Gingipain B is a protease released by Porphyromonas gingivalis, which contributes to the development of diseases such as periodontitis, Alzheimer's disease, rheumatoid arthritis and cancer. Therefore, it could be a key target for the design of inhibitors that could be used in new drug therapies. In this search for inhibitors, it is interesting to study their potentiation by Zn2+, mainly because this divalent cation can be a common additive in toothpastes or mouthwashes. To understand this potentiation process, the structural aspects that determine the effect of Zn2+ on the different potentiation of gingipain B inhibition by three inhibitors with benzamidine group were studied. For this purpose, molecular dynamics simulations of the enzyme/inhibitor complex, in the presence or absence of Zn2+, were performed to identify the residues involved in the interaction with the inhibitor or with the cation. From the simulations it is observed that the compounds that are potentiated by Zn interact with the ion via water molecules, which are oriented towards the carbonyl functional group. In addition, a higher solvent exposure of the catalytic site and a lower flexibility of a region adjacent to cysteine 244, which participates in catalysis, are observed in the simulations. The structural and dynamic results obtained through molecular dynamics simulations support the hypothesis that the potentiation of gingipain B inhibition by Zn2+ is associated with ion-mediated ligand stabilization, leading to a decrease in the number of water molecules in the second hydration shell, as well as polarization of the molecules in the vicinity of Zn2+. Taken together, these findings open new avenues for the design of inhibitors whose activity can be modulated or enhanced by the presence of Zn2+, with potential applications in the development of innovative therapeutic strategies.

Arginine-specific gingipain B (RgpB), a key cysteine protease from Porphyromonas gingivalis, is associated with several systemic diseases. It is synthesized as a zymogen bound to a propeptide inhibitor that blocks its catalytic activity until activation in the extracellular environment. To identify inhibitory peptide variants with enhanced affinity, mutants of the propeptide inhibitory loop were designed. A total of 52 mutants were generated and, for each model, four independent molecular dynamics replicas were performed, followed by binding free energy calculations using MM/GBSA. Most variants exhibited more favorable binding affinities than the wild-type (WT) loop (-109.8 ± 4.9 kcal mol-1). Among them, V126K (-128.1 ± 5.6 kcal mol-1), E131D (-120.6 ± 8.2 kcal mol-1), and N132R (-135.8 ± 4.6 kcal mol-1) emerged as promising, with the first two showing statistically significant improvements using ANOVA followed by Tukey's post-hoc test (p = 0.001). Thermodynamic integration calculations were consistent with increased binding affinity for the selected variants. Thermodynamic integration calculations further confirmed a significant increase in the relative binding affinity for the selected variants compared to the wild type. These results provide a solid basis for future in vitro validation and establish an in silico framework to accelerate the rational design of propeptide-based inhibitors and the development of novel therapeutic approaches targeting RgpB.

Agronomic modulation of secondary metabolism may influence not only crop resilience but also the biological activity of fruit-derived phytochemicals. In this study, we evaluated the impact of exogenous methyl jasmonate (MeJA) application under contrasting water regimes on the selected phenolic compounds and vascular bioactivity of Vaccinium corymbosum L. cv. Legacy. Antioxidant capacity was assessed by FRAP and DPPH assays, phytochemical composition was characterized by HPLC-DAD, and antiplatelet activity was evaluated through inhibition of TRAP-6-induced P-selectin (CD62P) expression in human platelets. Selected phenolic constituents were further examined using molecular docking and molecular dynamics simulations against a platelet receptor model. Although MeJA treatment altered the abundance of selected phenolic compounds identified by HPLC-DAD, total antioxidant capacity remained largely unchanged. Blueberry extracts significantly inhibited platelet activation in a concentration-dependent manner without cytotoxic effects, and antiplatelet potency was not strictly related to global antioxidant indices. Computational analyses revealed stable ligand-receptor interactions and favorable binding free energies for selected phenolics, providing a structural explanation for receptor-level modulation. These findings suggest that elicitor-driven responses in blueberries can influence platelet functional responses and highlight the importance of qualitative phytochemical composition in determining vascular bioactivity. This multiscale approach connects plant stress physiology, natural product chemistry, and human platelet biology, underscoring the translational relevance of agronomic strategies for nutraceutical functionality.

Fruit aroma is a key determinant of organoleptic quality in strawberry (Fragaria × ananassa), with volatile esters being the major contributors to its characteristic scent. Alcohol dehydrogenases (ADHs) catalyze the conversion of aldehydes into alcohols, which serve as essential precursors for ester biosynthesis via alcohol acyltransferases (AAT). Despite their potential role in aroma formation, the molecular identity and functional properties of ADH family members in strawberry remain largely unexplored. Here, we present a comprehensive genome-wide analysis of the FaADH gene family in the cultivated strawberry genome. Eleven FaADH genes were identified and characterized based on gene structure, phylogeny, and promoter cis-element composition. Expression profiling across five fruit developmental stages revealed dynamic regulation of several isoforms, with FaADH5, FaADH8, and FaADH11 showing strong expression during early fruit development. These patterns were consistent with biochemical assays, which showed declining total ADH enzymatic activity as ripening progressed. Homology-based protein modeling and molecular docking predicted differential substrate affinities among isoforms, with FaADH5 and FaADH8 displaying favorable binding to 1-butanol and 1-hexanol. Molecular dynamics (MD) simulations further confirmed the structural stability of these interactions over time. The integration of expression, activity, and structural data supports a functional specialization within the FaADH family, with select isoforms likely contributing directly to the alcohol pool required for ester formation. These findings provide new insights into the molecular basis of aroma development in strawberry and highlight FaADH genes as potential targets for breeding or metabolic engineering aimed at improving fruit flavor.

Helicobacter pylori urease is a key virulence factor and a validated target for anti-infective strategies. In this study, a comprehensive computational workflow was applied to identify potential urease inhibitors through a drug repurposing approach. A curated library was first filtered using permeability-related descriptors and multiparametric scoring. The resulting compounds were evaluated through ensemble and consensus docking across multiple protein conformations and docking engines, followed by XP rescoring, metal-ligand distance analysis, and molecular dynamics simulations. Binding stability and thermodynamic profiles were further assessed using MM-GBSA and well-tempered metadynamics. This integrative strategy led to the identification of several candidate compounds exhibiting favorable docking scores, stable coordination with the catalytic Ni2+ center, and consistent binding behavior during molecular dynamics simulations. Notably, selected compounds showed improved relative binding free energy profiles compared to reference inhibitors within the applied computational framework. Overall, this study provides a robust computational pipeline for urease inhibitor identification and highlights repurposed compounds as promising candidates for further experimental validation.

Calcium-mediated coordination plays a central role in determining the structural and functional properties of alginate-based polymer networks. Here, we investigate how calcium stoichiometry modulates ligand confinement and emergent biological response in calcium-alginate systems using methyl jasmonate (MeJA) as a model bioactive compound. An integrated multiscale approach combining molecular dynamics simulations with physicochemical and cellular analyses was employed. Atomistic simulations revealed that increasing Ca²⁺ coordination density (Alg:Ca²⁺ 1:2) produces a more compact and energetically stabilized polymer network, characterized by reduced solvent-accessible surface area, lower radius of gyration, enhanced ion-mediated contacts, and decreased MeJA diffusion. These descriptors indicate tighter ligand confinement within a coordination-driven matrix. Experimental validation through ATR-FTIR, thermogravimetric, and calorimetric analyses confirmed enhanced structural cohesion and thermal stability in highly crosslinked formulations, supporting competitive coordination between MeJA carbonyl groups and Ca²⁺ ions. Importantly, these structural differences translated into distinct biological outcomes. Only formulations exhibiting higher coordination density induced significant cytotoxicity in AGS gastric cancer cells, demonstrating that modulation of matrix organization directly influences functional response. Collectively, this study establishes calcium coordination density as a tunable structural parameter controlling ligand dynamics and biological performance, highlighting the predictive value of molecular modeling for rational design of coordination-engineered biopolymer systems.

The [Formula: see text] transporter AE4 (SLC4A9) plays a role in NaCl reabsorption and pH sensing in the kidney, and Cl--dependent fluid secretion in salivary glands. Sharing functional features with other Cl-/[Formula: see text] exchangers and Na+-[Formula: see text] cotransporters, it has been proposed that AE4 mediates Cl-/cation-[Formula: see text] exchange. Our sequence alignments and molecular dynamics (MD) analysis showed that three residues, reported as critical for transport activity in other SLC4 transporters, are conserved in AE4, suggesting similarities in their ion transport mechanism. Site-directed mutagenesis and further functional experiments showed that two out of the three conserved residues (D709 and T448) are functionally relevant, but in contrast to other SLC4 transporters, where transport was almost completely abolished, AE4 mutants conserved about 50% of transport activity. In addition, alanine scanning showed that S446A and T756A decreased transport by nearly 30%. Consistent with an additive effect of mutations at positions T756 and T448, the double mutant T756A-T448I completely abolished transport in the presence of extracellular Na+ but interestingly exhibited anion transporter activity in the presence of K+ as the main extracellular cation. MD simulations revealed that the [Formula: see text] and cation coordination site is at the interface between the transmembrane segments TM3-TM10. The interaction network was importantly disrupted in the double mutant in the presence of Na+, but it is partially conserved in the presence of K+, suggesting differences in the cation coordination. In summary, we identified the putative cation coordination site of AE4 and the critical functional role of residues T756 and T448 in its transport cycle.NEW & NOTEWORTHY AE4 is a [Formula: see text] transporter that is important for electrolyte transport and pH regulation in epithelia, which has been proposed to mediate Cl-/cation-[Formula: see text] exchange. However, critical residues sustaining transport activity are not known. In this study, we show that AE4 can coordinate [Formula: see text] and Na+ or K+ at the TM3-TM10 interface and that residues T448 and T756 are crucial for cation binding and transport cycle.

The transient receptor potential vanilloid 1 (TRPV1) ion channel is a key mediator of pain and inflammation, making it a crucial target for developing new analgesics. Despite progress in understanding TRPV1's role, novel modulators that effectively inhibit nociceptive transduction are needed. Additionally, robust drug design protocols capable of exploring vast chemical space remain imperative. In this study, we present a computational framework combining machine learning (ML), virtual screening, ensemble molecular docking, molecular dynamics (MD) simulations, and MM-GBSA calculations to identify potential TRPV1 modulators among FDA-approved drugs. ML models trained on bioactivity data from the ChEMBL database classified 670 candidates from a library of FDA-approved drugs. Ensemble docking simulations, conducted on four TRPV1 cryo-EM structures representing different functional states, assessed binding interactions at the vanilloid site, a critical modulation domain. The top 20 candidates were further analyzed using MD simulations and MM-GBSA calculations to evaluate their stability and binding energetics. Among these, CYM-5442 (CA6), Rociletinib (CA7), and SC-51089 (CA9) demonstrated strong binding affinities and thermodynamic stability, outperforming known modulators such as Capsazepine and Capsaicin. These findings highlight the effectiveness of combining ML and molecular simulations in drug discovery, offering valuable insights into the identification of novel TRPV1 modulators as a starting point for further experimental validation and optimization in the development of next-generation analgesics targeting the TRPV1 channel.

Phosphatidylcholine (PC) is a crucial membrane phospholipid involved in both cellular processes and stress responses. CTP:phosphocholine cytidylyltransferase 1 (CCT1) is considered to catalyze a key regulatory step in primary PC de novo biosynthesis, but its functions and regulation are yet to be well elucidated. This study explored the physiological functions of CCT1 in Arabidopsis (Arabidopsis thaliana) (AthCCT1) in PC biosynthesis under normal growth conditions and in root development under osmotic stress, as well as its regulation by phosphorylation. Arabidopsis cct1 knockdown cct2 knockout lines exhibited significantly decreased PC intensities under normal growth conditions and impaired root growth under osmotic stress, which was rescued by AthCCT1 overexpression. Moreover, based on our previous findings that AthCCT1 is phosphorylated at Serine 187 (S187), we further investigated how this phosphorylation affects its biochemical and biological functions. The S187D phosphomimetic protein mutant of AthCCT1 exhibited reduced lipid-induced conformational changes and decreased enzymatic activity compared to the native protein. Molecular dynamics simulations of the S187D protein mutant revealed that the auto-inhibitory region, a conserved regulatory domain across CCT enzymes, remained closer to the αE helix, maintaining a constrained interaction between them. Consistent with the results of the in vitro analyses, overexpression of AthCCT1-S187D did not rescue stress-induced short-root phenotypes in cct1 knockdown cct2 knockout Arabidopsis lines. Taken together, the results revealed that AthCCT1 regulates PC biosynthesis under normal conditions and root development under osmotic stress, with its phosphorylation state at S187 playing an important role in modulating its enzymatic activity and physiological functions.

The HCO3- transporter AE4 (SLC4A9) plays a role in NaCl reabsorption and pH sensing in the kidney, and Cl--dependent fluid secretion in salivary glands. Sharing functional features with other Cl-/HCO3- exchangers and Na+-HCO3- cotransporters, it has been proposed that AE4 mediates Cl-/cation-HCO3- exchange. Our sequence alignments and molecular dynamics (MD) analysis showed that three residues, reported as critical for transport activity in other SLC4 transporters, are conserved in AE4, suggesting similarities in their ion transport mechanism. Site-directed mutagenesis and further functional experiments showed that two out of the three conserved residues (D709 and T448) are functionally relevant, but in contrast to other SLC4 transporters, where transport was almost completely abolished, AE4 mutants conserved about 50% of transport activity. In addition, alanine scanning showed that S446A and T756A decreased transport by nearly 30%. Consistent with an additive effect of mutations at positions T756 and T448, the double mutant T756A-T448I completely abolished transport in the presence of extracellular Na+ but interestingly exhibited anion transporter activity in the presence of K+ as the main extracellular cation. MD simulations revealed that the HCO(3)(-)and cation coordination site is at the interface between the transmembrane segments TM3-TM10. The interaction network was importantly disrupted in the double mutant in the presence of Na+, but it is partially conserved in the presence of K+, suggesting differences in the cation coordination. In summary, we identified the putative cation coordination site of AE4 and the critical functional role of residues T756 and T448 in its transport cycle. NEW & NOTEWORTHY AE4 is a HCO3- transporter that is important for electrolyte transport and pH regulation in epithelia, which has been proposed to mediate Cl-/cation-HCO(3)(-)exchange. However, critical residues sustaining transport activity are not known. In this study, we show that AE4 can coordinate HCO3- and Na+ or K+ at the TM3-TM10 interface and that residues T448 and T756 are crucial for cation binding and transport cycle.

Commercial strawberries (Fragaria x ananassa) are recognized worldwide for their exceptional fruit quality, which includes outstanding aroma, flavor, color, and nutritional value. Hydroperoxide lyase (HPL) enzymes are central to the biosynthesis of green leaf volatiles (GLVs), which contribute to fruit aroma and stress responses. Here, we identify and characterize four FaHPL genes in commercial strawberry using integrative genomic, structural, and expression approaches. Phylogenetic and motif analyses reveal evolutionary conservation of catalytic domains across isoforms, while promoter analysis suggests differential transcriptional regulation in response to hormonal and environmental cues. All four FaHPL genes are predominantly expressed during early fruit development, with FaHPL4 showing the highest expression during small green stage and with FaHPL3 displayed the strongest predicted substrate interaction based on molecular docking and dynamics simulations. This isoform exhibits enhanced stability in complex with HPOD and HPOT, consistent with an active enzymatic role. These data suggest a model in which FaHPL3 and FaHPL4 mediates GLV production in green-stage fruits, shaping the aroma profile and potentially contributing to defense. Finally, we propose a conceptual model in which GLVs predominate during early fruit development, mediated by high FaHPL expression, while volatile esters become dominant in ripe stages, associated with increased expression of alcohol acyltransferases (AATs) genes. Additionally, we propose that FaHPL3 and FaHPL4 are a promising target for functional studies aimed at modulating volatile composition in strawberry. This work contributes to our understanding of aroma biosynthesis dynamics and provides a foundation for future targeted breeding strategies in strawberry.

Nanostructured lipid carriers (NLCs) are widely investigated as versatile drug delivery systems. In this study, we aimed to design, synthesize, and characterize a novel fluorescently labeled NLC platform incorporating a rhodamine B-oleic acid conjugate (Rd-OA) as a proof-of-concept model for future therapeutic applications. NLCs were prepared using Precirol (R) ATO 5, oleic acid and Rd-OA, and Tween (R) 20 (T20) or Tween (R) 80 (T80). The resulting nanoparticles exhibited sizes of 350-500 nm, narrow polydispersity indices (<0.2), and highly negative zeta potentials (similar to-30 mV), confirming stable colloidal properties. Rd-OA was efficiently integrated (100 % incorporation efficiency) with different concentrations. Molecular dynamics simulations provided atomic-level structural insights, revealing surfactant-dependent differences in component organization consistent with experimental data. NLCs were stable over a period of three months. In vitro assays in keratinocytes cells demonstrated a concentration-dependent cytotoxicity effect influenced by Rd-OA content, while fluorescence microscopy confirmed efficient cellular internalization of the NLCs. Collectively, these findings support the developed fluorescent NLC system as a stable, reproducible, and traceable platform suitable for future adaptation in drug delivery and imaging applications."

Structure-based virtual screening (SBVS) is a fundamental approach in drug discovery, yet its predictive accuracy is highly dependent on methodological choices, scoring functions, and data processing strategies. This study systematically evaluates five protocol variants integrating molecular docking, induced-fit docking (IFD), quantum-polarized ligand docking (QPLD), ensemble docking (ED), and molecular mechanics/generalized Born surface area (MM-GBSA) in Helicobacter pylori urease employing four distinct crystallographic structures obtained from the protein data bank (PDB). We assess their predictive performance using statistical correlation metrics (Spearman and Pearson) and error-based measures (mean absolute error, root-mean-squared error, and inlier ratio metric). Additionally, we investigate the influence of data fusion techniquesminimum, median, arithmetic, geometric, harmonic, and Euclidean meansand varying numbers of docking poses (ranging from 1 to 100) on ligand ranking accuracy. Results indicate that MM-GBSA and ED consistently outperform other methods in compound ranking, although MM-GBSA exhibits higher errors in absolute binding energy predictions. While increasing the number of poses generally reduces predictive accuracy, the minimum fusion approach remains robust across all conditions. Comparisons between IC50 and pIC50 as experimental reference values reveal that pIC50 provides higher Pearson correlations, reinforcing its suitability for affinity prediction, while both metrics perform similarly in Spearman rankings. These findings refine SBVS workflows by optimizing scoring and pose aggregation strategies, highlighting the importance of method selection and data fusion techniques. The proposed framework enhances ligand prioritization in virtual screening campaigns and can be adapted to other therapeutic targets. Future research should explore adaptive scoring frameworks and machine-learning approaches to further improve the SBVS predictive reliability.

Phospholipase Cζ (PLCζ), a sperm-specific enzyme, plays a critical role in mammalian fertilization. Mutations in PLCζ have been linked to male infertility, as they impair its ability to trigger calcium (Ca2+) oscillations necessary for egg activation and embryo development. During fertilization, PLCζ is introduced into the egg, where it hydrolyzes phosphatidylinositol 4,5-bisphosphate (PIP2) into inositol 1,4,5-trisphosphate and diacylglycerol, leading to Ca2+ release from the endoplasmic reticulum. Human infertility-associated mutations include H233L, H398P, and R553P, which disrupt PLCζ function. To elucidate the molecular consequences of the mutations, we employed full-atom molecular dynamics simulations to analyze structural perturbations and their impact on PIP2 and Ca2+ binding. Our results reveal that H233L and H398P mutations significantly reduce interactions with PIP2, disrupting hydrogen bonding and salt bridge formation, leading to misalignment of the substrate. Additionally, these mutations destabilize Ca2+ binding by altering its positioning within the active site. In contrast, the R553P mutation primarily affects intramolecular stability and enzyme dynamics without impairing substrate or ion binding. Free energy calculations indicate an increased affinity for PIP2 in H233L and H398P mutants, leading to an aberrant substrate positioning and compromised hydrolysis. These structural insights help explain the egg activation failure and infertility of patients carrying these mutations.

Host- guest complexes are commonly found in several disciplines such as biochemistry, cosmetics, food, pharmaceuticals, and the environment. Studying the relationships between host and guest is essential in this context to understand their physicochemical behavior. This study aimed to examine the intermolecular interactions of cyclic alcohols within n- cyclodextrin (n- CD). The experimental spectroscopic results demonstrated the formation of the studied complexes. In this work, two orientations were used: orientation A (hydroxyl group toward the primary hydroxyl of n- CD) and orientation B (hydroxyl group toward the secondary hydroxyl of n- CD). The results indicate that regardless of the orientation used, the profile energy is thermodynamically favorable. However, there are differences in terms of greater or less stability in terms of the thermodynamic parameters studied. Physicochemical properties demonstrate that the host-guest complex forms spontaneously, and exothermic mode. The interaction between cyclic alcohols and n- CD in orientation A promotes a more pronounced deformation of the secondary edge of n- CD. Moreover, the arrangement of molecules demonstrates that intramolecular hydrogen bonds are less stable between the glycosidic units of n- CD. This arrangement may help or hinder the development of intermolecular hydrogen bonds.

Helicobacter pylori urease (HpU) plays a central role in bacterial survival and virulence by hydrolyzing urea into ammonia and carbon dioxide, neutralizing gastric acidity, and facilitating host colonization. The increasing prevalence of antibiotic resistance underscores the need for alternative strategies targeting essential bacterial enzymes such as urease. In this study, a multistage computational pipeline integrating pharmacophore modeling, machine learning (ML), ensemble docking, and enhanced molecular dynamics simulations were applied to identify novel triazole-based HpU inhibitors. Starting from over seven million compounds in the ZINC15 database, pharmacophore- and ML-based filters progressively reduced the chemical space to 7062 candidates. Ensemble docking across 25 conformational frames of HpU, followed by quantum-polarized ligand docking (QPLD), identified seven promising ligands exhibiting strong binding energies and stable metal coordination. Molecular dynamics (MD) simulations under progressively relaxed restraints revealed three highly stable complexes (CA1, CA3, and CA6). Subsequent well-tempered metadynamics (WT-MetaD) simulations reconstructed free-energy landscapes showing deep, localized basins for CA3 and CA6, comparable to the potent reference inhibitor DJM, supporting their potential as strong urease binders. Finally, unsupervised chemical space mapping using the UMAP algorithm positioned these candidates within molecular regions associated with potent urease inhibitors, further validating their structural coherence and pharmacophoric relevance. An ADMET assessment confirmed that the selected candidates exhibit physicochemical and early safety properties compatible with subsequent in vitro evaluation. This multilevel screening strategy demonstrates the power of combining ML-driven classification, ensemble docking, and enhanced sampling simulations to discover non-hydroxamic urease inhibitors. Although the current findings are computational, they provide a rational foundation for future in vitro validation and for expanding the discovery of triazole-based scaffolds targeting ureolytic enzymes.

Plant–microbe interactions exert a significant influence on host stress responses; however, the molecular mechanisms underlying these effects remain inadequately understood. In this study, we characterize FaMAN8, an α-mannosidase from Fragaria × ananassa, to explore its role in adaptation to heat waves and water deficit, as well as its modulation by fungal endophytes. Transcriptomic analysis identified FaMAN8 as the sole α-mannosidase isoform highly conserved across reported sequences, with root-specific induction under conditions of heat stress, deficient irrigation, and endophytic colonization. Structural modeling revealed that FaMAN8 exhibits the canonical domain organization of glycoside hydrolase family 38 (GH38) enzymes, featuring a conserved catalytic architecture and metal-binding site. Molecular docking and dynamics simulations with the Man3GlcNAc2 ligand indicated a stable binding pocket involving key catalytic residues and strong electrostatic complementarity. MM-GBSA and free energy landscape analyses further supported the thermodynamic stability of the protein–ligand complex. Cavity analysis revealed a larger active site in FaMAN8 compared to its homolog JbMAN, suggesting broader substrate accommodation. Collectively, these findings identify FaMAN8 as a stress-responsive glycosidase potentially involved in glycan remodeling during beneficial root–fungus interactions. This work provides molecular insights into plant–microbe symbiosis and lays the groundwork for microbiome-informed strategies to enhance crop stress resilience.

The heat and capsaicin receptor TRPV1 channel is widely expressed in nerve terminals of dorsal root ganglia (DRGs) and trigeminal ganglia innervating the body and face, respectively, as well as in other tissues and organs including central nervous system. The TRPV1 channel is a versatile receptor that detects harmful heat, pain, and various internal and external ligands. Hence, it operates as a polymodal sensory channel. Many pathological conditions including neuroinflammation, cancer, psychiatric disorders, and pathological pain, are linked to the abnormal functioning of the TRPV1 in peripheral tissues. Intense biomedical research is underway to discover compounds that can modulate the channel and provide pain relief. The molecular mechanisms underlying temperature sensing remain largely unknown, although they are closely linked to pain transduction. Prolonged exposure to capsaicin generates analgesia, hence numerous capsaicin analogs have been developed to discover efficient analgesics for pain relief. The emergence of in silico tools offered significant techniques for molecular modeling and machine learning algorithms to indentify druggable sites in the channel and for repositioning of current drugs aimed at TRPV1. Here we recapitulate the physiological and pathophysiological functions of the TRPV1 channel, including structural models obtained through cryo-EM, pharmacological compounds tested on TRPV1, and the in silico tools for drug discovery and repositioning.

Urease, a pivotal enzyme in nitrogen metabolism, plays a crucial role in various microorganisms, including the pathogenic Helicobacter pylori. Inhibiting urease activity offers a promising approach to combating infections and associated ailments, such as chronic kidney diseases and gastric cancer. However, identifying potent urease inhibitors remains challenging due to resistance issues that hinder traditional approaches. Recently, machine learning (ML)-based models have demonstrated the ability to predict the bioactivity of molecules rapidly and effectively. In this study, we present ML models designed to predict urease inhibitors by leveraging essential physicochemical properties. The methodological approach involved constructing a dataset of urease inhibitors through an extensive literature search. Subsequently, these inhibitors were characterized based on physicochemical properties calculations. An exploratory data analysis was then conducted to identify and analyze critical features. Ultimately, 252 classification models were trained, utilizing a combination of seven ML algorithms, three attribute selection methods, and six different strategies for categorizing inhibitory activity. The investigation unveiled discernible trends distinguishing urease inhibitors from non-inhibitors. This differentiation enabled the identification of essential features that are crucial for precise classification. Through a comprehensive comparison of ML algorithms, tree-based methods like random forest, decision tree, and XGBoost exhibited superior performance. Additionally, incorporating the “chemical family type” attribute significantly enhanced model accuracy. Strategies involving a gray-zone categorization demonstrated marked improvements in predictive precision. This research underscores the transformative potential of ML in predicting urease inhibitors. The meticulous methodology outlined herein offers actionable insights for developing robust predictive models within biochemical systems.

This paper addresses the reference tracking control problem for Medical Cyber-Physical Systems (MCPS). The control theory is employed to guarantee the suitable concentration of drugs in the body of patients to guarantee a safe treatment. The MCPS is modeled as a switched system, and the modes consider the different scenarios for the problem. A discrete-time model is utilized for the pharmacokinetic process, and the zero input control strategy is employed to design state-feedback controllers with a guaranteed exponential convergence rate. A numerical experiment is presented to illustrate the validity and effectiveness of our method.