chiral centers, resulting in four stereoisomers which appear to be stereoselective upon binding. A combination of different molecular modeling methods, such as molecular dynamics, ensemble docking, and Density Functional Theory (DFT), were used to rationalize these differences in antagonism observed in vitro and to elucidate the mode of binding of PAM to P2Y12. PAM is found to bind to the closed P2Y12 conformation in a preferential way. Although the four stereoisomers have comparable affinity, the location of the RS stereoisomer makes the formation of a disulfide bond with cysteines more favorable, particularly with cysteine 175. Compared to the RR stereoisomer, the RS stereoisomer interacts less deeply with the P2Y12 receptor, interacting in particular with the second and third extracellular loops, explaining the competition observed with cangrelor and an intermediate metabolite of prasugrel. Furthermore, DFT calculations have shown that the formation of a disulfide bridge is energetically more favorable with the RS stereoisomer than with the RR stereoisomer. The physical interactions and chemical reaction between the RS stereoisomer and the P2Y12 receptor are key factors in explaining the stereoselective binding of PAM to P2Y12.

Mycoplasma synoviae is an avian pathogen that causes respiratory disease and synovitis, and its hemagglutinin plays a critical role in host cell adhesion. However, the key residues and structural mechanisms underlying hemagglutination remain unclear. In this study, domain analysis of the hemagglutinin family of Mycoplasma synoviae revealed that it contains long-chain and short-chain types, among which LAM HA (VY93_RS01465) was selected as the bait protein due to its complete C-terminal conserved domain. Through yeast two-hybrid screening, 18 host proteins interacting with LAM HA were identified. Furthermore, five key amino acid residues S83, R85, Y88, N124, and K192 were found to mediate hemagglutination activity. Deletion of these residues reduced the hemagglutination titer of LAM HA under acidic conditions. Secondary structure analysis showed that the deletion mutation decreased the -helix content while increasing the proportions of {beta}-sheet and random coil. Molecular dynamics simulations revealed that the mutant exhibited generally higher root mean square deviation and root mean square fluctuation values than the wild-type under different pH conditions, with a marked decrease in structural stability particularly at pH 5.0 and 6.0. These findings indicate that LAM HA, as a critical adhesin, exerts its hemagglutination function dependent on specific key residues and pH-sensitive conformational stability. IMPORTANCEMycoplasma synoviae (M. synoviae) causes significant economic losses to the poultry industry worldwide. Lipid-related membrane protein hemagglutinin (LAM HA) is a surface adhesin essential for host cell attachment, but its precise amino acid residues and structural features have not been defined. In this study, five key residues (S83, R85, Y88, N124, and K192) were identified as critical for LAM HA-mediated hemagglutination activity. Deletion of these residues altered the secondary structure composition, reduced conformational stability under acidic pH conditions, and decreased hemagglutination activity. These findings reveal a previously unknown structure-function relationship of M. synoviae LAM HA, demonstrating that its hemagglutination activity depends on specific residues and pH-sensitive structural integrity. This provides new insights into the molecular mechanisms of M. synoviae adhesion and offers potential targets for the development of novel intervention strategies against avian mycoplasmosis.

Cannabinoid receptors are therapeutically promising GPCRs that are also interesting test systems for structure-based methods, which have targeted them previously. Here we used the CB2 receptor as a template to explore several topical questions in library docking. Whereas an earlier campaign against the CB1 receptor led to potent but relatively non-selective ligands, here we found that targeting interactions with polar, orthosteric site residues led to subtype-selective ligands. Docking hit rate and especially hit affinity improved in moving from a 7 million to a 2.6 billion molecule library. Similar to earlier studies, docking against active and inactive states of the receptor did not reliably bias toward the discovery of agonists or inverse agonists. Cryo-EM structures of two of the new agonists, each in a different chemotype, superposed well on the docking predictions. Correspondingly, structure-based optimization led to 10- to 140-fold improvements within three different series, also consistent with well-behaved ligand families. Hit rates with a fully enumerated 2.6 billion molecule library resembled those of an implied 11 billion molecule library from a building-block method, consistent with the latters ability to explore this space, though higher affinities were discovered from the fully enumerated set. Overall, eight diverse families of ligands, with potencies <100 nM and mostly unrelated to previously known ligands were found. Implications for future studies are considered.

Malaria is one of the deadliest diseases in sub-Saharan Africa and Southeast Asia. The majority of the fatalities occur mostly in children under 5 years and pregnant women and this is due to infection by Plasmodium spp, of which Plasmodium falciparum is the most virulent and is responsible for most of the morbidity and mortality. Despite various public health interventions such as use of insecticide-treated bed nets, spraying of homes with insecticides and use of WHO recommended artemisinin-based combination therapies (ACT), malaria prevention still faces major setback due to drug and insecticide resistance by P. falciparum and mosquitoes respectively. The study uses molecular docking and immunoinformatics to screen various Plasmodium spp antigens and evaluate their antigenicity and suitability as vaccine candidates. The P. falciparum antigens and T-cell receptor (TCR) structures were obtained from Protein Data Bank (PDB) based on a range of factors related to their role in the lifecycle of the parasite and their status as vaccine targets. Protein structures not available in the PDB were predicted using AlphaFold. The 3D structures of selected P. falciparum antigens and TCR structures were downloaded in PDB format then all water molecules, Hetatm, and bound ligands were deleted from the protein structures using BIOVIA Discovery Studio Visualizer. Subsequently, molecular docking was done using ClusPro v2.0 server and docked complexes were compared. The findings of this study gave valuable insights into the interaction of human immune response with P. falciparum antigens. The best three ranked antigen complexes are PfCyRPA, PfMSP10 and PfCSP and this confirm their use as potential candidates for vaccine development. This study highlights the usefulness of computational docking in identifying P. falciparum antigens of excellent immunogenic potential as vaccine candidates.

Inflammation serves as a vital physiological process essential for preserving health and countering illness. Yet, persistent inflammation drives osteoclastogenesis and ongoing bone erosion in rheumatoid arthritis (RA), mainly via macrophage activation and overproduction of pro-inflammatory cytokines like TNF-, IL-1{beta}, and IL-6. Limitations of prolonged conventional treatments underscore the need for safer small-molecule inhibitors that address both inflammation and osteoclast formation. Chalcones, natural plant defense compounds, exhibit diverse pharmacological properties including anti-inflammatory, anticancer, antibacterial, antifungal, and antiparasitic actions, owing to their characteristic reactive , {beta}- unsaturated carbonyl moiety. This study assessed chalcone derivative 7a for its anti-inflammatory effects in vitro and in vivo, alongside its capacity to modulate osteoclast differentiation, offering the inaugural demonstration of its dual anti-inflammatory and anti-osteoclastogenic properties. In LPS-stimulated macrophages, 7a substantially curtailed nitric oxide production, curbed pro-inflammatory cytokines (TNF-, IL-1{beta}, IL-6), and concentration-dependently diminished iNOS and COX-2 expression while inhibiting reactive oxygen species levels. In vivo, oral 7a dosing potently alleviated carrageenan-evoked paw swelling and restored serum lactate dehydrogenase and C-reactive protein to normalcy. In LPS-exposed mice, it further lowered systemic cytokines and rectified dysregulated biomarkers such as LDH, ALP, ALT, AST, creatinine, and urea. Moreover, in RANKL-stimulated osteoclast cultures, 7a markedly suppressed osteoclastogenesis by downregulating pivotal markers like tartrate-resistant acid phosphatase (TRAP) and matrix metalloproteinase-9 (MMP-9). Derivative 7a also enhances antioxidant defense--superoxide dismutase and catalase--via blockade of NF-{kappa}B and MAPK pathways. Overall, chalcone derivative 7a displays robust anti-inflammatory and anti-osteoclastogenic activity, positioning it as a compelling candidate for RA therapy.

GPI-anchored proteins are crucial cell surface proteins with diverse, organism-specific functions, in eukaryotes. They are produced when the GPI transamidase (GPIT), a five-subunit membrane-bound enzyme complex, attaches a pre-formed GPI anchor to the C-terminal end of nascent proteins on the lumenal face of the endoplasmic reticulum. This process requires the removal of a C-terminal signal sequence (SS) on the substrate protein by the action of an endopeptidase subunit of the GPIT, Gpi8/ PIG-K. Using an AMC-tagged peptide in a cell free (post-mitochondrial fraction) assay, this manuscript studies the steady state kinetics of enzymatic cleavage of the substrate by GPIT of the human pathogenic fungus, C. albicans. We show that Mn+2 enhances activity by improving substrate binding but plays no direct role in substrate cleavage per se. Molecular dynamics simulations suggest that the divalent cation binds at a site away from the active site but provides compactness and stability to Gpi8. It also enables a conformation in which a flexible loop (219-244 residues) in the vicinity of the catalytic pocket is able to interact with and position the scissile bond for cleavage by Cys202. Steady state kinetics also indicate that peptides of lengths 7-mer to 9-mer are better bound than 4-mer or 15-mer peptide substrates. A bulky residue at the site of cleavage reduces the catalytic activity of the GPIT. This is the first detailed steady state kinetics study on the endopeptidase activity of a GPIT from any organism.

The importance of human aldehyde oxidase (hAOX1) has increased over the last decades due to its involvement in drug metabolism. Inhibition studies concerning hAOX1 are extensive and a common reducing agent, dithiothreitol (DTT), was recently found to inactivate the enzyme. However, in previous crystallographic studies of hAOX1, DTT was found to be essential for crystallization. To surpass this concern another reducing agent used in crystallization trials. Using tris(2-carboxyethyl)phosphine (TCEP), a sulphur-free reducing agent, it was possible to obtain well-ordered crystals from hAOX1 wild type and variant, hAOX1_6A, which diffracted beyond 2.3 [A]. Instead of the typical star-shaped crystals of hAOX1, at pH 4.7, plates are obtained in the orthorhombic space group (P22121) with two molecules in the asymmetric unit. Activity assays with the enzyme incubated with both reducing agents show that contrary to DTT, TCEP does not lead to irreversible inactivation of the enzyme. The replacement of DTT with TCEP in crystallization of hAOX1 provides a strategy to circumvent enzyme inactivation during crystallographic studies, allowing future applications of new assays, such as time-resolved crystallography.

Purpose/ObjectiveThis study aimed to design and computationally evaluate a synthetic GluN1-mimetic peptide as a decoy to bind and neutralize pathogenic autoantibodies in anti-NMDA receptor (NMDAR) encephalitis, a severe autoimmune neurological disorder affecting approximately 1.5 per million individuals annually. MethodsKey GluN1 epitope residues (351-390 of the amino-terminal domain) were identified from crystallographic evidence and patient-derived antibody binding studies. Multiple peptide variants were rationally designed to mimic the antibody-binding interface. AlphaFold2 was used to predict peptide structures. Rigid-body docking simulations were conducted with HADDOCK 2.4 to model peptide-antibody complexes, and binding affinities were quantified using PRODIGY. A scrambled peptide control was included to establish docking specificity. ResultsThe top-performing peptide demonstrated favorable predicted binding ({Delta}G = -21.5 kcal/mol, Kd = 1.7 x 10-{superscript 1} M) with an average pLDDT score of 90%, a buried surface area of 3,255.5 [A]{superscript 2}, and 18 intermolecular hydrogen bonds. Relative to the scrambled control ({Delta}G = -8.3 kcal/mol), the designed peptide showed substantially stronger predicted binding. Conclusion/ImplicationsThese results support the validity of an epitope-mimicry design strategy and establish a scalable computational framework for prioritizing peptide decoy candidates applicable to other antibody-mediated autoimmune disorders. Experimental validation remains necessary to confirm real-world efficacy.

Alzheimers disease remains a major therapeutic challenge, and no {beta}-secretase (BACE1) inhibitor has achieved clinical approval. A key limitation of prior discovery efforts is reliance on single-parameter optimization, often resulting in candidates with limited translational potential. In this study, we developed a biology-informed computational framework integrating meta-ensemble QSAR modeling, molecular docking, Protein Language Model (ESM-1b)-guided residue interaction weighting, and ADMET profiling within a normalized multi-parameter ranking scheme. Model performance was validated using cross-validation, external validation, and Y-randomization (n = 100; p = 0.009), while applicability domain analysis based on Tanimoto similarity highlighted reduced reliability for extrapolative predictions. Sensitivity analysis showed high ranking stability under moderate perturbations (Spearman {rho} = 0.998 for {+/-}10%; 0.963 for {+/-}25%), with reduced agreement under randomized weighting ({rho} = 0.821), indicating that prioritization is robust but influenced by weight selection. Screening of 16,196 compounds identified 153 predicted actives (accuracy = 0.852; ROC-AUC = 0.920), which were refined to 111 candidates and seven prioritized leads. Molecular dynamics simulations (200 ns) indicated stable binding and persistent catalytic interactions, with Mol-2 showing favorable dynamic stability and ADMET characteristics. Overall, this study presents an interpretable and quantitatively evaluated framework for multi-parameter compound prioritization, supporting more reliable virtual screening in early-stage CNS drug discovery.

The emergence of antimicrobial resistance (AMR) has driven the search for alternative therapeutic strategies, including antivirulence approaches targeting bacterial quorum sensing (QS). Azelaic acid (AzA), a naturally occurring dicarboxylic acid with known antimicrobial properties, has not previously been characterized as a QS inhibitor in Gram-negative pathogens. This study evaluated the dual antimicrobial and antivirulence activity of AzA against reference strains and clinical isolates of Pseudomonas aeruginosa, Enterobacteriaceae, and Staphylococcus aureus through in vitro assays and molecular docking analyses. Minimum inhibitory concentration (MIC) values ranged from 250 to 1000 {micro}g/mL, with lower MICs observed in clinical isolates of E. coli and S. aureus. Subinhibitory concentrations (250, 500 and 750 {micro}g/mL) were used to assess QS-regulated virulence factors in P. aeruginosa, including pyocyanin, elastase, alginate, and protease production. AzA exhibited a significant, dose-dependent inhibition of all evaluated virulence factors across both reference and multidrug-resistant (MDR) and pan-drug-resistant (PDR) clinical strains (p < 0.001), achieving inhibition levels exceeding 90% in several cases, particularly for protease activity. Molecular docking analyses revealed that AzA interacts with key QS-related proteins (LasI, LasR, PqsD, and PqsR), showing moderate binding affinities (-5.3 to -6.5 kcal/mol) and stable interactions within conserved ligand-binding domains. These findings suggest a multitarget modulatory mechanism affecting interconnected QS pathways. Overall, this study demonstrates, for the first time, that AzA acts as a quorum sensing inhibitor in P. aeruginosa, attenuating virulence without directly affecting bacterial growth, highlighting its potential as a promising antivirulence therapeutic strategy.

Ubiquitin-like protein 3 (UBL3) is a post-translational modifier that sorts proteins into small extracellular vesicles and regulates the trafficking of disease-associated proteins such as -synuclein. The structural and dynamic features of the UBL domain that underlie these functions, however, remain poorly understood. Here we performed in silico structural dynamics analysis of the UBL3 UBL domain using an NMR structure ensemble combined with anisotropic network modeling (ANM) and perturbation response scanning (PRS). Principal component analysis and residue-wise fluctuation analysis consistently revealed high flexibility in the C-terminal region of UBL3. Comparative ANM analysis across 20 ubiquitin-like proteins (UBLs) further showed that C-terminal flexibility is a conserved yet variable property within the UBL family. PRS analysis demonstrated that residues forming the central -helix of the {beta}-grasp fold exert greater dynamic control over collective motions than {beta}-sheet residues. Notably, UBL3 displayed the highest helix/sheet PRS effectiveness ratio among all UBLs analyzed, highlighting the prominent dynamic contribution of helix residues in this domain. Together, these results provide a structural basis for understanding UBL3-dependent protein interactions and disease-related mechanisms, and suggest that helix-centered dynamic control in the UBL domain may represent a potential target for modulating UBL3 function.

Bone morphogenetic protein receptor type IA (BMPR1A) is a key mediator of chondrogenesis and a validated therapeutic target for cartilage repair, yet existing BMP mimetic peptides suffer from low potency and the full-length protein (rhBMP-2) carries significant safety risks. Generative AI tools for protein design can now produce de novo peptide binders, but none have been applied to cartilage regeneration targets. Here, we benchmarked four architecturally distinct AI tools--RFdiffusion, BindCraft, PepMLM, and RFpeptides--to design candidate BMPR1A-binding peptides. We generated 192 candidates alongside 98 negative controls (290 total) and evaluated all complexes using AlphaFold 3 structure prediction, dual physics-based energy scoring (PyRosetta and FoldX), and contact recapitulation against the crystallographic BMP-2:BMPR1A interface (PDB: 1REW). A four-metric composite ranking identified a 15-residue PepMLM design (pepmlm_L15_0026) as the top candidate, combining favorable binding energy (PyRosetta dGseparated = -45.9 REU; FoldX {Delta}G = -19.4 kcal/mol) with the highest contact recapitulation among top-ranked peptides (11/30 gold-standard interface residues). Designed candidates significantly outperformed controls on ipTM (p = 0.002) and FoldX {Delta}G (p < 0.001). BindCraft candidates achieved the highest structural confidence (ipTM up to 0.81) but exhibited moderate contact recapitulation (mean 0.224), consistent with the computational hypothesis that they may engage alternative BMPR1A binding surfaces rather than the native BMP-2 interface. Physicochemical filtering yielded a shortlist of 54 candidates across all four tools. These results establish a reproducible computational framework for AI-guided peptide design targeting cartilage regeneration and identify specific candidates for future experimental validation via binding assays and chondrocyte differentiation studies. Author summaryDamaged cartilage has limited capacity to heal, and current biological therapies based on bone morphogenetic protein 2 (BMP-2) carry serious safety concerns including ectopic bone formation and inflammation. Short peptides that mimic BMP-2s interaction with its receptor BMPR1A could offer a safer, more targeted alternative, but designing such peptides from scratch is challenging. We used four different artificial intelligence tools--each employing a distinct computational strategy--to generate 192 candidate peptides designed to bind BMPR1A. We then evaluated all candidates using multiple independent computational methods to assess binding quality, energy favorability, and whether each peptide targets the correct site on the receptor. Our analysis identified a shortlist of 54 promising candidates, with a 15-residue peptide from the language model-based tool PepMLM emerging as the top-ranked design. We also found evidence that one tool (BindCraft) may produce peptides that bind BMPR1A at sites different from the natural BMP-2 interface, highlighting the importance of validating not just whether a peptide binds, but where it binds. Our computational framework and candidate peptides provide a foundation for future laboratory testing toward cartilage repair therapies.

BackgroundTo control leishmaniasis, chemotherapy drugs are currently under development. However, these drugs often exhibit poor efficacy and are associated with toxicity, adverse effects, and drug resistance. At present, no specific drug is available for the treatment of leishmaniasis. Meanwhile, vaccine research is ongoing. Recent studies have analysed some experimental vaccines using mathematical models. AimIn previous work, drug targeting was focused on the entire human body rather than specifically addressing infected macrophages and parasites. In our current approach, we aim to eliminate infected macrophages and parasites through nano-drug design. Specifically, we utilise two types of nanoparticles: iron oxide and citric acid-coated iron oxide. Moving forward, we plan to advance this strategy using mathematical modelling of macrophage-parasite interactions. MethodsWe design PDE-based models of macrophages and parasites, incorporating cytokine dynamics, to support nano-drug development. Drug efficacy is estimated using posterior distributions to analyse phenotypic fluctuations of macrophages and parasites during the design phase. We investigate implicit and semi-implicit treatment schemes, focusing on energy decay properties. To model drug flow during treatment, we introduce a three-phase moving boundary problem. Comparative analyses are conducted to evaluate macrophage and parasite behaviour with and without treatment. Finally, the entire framework is implemented within a virtual lab environment. ResultsThe results show that the nano-drug exhibits better efficacy compared to combined drug doses. We analysed and compared two types of nano-drug particles: iron oxide and citric acid-coated iron oxide. We discuss how the drug effectively targets and eliminates infected macrophages and parasites. ConclusionOur models results and simulations will support researchers conducting further studies in nano-drug design for leishmaniasis. These simulations are performed within a virtual lab environment.

The identification of suitable lead molecules in the vast chemical space is a critical and challenging task in drug discovery campaigns. Recently, it has been demonstrated that large-scale virtual screening provides a powerful approach to accelerate the identification of novel drug candidates by screening ever increasing virtual ligand libraries, which have reached magnitudes of > 1020 compounds. However, this desirable increase in potentially bioactive molecules poses a new challenge as enumerating and virtually screening such huge compound libraries is computationally prohibitive. Consequently, advanced approaches to navigate ultra-large chemical spaces and to identify suitable candidate molecules therein are urgently needed. Here, we present an evolutionary algorithm framework using molecular generative AI, reaction-based substructure searching, and iterative model fine-tuning for a targeted and efficient exploration of chemical fragment spaces. Combining this approach with large-scale virtual screening we are able to identify target-specific candidate molecules within the commercially available Enamine REAL Space ([~]1015). We demonstrate the applicability of the approach by successfully identifying and biochemically validating pH-specific ligands of the {micro}-opioid receptor. Our results demonstrate that integrating generative AI with evolutionary algorithms provides a promising route to explore ultra-large chemical spaces for the discovery of novel, synthetically accessible lead molecules.

BackgroundThere is currently no approved antiviral therapy against measles virus (MeV). Repurposing available compounds with broad antiviral activity may rapidly identify candidate drugs for clinical evaluation. Here we evaluated the antiviral activity of the clinically approved drugs azelastine hydrochloride and zafirlukast as well as the flavonoids quercetin and isoquercetin against MeV in preventative and therapeutic in vitro studies. MethodsCompounds were tested for antiviral activity against MeV in preventative (prophylactic and virucidal) and therapeutic (steady-state and persistent) assays in Vero/hSLAM cells. Viral loads and cell viability were measured 48h post-infection, and dose-response curves were used to calculate EC50 values. Flavonoids were also tested in the presence of 1 mM ascorbic acid. ResultsAzelastine hydrochloride did not show evidence of antiviral activity against MeV under these conditions, whereas zafirlukast, quercetin, and isoquercetin showed therapeutic activity against MeV. The addition of ascorbic acid enhanced the therapeutic potency of quercetin to 4.2-4.8 {micro}M and of isoquercetin to 10.7-10.9 {micro}M. Antiviral activity was dose-dependent when administered post-infection. ConclusionAmong the four compounds tested, quercetin showed the most potent therapeutic antiviral activity against MeV in vitro. Isoquercetin and zafirkulast also showed therapeutic activity. These findings support further evaluation of quercetin, isoquercetin, and zafirlukast as candidate antiviral drugs for MeV and highlight the utility of in vitro platforms for rapid antiviral drug screening.

NLRP3 inflammasome is a cytosolic multi-protein complex that plays a crucial role in the immune system, responding to various exogenous and endogenous stimuli by triggering protective inflammatory responses. However, aberrant NLRP3 inflammasome activation is implicated in numerous inflammatory diseases. Therefore, the NLRP3 inflammasome is an important pharmacological target for the treatment of multiple diseases. In this context, we screened various US-FDA-approved drugs for NLRP3 inflammasome inhibition. We found that among various drugs, minoxidil hydrochloride (MXL) effectively inhibits NLRP3 inflammasome, evidenced by reduced secretion of IL-1{beta} and IL-18 in J774A.1 cells treated with MXL. The IC50 values of MXL for inhibition of IL-1{beta} and IL-18 were calculated to be 1.2 and 1.06 {micro}M, respectively. MXL was found to prevent ASC oligomerization, thereby inhibiting the NLRP3 inflammasome and leading to CASP1 cleavage. Further investigation revealed that MXL also utilizes AMPK-mediated autophagy to modulate NLRP3 inflammasome activity. Using siAMPK and bafilomycin A1, an end-stage autophagy inhibitor, we elucidated crosstalk between the NLRP3 inflammasome and autophagic pathways, which was modulated by MXL. Furthermore, we demonstrated the efficacy of MXL in two different mouse models of inflammation, involving the NLRP3 inflammasome. MXL at doses of 10 and 20 mg/kg effectively inhibited the activation of NLRP3 inflammasome by monosodium urate in the air pouch model and by ATP in the peritoneal inflammation model, as evidenced by reduced secretion of 1{beta} and IL-18 in the lavage. Our study identifies MXL as a potent NLRP3 inflammasome inhibitor, warranting further investigation as a potential therapeutic agent for inflammatory diseases.

BackgroundMultidrug-resistant (MDR) Gram-negative bacteria have triggered a critical global health crisis. Polymyxin lipopeptide antibiotics are used as a last-line therapy against these problematic pathogens, but their clinical use is largely limited by severe nephrotoxicity. Human oligopeptide transporter 2 (hPepT2) is a membrane transporter mediating the reabsorption of polymyxins in renal proximal tubular cells, substantially contributing to their nephrotoxicity. However, it remains unclear how polymyxins interact with hPepT2. MethodsIn this study, we investigated the structure-interaction relationship (SIR) of polymyxins with hPepT2 by integrating computational, chemical and cell biology approaches. Bioinformatic modelling predicted the residues essential for the binding of polymyxins with hPepT2. Transporter mutagenesis and molecular analysis were employed to explore the role of each residue in the interaction of hPepT2 and polymyxins. Moreover, we synthesised a series of polymyxin-like analogues with altering the moieties that are critical for binding with hPepT2. The antibacterial activity and nephrotoxicity of these analogues were subsequently assessed. ResultsOur bioinformatic modelling proposed an outward-facing structure of hPepT2 with a possible transport pathway that polymyxins bind to the lateral opening site of hPepT2 (e.g. E214, D215, D317, D342, E622). Molecular assays for transporter function and expression confirmed that D215 residue of hPepT2 is critical for polymyxin binding, while several other residues significantly impact on transporter turnover rate and/or protein expression. Our experimental validations showed that the lipopeptide analogues with altering the Dab1, Dab3, Dab5 and Dab9 moieties of polymyxins demonstrated decreased interactions with hPepT2. Among these synthetic analogues, alanine substitution at Dab3 showed reduced nephrotoxicity in mice while reserved antibacterial activity against a range of bacterial strains. ConclusionsOverall, this proof-of-concept study demonstrated that the computationally predicted and experimentally validated polymyxin-hPepT2 SIR model provides a viable approach for the discovery of novel, safer lipopeptide antibiotics.

Single-chain variable fragments (scFvs) are widely used in diagnostic and therapeutic applications. These antibody fragments comprise two antibody variable domains connected by a flexible peptide linker whose properties critically influence folding, stability, oligomeric state, and antigen-binding. Therefore, careful linker selection represents a key step in scFv design. Guanylyl Cyclase C (GUCY2C) is a tumor-associated cell surface receptor expressed in gastrointestinal malignancies, including more than 90% of colorectal cancer (CRC) cases across all disease stages. Its restricted physiological expression pattern makes GUCY2C an attractive target for immunotherapy and precision oncology therapies. Here, we investigated the structural and functional consequences of incorporating alternative linker designs into an anti-GUCY2C scFv. Using molecular modeling, protein-protein docking, and molecular dynamics (MD) simulations, we evaluated the conformational stability, interdomain organization, and antigen-binding interactions of each construct. Our results provide a dynamic, structure-based assessment of how linker composition influences GUCY2C recognition and scFv structural behavior. Furthermore, this work establishes a computational framework for the rational optimization of GUCY2C-targeted antibody fragments.