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Search Results (2,217)

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Keywords = molecular docking and dynamics

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18 pages, 815 KiB  
Article
Discovery of Effective Inhibitors Against Phosphodiesterase 9, a Potential Therapeutic Target of Alzheimer’s Disease with Antioxidant Capacities
by Qian Zhou, Xu-Nian Wu, Wei-Hao Luo, Qing-Hua Huang, Ling-Ling Feng, Yinuo Wu and Chen Zhang
Antioxidants 2025, 14(2), 123; https://doi.org/10.3390/antiox14020123 - 21 Jan 2025
Viewed by 144
Abstract
Alzheimer’s disease (AD) is a widely recognized type of dementia that leads to progressive cognitive decline and memory loss, affecting a significant number of people and their families worldwide. Given the multifactorial nature of AD, multitarget-directed ligands (MTDLs) hold promise in developing effective [...] Read more.
Alzheimer’s disease (AD) is a widely recognized type of dementia that leads to progressive cognitive decline and memory loss, affecting a significant number of people and their families worldwide. Given the multifactorial nature of AD, multitarget-directed ligands (MTDLs) hold promise in developing effective drugs for AD. Phosphodiesterase-9 (PDE9) is emerging as a promising target for AD therapy. In this study, by combining a PDE9 inhibitor C33 with the antioxidant melatonin, we designed and discovered a series of pyrazolopyrimidinone derivatives that simultaneously inhibit PDE9 and possess antioxidant activities. Molecular docking, together with dynamics simulations, were applied to accelerate compound design and reduce synthetic work. Four out of the 14 compounds were validated as effective PDE9 inhibitors with comparable antioxidant activity. Notably, compounds 17b and 17d demonstrated IC50 values of 91 and 89 nM against PDE9, respectively, with good antioxidant activities (ORAC (Trolox) of 2.00 and 2.60). This work provides a new approach for designing MTDLs for the treatment of AD and offers insights for further structural modifications of PDE9 inhibitors with antioxidant capacities. Full article
(This article belongs to the Special Issue Oxidative Stress as a Therapeutic Target of Alzheimer’s Disease)
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23 pages, 3517 KiB  
Article
Isolation and Characterization of the First Antigen-Specific EGFRvIII vNAR from Freshwater Stingray (Potamotrygon spp.) as a Drug Carrier in Glioblastoma Cancer Cells
by Alejandro Manzanares-Guzmán, Andrea C. Alfonseca-Ladrón de Guevara, Elia Reza-Escobar, Mirna Burciaga-Flores, Alejandro Canales-Aguirre, Hugo Esquivel-Solís, Pavel H. Lugo-Fabres and Tanya A. Camacho-Villegas
Int. J. Mol. Sci. 2025, 26(3), 876; https://doi.org/10.3390/ijms26030876 - 21 Jan 2025
Viewed by 346
Abstract
Glioblastoma is the most common and highly malignant brain tumor in adults. New targeted therapeutic approaches are imperative. EGFRvIII has appealing therapeutic targets using monoclonal antibodies. Thus, endeavors toward developing new mAbs therapies for GBM capable of targeting the tumor EGFRvIII biomarker must [...] Read more.
Glioblastoma is the most common and highly malignant brain tumor in adults. New targeted therapeutic approaches are imperative. EGFRvIII has appealing therapeutic targets using monoclonal antibodies. Thus, endeavors toward developing new mAbs therapies for GBM capable of targeting the tumor EGFRvIII biomarker must prevail to improve the patient’s prognosis. Here, we isolated and characterized an anti-EGFRvIII vNAR from a non-immune freshwater stingray mixed library, termed vNAR R426. The vNAR R426 and pEGFRvIII interaction was demonstrated by molecular docking and molecular dynamics, and the recognition of EGFRvIII in vitro was further confirmed by cell immunofluorescence staining. Moreover, the vNAR R426 was shown to be an effective cisplatin drug carrier in the U87-MG glioma cell line. The cisplatin-coupled vNAR demonstrated highly significant differences when compared to free CDDP at 72 h. Notably, the cisplatin-vNAR carrier achieved better efficacy in the U87-MG cell line. Thus, we described the vNAR R426 internalization by receptor-mediated endocytosis and the subsequent COPI-mediated nuclear translocation of EGFRvIII and highlighted the importance of this shuttle mechanism to enhance the targeted delivery of cisplatin within the glioma cell’s nucleus and improved cytotoxic effect. In conclusion, vNAR R426 could be a potential therapeutic carrier for EGFRvIII-targeted glioblastoma and cancer therapies. Full article
(This article belongs to the Special Issue New Agents and Novel Drugs Use for the Oncological Diseases Treatment)
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Figure 1

Figure 1
<p>Identification of freshwater stingray-derived anti-EGFRvIII vNAR. (<b>a</b>) Schematic representation of non-immune phage library construction from freshwater stingray and sequencing. (<b>b</b>) Panning rounds with non-immune vNAR library. R = Round, CFU = Colony-forming units. (<b>c</b>) Recognition ELISA for vNAR R426-EGFRvIII peptide (LEEKKGNYVVTDHC). The hatched bars depict specific recognition of vNAR R426 towards the EGFRvIII receptor. Negative control: BSA 3%. Student’s <span class="html-italic">t</span> test = ** greatest significant difference with <span class="html-italic">p</span> &lt; 0.01. (<b>d</b>) SDS-PAGE showing the purification steps of vNAR R426 in a 15% SDS-PAGE gel stained with Coomassie blue. MW = Molecular weight marker. 1 = non-retained fraction (NR), 2 = Negative control (C−) <span class="html-italic">E. coli</span> BL21 extract without plasmid, 3 = Wash 1 (50 mM imidazole), 4 = Elution 1 (E1), 5 = Elution 2 (E2), 6 = Elution 3 (E3), 7 = Elution 4 (E4), 8 = Elution 8 (E4). (<b>e</b>) Western blot of vNAR R426, MW = Molecular weight marker, 1 = Positive control (a non-related vNAR with 6xHis-Tag), 2 = Purified and endotoxin-free vNAR R426. Black boxes indicate the presence of vNAR R426 (~14.9 kDa).</p>
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<p>Molecular dynamics of the interaction between vNAR R426 and EGFRvIII peptide. (<b>a</b>) vNAR R426 sequence. CDR = Complementarity-determining region 1 and 3 (CDR1 and CDR3); HV = Hypervariable regions 2 and 4 (HV2 and HV4) are indicated in the box. Canonical cysteine residues are highlighted in blue and non-canonical cysteine residues are highlighted in orange. (<b>b</b>) Molecular representation of vNAR R426/pEGFRvIII complex. (<b>c</b>) Root Mean Square Deviation (RMSD) of vNAR and pEGFRvIII in the complex over 100 ns of molecular dynamics. The RMSD of vNAR R426 (Cα atoms) is blue. The RMSD of EGFRvIII peptide (Cα atoms) are in cyan. On average, there are 3 experimental replicates.</p>
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<p>Immunofluorescence evaluation of vNAR<sub>FITC</sub> binding to EGFRvIII and cell internalization. (<b>a</b>) U87-MG (EGFRvIII<sup>+</sup> cells) as positive control (C+) and (<b>b</b>) HBEC-5i (wtEGFR<sup>−</sup>/EGFRvIII<sup>−</sup> cells) as negative control (C−), were incubated for 4 h with vNAR<sub>FITC</sub> (0.081 µM, final concentration), and treated with cytochalasin B at 0.5 nM (<b>c</b>) or 1 nM (<b>d</b>). vNAR<sub>FITC</sub> signal (in green, as remarked by green arrows) and nucleus staining with propidium iodide (red). Scale bar, 60 µm. All experiments were conducted in triplicate.</p>
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<p>Inhibition of EGFRvIII nuclear translocation hampers vNAR<sub>FITC</sub> signal within the cell nucleus. U87-MG cells were treated with or without DMSO or different concentrations of BFA for 30 min. Next, all cell groups were incubated with vNAR<sub>FITC</sub> (0.081 µM) for 24 h and then immunofluorescence detected with confocal microscopy. (<b>a</b>) Negative control group (C−). (<b>b</b>) BFA treatment group at 0.36 µM. (<b>c</b>) BFA treatment group at 0.72 µM. (<b>d</b>) DMSO control group (C−). DMSO is the control of BFA. vNAR<sub>FITC</sub> signal (in green, marked by green arrows). Nucleus staining with propidium iodide (red). Scale bar, 60 µm.</p>
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<p>(<b>a</b>) Cell viability assay of U87-MG cells incubated with cisplatin. U87-MG cells were incubated with different concentrations of cisplatin (CDDP) for 72 h. The means for each concentration are represented. The experiments were performed in triplicate. (<b>b</b>) Schematic representation of vNAR R426 and cisplatin conjugation. The anti-EGFRvIII vNAR was coupled with cisplatin following the EDC coupling protocol. The cisplatin-vNAR R426 carrier (vNAR<sub>CDDP</sub>) was obtained. Anti-EGFRvIII vNAR (Blue), cisplatin (Cyan), EDC (Orange), and vNAR<sub>CDDP</sub> (White) are depicted. (<b>c</b>,<b>d</b>) Cell viability assay of vNAR R426 alone and cisplatin-vNAR R426 carrier in U87-MG cells. U87-MG cells were incubated with different concentrations of vNAR R426 alone for 48 h (<b>c</b>) and 72 h (<b>d</b>). (<b>e</b>,<b>f</b>) U87-MG cells were incubated with varying concentrations of cisplatin-vNAR R426 carrier (vNAR<sub>CDDP</sub>) for 48 h (<b>e</b>) and 72 h (<b>f</b>). Data are presented as mean ± SD. The experiments were performed in triplicate. One-way ANOVA for the comparison of the free CDDP treatment (10 μM) to the means of the other therapies, considering <span class="html-italic">p</span> &lt; 0.05 significant, ns &gt; 0.05, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Schematic representation of the hypothetical vNAR<sub>CDDP</sub> cytotoxic mechanism in U87-MG cells. The vNAR<sub>CDDP</sub> recognizes the EGFRvIII receptor in the plasma membrane of U87-MG cells. Then, receptor-mediated endocytosis and endosome formation take place (<b>1</b>). The nuclear localization signal (NLS) within the EGFRvIII is recognized by syntaxin 6, which transports the vNAR<sub>CDDP</sub>-EGFRvIII complex to trans-Golgi (<b>2</b>). Next, COPI vesicle formation occurs in cis-Golgi and mediates the retro-translocation to the rough endoplasmic reticulum (<b>3</b>). Finally, vNAR<sub>CDDP</sub> reaches the cell nucleus along with EGFRvIII, degrades, and CDDP can form adducts with DNA, culminating in cell death (<b>4</b>). The previously reported COPI-mediated EGFRvIII nuclear translocation [<a href="#B50-ijms-26-00876" class="html-bibr">50</a>] helped us hypothesize the vNAR<sub>CDDP</sub> cytotoxic mechanism within the U87-MG cells. Red lines depict the inhibition checkpoints we employed in the cell assays.</p>
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18 pages, 3807 KiB  
Article
Dummy Template Molecularly Imprinted Polymers for Electrochemical Detection of Cardiac Troponin I: A Combined Computational and Experimental Approach
by Mohammad Sadegh Sadeghi Googheri, Davide Campagnol, Paolo Ugo, Samira Hozhabr Araghi and Najmeh Karimian
Chemosensors 2025, 13(1), 26; https://doi.org/10.3390/chemosensors13010026 - 20 Jan 2025
Viewed by 417
Abstract
Cardiac troponin I (cTnI) is a crucial biomarker for the early detection of acute myocardial infarction (AMI), playing a significant role in cardiac health assessment. Molecularly imprinted polymers (MIPs) are valued for their stability, ease of fabrication, reusability, and selectivity. However, using the [...] Read more.
Cardiac troponin I (cTnI) is a crucial biomarker for the early detection of acute myocardial infarction (AMI), playing a significant role in cardiac health assessment. Molecularly imprinted polymers (MIPs) are valued for their stability, ease of fabrication, reusability, and selectivity. However, using the analyte as a template can be costly, especially if the analyte is expensive. In such cases, a dummy template (DT) with similar chemico-physical properties can be useful. This study aimed to design a DT-MIP for cTnI detection using cytochrome c (Cyt c) as the template, combining computational and experimental approaches. Molecular docking identified binding sites on Cyt c and cTnI for poly(o-phenylenediamine) (5PoPD) pentamers. Interactions and binding energies were examined using all-atom molecular dynamics (MDs) simulations and structural interaction fingerprint (SIFt) calculations. A DT-MIP-modified electrode for cTnI detection was prepared by electropolymerizing o-PD in the presence of Cyt c as a dummy template. Electrochemical techniques monitored the electropolymerization, template removal, and binding of the target analyte. The experimental results showed that the DT-MIPs exhibited a high binding affinity for cTnI, consistent with the binding energies observed in MD simulations. The satisfactory correlation between experimental and computational results validated our model-based approach for the rational design of dummy template molecularly imprinted polymers. Full article
(This article belongs to the Special Issue Recent Advances in Electrode Materials for Electrochemical Sensing)
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Figure 1
<p>Representative docking poses of all modeled polymers into (<b>a</b>) Cyt <span class="html-italic">c</span> and (<b>b</b>) cTnI. The protein backbones are represented as a lime green cartoon, while the 5PH<sub>Red</sub>, 5PH<sub>Ox</sub>, 5BQ<sub>Red</sub>, and 5BQ<sub>Ox</sub> modeled polymers are shown as gray, yellow, orange, and magenta sticks, respectively. Figures were created by UCSF Chimera software 1.17.3 [<a href="#B86-chemosensors-13-00026" class="html-bibr">86</a>].</p>
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<p>Schematic representation of the comparison between the initial and final conformations of (<b>a</b>) 5PH<sub>Red</sub>-Cyt <span class="html-italic">c</span>, (<b>b</b>) 5PH<sub>Ox</sub>-Cyt <span class="html-italic">c</span>, (<b>c</b>) 5BQ<sub>Red</sub>-Cyt <span class="html-italic">c</span>, and (<b>d</b>) 5BQ<sub>Ox</sub>-Cyt <span class="html-italic">c</span> complexes. The Cyt <span class="html-italic">c</span> conformations are represented as green and blue cartoons, and the 5PoPD conformations are represented as magenta and orange sticks for the before and after MD simulations, respectively. Figures were created by PyMOL 2.5.5 [<a href="#B87-chemosensors-13-00026" class="html-bibr">87</a>].</p>
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<p>Schematic representation of the comparison between the initial and final conformations of (<b>a</b>) 5PH<sub>Red</sub>-cTnI, (<b>b</b>) 5PH<sub>Ox</sub>-cTnI, (<b>c</b>) 5BQ<sub>Red</sub>-cTnI, and (<b>d</b>) 5BQ<sub>Ox</sub>-cTnI complexes. The cTnI conformations are represented as green and blue cartoons, and the 5PoPD conformations are represented as magenta and orange sticks for the before and after MD simulations, respectively. Figures were created by PyMOL 2.5.5 [<a href="#B87-chemosensors-13-00026" class="html-bibr">87</a>].</p>
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<p>SIFt and HBs analyses of the involved residues in the (<b>a</b>) 5PoPD-Cyt <span class="html-italic">c</span> and (<b>b</b>) 5PoPD-cTnI complexes obtained from MD simulations. The residues that are most involved in interactions are also represented.</p>
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<p>DPVs recorded with the DT-MIP electrode in 1 mM FcCOOH, PBS, pH 7.4: (a) DT-MIP electrode before template removal; (b) DT-MIP electrode after template removal; (c) DT-MIP electrode after 10 min incubation in a 1.7 × 10<sup>−12</sup> M cTnI solution; (d) NIP electrode before treatment; (e) NIP electrode after treatment with the extraction solvent.</p>
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<p>(<b>a</b>) DPV recorded with the DT-MIP electrode after 10 min of incubation in 1 mM FcCOOH, PBS, pH 7.4, containing the different cTnI concentrations. From lower to higher, the addition of cTnI includes 0 M, 4.2 × 10<sup>−14</sup> M, 3.8 × 10<sup>−13</sup> M, 1.7 × 10<sup>−12</sup> M, 3.8 × 10<sup>−12</sup> M, 1.7 × 10<sup>−11</sup> M, 3.3 × 10<sup>−11</sup> M, and 3.0 × 10<sup>−10</sup> M, respectively. (<b>b</b>) Correlation between ΔI/I<sub>0</sub> and the cTnI concentration.</p>
Full article ">Scheme 1
<p>A schematic representation of molecular imprinting and the recognition principle. (<b>A</b>) Electrochemical characterization of the bare surface electrode; (<b>B</b>) the dummy template and functional monomer interact to form a complex preserved by electropolymerization; (<b>C</b>) electrochemical characterization of the deposited PoPD film on the electrode surface using FcCOOH as the redox probe; (<b>D</b>) electrochemical characterization of the DT-MIP electrode after template removal; and (<b>E</b>) electrochemical detection of analyte binding by the DMIP electrode through the association process in the presence of FcCOOH.</p>
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14 pages, 2487 KiB  
Article
Targeting SLC4A4: A Novel Approach in Colorectal Cancer Drug Repurposing
by Krunal Pawar, Pramodkumar P. Gupta, Pooran Singh Solanki, Ravi Ranjan Kumar Niraj and Shanker L. Kothari
Curr. Issues Mol. Biol. 2025, 47(1), 67; https://doi.org/10.3390/cimb47010067 - 20 Jan 2025
Viewed by 372
Abstract
Background: Colorectal cancer (CRC) is a complex and increasingly prevalent malignancy with significant challenges in its treatment and prognosis. This study aims to explore the role of the SLC4A4 transporter as a biomarker in CRC progression and its potential as a therapeutic target, [...] Read more.
Background: Colorectal cancer (CRC) is a complex and increasingly prevalent malignancy with significant challenges in its treatment and prognosis. This study aims to explore the role of the SLC4A4 transporter as a biomarker in CRC progression and its potential as a therapeutic target, particularly in relation to tumor acidity and immune response. Methods: The study utilized computational approaches, including receptor-based virtual screening and high-throughput docking, to identify potential SLC4A4 inhibitors. A model of the human SLC4A4 structure was generated based on CryoEM data (PDB ID 6CAA), and drug candidates from the DrugBank database were evaluated using two computational tools (DrugRep and CB-DOCK2). Results: The study identified the compound (5R)-N-[(1r)-3-(4-hydroxyphenyl)butanoyl]-2-decanamide (DB07991) as the best ligand, demonstrating favorable binding affinity and stability. Molecular dynamics simulations revealed strong protein–ligand interactions with consistent RMSD (~0.25 nm), RMSF (~0.5 nm), compact Rg (4.0–3.9 nm), and stable SASA profiles, indicating that the SLC4A4 structure remains stable upon ligand binding. Conclusions: The findings suggest that DB07991 is a promising drug candidate for further investigation as a therapeutic agent against CRC, particularly for targeting SLC4A4. This study highlights the potential of computational drug repositioning in identifying effective treatments for colorectal cancer. Full article
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Figure 1
<p>(<b>a</b>) Q9Y6R1.1.A template-based model of SLC4A4 protein (6CAA); (<b>b</b>) Ramachandran Favoured plot for model, based on Q9Y6R1.1.A template.</p>
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<p>(<b>a</b>) Complex of modeled 6CAA and DB07991—visualization through Pymol; (<b>b</b>) molecular lever interaction of 6CAA protein and DB07991—visualization through LigPlot+.</p>
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<p>(<b>a</b>) Root mean square deviation of modeled 6CAA protein and ligand DB07991. (<b>b</b>) Root mean square fluctuation of atoms during MD simulation process of modeled 6CAA protein and ligand DB07991. (<b>c</b>) Radius of gyration (Rg) plots for modeled 6CAA protein and ligand DB07991. (<b>d</b>) Hydrogen bond plots for modeled 6CAA protein and ligand DB07991. (<b>e</b>) Solvent-accessible surface area for modeled 6CAA protein and ligand DB07991.</p>
Full article ">Figure 3 Cont.
<p>(<b>a</b>) Root mean square deviation of modeled 6CAA protein and ligand DB07991. (<b>b</b>) Root mean square fluctuation of atoms during MD simulation process of modeled 6CAA protein and ligand DB07991. (<b>c</b>) Radius of gyration (Rg) plots for modeled 6CAA protein and ligand DB07991. (<b>d</b>) Hydrogen bond plots for modeled 6CAA protein and ligand DB07991. (<b>e</b>) Solvent-accessible surface area for modeled 6CAA protein and ligand DB07991.</p>
Full article ">Figure 3 Cont.
<p>(<b>a</b>) Root mean square deviation of modeled 6CAA protein and ligand DB07991. (<b>b</b>) Root mean square fluctuation of atoms during MD simulation process of modeled 6CAA protein and ligand DB07991. (<b>c</b>) Radius of gyration (Rg) plots for modeled 6CAA protein and ligand DB07991. (<b>d</b>) Hydrogen bond plots for modeled 6CAA protein and ligand DB07991. (<b>e</b>) Solvent-accessible surface area for modeled 6CAA protein and ligand DB07991.</p>
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<p>(<b>a</b>) Conformation at 0th ns. (<b>b</b>) Conformation at 25th ns. (<b>c</b>) Conformation at 50th ns.</p>
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<p>(<b>a</b>) Conformation at 0th ns. (<b>b</b>) Conformation at 25th ns. (<b>c</b>) Conformation at 50th ns.</p>
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15 pages, 4052 KiB  
Article
Engineering of an Alkaline Feruloyl Esterase PhFAE for Enhanced Thermal Stability and Catalytic Efficiency Through Molecular Dynamics and FireProt
by Sheng Yang, Miaofang Lin, Jiyang Chen, Min Liu and Qi Chen
Catalysts 2025, 15(1), 92; https://doi.org/10.3390/catal15010092 - 19 Jan 2025
Viewed by 442
Abstract
Feruloyl esterases (FAEs) play critical roles in industrial applications such as food processing, pharmaceuticals, and paper production by breaking down plant cell walls and releasing ferulic acid. However, most bacterial FAEs function optimally in acidic environments, limiting their use in alkaline industrial processes. [...] Read more.
Feruloyl esterases (FAEs) play critical roles in industrial applications such as food processing, pharmaceuticals, and paper production by breaking down plant cell walls and releasing ferulic acid. However, most bacterial FAEs function optimally in acidic environments, limiting their use in alkaline industrial processes. Additionally, FAEs with alkaline activity often lack the thermal stability required for demanding industrial conditions. In this study, an alkaline feruloyl esterase, PhFAE, from Pandoraea horticolens was identified that exhibits high catalytic activity but suffers from thermal instability, restricting its broader industrial applications. To address this limitation, molecular dynamics simulations were used to analyze enzyme stability, and FireProt, an automated computational tool, was employed to design stabilizing mutations. The engineered S155F mutant demonstrated a 7.8-fold increase in half-life at 60 °C and a 1.72-fold improvement in catalytic efficiency (Kcat/Km), corresponding to 680% and 72% enhancements, respectively, compared to the wild-type enzyme. Molecular docking and dynamics simulations revealed that these enhancements were likely due to increased hydrophobic interactions and altered surface charge, which stabilized the enzyme’s structure. This study provides an effective strategy for improving the functional properties of FAEs and other industrial enzymes, broadening their applicability in diverse industrial processes. Full article
(This article belongs to the Special Issue Recent Advances in Biocatalysis and Enzyme Engineering)
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Figure 1
<p>(<b>A</b>) Alignment of PhFAE with its homologous proteins is presented; matching amino acids are displayed in white text against a crimson background, while similar amino acids are highlighted with dark text within azure rectangles. The secondary structure elements, including α-helices, strands, bends, and 2η helices, are illustrated at the top and represented by coils, pointers, TT marks, and η symbols, respectively. The catalytic triad residues (serine, aspartate, and histidine) are indicated with a dark blob beneath the strand, while the invariant pentapeptide sequence GQSMG is highlighted with a dark rectangle. The amino acid sequences analyzed were derived from <span class="html-italic">B. intermedia</span>, <span class="html-italic">P. salinisoli</span>, <span class="html-italic">Bordetella</span> sp. H567, <span class="html-italic">L. pneumophila</span>, and <span class="html-italic">Legionella</span> sp. P1, exhibiting sequence identities of 74.9%, 60.92%, 49.8%, 48.47%, and 45.41%, respectively. (<b>B</b>) Phylogenetic analysis of PhFAE homologs. (<b>C</b>) Measurement of relative enzyme activity (%) of wild-type PhFAE and predicted catalytic triplet amino acid residue mutants.</p>
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<p>Preparation and characterization of PhFAE. (<b>A</b>) Size-exclusion chromatography. (<b>B</b>) Substrate specificity. (<b>C</b>) Influence of thermal conditions. (<b>D</b>) Thermostability of the enzyme. (<b>E</b>) Influence of acidity and basicity. (<b>F</b>) Activity kinetics graph of PhFAE. The presented data represent average values ± standard deviation.</p>
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<p>Thermodynamic analysis through MD for PhFAE. (<b>A</b>) Analysis of RMSF for PhFAE, with the dashed box indicating different flexible regions. Regions I-III correspond to the three highly flexible areas identified through dynamic simulations. (<b>B</b>) Highlighted flexible regions in the three-dimensional conformation of PhFAE, with different colors representing three distinct regions. (<b>C</b>) Determination of melting points (Tm) via assessing the nanoDSF signal ratio at 350 nm and 330 nm, accompanied by a graphical representation of the first derivative in relation to temperature changes. The x-axis represents temperature, and the y-axis represents the first derivative and the ratio at 350 nm and 330 nm. (<b>D</b>) Evaluation of the decay period (t1/2) for PhFAE and its mutants, conducted at a temperature of 55 °C. The x-axis represents time, and the y-axis represents relative enzyme activity.</p>
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<p>Analysis of molecular docking results and comparison of surface potential between PhFAE and its S155F mutant. (<b>A</b>) Docking of PhFAE with <span class="html-italic">p</span>NPF, with light brown representing <span class="html-italic">p</span>NPF and green representing PhFAE amino acid residues interacting with <span class="html-italic">p</span>NPF. (<b>B</b>) Docking of S155F with <span class="html-italic">p</span>NPF, with light brown representing <span class="html-italic">p</span>NPF and green representing PhFAE amino acid residues interacting with <span class="html-italic">p</span>NPF. (<b>C</b>) Hydrophobic interactions of S155 with <span class="html-italic">p</span>NPF, with green indicating amino acid residues that directly interact with <span class="html-italic">p</span>NPF. (<b>D</b>) Hydrophobic interactions of F155 with <span class="html-italic">p</span>NPF, with green indicating amino acid residues that directly interact with pNPF. (<b>E</b>) Electrostatic potential of S155, with red representing negative charge regions and blue representing positive charge regions. (<b>F</b>) Electrostatic potential of F155, with red representing negative charge regions and blue representing positive charge regions.</p>
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<p>Investigation of PhFAE and S155F through MD simulation: (<b>A</b>) examination of RMSD values, (<b>B</b>) evaluation of SASA metrics, (<b>C</b>) analysis of RMSF data, and (<b>D</b>) assessment of RG measurements.</p>
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19 pages, 4186 KiB  
Article
Thiamine and Thiamine Pyrophosphate as Non-Competitive Inhibitors of Acetylcholinesterase—Experimental and Theoretical Investigations
by Łukasz Szeleszczuk, Dariusz Maciej Pisklak and Błażej Grodner
Molecules 2025, 30(2), 412; https://doi.org/10.3390/molecules30020412 - 19 Jan 2025
Viewed by 309
Abstract
Vitamin B1 (thiamine) plays an important role in human metabolism. It is essential for the proper growth and development of the body and has a positive effect on the functioning of the digestive, cardiovascular, and nervous systems. Additionally, it stimulates the brain [...] Read more.
Vitamin B1 (thiamine) plays an important role in human metabolism. It is essential for the proper growth and development of the body and has a positive effect on the functioning of the digestive, cardiovascular, and nervous systems. Additionally, it stimulates the brain and improves the psycho-emotional state. In vivo, vitamin B1 occurs in free form as thiamine or as its ester with phosphate residue(s), i.e., as mono-, di-, or triphosphate. It has been proven that supportive therapy with vitamin B1 can not only provide neuroprotection but also has a positive effect on advanced neurodegenerative diseases, such as Parkinson’s disease, Alzheimer’s disease, Wernicke–Korsakoff syndrome, or Huntington’s disease. This paper presents studies on the effect of free thiamine (T) and thiamine pyrophosphate (TPP) on the activity of acetylcholinesterase (AChE), which is an enzyme considered to play an important role in the therapies for neurodegenerative diseases, especially Alzheimer’s disease. The mechanisms of action of these compounds as potential inhibitors of AChE were evaluated using both experimental (enzymatic activity) as well as computational (molecular docking, molecular dynamics simulations, and MM-GBSA calculations) methods. The results of the current study indicate a non-competitive type of enzyme inhibition, in contrast to the previously published works suggesting a competitive one. Full article
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<p>Chemical structures of thiamine (<b>T</b>) and thiamine pyrophosphate (<b>TPP</b>).</p>
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<p>Lineweaver–Burk plots for systems with inhibitors (<b>T</b>) and (<b>TPP</b>) at concentrations of 17.5 mg/mL and 35.0 mg/mL.</p>
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<p>Binding modes of <b>T</b> and <b>TPP</b> with AChE obtained from molecular docking. Description of the models (1–4) is presented in <a href="#molecules-30-00412-t002" class="html-table">Table 2</a>.</p>
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<p>RMSD values obtained from the MD simulations. Cyan color represents the protein RMSD while pink color represents the ligand RMSD. Description of the models (1–4) is presented in <a href="#molecules-30-00412-t002" class="html-table">Table 2</a>.</p>
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<p>RMSF values obtained from the MD simulations. Black numbers represent carbon atoms, blue numbers represent nitrogen atoms, red numbers represent oxygen atoms, yellow numbers represent sulfur atoms, brown numbers represent phosphorus atoms. Description of the models (1–4) is presented in <a href="#molecules-30-00412-t002" class="html-table">Table 2</a>.</p>
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<p>Distribution of the ligand’s torsion values, obtained from the MD simulations. Description of the models (1–4) is presented in <a href="#molecules-30-00412-t002" class="html-table">Table 2</a>. The colors used in the graphs represent the torsion angles indicated in the corresponding molecular structures, below the graphs.</p>
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<p>Binding modes of T and TPP with AChE obtained from molecular dynamics. Description of the models (1–4) is presented in <a href="#molecules-30-00412-t002" class="html-table">Table 2</a>.</p>
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<p>Interactions of ligands, T, and TPP, with AChE obtained from molecular docking. Description of the models (1–4) is presented in <a href="#molecules-30-00412-t002" class="html-table">Table 2</a>. The stacked bar charts are normalized over the course of the trajectory; for example, a value of 0.7 suggests that 70% of the simulation time for the specific interaction is maintained. Values over 1.0 are possible as some protein residue may make multiple contacts of same subtype with the ligand. Green color represents H-bonds, violet color represents hydrophobic interactions, pink color represents ionic interactions, blue color represents H-bonds through water bridges.</p>
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<p>Interactions of ligands, T, and TPP, with AChE obtained from molecular docking. Description of the models (1–4) is presented in <a href="#molecules-30-00412-t002" class="html-table">Table 2</a>. The stacked bar charts are normalized over the course of the trajectory; for example, a value of 0.7 suggests that 70% of the simulation time for the specific interaction is maintained. Values over 1.0 are possible as some protein residue may make multiple contacts of same subtype with the ligand. Green color represents H-bonds, violet color represents hydrophobic interactions, pink color represents ionic interactions, blue color represents H-bonds through water bridges.</p>
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<p>Reaction scheme for the determination of acetylcholinesterase activity [<a href="#B35-molecules-30-00412" class="html-bibr">35</a>].</p>
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<p>Schematic representation of the study.</p>
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30 pages, 8378 KiB  
Article
Examining Prenylated Xanthones as Potential Inhibitors Against Ketohexokinase C Isoform for the Treatment of Fructose-Driven Metabolic Disorders: An Integrated Computational Approach
by Tilal Elsaman and Magdi Awadalla Mohamed
Pharmaceuticals 2025, 18(1), 126; https://doi.org/10.3390/ph18010126 - 18 Jan 2025
Viewed by 579
Abstract
Background/Objectives: Fructose-driven metabolic disorders, such as obesity, non-alcoholic fatty liver disease (NAFLD), dyslipidemia, and type 2 diabetes, are significant global health challenges. Ketohexokinase C (KHK-C), a key enzyme in fructose metabolism, is a promising therapeutic target. α-Mangostin, a naturally occurring prenylated xanthone, has [...] Read more.
Background/Objectives: Fructose-driven metabolic disorders, such as obesity, non-alcoholic fatty liver disease (NAFLD), dyslipidemia, and type 2 diabetes, are significant global health challenges. Ketohexokinase C (KHK-C), a key enzyme in fructose metabolism, is a promising therapeutic target. α-Mangostin, a naturally occurring prenylated xanthone, has been identified as an effective KHK-C inhibitor, prompting exploration of its analogs for enhanced efficacy. This study aimed to identify α-Mangostin analogs with improved inhibitory properties against KHK-C to address these disorders. Methods: A library of 1383 analogs was compiled from chemical databases and the literature. Molecular docking, binding free energy calculations, pharmacokinetic assessments, molecular dynamics simulations, and quantum mechani–cal analyses were used to screen and evaluate the compounds. α-Mangostin’s binding affinity (37.34 kcal/mol) served as the benchmark. Results: Sixteen analogs demonstrated binding affinities superior to α-Mangostin (from −45.51 to −61.3 kcal/mol), LY-3522348 (−45.36 kcal/mol), and reported marine-derived inhibitors (from −22.74 to −51.83 kcal/mol). Hits 7, 8, 9, 13, and 15 not only surpassed these benchmarks in binding affinity, but also exhibited superior pharmacokinetic properties compared to α-Mangostin, LY-3522348, and marine-derived inhibitors, indicating strong in vivo potential. Among these, hit 8 emerged as the best performer, achieving a binding free energy of −61.30 kcal/mol, 100% predicted oral absorption, enhanced metabolic stability, and stable molecular dynamics. Conclusions: Hit 8 emerged as the most promising candidate due to its superior binding affinity, favorable pharmacokinetics, and stable interactions with KHK-C. These findings highlight its potential for treating fructose-driven metabolic disorders, warranting further experimental validation. Full article
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<p>Hepatic metabolism of fructose begins with its phosphorylation by the enzyme KHK-C, producing fructose-1-phosphate. This intermediate is subsequently cleaved by aldolase B to yield the trioses glyceraldehyde and dihydroxyacetone phosphate. Glyceraldehyde is then phosphorylated to form glyceraldehyde-3-phosphate. At this stage, the phosphorylated trioses enter the glycolytic pathway and are ultimately converted into triglycerides and VLDL particles, thereby promoting lipogenesis.</p>
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<p>Chemical structures and IC<sub>50</sub> values of reported KHK-C inhibitors.</p>
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<p>Virtual screening pipeline for the identification of prenylated xanthones as potential KHK-C inhibitors.</p>
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<p>Binding mode of the co-crystal ligand in the dimeric active site of KHK-C.</p>
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<p>(<b>A</b>) The co-crystal ligand is positioned in the active site of KHK-C (PDB ID: <b>8UG1</b>). (<b>B</b>) Three-dimensional interactions of the co-crystal ligand with key residues, displayed in their three-letter codes. Hydrogen bonds and water bridges are shown as green dotted lines, while salt bridges are indicated by pink dotted lines. (<b>C</b>) A visual representation of the redocking process, where the original pose of the ligand is shown in green, and the redocked pose is depicted in red. (<b>D</b>) Two-dimensional interactions, with hydrogen bonds highlighted in magenta and salt bridges illustrated as a gradient line from purple to red, where purple represents the positive center and red indicates the negative center.</p>
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<p>Chemical structures of leading compounds identified in virtual screening.</p>
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<p>Three-dimensional interaction diagrams of two prenylated xanthones, hit <b>8</b> (<b>A</b>) and hit <b>13</b> (<b>B</b>), with KHK-C (PDB ID: <b>8UG1</b>): key residue interactions in the binding site highlighting H-bonds and water bridges (green) and Pi–cation interactions (red).</p>
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<p>The designed analogs of hit <b>8</b> (<b>I–VI</b>) and hit <b>13</b> (<b>VII–XII</b>).</p>
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<p>The designed analogs of hit <b>8</b> (<b>I–VI</b>) and hit <b>13</b> (<b>VII–XII</b>).</p>
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<p>Chemical structures of the top five marine-derived natural product inhibitors identified by Alturki and reported as potential inhibitors of KHK-C.</p>
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<p>Detailed energy contributions for the co-crystal ligand, LY-3522348, γ-Mangostin, hit <b>8</b>, and hit <b>13</b>.</p>
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<p>RMSD analysis of hits <b>7</b>, <b>8</b>, <b>9</b>, <b>13</b>, <b>15</b>, γ-Mangostin, LY-3522348, co-crystal ligand and apoprotein during 100 ns MD simulations.</p>
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<p>RMSF analysis of hits <b>7</b>, <b>8</b>, <b>9</b>, <b>13</b>, <b>15</b>, γ-Mangostin, LY-3522348, co-crystal ligand, and apoprotein, highlighting residue flexibility during 100 ns MD simulations.</p>
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<p>Analysis of H-bonds (<b>A</b>), hydrophobic contacts (<b>B</b>), and water bridges (<b>C</b>) for the co-crystal ligand, LY-3522348, γ-Mangostin, and hit <b>8</b> during 100 ns MD simulations.</p>
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<p>Analysis of H-bonds (<b>A</b>), hydrophobic contacts (<b>B</b>), and water bridges (<b>C</b>) for the co-crystal ligand, LY-3522348, γ-Mangostin, and hit <b>8</b> during 100 ns MD simulations.</p>
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<p>Spatial Distribution of HOMO (<b>A</b>) and LUMO (<b>B</b>) orbitals in hit <b>8</b>.</p>
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<p>Spatial Distribution of HOMO (<b>A</b>) and LUMO (<b>B</b>) orbitals in hit <b>8</b>.</p>
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19 pages, 5699 KiB  
Article
Molecular Docking Studies and In Vitro Activity of Pancreatic Lipase Inhibitors from Yak Milk Cheese
by Peng Wang, Xuemei Song and Qi Liang
Int. J. Mol. Sci. 2025, 26(2), 756; https://doi.org/10.3390/ijms26020756 - 17 Jan 2025
Viewed by 261
Abstract
Pancreatic lipase serves as a primary trigger for hyperlipidemia and is also a crucial target in the inhibition of hypercholesterolemia. By synthesizing anti-hypercholesterolemic drugs such as atorvastatin, which are used to treat hypercholesterolemia, there were some side effects associated with the long-term use [...] Read more.
Pancreatic lipase serves as a primary trigger for hyperlipidemia and is also a crucial target in the inhibition of hypercholesterolemia. By synthesizing anti-hypercholesterolemic drugs such as atorvastatin, which are used to treat hypercholesterolemia, there were some side effects associated with the long-term use of statins. Based on this idea, in the present study, we identified peptides that inhibited PL by virtual screening and in vitro activity assays. In addition, to delve into the underlying mechanisms, we undertook a dual investigative approach involving both molecular docking analyses and molecular dynamics simulations. The results showed that peptides RK7, KQ7, and TL9, all with molecular weights of <1000 Da and a high proportion of hydrophobic amino acids, inhibited PL well. Molecular docking and molecular dynamics showed that peptides RK7, KQ7, and TL9 bound to important amino acid residues of PL, such as Pro and Leu, through hydrogen bonding, hydrophobic interactions, salt bridges, and π-π stacking to occupy the substrate-binding site, which inhibited PL and identified them as potential PL inhibitors. In vitro tests showed that the IC50 of RK7 and KQ7 on PL were 0.690 mg/mL and 0.593 mg/mL, respectively, and the inhibitory effects of RK7 and KQ7 on PL were significantly enhanced after simulated gastrointestinal digestion. Our results suggested that peptides RK7 and KQ7 from yak milk cheese can be identified as a novel class of potential PL inhibitors. Full article
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<p>Close-up 2D view of the active site of PL bound to (<b>a</b>) RK7, (<b>b</b>) KQ7, (<b>c</b>) TL9, and (<b>d</b>) atorvastatin. Key residues of the peptide interacting with atorvastatin and PL binding are shown as bars and marked in green. Green dashed lines represent hydrogen bonds. Pink dashed lines represent hydrophobic interactions.</p>
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<p>Analysis of molecular dynamics simulations for the peptide–PL complexes. (<b>a</b>) Depiction of the root mean square deviation (RMSD) trajectories for KQ7 (represented by the blue line) and RK7 (the red line) alongside TL9 (illustrated with the black line). (<b>b</b>) Display of the root mean square fluctuation (RMSF) profiles for KQ7 (denoted by the blue line), RK7 (signified with the red line), and TL9 (portrayed with the black line). (<b>c</b>) Presentation of hydrogen bond dynamics involving KQ7 (identified by the blue line), RK7 (marked with the red line), and TL9 (emphasized by the black line). (<b>d</b>) Illustration of solvent-accessible surface area (SASA) fluctuations for KQ7 (captured by the blue line), RK7 (highlighted with the red line), and TL9 (depicted by the black line). (<b>e</b>–<b>g</b>) Three-dimensional visualizations of the Gibbs free energy landscapes for the complexes formed by PL with RK7, KQ7, and TL9, respectively, offer insights into their thermodynamic stability.</p>
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<p>Analysis of molecular dynamics simulations for the peptide–PL complexes. (<b>a</b>) Depiction of the root mean square deviation (RMSD) trajectories for KQ7 (represented by the blue line) and RK7 (the red line) alongside TL9 (illustrated with the black line). (<b>b</b>) Display of the root mean square fluctuation (RMSF) profiles for KQ7 (denoted by the blue line), RK7 (signified with the red line), and TL9 (portrayed with the black line). (<b>c</b>) Presentation of hydrogen bond dynamics involving KQ7 (identified by the blue line), RK7 (marked with the red line), and TL9 (emphasized by the black line). (<b>d</b>) Illustration of solvent-accessible surface area (SASA) fluctuations for KQ7 (captured by the blue line), RK7 (highlighted with the red line), and TL9 (depicted by the black line). (<b>e</b>–<b>g</b>) Three-dimensional visualizations of the Gibbs free energy landscapes for the complexes formed by PL with RK7, KQ7, and TL9, respectively, offer insights into their thermodynamic stability.</p>
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<p>Structure of peptide interaction with PL in the 100 ns molecular dynamics regime: the 3D interaction maps of (<b>a</b>) RK7, (<b>b</b>) KQ7, and (<b>c</b>) TL9 obtained at 0 ns and 100 ns, with elaboration on the interaction diagrams and associated forces between the protein and peptide at 0 ns and 100 ns.</p>
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<p>The effect of peptide mass concentration and atorvastatin on PL inhibition rate. (<b>a</b>) Effect of RK7 mass concentration on PL inhibition rate. (<b>b</b>) Effect of KQ7 mass concentration on PL inhibition rate. (<b>c</b>) Effect of atorvastatin mass concentration on PL inhibition rate.</p>
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<p>Effect on PL inhibitory activity after simulated gastrointestinal digestion.</p>
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<p>The two-dimensional molecular structures of (<b>a</b>) RK7, (<b>b</b>) KQ7, and (<b>c</b>) TL9.</p>
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<p>Three-dimensional structure of PL (PDB: 1ETH); the N-terminal and C-terminal positions in the structural domain of PL are labeled in the figure.</p>
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7 pages, 3141 KiB  
Proceeding Paper
A Computational Investigation of Potential 5-HT 2C Receptor Inhibitors for Treating Schizophrenia by ADMET Profile Analysis, Molecular Docking, DFT, Network Pharmacology, and Molecular Dynamic Simulation
by Mohammed Raihan Uddin, Mahira Rahman, Mosammad Jannatun Nayem Rafin and Joya Datta Ripa
Chem. Proc. 2024, 16(1), 69; https://doi.org/10.3390/ecsoc-28-20242 - 16 Jan 2025
Viewed by 182
Abstract
Background: Schizophrenia manifests through behavioral abnormalities, suicidal ideation, and neuropsychological deficits. Hence, this study focused on 5-hydroxytryptamine (5-HT 2C) which influenced the modulation of the series of events that lead to schizophrenia. Methodology: Based on the computational study, the potential 5-HT 2C inhibitors [...] Read more.
Background: Schizophrenia manifests through behavioral abnormalities, suicidal ideation, and neuropsychological deficits. Hence, this study focused on 5-hydroxytryptamine (5-HT 2C) which influenced the modulation of the series of events that lead to schizophrenia. Methodology: Based on the computational study, the potential 5-HT 2C inhibitors such as Ephemeranthoquinone from Arundina graminifolia and Actinodaphnine from Litsea polyantha were determined. The candidate ligands were optimized using the Gaussian 16 software package and the DFT 6-31g (d,p) basis set. The interaction between the ligands and proteins was examined with PyRx 0.8. Additionally, pharmacokinetics was assessed using SwissADME, and Protox II for toxicity prediction. The network pharmacology study was examined by using the STRING database and the Cytoscape 3.10.1 tool. Moreover, a 100-nanosecond molecular dynamics simulation analysis using Desmond to ensure the stability of these two compounds was carried out. Result: This computational research observed that ephemeranthoquinone and actinodaphnine are the most selective 5-HT 2C inhibitors due to their docking score, optimization, and molecular dynamics simulation results. Conclusions: These compounds are required to be studied further to develop a useful 5-HT 2C inhibitors for the treatment of schizophrenia. Full article
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<p>Protein–ligand binding interaction of top two compounds based on binding score: (<b>a</b>) ephemeranthoquinone and (<b>b</b>) actinodaphnine.</p>
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<p>Network pharmacology analysis of 5-HT 2C protein (<b>a</b>) and top two compounds (<b>b</b>).</p>
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<p>The optimization structure of the top two compounds, (<b>a</b>) Actinodaphnine and (<b>b</b>) Ephemeranthoquinone.</p>
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<p>RMSD values of top 2 compounds.</p>
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<p>RMSF value of top 2 compounds.</p>
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<p>SASA value of top 2 compounds.</p>
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<p>rGyr value of top 2 compounds.</p>
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16 pages, 4496 KiB  
Article
Identification of Oligopeptides in the Distillates from Various Rounds of Soy Sauce-Flavored Baijiu and Their Effect on the Ester–Acid–Alcohol Profile in Baijiu
by Qiang Wu, Shanlin Tian, Xu Zhang, Yunhao Zhao and Yougui Yu
Foods 2025, 14(2), 287; https://doi.org/10.3390/foods14020287 - 16 Jan 2025
Viewed by 491
Abstract
Endogenous peptides in Baijiu have primarily focused on finished liquor research, with limited attention given to the peptides in base liquor prior to blending. Liquid chromatography–tandem mass spectrometry (LC-MS) was employed to identify endogenous peptides in the distillates from the first to seventh [...] Read more.
Endogenous peptides in Baijiu have primarily focused on finished liquor research, with limited attention given to the peptides in base liquor prior to blending. Liquid chromatography–tandem mass spectrometry (LC-MS) was employed to identify endogenous peptides in the distillates from the first to seventh rounds of soy sauce-flavored Baijiu. Two hundred and five oligopeptides were identified from these distillates, all of which had molecular weights below 1000 Da and were composed of amino acid residues associated with flavor (sweet, sour, and bitter) and biological activity. Furthermore, full-wavelength scanning, content determination of the main compounds, and molecular docking were performed to analyze these oligopeptides’ effect on the ester–acid–alcohol profile in Baijiu. This determination revealed a negative correlation between the peptide content and total ester content (r = −0.691), as well as the total acid content (r = −0.323), and a highly significant negative correlation with ethanol content (r = −0.916). Notably, the screened peptides (TRH, YHY, RQTQ, PLDLTSFVLHEAI, KHVS, LPQRHRMVYSLL, and NEWH) had specific interactions with the major flavor substances via hydrogen bonds, including esters (ethyl acetate, ethyl butanoate, ethyl hexanoate, and ethyl lactate), acids (acetate acid, butanoate acid, hexanoate acid, lactate acid), and alcohols (ethanol, 1-propanol, 1-butanol, and 1-hexanol). These findings elucidate the distribution and dynamic changes of endogenous peptides in the distillates from various rounds of soy sauce-flavored Baijiu, providing a theoretical foundation for further investigation into their interaction mechanisms associated with flavor compounds. Full article
(This article belongs to the Section Food Physics and (Bio)Chemistry)
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<p>Statistical analysis of the amino acid residues that occurred in peptides of the liquors from different rounds. (<b>A</b>) Distribution diagram of the length of the peptides. It was calculated as the proportion in the totality of amino acid residues of all peptides. The circles from the inside to the outside represent the one to seven rounds of liquor. (<b>B</b>) Distribution diagram of the molecular weight of the peptides. It was calculated as the proportion in the totality of amino acid residues of all peptides. The circles from the inside to the outside represent the one to seven rounds of liquor. (<b>C</b>) Distribution diagram of the retention time of the peptides. It was calculated as the proportion in the totality of amino acid residues of all peptides. The circles from the inside to the outside represent the one to seven rounds of liquor. (<b>D</b>) Distribution diagram of the amino acid properties of the peptides. It was calculated as the proportion in the totality of amino acid residues of all peptides. The circles from the inside to the outside represent the one to seven rounds of liquor. (<b>E</b>) Distribution diagram of amino acids influenced the Baijiu quality of the liquors. It was calculated as the proportion of the totality of amino acid residues of all peptides. (<b>F</b>) Distribution diagram of the flavor-related amino acid of the liquors. It was calculated as the proportion in the totality of amino acid residues of all peptides. (<b>G</b>) Distribution diagram of the bioactive amino acid of the liquors. It was calculated as the proportion in the totality of amino acid residues of all peptides.</p>
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<p>Correlation analysis between the peptides and the main flavor substances in the liquors collected at different rounds. (<b>A</b>) Spectra of the standard substances involved in Baijiu. (<b>B</b>) Spectra of the liquors from different rounds. (<b>C</b>) Correlation analysis among the characteristic peaks based on the maximum absorption value. (<b>D</b>) Content determination of total acid, total ester, total alcohol, and the peptide. (<b>E</b>) Correlation analysis between the content of the peptide and total acid, total ester, or total alcohol. Red represents positive, and blue represents negative. ** <span class="html-italic">p</span> ≤ 0.01.</p>
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<p>Molecular docking of the peptides with a flavor substance. (<b>A1</b>) PLDLTSFVLHEAI and the alcohols. (<b>B1</b>) PLDLTSFVLHEAI and the ester substances. (<b>C1</b>) KHVS or TRH and the acid substances. The interaction force model of the peptide and flavor substance molecules. (<b>A2</b>) PLDLTSFVLHEAI and the alcohols. (<b>B2</b>) PLDLTSFVLHEAI and the ester substances. (<b>C2</b>) KHVS or TRH and the acid substances.</p>
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28 pages, 10002 KiB  
Article
Silymarin as a Therapeutic Agent for Hepatocellular Carcinoma: A Multi-Approach Computational Study
by Ouided Benslama, Sabrina Lekmine, Hamza Moussa, Hichem Tahraoui, Mohammad Shamsul Ola, Jie Zhang and Abdeltif Amrane
Metabolites 2025, 15(1), 53; https://doi.org/10.3390/metabo15010053 - 15 Jan 2025
Viewed by 475
Abstract
Background: Hepatocellular carcinoma (HCC) is a prevalent and lethal form of liver cancer with limited treatment options. Silymarin, a flavonoid complex derived from milk thistle, has shown promise in liver disease treatment due to its antioxidant, anti-inflammatory, and anticancer properties. This study aims [...] Read more.
Background: Hepatocellular carcinoma (HCC) is a prevalent and lethal form of liver cancer with limited treatment options. Silymarin, a flavonoid complex derived from milk thistle, has shown promise in liver disease treatment due to its antioxidant, anti-inflammatory, and anticancer properties. This study aims to explore the therapeutic potential of silymarin in HCC through a comprehensive in silico approach. Methods: This study employed a network pharmacology approach to identify key molecular targets of silymarin in HCC. The Genecards and Metascape databases were used for target identification and functional annotation. Molecular docking analysis was conducted on the primary silymarin components against VEGFA and SRC proteins, which are critical in HCC progression. MD simulations followed to assess the stability and interactions of the docked complexes. Results: Network pharmacology analysis identified several key molecular targets and pathways implicated in HCC. The molecular docking results revealed strong binding affinities of silymarin components to VEGFA and SRC, with Silybin A and Isosilybin B showing the highest affinities. MD simulations confirmed the stability of these interactions, indicating potential inhibitory effects on HCC progression. Conclusions: This study provides a comprehensive in silico evaluation of silymarin’s therapeutic potential in HCC. The findings suggest that silymarin, particularly its components Silybin A and Isosilybin B, may effectively target VEGFA and SRC proteins, offering a promising avenue for HCC treatment. Further experimental validation is warranted to confirm these findings and facilitate the development of silymarin-based therapeutics for HCC. Full article
(This article belongs to the Special Issue Metabolism of Bioactives and Natural Products)
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<p>Overview of the research workflow.</p>
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<p>PPI network of 102 coincident targets of HC and silymarin.</p>
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<p>(<b>A</b>) Venn diagram shows the targets of silymarin and HCC. (<b>B</b>) The network of top 10 core HCC-related targets screened using degree values. (<b>C</b>) Ten core targets ranked by degree values.</p>
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<p>Functional annotation and KEGG pathway enrichment analysis of 10 core gene targets. (<b>A</b>) Dotplot chart showing the top 20 BPs. (<b>B</b>) Dotplot chart showing the top 20 CCs. (<b>C</b>) Dotplot chart showing the top 20 MFs. (<b>D</b>) Dotplot chart showing the top 20 KEGG pathways.</p>
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<p>Diagram of pathways involving potential targets identified through KEGG analysis. The red sections highlight the silymarin targets associated with HC.</p>
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<p>Silymarin–target–pathway network. The orange circles represent the three active components of silymarin, the cyan rectangles represent the core genes, and the green triangles represent the pathways linked to the core genes.</p>
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<p>Molecular docking 2D and 3D diagrams of silybin with the highest degree hub target VEGFA (1VPF).</p>
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<p>Molecular docking 2D and 3D diagrams of silybin with the highest degree hub target SRC (3U51).</p>
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<p>(<b>A</b>) RMSD of the VEGFA–silybin and VEGFA-PGG complexes during 100 ns MD simulation. (<b>B</b>) RMSF of the VEGFA–silybin and VEGFA-PGG complexes during 100 ns MD simulation. (<b>C</b>) SASA of the VEGFA–silybin and VEGFA-PGG complexes during 100 ns MD simulation. (<b>D</b>) Radius of gyration of the VEGFA–silybin and VEGFA-PGG complexes during 100 ns MD simulation. (<b>E</b>) Number of H-bonds of the VEGFA–silybin and VEGFA-PGG complexes during 100 ns MD simulation.</p>
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<p>(<b>A</b>) RMSD of the SRC–silybin and SRC co-crystallized inhibitor complexes during 100 ns MD simulation. (<b>B</b>) RMSF of the SRC–silybin and SRC co-crystallized inhibitor complexes during 100 ns MD simulation. (<b>C</b>) SASA of the SRC–silybin and SRC co-crystallized inhibitor complexes during 100 ns MD simulation. (<b>D</b>) Radius of gyration of the SRC–silybin and SRC co-crystallized inhibitor complexes during 100 ns MD simulation. (<b>E</b>) Number of H-bonds of the SRC–silybin and SRC co-crystallized inhibitor complexes during 100 ns MD simulation.</p>
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23 pages, 7871 KiB  
Article
Resveratrol-Based Carbamates as Selective Butyrylcholinesterase Inhibitors: Design, Synthesis, Computational Study and Biometal Complexation Capability
by Maja Sviben, Ilijana Odak, Danijela Barić, Milena Mlakić, Ottó Horváth, Lajos Fodor, Sunčica Roca, Ivana Šagud and Irena Škorić
Molecules 2025, 30(2), 316; https://doi.org/10.3390/molecules30020316 - 15 Jan 2025
Viewed by 372
Abstract
Considering our previous experience in the design of new cholinesterase inhibitors, especially resveratrol analogs, in this research, the basic stilbene skeleton was used as a structural unit for new carbamates designed as potentially highly selective butyrylcholinesterase (BChE) inhibitors with excellent absorption, distribution, metabolism, [...] Read more.
Considering our previous experience in the design of new cholinesterase inhibitors, especially resveratrol analogs, in this research, the basic stilbene skeleton was used as a structural unit for new carbamates designed as potentially highly selective butyrylcholinesterase (BChE) inhibitors with excellent absorption, distribution, metabolism, excretion and toxicity ADMET properties. The inhibitory activity of newly prepared carbamates 113 was tested toward the enzymes acetylcholinesterase (AChE) and BChE. In the tested group of compounds, the leading inhibitors were 1 and 7, which achieved excellent selective inhibitory activity for BChE with IC50 values of 0.12 ± 0.09 μM and 0.38 ± 0.01 μM, respectively. Both were much more active than the standard inhibitor galantamine against BChE. Molecular docking of the most promising inhibitor candidates, compounds 1 and 7, revealed that stabilizing interactions between the active site residues of BChE and the ligands involve π-stacking, alkyl-π interactions, and, when the carbamate orientation allows, H-bond formation. MD analysis confirmed the stability of the obtained complexes. Some bioactive resveratrol-based carbamates displayed complex-forming capabilities with Fe3+ ions as metal centers. Spectrophotometric investigation indicated that they coordinate one or two metal ions, which is in accordance with their chemical structure, offering two binding sites: an amine and a carboxylic group in the carbamate moiety. Based on the obtained in silico, experimental and computational results on biological activity in the present work, new carbamates 1 and 7 represent potential selective BChE inhibitors as new therapeutics for neurological disorders. Full article
(This article belongs to the Special Issue Synthesis of Bioactive Compounds: Volume II)
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<p>Structures of rivastigmine, a progressive non-selective inhibitor of AChE and BChE; resveratrol analogs (<b>A</b>) exhibiting significant BChE inhibitory potential [<a href="#B30-molecules-30-00316" class="html-bibr">30</a>,<a href="#B32-molecules-30-00316" class="html-bibr">32</a>] and newly designed compounds from this work (<b>B</b>).</p>
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<p>Newly synthesized carbamates <b>1</b>–<b>13</b> bearing various functionalities (isolated yields for individual compounds are shown in parentheses).</p>
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<p>The portions of the <sup>1</sup>H NMR spectra of the most active carbamates as selective BChE inhibitors were compared: (<b>a</b>) <b>1</b>, (<b>b</b>) <b>5</b>, and (<b>c</b>) <b>8</b>.</p>
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<p>Structure of bambuterol.</p>
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<p>(<b>a</b>) Representative pose of the most populated cluster of molecule <b>1</b> docked into the active site of BChE. (<b>b</b>) The most stable pose of molecule <b>1</b> obtained by docking to BChE. Distances given in angstroms. Docked molecules are presented using ball-and-stick models.</p>
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<p>Representative pose of the most populated and most stable cluster of molecule <b>7</b> docked into the active site of BChE. Distances in angstroms. Docked molecules are presented using ball-and-stick models.</p>
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<p>Root mean square deviation values from molecular dynamics simulation of a protein–ligand complex of BChE with compounds <b>1</b> and <b>7</b>, respectively. Complexes BChE-<b>1a</b> and BChE-<b>1b</b> correspond to the ligand conformations presented in <a href="#molecules-30-00316-f005" class="html-fig">Figure 5</a>a and <a href="#molecules-30-00316-f005" class="html-fig">Figure 5</a>b, respectively.</p>
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<p>Root mean square fluctuation values from molecular dynamics simulation of a protein–ligand complex of BChE with compounds <b>1</b> (dotted violet and blue lines) and <b>7</b> (red line), respectively.</p>
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<p>Values of a radius of gyration from molecular dynamics simulation of a protein–ligand complex of BChE with compounds <b>1</b> (violet for conformation <b>1a</b>, and blue for <b>1b</b>) and <b>7</b> (red line), respectively.</p>
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<p>Distance between hydroxyl oxygen of the catalytic serine and carbonyl carbon of ligand <b>1</b> based on molecular dynamics simulation of a protein–ligand complex BChE-<b>1b</b>.</p>
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<p>Molar absorptivity spectra of compound <b>1</b> (black), <b>3</b> (blue), <b>6</b> (green) and <b>7</b> (red).</p>
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<p>Difference absorption spectra obtained through titration of 1.93 × 10<sup>–5</sup> M of Fe<sub>2</sub>(SO<sub>4</sub>)<sub>3</sub> aqueous solution with compound <b>6</b> in the 0–0.35 mM concentration range. The orange curve is the spectrum of the starting pure Fe<sub>2</sub>(SO<sub>4</sub>)<sub>3</sub> solution, while the green spectrum belongs to the pure ligand. The inset shows the changes in absorbance measured at 330 nm (blue) and the theoretical absorbance calculated for the ligand at the same wavelength (red) as a function of the concentration of <b>6</b>.</p>
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<p>Newly designed and synthesized carbamates <b>1</b>–<b>13</b>.</p>
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<p>Possible mechanism of action of carbamate <b>1</b> in the BChE enzyme active site by analogy with acetylcholine and rivastigmine.</p>
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24 pages, 39013 KiB  
Article
Computational Assessment of Biocompatibility and Toxicity of Graphene and Its Derivatives for Dental Adhesives
by Ravinder Saini
Oral 2025, 5(1), 4; https://doi.org/10.3390/oral5010004 - 14 Jan 2025
Viewed by 420
Abstract
Background/Objectives: Graphene and its derivatives have garnered attention for their unique properties that could enhance dental biomaterials. Understanding their interactions with biological systems is crucial for optimizing their application in dentistry. This study aimed to comprehensively evaluate the biocompatibility, molecular interactions, and toxicity [...] Read more.
Background/Objectives: Graphene and its derivatives have garnered attention for their unique properties that could enhance dental biomaterials. Understanding their interactions with biological systems is crucial for optimizing their application in dentistry. This study aimed to comprehensively evaluate the biocompatibility, molecular interactions, and toxicity profiles of graphene and its derivatives for potential dental applications using in silico approaches. Methods: The study employed molecular-docking simulations, 100 ns molecular dynamics (MD) simulations, pharmacophore modeling, and in silico toxicity assessments. Key bone-related proteins and receptors were selected to assess the potential of graphene-based materials in dental restorative and regenerative therapies. Results: Molecular-docking simulations revealed strong interactions of Graphene Quantum Dots (GQDs) and sulfur-doped graphene with critical bone-related receptors, suggesting their potential for reinforcing dentin and promoting bone regeneration. MD simulations demonstrated stable complex formations, with occasional fluctuations indicating areas for material optimization. In silico toxicity assessments indicated favorable profiles for high-purity graphene and selected doped graphenes (nitrogen-, fluorine-, and sulfur-doped), while graphene oxide (GO) exhibited concerning toxicity levels, highlighting the importance of mitigating strategies. Conclusions: Graphene and its derivatives exhibit promising biocompatibility and molecular interaction profiles relevant to dental applications. Challenges such as GO’s toxicity and occasional instability in simulations suggest the need for further research into surface modifications and material refinement. These findings pave the way for advancing graphene-based dental materials toward clinical implementation, potentially revolutionizing dental prosthetics and treatments. Full article
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<p>Molecular-docking simulation results: (<b>a</b>) 3D perspective of osteonectin/Graphene Quantum Dot complex; (<b>b</b>) 2D perspective of osteonectin/Graphene Quantum Dot complex; (<b>c</b>) 3D perspective of BMP2/sulfur-doped graphene complex; and (<b>d</b>) 2D perspective of BMP2/sulfur-doped graphene complex. Conventional hydrogen bonds are depicted in firm green, van der Waals interactions in light green, salt bridges, attractive charges, and pi-cation interactions in orange, while alkyl interactions are shown in pink.</p>
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<p>Molecular dynamics (MD) simulation results of top complexes of graphene and its derivatives with each target receptor.</p>
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21 pages, 2535 KiB  
Article
Prediction of the Binding to the Nuclear Factor NF-Kappa-B by Constituents from Teucrium polium L. Essential Oil
by Renilson Castro de Barros, Renato Araújo da Costa, Nesrine Guenane, Boulanouar Bakchiche, Farouk Benaceur, Omer Elkiran, Suelem Daniella Pinho Farias, Vanessa Regina Silva Mota and Maria Fani Dolabela
Curr. Issues Mol. Biol. 2025, 47(1), 48; https://doi.org/10.3390/cimb47010048 - 14 Jan 2025
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Abstract
Teucrium polium L. is a plant with various claims of ethnobotanical use, primarily for inflammatory diseases. Chemical studies have already isolated different types of terpenes from the species, and studies have established its pharmacological potential. The present study evaluates the components of T. [...] Read more.
Teucrium polium L. is a plant with various claims of ethnobotanical use, primarily for inflammatory diseases. Chemical studies have already isolated different types of terpenes from the species, and studies have established its pharmacological potential. The present study evaluates the components of T. polium essential oil cultivated in the Algerian Saharan Atlas. GC-MS identified the major components as fenchone (31.25%), 3-carene (15.77%), cis-limonene oxide (9.77%), and myrcene (9.15%). In the in silico prediction, molecules with more than 1% abundance were selected. Regarding Lipinski’s rule, all molecules followed the rule. All molecules were found to be toxic in at least one model, with some molecules being non-genotoxic (6, 8, 10, 11, 12, 13) and others being non-mutagenic (5, 7, 9, 14). Three molecules were selected that showed the best results in pharmacokinetic and toxicity studies: the molecules that did not present carcinogenic potential (7—myrtenal; 9—myrtenol; 14—verbenol). The molecular target was established, and it seems that all three bound to the nuclear factor NF-kappa-B. Based on the docking and molecular dynamics results, these molecules have potential as anti-inflammatory and antitumor therapies, with further in vitro and in vivo studies needed to evaluate their activity and toxicity. Full article
(This article belongs to the Special Issue Molecular Research in Bioactivity of Natural Products, 2nd Edition)
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<p>Gas chromatography flame ionization detector (GC-FID) profile of the essential oil of <span class="html-italic">Teucrium polium</span>.</p>
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<p>Molecules found in <span class="html-italic">T. polium</span> essential oil: (<b>a</b>)—fenchone; (<b>b</b>)—3-carene; (<b>c</b>)—limonene oxide, cis-; (<b>d</b>)—myrcene; (<b>e</b>)—cis-pinocarveol; (<b>f</b>)—germacrene D; (<b>g</b>)—myrtenal; (<b>h</b>)—bicyclogermacrene; (<b>i</b>)—myrtenol; (<b>j</b>)—spathulenol; (<b>k</b>)—(Z)-nerolidyl acetate; (<b>l</b>)—δ-cadinene; (<b>m</b>)—β-ocimene, (E)-; (<b>n</b>)—verbenol.</p>
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<p>Illustration of the compounds docked on the active site of the NF-κB protein.</p>
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<p>Representation of the 2D interactions of the molecules parthenolide, myrtenal, myrtenol, verbenol, and the protein nuclear factor NF-kappa-B. Image generated with Discovery Studio 3.5 Visualizer.</p>
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<p>Chart of RMSD (<b>A</b>) and RSMF (<b>B</b>) of the apo form of the protein nuclear factor NF-kappa-B and complexed with parthenolide, myrtenal, myrtenol, and verbenol.</p>
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33 pages, 2203 KiB  
Article
Predicting the Anti-SARS-CoV-2 Potential of Isoquinoline Alkaloids from Brazilian Siparunaceae Species Using Chemometric Tools
by Brendo Araujo Gomes, Diégina Araújo Fernandes, Simony Carvalho Mendonça, Mariana Freire Campos, Thamirys Silva da Fonseca, Larissa Esteves Carvalho Constant, Natalia Ferreira de Sousa, Renata Priscila Barros de Menezes, Beatriz Albuquerque Custódio de Oliveira, Stephany da Silva Costa, Giovanna Barbosa Frensel, Alice Santos Rosa, Thamara Kelcya Fonseca Oliveira, Amanda Resende Tucci, Júlia Nilo Henrique Lima, Vivian Neuza Santos Ferreira, Milene Dias Miranda, Diego Allonso, Marcus Tullius Scotti, Suzana Guimarães Leitão and Gilda Guimarães Leitãoadd Show full author list remove Hide full author list
Int. J. Mol. Sci. 2025, 26(2), 633; https://doi.org/10.3390/ijms26020633 - 13 Jan 2025
Viewed by 784
Abstract
The COVID-19 pandemic has caused over 7 million deaths globally in the past four years. Siparuna spp. (Siparunaceae), which is used in Brazilian folk medicine, is considered a genus with potential antiviral alternatives. This study explored the correlation between phytochemicals in Siparuna leaf [...] Read more.
The COVID-19 pandemic has caused over 7 million deaths globally in the past four years. Siparuna spp. (Siparunaceae), which is used in Brazilian folk medicine, is considered a genus with potential antiviral alternatives. This study explored the correlation between phytochemicals in Siparuna leaf extracts (S. ficoides, S. decipiens, S. glycycarpa, S. reginae, and S. cymosa) and their potential against various SARS-CoV-2 targets. In vitro assays examined interactions between the spike protein and the ACE2 receptor, protease activity, and viral replication inhibition in Calu-3 cell models. UHPLC-MS/MS analysis, processed with MZmine and evaluated chemometrically, revealed isoquinoline alkaloids with bulbocapnine, showing promising therapeutic potential. Predictions regarding absorption, distribution, metabolism, excretion, and toxicity were conducted, along with molecular docking and dynamics simulations, to evaluate protein−ligand interaction stability. The results confirmed the antiviral activity of the Siparuna genus against SARS-CoV-2 targets, with 92% of the extracts maintaining over 70% cellular viability at 200 μg·mL−1 and 80% achieving more than 50% viral activity suppression at 50 μg·mL−1. These findings highlight the potential of isoquinoline alkaloids as novel anti-coronavirus agents and support the need for further exploration, isolation, and testing of Siparuna compounds in the fight against COVID-19. Full article
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