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Cytochrome P450 2B4 (CYP 2B4) is one of the best-characterized CYPs and serves as a key model system for understanding the mechanisms of microsomal class II CYPs, which metabolize most known drugs. The highly flexible nature of CYP 2B4 is apparent from crystal structures that show the active site with either a wide open or a closed heme binding cavity. Here, we investigated the conformational ensemble of the full-length CYP 2B4 in a phospholipid bilayer, using multiresolution molecular dynamics (MD) simulations. Coarse-grained MD simulations revealed two predominant orientations of CYP 2B4's globular domain with respect to the bilayer. Their refinement by atomistic resolution MD showed adaptation of the enzyme's interaction with the lipid bilayer, leading to open configurations that facilitate ligand access to the heme binding cavity. CAVER analysis of enzyme tunnels, AquaDuct analysis of water routes, and Random Acceleration Molecular Dynamics simulations of ligand dissociation support the conformation-dependent passage of molecules between the active site and the protein surroundings. Furthermore, simulation of the re-entry of the inhibitor bifonazole into the open conformation of CYP 2B4 resulted in binding at a transient hydrophobic pocket within the active site cavity that may play a role in substrate binding or allosteric regulation. Together, these results show how the open conformation of CYP 2B4 facilitates the binding of substrates from and release of products to the membrane, whereas the closed conformation prolongs the residence time of substrates or inhibitors and selectively allows the passage of smaller reactants via the solvent and water channels.

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Calculation of binding free energies between a protein and a ligand are highly desired for computer-aided drug design. Here we approximate the binding energies of ABL1, an enzyme which is the target for drugs used in the treatment of chronic myeloid leukaemia, with minimal models and density functional theory (DFT). Starting from the crystal structures of protein-drug complexes, we estimated the binding free energies having used all available individual molecules (protein chains) within each structure, not only a single one as commonly used, in order to see if the choice of the protein chain is important in such calculations. Differences were observed between chains in the same file. Energy decomposition analysis (EDA) revealed that the most important factors for binding were exchange, repulsion and electrostatics. The desolvation term varied dramatically between the inhibitors (between 4.2 and 92.3 kcal/mol). All functionals showed similar patterns in the EDA and in discriminating between the ligands. Non-covalent interactions (NCI) analysis was used to further explain the differences between protein chains and functionals. Overall, it is shown that small minimal models of a drug binding site can be useful to infer on the suitability of an initial crystal structure for further analysis such as EDA.

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Anionic redox chemistry has attracted increasing attention for the improvement in the reversible capacity and energy density of cathode materials in Li/Na-ion batteries. However, adverse electrochemical behaviors, such as voltage hysteresis and sluggish kinetics resulting from weak metal-ligand interactions, commonly occur with anionic redox reactions. Currently, the mechanistic investigation driving these issues still remains foggy. Here, we chemically designed Na0.8Fe0.4Ti0.6S2 and Na0.8Fe0.4Ti0.6O2 as model cathodes to explore the covalency effects on metal-ligand interactions during anionic redox process. Na0.8Fe0.4Ti0.6S2 with strengthened covalent interaction of metal-ligand bonds exhibits smaller voltage hysteresis and faster kinetics than Na0.8Fe0.4Ti0.6O2 during (de)sodiation process. Theoretical calculations suggest that Fe is the dominant redox-active center in Na0.8Fe0.4Ti0.6S2, whereas the redox-active center moves from Fe to O with the removal of Na+ in Na0.8Fe0.4Ti0.6O2. We attribute the above different redox behaviors between Na0.8Fe0.4Ti0.6S2 and Na0.8Fe0.4Ti0.6O2 to the charge transfer kinetics from ligand to metal. Moreover, the structural stability of Na0.8Fe0.4Ti0.6S2 is enhanced by increasing the cation migration barriers through strong metal-ligand bonds during desodiation. These insights into the originality of metal-ligand interactions provide guidance for the design of high-capacity and structurally stable cathode materials for Li/Na-ion batteries.

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Quantitative tools to compile and analyze biomolecular interactions among chemically diverse binding partners would improve therapeutic design and aid in studying molecular evolution. Here we present Mapping Areas of Genetic Parsimony In Epitopes (MAGPIE), a publicly available software package for simultaneously visualizing and analyzing thousands of interactions between a single protein or small molecule ligand (the "target") and all of its protein binding partners ("binders"). MAGPIE generates an interactive three-dimensional visualization from a set of protein complex structures that share the target ligand, as well as sequence logo-style amino acid frequency graphs that show all the amino acids from the set of protein binders that interact with user-defined target ligand positions or chemical groups. MAGPIE highlights all the salt bridge and hydrogen bond interactions made by the target in the visualization and as separate amino acid frequency graphs. Finally, MAGPIE collates the most common target-binder interactions as a list of "hotspots," which can be used to analyze trends or guide the de novo design of protein binders. As an example of the utility of the program, we used MAGPIE to probe how different antibody fragments bind a viral antigen; how a common metabolite binds diverse protein partners; and how two ligands bind orthologs of a well-conserved glycolytic enzyme for a detailed understanding of evolutionarily conserved interactions involved in its activation and inhibition. MAGPIE is implemented in Python 3 and freely available at https://github.com/glasgowlab/MAGPIE, along with sample datasets, usage examples, and helper scripts to prepare input structures.

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The main protease (Mpro) of SARS-CoV-2 is an essential enzyme that plays a critical part in the virus's life cycle, making it a significant target for developing antiviral drugs. The inhibition of SARS-CoV-2 Mpro has emerged as a promising approach for developing therapeutic agents to treat COVID-19. This review explores the structure of the Mpro protein and analyzes the progress made in understanding protein-ligand interactions of Mpro inhibitors. It focuses on binding kinetics, origin, and the chemical structure of these inhibitors. The review provides an in-depth analysis of recent clinical trials involving covalent and non-covalent inhibitors and emerging dual inhibitors targeting SARS-CoV-2 Mpro. By integrating findings from the literature and ongoing clinical trials, this review captures the current state of research into Mpro inhibitors, offering a comprehensive understanding of challenges and directions in their future development as anti-coronavirus agents. This information provides new insights and inspiration for medicinal chemists, paving the way for developing more effective Mpro inhibitors as novel COVID-19 therapies.

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The long story of NK cells started about 50 y ago with the first demonstration of a natural cytotoxic activity within an undefined subset of circulating leukocytes, has involved an ever-growing number of researchers, fascinated by the apparently easy-to-reach aim of getting a "universal anti-tumor immune tool". In fact, in spite of the impressive progress obtained in the first decades, these cells proved far more complex than expected and, paradoxically, the accumulating findings have continuously moved forward the attainment of a complete control of their function for immunotherapy. The refined studies of these latter years have indicated that NK cells can epigenetically calibrate their functional potential, in response to specific environmental contexts, giving rise to extraordinarily variegated subpopulations, comprehensive of memory-like cells, tissue-resident cells, or cells in various differentiation stages, or distinct functional states. In addition, NK cells can adapt their activity in response to a complex body of signals, spanning from the interaction with either suppressive or stimulating cells (myeloid-derived suppressor cells or dendritic cells, respectively) to the engagement of various receptors (specific for immune checkpoints, cytokines, tumor/viral ligands, or mediating antibody-dependent cell-mediated cytotoxicity). According to this picture, the idea of an easy and generalized exploitation of NK cells is changing, and the way is opening toward new carefully designed, combined and personalized therapeutic strategies, also based on the use of genetically modified NK cells and stimuli capable of strengthening and redirecting their effector functions against cancer.

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Occupancy of prostate cancer (PCa) cell androgen receptors (AR) signals proliferation, therefore testosterone biosynthesis inhibitors and AR antagonists are important PCa treatments. Conversely, androgen mimics (e.g., prednisone) used in management of PCa might cause proliferation. The balance between PCa proliferation and inhibition predicts treatment success. We used in silico molecular modelling to explore interactions between ARs, androgens (testosterone, dihydrotestosterone (DHT)) and drugs used to treat (bicalutamide) and manage (dexamethasone, prednisone, hydrocortisone) PCa. We found that hydrogen (H-) bonds between testosterone, DHT and Arg752, Asn705 and Thr877 followed by ligand binding cleft hydrophobic interactions signal proliferation, whereas bicalutamide antagonism is via Phe764 interactions. Hydrocortisone, dexamethasone and prednisone H-bond Asn705 and Thr877, but not Arg752 in the absence of a water molecule. Studies with a bicalutamide agonist AR mutation showed different amino acid interactions, indicating testosterone and DHT would not promote proliferation as effectively as via the native receptor. However, hydrocortisone and bicalutamide form Arg752 and Asn705 H-bonds indicating agonism. Our results suggest that as PCa progresses the resulting mutations will change the proliferative response to androgens and their drug mimics, which have implications for the treatment of prostate cancer.

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Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein-ligand structures and affinity data make it possible to develop machine-learning models focused on specific protein systems with superior predictive performance. Here, we report a new methodology named SAnDReS that combines AutoDock Vina 1.2 with 54 regression methods available in Scikit-Learn to calculate binding affinity based on protein-ligand structures. This approach allows exploration of the scoring function space. SAnDReS generates machine-learning models based on crystal, docked, and AlphaFold-generated structures. As a proof of concept, we examine the performance of SAnDReS-generated models in three case studies. For all three cases, our models outperformed classical scoring functions. Also, SAnDReS-generated models showed predictive performance close to or better than other machine-learning models such as KDEEP, CSM-lig, and ΔVinaRF20. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.

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In the field of human health research, the homeostasis of copper (Cu) is receiving increased attention due to its connection to pathological conditions, including diabetes mellitus (DM). Recent studies have demonstrated that proteins associated with Cu homeostasis, such as ATOX1, FDX1, ATP7A, ATPB, SLC31A1, p53, and UPS, also contribute to DM. Cuproptosis, characterized by Cu homeostasis dysregulation and Cu overload, has been found to cause the oligomerization of lipoylated proteins in mitochondria, loss of iron-sulfur protein, depletion of glutathione, production of reactive oxygen species, and cell death. Further research into how cuproptosis affects DM is essential to uncover its mechanism of action and identify effective interventions. In this article, we review the molecular mechanism of Cu homeostasis and the role of cuproptosis in the pathogenesis of DM. The study of small-molecule drugs that affect these proteins offers the possibility of moving from symptomatic treatment to treating the underlying causes of DM.

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While they account for a large portion of drug targets, membrane proteins (MPs) present a unique challenge for drug discovery. Peripheral membrane proteins (PMPs), a class of proteins that bind reversibly to membranes, are also difficult targets, particularly those that function only while bound to membranes. The protein-membrane interface in PMPs is often where functional interactions and catalysis occur, making it a logical target for inhibition. However, interfaces are underexplored spaces in inhibitor design and there is a need for enhanced methods for small-molecule ligand discovery. In an effort to better initiate drug discovery efforts for PMPs, this study presents a screening methodology using membrane-mimicking reverse micelles (mmRM) and NMR-based fragment screening to assess ligandability in the protein-membrane interface. The proof-of-principle target, glutathione peroxidase 4 (GPx4), is a lipid hydroperoxidase which is essential for the oxidative protection of membranes and thereby the prevention of ferroptosis. GPx4 inhibition is promising for therapy-resistant cancer therapy, but current inhibitors are generally covalent ligands with limited clinical utility. Presented here is the discovery of non-covalent small-molecule ligands for membrane-bound GPx4 revealed through the mmRM fragment screening methodology. The fragments were tested against GPx4 in bulk aqueous conditions and displayed little to no binding to the protein without embedment into the membrane. The 9 hits had varying affinities and partitioning coefficients and revealed properties of fragments that bind within the protein-membrane interface. Additionally, a secondary screen confirmed the potential to progress the fragments by enhancing the affinity from > 200 µM to ~15 µM with the addition of certain hydrophobic groups. This study presents an advancement of screening capabilities for membrane associated proteins, reveals ligandability within the GPx4 protein-membrane interface, and may serve as a starting point for developing non-covalent inhibitors of GPx4.

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Aristolochic acids I and II (AA-I/II) are carcinogenic principles of Aristolochia plants, which have been employed in traditional medicinal practices and discovered as food contaminants. While the deleterious effects of AAs are broadly acknowledged, there is a dearth of information to define the mechanisms underlying their carcinogenicity. Following bioactivation in the liver, N-hydroxyaristolactam and N-sulfonyloxyaristolactam metabolites are transported via circulation and elicit carcinogenic effects by reacting with cellular DNA. In this study, we apply DNA adduct analysis, X-ray crystallography, isothermal titration calorimetry, and fluorescence quenching to investigate the role of human serum albumin (HSA) in modulating AA carcinogenicity. We find that HSA extends the half-life and reactivity of N-sulfonyloxyaristolactam-I with DNA, thereby protecting activated AAs from heterolysis. Applying novel pooled plasma HSA crystallization methods, we report high-resolution structures of myristic acid-enriched HSA (HSAMYR) and its AA complexes (HSAMYR/AA-I and HSAMYR/AA-II) at 1.9 Å resolution. While AA-I is located within HSA subdomain IB, AA-II occupies subdomains IIA and IB. ITC binding profiles reveal two distinct AA sites in both complexes with association constants of 1.5 and 0.5 · 106 M-1 for HSA/AA-I versus 8.4 and 9.0 · 105 M-1 for HSA/AA-II. Fluorescence quenching of the HSA Trp214 suggests variable impacts of fatty acids on ligand binding affinities. Collectively, our structural and thermodynamic characterizations yield significant insights into AA binding, transport, toxicity, and potential allostery, critical determinants for elucidating the mechanistic roles of HSA in modulating AA carcinogenicity.

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The N-methyl-d-aspartate (NMDA) receptors, which belong to the ionotropic Glutamate receptors, constitute a family of ligand-gated ion channels. Within the various subtypes of NMDA receptors, the GluN1/2A subtype plays a significant role in central nervous system (CNS) disorders. The present article aims to provide a comprehensive review of ligands targeting GluN2A-containing NMDA receptors, encompassing negative allosteric modulators (NAMs), positive allosteric modulators (PAMs) and competitive antagonists. Moreover, the ligands' structure-activity relationships (SARs) and the binding models of representative ligands are also discussed, providing valuable insights for the clinical rational design of effective drugs targeting CNS diseases.

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The current study addresses the growing demand for sustainable plant-based cheese alternatives by employing molecular docking and deep learning algorithms to optimize protein-ligand interactions. Focusing on key proteins (zein, soy, and almond protein) along with tocopherol and retinol, the goal was to improve texture, nutritional value, and flavor characteristics via dynamic simulations. The findings demonstrated that the docking analysis presented high accuracy in predicting conformational changes. Flexible docking algorithms provided insights into dynamic interactions, while analysis of energetics revealed variations in binding strengths. Tocopherol exhibited stronger affinity (-5.8Kcal/mol) to zein compared to retinol (-4.1Kcal/mol). Molecular dynamics simulations offered comprehensive insights into stability and behavior over time. The integration of machine learning algorithms improved the classification and the prediction accuracy, achieving a rate of 71.59%. This study underscores the significance of molecular understanding in driving innovation in the plant-based cheese industry, facilitating the development of sustainable alternatives to traditional dairy products.

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A crucial feature of life is its spatial organization and compartmentalization on the molecular, cellular, and tissue levels. Spatial transcriptomics (ST) technology has opened a new chapter of the sequencing revolution, emerging rapidly with transformative effects across biology. This technique produces extensive and complex sequencing data, raising the need for computational methods for their comprehensive analysis and interpretation. We developed the ST browser web tool for the interactive discovery of ST images, focusing on different functional aspects such as single gene expression, the expression of functional gene sets, as well as the inspection of the spatial patterns of cell-cell interactions. As a unique feature, our tool applies self-organizing map (SOM) machine learning to the ST data. Our SOM data portrayal method generates individual gene expression landscapes for each spot in the ST image, enabling its downstream analysis with high resolution. The performance of the spatial browser is demonstrated by disentangling the intra-tumoral heterogeneity of melanoma and the microarchitecture of the mouse brain. The integration of machine-learning-based SOM portrayal into an interactive ST analysis environment opens novel perspectives for the comprehensive knowledge mining of the organization and interactions of cellular ecosystems.

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Multivalent receptor-ligand interactions (RLIs) exhibit excellent affinity for binding when targeting cell membrane receptors with low expression. However, existing strategies only allow for limited control of the valency and spacing of ligands for a certain receptor, lacking recognition patterns for multiple interested receptors with complex spatial distributions. Here, we developed flexible DNA nanoclaws with multivalent aptamers to achieve powerful cell recognition by controlling the spacing of aptamers to match the spatial patterns of receptors. The DNA nanoclaw with spacing-controllable binding sites was constructed via hybrid chain reaction (HCR), enabling dual targeting of HER2 and EpCAM molecules. The results demonstrate that the binding affinity of multivalent DNA nanoclaws to tumor cells is enhanced. We speculate that the flexible structure may conform better to irregularly shaped membrane surfaces, increasing the probability of intermolecular contact. The capture efficiency of circulating tumor cells successfully verified the high affinity and selectivity of this spatial pattern. This strategy will further promote the potential application of DNA frameworks in future disease diagnosis and treatment.

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A common approach in therapeutic protein development involves employing synthetic ligands with multivalency, enabling sophisticated control of signal transduction. Leveraging the emerging concept of liquid-liquid phase separation (LLPS) and its ability to organize cell surface receptors into functional compartments, we herein have designed modular ligands with phase-separation modalities to engineer programmable interreceptor communications and precise control of signal pathways, thus inducing the rapid, potent, and specific apoptosis of tumor cells. Despite their simplicity, these "triggers", named phase-separated Tumor Killers (hereafter referred to as psTK), are sufficient to yield interreceptor clustering of death receptors (represented by DR5) and tumor-associated receptors, with notable features: LLPS-mediated robust high-order organization, well-choreographed conditional activation, and broad-spectrum capacity to potently induce apoptosis in tumor cells. The development of novel therapeutic proteins with phase-separation modalities showcases the power of spatially reorganizing signal transduction. This approach facilitates the diversification of cell fate and holds promising potential for targeted therapies against challenging tumors.

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A new adsorbent based on commercial granular activated carbon (GAC) and loaded with Cu(II) (GAC-Cu) was prepared to enhance the adsorption capacity of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS). The surface area (SA) and pore volume of GAC-Cu decreased by ∼15% compared to those of pristine GAC. The scanning electron microscopy-energy dispersive spectrometry (SEM-EDS) and leaching test results indicated that, compared with GAC, the Cu atomic ratio and Cu amount in GAC-Cu increased by 2.91 and 2.43 times, respectively. The point of zero charge (PZC) measured using a salt addition method obtained a pH of 6.0 (GAC) and 5.0 (GAC-Cu). According to the isotherm models obtaining highest coefficient of determination (R2), GAC-Cu exhibited a 20.4% and 35.2% increase for PFOA and PFOS in maximum uptake (qm), respectively, compared to those of GAC. In addition, the adsorption affinity (b) for GAC-Cu increased by 1045% and 175% for PFOA and PFOS, respectively. The pH effect on the adsorption capacity of GAC-Cu was investigated. The uptake of PFOA and PFOS decreased with an increase in pH for both GAC and GAC-Cu. GAC-Cu exhibited higher uptake than GAC at pH 6 and 7, but no enhanced uptake was observed at pH 4.0, 5.0, and 8.5. Therefore, ligand interaction was effective at weak acid or neutral pH.

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Schizophrenia is a serious mental illness with unknown etiology, and shows increasing incidence and high lifetime prevalence rate. The main receptors related to the disease are DRD2 and 5-HTR2A. Thus, a comprehensive understanding of the interaction mode between antipsychotic drugs with relevant receptors is very important for developing more effective drugs. 5-HTR2A-SNAP-Tag/CMC and DRD2-SNAP-Tag/CMC models constructed in this work provided a new method for studying the interaction between atypical antipsychotics and the two receptors. The results of comparative experiments showed that the new models not only met the high selectivity and specificity of the screening requirements but were also more stable and long-lasting than the traditional CMC model. Binding assays showed that the effects of three atypical antipsychotics (including clozapine, olanzapine, and quetiapine) on 5-HTR2A were stronger than their effects on DRD2. Additionally, two potentially active components, magnolol and honokiol, were screened in Magnolia officinalis methanol extract using the 5-HTR2A-SNAP-Tag/CMCHPLC-MS system. Nonlinear chromatographic analysis and molecular docking were conducted to study the interactions between screened compounds and the two receptors. The binding constants (KA) of magnolol and honokiol with 5-HTR2A were 17,854 ± 1,117 M-1 and 38,858 ± 4,964 M-1, respectively, and KA values with DRD2 were 4,872 ± 1,618 M-1 and 20,692 ± 10,267 M-1, respectively. We concluded that the established models are reliable for studying receptor-ligand interactions and screening antagonists of schizophrenia.

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Staphylococcus aureus, a gram-positive bacterial pathogen, develops antibiotic resistance partly through enhanced activity of transmembrane multi-drug efflux pump proteins like NorA. Being a prominent member of the Major Facilitator Superfamily (MFS), NorA transports various small molecules including hydrophilic fluoroquinolone antibiotics across the cell membrane. Intriguingly, NorA is inhibited by a structurally diverse set of small molecule inhibitors as well, indicating a highly promiscuous ligand/inhibitor recognition. Our study aims to elucidate the structural facets of this promiscuity. Known NorA inhibitors were grouped into five clusters based on chemical class and docked into ligand binding pockets on NorA conformations generated via molecular dynamics simulations. We discovered that several key residues, such as I23, E222, and F303, are involved in inhibitor binding. Additionally, residues I244, T223, F303, and F140 were identified as prominent in interactions with specific ligand clusters. Our findings suggest that NorA's substrate binding site, encompassing residues aiding ligand recognition based on chemical nature, facilitates the recognition of chemically diverse ligands. This insight into NorA's structural promiscuity in ligand recognition not only enhances understanding of antibiotic resistance mechanisms in S. aureus but also sets the stage for the development of more effective efflux pump inhibitors, vital for combating multidrug resistance.Communicated by Ramaswamy H. Sarma.

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We present an optimization of Reverse NOE-pumping (RNP) in order to observe the 1H signals of ligands bound to proteins. Although various ligand-based NMR screening methods have been proposed, the most frequently used method has been Saturation-Transfer Difference (STD), owing to the relatively easy setup of experiments. Yet the critical point of STD is the selective irradiation of protein without irradiating ligand, and thus the STD technique is unable to observe 1H ligand signals, which resonate across the entire 1H spectral width. In the present study, the RNP experiment has been improved to develop an effective NMR-based screening technique. The optimized RNP spectra reveal less subtraction artifacts and phase distortion than the original RNP spectra, indicating its applicability to any type of ligand molecules.