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15 pages, 6407 KiB  
Article
Identification of Potential Selective PAK4 Inhibitors Through Shape and Protein Conformation Ensemble Screening and Electrostatic-Surface-Matching Optimization
by Xiaoxuan Zhang, Meile Zhang, Yihao Li and Ping Deng
Curr. Issues Mol. Biol. 2025, 47(1), 29; https://doi.org/10.3390/cimb47010029 - 6 Jan 2025
Viewed by 678
Abstract
P21-activated kinase 4 (PAK4) plays a crucial role in the proliferation and metastasis of various cancers. However, developing selective PAK4 inhibitors remains challenging due to the high homology within the PAK family. Therefore, developing highly selective PAK4 inhibitors is critical to overcoming the [...] Read more.
P21-activated kinase 4 (PAK4) plays a crucial role in the proliferation and metastasis of various cancers. However, developing selective PAK4 inhibitors remains challenging due to the high homology within the PAK family. Therefore, developing highly selective PAK4 inhibitors is critical to overcoming the limitations of existing inhibitors. We analyzed the structural differences in the binding pockets of PAK1 and PAK4 by combining cross-docking and molecular dynamics simulations to identify key binding regions and unique structural features of PAK4. We then performed screening using shape and protein conformation ensembles, followed by a re-evaluation of the docking results with deep-learning-driven GNINA to identify the candidate molecule, STOCK7S-56165. Based on this, we applied a fragment-replacement strategy under electrostatic-surface-matching conditions to obtain Compd 26. This optimization significantly improved electrostatic interactions and reduced binding energy, highlighting its potential for selectivity. Our findings provide a novel approach for developing selective PAK4 inhibitors and lay the theoretical foundation for future anticancer drug design. Full article
(This article belongs to the Special Issue New Insight: Enzymes as Targets for Drug Development, 2nd Edition)
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Figure 1

Figure 1
<p>Overall workflow.</p>
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<p>Structural comparison of the binding cavities in PAK1 and PAK4 with their ligands. (<b>a</b>) Crystal structure of PAK4 (PDB ID: 7CP4) and (<b>b</b>) crystal structure of PAK1 (PDB ID: 5DEY). The hydrophobic surfaces of the binding cavities are visualized using color shading. The secondary structures of the receptors, including the α-helices, are highlighted: PAK1 is shown in blue (<b>c</b>) and PAK4 is shown in pink (<b>d</b>).</p>
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<p>Chemical structures of representative inhibitors targeting PAK1 and PAK4.</p>
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<p>Binding cavities of PAK1 and PAK4. Left: Crystal structure of the PAK4 (PDB ID: 7CP4). Right: Crystal structure of the PAK1 (PDB ID: 5DEY). The binding cavities of both receptors are highlighted using electrostatic potential coloring, with key binding site residues labeled in blue.</p>
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<p>Heatmap of the free energy decomposition for PAK1 and PAK4 systems. Red indicates the interactions of inhibitors with key residues around the binding site in PAK1, while blue indicates the interactions of inhibitors with key residues around the binding site in PAK4. Key residues are highlighted in yellow boxes.</p>
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<p>(<b>a</b>) The chemical structure of STOCK7S-56165, (<b>b</b>) the chemical structure of Compd 26, and (<b>c</b>) binding free energy contributions of Compd 55, STOCK7S-56165, and Compd 26 to PAK4 (energy unit: kcal/mol). The result for Compd 26 is the average value of stable 100-250 ns from three independent replicate simulations.</p>
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<p>(<b>a</b>) RMSD analysis of Compd 26 during MD simulations from three independent replicate calculations, (<b>b</b>) 3D binding pose of Compd 26 with PAK4, where hydrogen bonds and hydrophobic interactions are represented by green and pink lines, respectively, and (<b>c</b>) IGMH analysis of the interaction between Compd 26 and PAK4; the green color block indicates that the main interaction is van der Waals interaction.</p>
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<p>ESP surface of PAK4-binding site (PDB ID: 7CP4), Compd 55, STOCK7S-56165, and Compd 26.</p>
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14 pages, 6805 KiB  
Article
Transient Flow Dynamics in Tesla Valve Configurations: Insights from Computational Fluid Dynamics Simulations
by Mohamad Zeidan, Márton Németh, Gopinathan R. Abhijith, Richárd Wéber and Avi Ostfeld
Water 2024, 16(23), 3492; https://doi.org/10.3390/w16233492 - 4 Dec 2024
Cited by 1 | Viewed by 1104
Abstract
This study investigates the transient flow dynamics and pressure interactions within Tesla valve configurations through comprehensive CFD simulations. Tesla valves offer efficient passive fluid control without the need for external power, making them favorable in various applications. Previous observations indicated that Tesla valves [...] Read more.
This study investigates the transient flow dynamics and pressure interactions within Tesla valve configurations through comprehensive CFD simulations. Tesla valves offer efficient passive fluid control without the need for external power, making them favorable in various applications. Previous observations indicated that Tesla valves effectively reduce the amplitude of pressure transients, prolonging their duration and distributing energy over an extended timeframe. While suggesting a potential role for Tesla valves as pressure dampers during transient events, the specific mechanisms behind this behavior remain unexplored. This research focuses on elucidating the internal dynamics of Tesla valves during transient events, aiming to unravel the processes responsible for the observed attenuation in pressure transients. This study reveals the emergence of “pressure pockets” within Tesla valves, deviating from conventional uniform pressure fronts. These pockets manifest as discrete chambers with varying lengths and volumes, contributing to the non-uniform propagation of pressure throughout the system. This investigation employs advanced CFD simulations as a crucial tool to unravel the governing dynamics of transient flow within Tesla valve configurations. By elucidating underlying fluid dynamics, this study lays the groundwork for future Tesla valve design optimization, holding potential implications for applications where the control of transient flow events is crucial. Full article
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<p>The cross-section of a Tesla valve, displaying its cavity design, from the original patent application (link).</p>
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<p>Tesla valve configuration for CFD modeling.</p>
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<p>Two-dimensional mesh configuration (<b>left</b>); reference mesh of Tesla valve (<b>right</b>).</p>
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<p>Normal pressure characteristics in Tesla valve.</p>
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<p>Network layout for simulating transient flow in normal pipe (<b>above</b>) vs. Tesla valve (<b>bottom</b>). Color map depicts velocity values.</p>
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<p>The transient response for the Tesla valve and pipe system for 3 different diameters.</p>
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<p>The normalized pressure response for the Tesla valve and pipe systems.</p>
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<p>The transient response for the Tesla valve vs. pipes for 5 s.</p>
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<p>Pressure pockets highlighted in the CFD simulation at different time steps.</p>
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<p>The projected trajectory for the pressure fronts from high to low pressure. (<b>a</b>) illustrate the pressure pockets at 0.01049 s, while (<b>b</b>) shows the pressures a 0.16 millisecond after.</p>
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<p>Measurement points in the third lower chamber of the Tesla valve. The numbering (1–7) indicates specific locations used in the CFD simulation to extract detailed flow and pressure data for analysis.</p>
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<p>(<b>a</b>) Pressure readings at points 1, 3, and 5. Dashed lines indicate timestamps corresponding to accompanying snapshots at different timestamps shown in (<b>b</b>).</p>
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<p>Pressure plot at points 1, 6, and 7.</p>
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<p>Acceleration at point 7.</p>
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<p>(<b>a</b>) Pressure fluctuations at points 1 and 3 variations over time. (<b>b</b>) The superposition of the two pressure signals, illustrating their combined effect and the resulting net pressure at point 2.</p>
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<p>Velocity plots at the 7 measuring points over the simulation period.</p>
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<p>Pressure plots at the 7 measuring points over the simulation period.</p>
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2 pages, 148 KiB  
Abstract
Sensory Evaluation of Prototypes of Novel Dishes and Recipes Based on Underutilized Foods
by Marija Ranić, Marija Knez, Jelena Milešević, Nevena Vidović, Vuk Stevanović, Agneš Kadvan and Mirjana Gurinović
Proceedings 2023, 91(1), 378; https://doi.org/10.3390/proceedings2023091378 - 27 Feb 2024
Viewed by 833
Abstract
Background and objectives: Since the beginning of the 20th century, more than 75% of genetic diversity has been lost. As a result of this homogenization, thousands of cultivated and wild food plants are no longer used, although they have high nutritional value. This [...] Read more.
Background and objectives: Since the beginning of the 20th century, more than 75% of genetic diversity has been lost. As a result of this homogenization, thousands of cultivated and wild food plants are no longer used, although they have high nutritional value. This work aims to develop recipes for new dishes and bring biodiversity to the plate in a way that consumers desire. Methods: Prototypes of new foods were prepared in the experimental kitchen. Each recipe was blind-tasted, evaluated, and ranked according to its organoleptic quality using a systematic approach. Both independent professional taste experts and lay public representatives were involved in the sensory evaluation of dishes. After the initial sensory evaluation (discrimination and hedonic scoring tests) in Serbia, the following recipes were selected for further evaluation in four other countries—Greece, Hungary, France, and Turkey: Dandelion and Tomato Salad; Buckwheat and Grass Pea Stew with Eggplant; Baked Eggplant and Potato à la Papa Alexie; Lentils as a Starter and Buckwheat Pockets Filled with Walnuts and Dried Fruit. The recipes were tested by 132 lay public representatives and 24 professionals. Results and Discussion: Of the five dishes tested, Buckwheat Pockets Filled with Walnuts and Dried Fruit were the most popular, followed by Baked Eggplant and Potato à la Papa Alexie and Dandelion and Tomato Salad. Although cultural differences and individual preferences play a role, none of the dishes was considered unacceptable or undesirable, and most were rated as likable to very likable. In line with the feedback, the optimization of the recipe design was discussed to optimize the sensory perception of the new dishes and to achieve a stimulating and satisfying taste and smell with appropriate texture and mouthfeel. The sensory evaluation showed that the new dishes offered, based on the underutilized foods studied in this project, were highly recognized and well received by consumers. Finally, a recipe book was created that includes a detailed explanation of the preparation methods and a comprehensive presentation of the relevant nutritional information of the new food dishes. Full article
(This article belongs to the Proceedings of The 14th European Nutrition Conference FENS 2023)
12 pages, 2483 KiB  
Article
Single Amino Acid Polymorphisms in the Fasciola hepatica Carboxylesterase Type B Gene and Their Potential Role in Anthelmintic Resistance
by Estefan Miranda-Miranda, Raquel Cossío-Bayúgar, Lauro Trejo-Castro and Hugo Aguilar-Díaz
Pathogens 2023, 12(10), 1255; https://doi.org/10.3390/pathogens12101255 - 18 Oct 2023
Cited by 2 | Viewed by 1563
Abstract
The expression of the Fasciola hepatica carboxylesterase type B (CestB) gene is known to be induced upon exposure to the anthelmintic triclabendazole (TCBZ), leading to a substantial rise in enzyme-specific activity. Furthermore, the nucleotide sequence of the CestB gene displays variations that can [...] Read more.
The expression of the Fasciola hepatica carboxylesterase type B (CestB) gene is known to be induced upon exposure to the anthelmintic triclabendazole (TCBZ), leading to a substantial rise in enzyme-specific activity. Furthermore, the nucleotide sequence of the CestB gene displays variations that can potentially result in radical amino acid substitutions at the ligand binding site. These substitutions hold the potential to impact both the ligand–protein interaction and the catalytic properties of the enzyme. Thus, the objective of our study was to identify novel CestB polymorphisms in TCBZ-resistant parasites and field isolates obtained from a highly endemic region in Central Mexico. Additionally, we aimed to assess these amino acid polymorphisms using 3D modeling against the metabolically oxidized form of the anthelmintic TCBZSOX. Our goal was to observe the formation of TCBZSOX-specific binding pockets that might provide insights into the role of CestB in the mechanism of anthelmintic resistance. We identified polymorphisms in TCBZ-resistant parasites that exhibited three radical amino acid substitutions at positions 147, 215, and 263. These substitutions resulted in the formation of a TCBZSOX-affinity pocket with the potential to bind the anthelmintic drug. Furthermore, our 3D modeling analysis revealed that these amino acid substitutions also influenced the configuration of the CestB catalytic site, leading to alterations in the enzyme’s interaction with chromogenic carboxylic ester substrates and potentially affecting its catalytic properties. However, it is important to note that the TCBZSOX-binding pocket, while significant for drug binding, was located separate from the enzyme’s catalytic site, rendering enzymatic hydrolysis of TCBZSOX impossible. Nonetheless, the observed increased affinity for the anthelmintic may provide an explanation for a drug sequestration type of anthelmintic resistance. These findings lay the groundwork for the future development of a molecular diagnostic tool to identify anthelmintic resistance in F. hepatica. Full article
(This article belongs to the Special Issue One Health: New Approaches, Research and Innovation to Zoonoses)
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Graphical abstract

Graphical abstract
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<p>Location of <span class="html-italic">F. hepatica</span> field samples obtained from a highly endemic area that spans four states in Central Mexico. The coordinates of each location, along with the developmental stage of the samples, are comprehensively summarized in <a href="#pathogens-12-01255-t001" class="html-table">Table 1</a>.</p>
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<p>TCBZSOX-binding pocket in both TCBZ-susceptible and TCBZ-resistant strains of <span class="html-italic">F. hepatica</span>. Although TCBZSOX-binding pockets were identified in both anthelmintic-susceptible and anthelmintic-resistant parasites, marked differences were observed between the two polymorphisms. In the susceptible helminth polymorphism depicted in (<b>A</b>), the amino acids R147, E215, and R263 were located in separate domains, disconnected from each other, and distant from the position of the anthelmintic. Conversely, in the resistant strain shown in (<b>B</b>), the amino acid substitutions at K147, K215, and K263 led to the modification of the TCBZSOX-binding pocket, forming a compact domain that surrounds the anthelmintic. Notably, two of these amino acid substitutions, K147 and K263, highlighted in yellow, were found to directly interact with TCBZSOX. Amino acids at positions 147, 215 and 263 were highlighted in yellow, TCBZSOX was highlighted in red.</p>
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<p>Detailed view of the CestB catalytic site featuring the 3D structures of the protein–ligand complexes. When analyzing the active site, the catalytic serine S336 highlighted in yellow was identified as a key component. Through protein–ligand docking analysis, different configurations were observed for the TCBZ-susceptible and TCBZ-resistant strains due to three amino acid substitutions. While these substitutions may not be visible in this figure and are not in close proximity to the catalytic serine 336, they still induce a structural reconfiguration at the core of the catalytic site. Notably, in the susceptible strain enzyme viewed in subfigure (<b>A</b>), the absence of N735 is replaced by I375 highlighted in yellow. In contrast, in the resistant strain viewed in subfigure (<b>B</b>), the presence of N753 highlighted in yellow, which is the last amino acid in the enzyme’s sequence, significantly brings it close to the catalytic S336, forming hydrogen bonds. The ANA ligand highlighted in red was employed to ascertain the position of the catalytic serine within the active site.</p>
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20 pages, 2919 KiB  
Article
Redox Modification of PKA-Cα Differentially Affects Its Substrate Selection
by Jeannette Delva-Wiley, Ese S. Ekhator, Laquaundra L. Adams, Supriya Patwardhan, Ming Dong and Robert H. Newman
Life 2023, 13(9), 1811; https://doi.org/10.3390/life13091811 - 26 Aug 2023
Viewed by 1658
Abstract
The cyclic AMP-dependent protein kinase (PKA) plays an essential role in the regulation of many important cellular processes and is dysregulated in several pervasive diseases, including diabetes, cardiovascular disease, and various neurodegenerative disorders. Previous studies suggest that the alpha isoform of the catalytic [...] Read more.
The cyclic AMP-dependent protein kinase (PKA) plays an essential role in the regulation of many important cellular processes and is dysregulated in several pervasive diseases, including diabetes, cardiovascular disease, and various neurodegenerative disorders. Previous studies suggest that the alpha isoform of the catalytic subunit of PKA (PKA-Cα) is oxidized on C199, both in vitro and in situ. However, the molecular consequences of these modifications on PKA-Cα’s substrate selection remain largely unexplored. C199 is located on the P + 1 loop within PKA-Cα’s active site, suggesting that redox modification may affect its kinase activity. Given the proximity of C199 to the substrate binding pocket, we hypothesized that oxidation could differentially alter PKA-Cα’s activity toward its substrates. To this end, we examined the effects of diamide- and H2O2-dependent oxidation on PKA-Cα’s activity toward select peptide and protein substrates using a combination of biochemical (i.e., trans-phosphorylation assays and steady-state kinetics analysis) and biophysical (i.e., surface plasmon resonance and fluorescence polarization assays) strategies. These studies suggest that redox modification of PKA-Cα differentially affects its activity toward different substrates. For instance, we found that diamide-mediated oxidation caused a marked decrease in PKA-Cα’s activity toward some substrates (e.g., Kemptide and CREBtide) while having little effect on others (e.g., Crosstide). In contrast, H2O2-dependent oxidation of PKA-Cα led to an increase in its activity toward each of the substrates at relatively low H2O2 concentrations, with differential effects at higher peroxide concentrations. Together, these studies offer novel insights into crosstalk between redox- and phosphorylation-dependent signaling pathways mediated by PKA. Likewise, since C199 is highly conserved among AGC kinase family members, they also lay the foundation for future studies designed to elucidate the role of redox-dependent modification of kinase substrate selection in physiological and pathological states. Full article
(This article belongs to the Section Biochemistry, Biophysics and Computational Biology)
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Figure 1
<p>Impact of diamide-mediated oxidation and subsequent glutathionylation on PKA-Cα activity toward different model substrates. (<b>A</b>). Normalized relative activity of PKA-Cα toward Kemptide (red), CREBtide (green) and Crosstide (blue) following pre-treatment with the indicated concentrations of diamide for 10 min at room temperature. Wild-type curves are shown as solid lines while PKA-Cα (C199S) curves are shown as dashed lines. (<b>B</b>). PKA-Cα activity toward Kemptide, CREBtide, and Crosstide after pre-treatment with diamide for 10 min at room temperature followed immediately by incubation with a 1.25-fold molar excess of reduced glutathione (GSH) for an additional 10 min at room temperature. (<b>C</b>). Difference in the normalized relative activity of PKA-Cα treated with diamide followed by GSH as in (<b>B</b>) (diamide and GSH) and PKA-Cα treated with diamide alone as in (<b>A</b>) (diamide alone). (<b>D</b>). Purified PKA-Cα was incubated in the presence of 100 μM diamide alone (lanes 3 and 9), 100 μM diamide followed by 125 μM GSH (lanes 4 and 10), 125 μM GSH alone (lanes 5 and 11), or 100 µM diamide and 125 μM GSH simultaneously (lanes 6 and 12). Samples were then resolved by SDS-PAGE in the presence (right) or absence (left) of dithiothreitol (DTT) reducing agent and analyzed by western blotting using an anti-PKA-Cα antibody. (<b>A</b>) black arrowhead indicates the position of a faster migrating species caused by the formation of an intramolecular disulfide bond (Intra-S-S) while brackets to the right indicate a series of slower migrating species caused by the formation of intermolecular disulfide bonds (Inter-S-S).</p>
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<p>Impact of H<sub>2</sub>O<sub>2</sub>-dependent oxidation of PKA on substrate selection. (<b>A</b>). Effect of H<sub>2</sub>O<sub>2</sub>-dependent oxidation on PKA-Cα activity toward Crosstide (blue), Kemptide (red), and CREBtide (green). Wild-type curves are shown as solid lines while PKA-Cα (C199S) curves are shown as dashed lines. (<b>B</b>). Non-reducing PAGE following treatment of PKA-Cα with 5 µM H<sub>2</sub>O<sub>2</sub> alone (lanes 3 and 9), 5 µM H<sub>2</sub>O<sub>2</sub> followed by 25 µM reduced glutathione (GSH) (lanes 4 and 10), GSH alone (lanes 5 and 11), or 5 µM H<sub>2</sub>O<sub>2</sub> and 25 µM GSH at the same time. Samples were then resolved by SDS-PAGE in the presence (right) or absence (left) of dithiothreitol (DTT) reducing agent and analyzed by western blot using an anti-PKA-Cα antibody. (<b>C</b>). Western blot following treatment with dH<sub>2</sub>O (lane 1), 5 µM H<sub>2</sub>O<sub>2</sub> alone (lane 2), or 5 µM H<sub>2</sub>O<sub>2</sub> followed by 25 µM GSH-biotin (lane 3). The blot was then probed with streptavidin-HRP (SA-HRP) followed by mouse anti-PKA-Cα antibody (α-PKA-C).</p>
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<p>Effects of H<sub>2</sub>O<sub>2</sub>-dependent oxidation on PKA-C<span class="html-italic">α</span>-CREBtide interactions. (<b>A</b>). Representative equilibrium binding experiment measuring the affinity of oxidized PKA-Cα for CREBtide using surface plasmon resonance imaging (SPRi) at PKA-Cα concentrations ranging from 0.156–10 µM. Different colored lines represent PKA-Cα concentrations ranging from 10 μM (black) to 0.156 μM (royal blue). The binding curve is shown in the inset. (<b>B</b>). Average K<sub>D,app</sub> for untreated PKA-Cα (blue) and PKA-Cα treated with 5.0 μM H<sub>2</sub>O<sub>2</sub> before injection (red). Error bars represent standard error about the mean (n = 4). Statistically significant differences are indicated by an asterisk * (<span class="html-italic">p</span> &lt; 0.05). (<b>C</b>). Representative kinetic binding experiment measuring the on- and off-rates (<span class="html-italic">k<sub>on</sub></span> and <span class="html-italic">k<sub>off</sub></span>, respectively) of oxidized PKA-Cα for CREBtide using SPRi. Different collored lines represent PKA-Cα concentrations ranging from 10 μM (light blue) to 0.156 μM (magenta).</p>
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<p>Steady-state kinetics analysis of H<sub>2</sub>O<sub>2</sub>-dependent changes in PKA-C<span class="html-italic">α</span>-substrate interactions. PKA-Cα (250 nM) was pre-treated with either dH<sub>2</sub>O (blue; untreated) or 5 µM H<sub>2</sub>O<sub>2</sub> (red; 5 µM H<sub>2</sub>O<sub>2</sub>) for 10 min before excess H<sub>2</sub>O<sub>2</sub> was scavenged with catalase for 1 min. The treated kinase was then incubated with the indicated concentrations of GST-CREB (<b>A</b>), Kemptide (<b>B</b>), or Crosstide (<b>C</b>) for 30 min at 30 °C before measuring kinase activity using the ADP-Glo assay (Promega). Error bars represent standard error about the mean of at least three independent experiments conducted in duplicate.</p>
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<p>Models of redox-dependent changes in PKA-Cα-substrate interactions. (<b>A</b>). Co-crystal structure of murine PKA-Cα (green) bound to the inhibitory peptide, PKI (5–24) (yellow). PKA-Cα residues E127 and T51 (blue sticks) in the hinge region and N-lobe, respectively, interact with R18 in the P-3 position of PKI (5–24) (yellow sticks) while PKA-Cα residues E170 and E230 (blue sticks) in the C-lobe interact with R19 in the P-2 position of PKI (5–24) (yellow sticks). The positions of C199 and C343, the only two Cys residues present in PKA-Cα, are shown as green sticks. The structure was generated from PDB ID: 1ATP using ChimeraX [<a href="#B34-life-13-01811" class="html-bibr">34</a>,<a href="#B37-life-13-01811" class="html-bibr">37</a>]. (<b>B</b>)<b>.</b> Cartoon models depicting how diamide- and H<sub>2</sub>O<sub>2</sub>-dependent oxidation of PKA-Cα, as well as subsequent glutathionylation with reduced glutathione (GSH), may differentially affect its interactions with different substrates. Residues involved in interactions with basic residues in the substrates (i.e., E127 and T51 in the hinge region and N-lobe, respectively, and E170 and E230 in the C-lobe) are depicted as blue diamonds while C199 and C343 are depicted as green sticks. Diamide-dependent oxidation leads to the formation of either intermolecular disulfide bonds between two or more PKA-Cα subunits or an intramolecular disulfide bond between C199 and C343 in the same molecule. Intermolecular disulfide bond formation may block access of some substrate molecules to the active site (i.e., “exclusion”). Meanwhile, rotation of the N-lobe about the hinge region to promote intramolecular disulfide bond formation may displace residues in the hinge region/N-lobe involved in substrate binding (e.g., E127 and T51) while having little effect on the position of residues in the C-lobe (e.g., E170 and E230). Displacement of residues in the hinge region/N-lobe may cause some PKA-Cα-substrate interactions to be lost (yellow quotation marks) while those with residues in the C-lobe remain unaffected. Subsequent glutathionylation forms a mixed disulfide that may cause steric clashes with some substrates (orange starburst). In the case of H<sub>2</sub>O<sub>2</sub>, redox modification (e.g., sulfenylation) may promote interactions with some substrates (e.g., yellow Sub 1) through the introduction of additional binding sites while preventing interactions with other substates (e.g., light blue Sub 2) due to steric clashes and/or charge repulsion.</p>
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18 pages, 10620 KiB  
Article
To Target or Not to Target Schistosoma mansoni Cyclic Nucleotide Phosphodiesterase 4A?
by Yang Zheng, Susanne Schroeder, Georgi K. Kanev, Sanaa S. Botros, Samia William, Abdel-Nasser A. Sabra, Louis Maes, Guy Caljon, Carmen Gil, Ana Martinez, Irene G. Salado, Koen Augustyns, Ewald Edink, Maarten Sijm, Erik de Heuvel, Iwan J. P. de Esch, Tiffany van der Meer, Marco Siderius, Geert Jan Sterk, David Brown and Rob Leursadd Show full author list remove Hide full author list
Int. J. Mol. Sci. 2023, 24(7), 6817; https://doi.org/10.3390/ijms24076817 - 6 Apr 2023
Cited by 2 | Viewed by 2203
Abstract
Schistosomiasis is a neglected tropical disease with high morbidity. Recently, the Schistosoma mansoni phosphodiesterase SmPDE4A was suggested as a putative new drug target. To support SmPDE4A targeted drug discovery, we cloned, isolated, and biochemically characterized the full-length and catalytic domains of SmPDE4A. The [...] Read more.
Schistosomiasis is a neglected tropical disease with high morbidity. Recently, the Schistosoma mansoni phosphodiesterase SmPDE4A was suggested as a putative new drug target. To support SmPDE4A targeted drug discovery, we cloned, isolated, and biochemically characterized the full-length and catalytic domains of SmPDE4A. The enzymatically active catalytic domain was crystallized in the apo-form (PDB code: 6FG5) and in the cAMP- and AMP-bound states (PDB code: 6EZU). The SmPDE4A catalytic domain resembles human PDE4 more than parasite PDEs because it lacks the parasite PDE-specific P-pocket. Purified SmPDE4A proteins (full-length and catalytic domain) were used to profile an in-house library of PDE inhibitors (PDE4NPD toolbox). This screening identified tetrahydrophthalazinones and benzamides as potential hits. The PDE inhibitor NPD-0001 was the most active tetrahydrophthalazinone, whereas the approved human PDE4 inhibitors roflumilast and piclamilast were the most potent benzamides. As a follow-up, 83 benzamide analogs were prepared, but the inhibitory potency of the initial hits was not improved. Finally, NPD-0001 and roflumilast were evaluated in an in vitro anti-S. mansoni assay. Unfortunately, both SmPDE4A inhibitors were not effective in worm killing and only weakly affected the egg-laying at high micromolar concentrations. Consequently, the results with these SmPDE4A inhibitors strongly suggest that SmPDE4A is not a suitable target for anti-schistosomiasis therapy. Full article
(This article belongs to the Special Issue Role of Phosphodiesterase in Biology and Pathology 2.0)
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Figure 1
<p>Expression and functional comparison of SmPDE4a_FL and SmPDE4a_CD. (<b>A</b>) Schematic view of the constructs for <span class="html-italic">SmPDE4a</span>_FL (amino acids 1–699) and <span class="html-italic">SmPDE4a</span>_CD (amino acids 303–671) with a 6×His tag on the <span class="html-italic">N</span>-terminus. The SmPDE4A_FL protein contains next to the catalytic domain (amino acids 416–657 in the red box) two potential regulatory UCR domains (gray boxes). (<b>B</b>) SDS-PAGE analysis of the purified SmPDE4A_FL and SmPDE4A_CD (indicated with an arrow) with expected molecular weights of 80.0 and 42.3 kDa, respectively. (<b>C</b>) Michaelis–Menten kinetics, as analyzed with GraphPad Prism, version 8, of both SmPDE4A enzyme constructs at 10 nM and 11 nM for, respectively, SmPDE4A_FL and SmPDE4A_CD.</p>
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<p>Crystal structure of SmPDE4A and comparison with other PDEs. (<b>A</b>) Crystallization of SmPDE4A_CD in 0.4 M ammonium sulfate 12–14% (<span class="html-italic">v/v</span>) PEG 3350 and structural features of the catalytic domain of SmPDE4A. (<b>B</b>) The protein crystallizes in a dimer form and contains two metal ions (the grey ball represents Mg<sup>2+</sup>, and the purple ball represents Zn<sup>2+</sup>). (<b>C</b>) These metal ions are coordinated by a number of histidine and aspartic acid residues in the active site. (<b>D</b>) the SmPDE4A structure (dark purple) is superimposed on the catalytic domain structures of LmjPDEB1 (PDB code: 2R8Q, green) and hPDE4D (PDB code: 3SL3, light blue). The surface is shown of residues within the P-pocket region in SmPDE4A (left) and LmjPDEB1 (right).</p>
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<p>Interaction of the catalytic domain of SmPDE4A with cAMP (purple) as found in the chain A of 6FG5. The cyclic phosphate group interacts with the Mg<sup>2+</sup> (green sphere) and Zn<sup>2+</sup> (grey sphere) ions and interferes with the water (red dots) network on the left side of the substrate binding pocket.</p>
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<p>PDE4NPD Toolbox screen to identify inhibitors of SmPDE4A. (<b>A</b>) Inhibition of SmPDE4A_FL (red circles) and SmPDE4A_CD activity (blue circles) after incubation with PDE inhibitors at 10 µM. (<b>B</b>) Representative dose-response curves for inhibition of the enzymatic activity of SmPDE4A_CD with the toolbox hits <b>NPD-0001</b> (blue; pIC<sub>50</sub> = 5.8 ± 0.06), <b>NPD-0005</b> (red; pIC<sub>50</sub> = 5.5 ± 0.14), <b>NPD-0006</b> (green; pIC<sub>50</sub> = 6.2 ± 0.10) and <b>NPD-0007</b> (black; pIC<sub>50</sub> = 5.4 ± 0.11).</p>
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<p>Docking results of roflumilast in SmPDE4A and comparison with the docking in hPDE4D2. (<b>A</b>) The docking pose of roflumilast in the catalytic site of SmPDE4A. The residue nomenclature follows the PDEStrIAn database [<a href="#B35-ijms-24-06817" class="html-bibr">35</a>]. (<b>B</b>) The docking pose of roflumilast in SmPDE4A (purple) and reference roflumilast (yellow) in hPDE4D (PDB:1XOQ). The docking pose makes hydrogen bonds with Q626 (Q<sup>Q.50</sup>) and face-to-face π-stacking interaction with F629 (F<sup>HC.52</sup>) as roflumilast in hPDE4D (PDB:1XOQ). (<b>C</b>) Alignment of the binding site residues of the SmPDE4A and hPDE4D2. The color bar above the residues indicates their location in the binding site. The different residues are highlighted in red. The interactions between SmPDE4A and cAMP are shown with dotted points with hydrophobic colored in grey, hydrogen bond donor colored in red and hydrogen bond acceptor colored in blue.</p>
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<p>Synthetic route of the roflumilast analog library. Reagents and conditions: a: acid A, THF, (COCl)<sub>2</sub>, <span class="html-italic">cat</span>. DMF, 0 °C to RT, 30 min; b: amine/aniline B, NaH, DMF, 0 °C to RT, 30 min; c: A’, B’, 0 °C to RT, 30 min; d: C, <span class="html-italic">m-</span>CPBA, DCM, RT, 30 min. Further details of all synthesized compounds can be found in the Supporting Information.</p>
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21 pages, 16859 KiB  
Article
Latitudinal Trend Analysis of Land Surface Temperature to Identify Urban Heat Pockets in Global Coastal Megacities
by Dyutisree Halder, Rahul Dev Garg and Alexander Fedotov
Remote Sens. 2023, 15(5), 1355; https://doi.org/10.3390/rs15051355 - 28 Feb 2023
Cited by 7 | Viewed by 2335
Abstract
Recent global warming has led to increased coastal disturbances through a significant transfer of heat between the land and the ocean surface. The polar regions show excessive temperature changes resulting in massive ice sheet melting. Mid-latitudinal storms pull heat away from the equator [...] Read more.
Recent global warming has led to increased coastal disturbances through a significant transfer of heat between the land and the ocean surface. The polar regions show excessive temperature changes resulting in massive ice sheet melting. Mid-latitudinal storms pull heat away from the equator towards the poles; therefore, the global sea level is rising, making coastal cities the most vulnerable. In last few decades, rapid urbanization in big cities has drastically changed the land cover and land use due to deforestation, which has led to increased land surface temperatures (LSTs). This eventually leads to urban flooding due to oceanic storm surges frequently created by low pressure over the ocean during summer. This paper considered factors such as drastic unplanned urbanization to analyze coastal cities as the focal point of the generation of heat yielding the annihilation of the natural topography. Urban heat pockets (UHP) were studied for nine megacities, which were selected at an interval of 5° of latitudinal difference in the northern hemisphere (NH) since 70% of densely populated megacities are located in coastal regions. A comparative surface temperature analysis was effectively carried out with the same latitudinal reference for nine mid-sized cities using the derived LST data from Landsat 8. The results provide a comparative classification of surface temperature variations across the coastal cities over the NH. This study infers that the issues pertaining to growing urbanization are very important for analyzing the proportional impact caused by the settlement hierarchy and lays a robust foundation for advanced studies of global warming in coastal urban environments. Full article
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<p>Coastal megacities and cities selected for the study (red dots: megacities, green dots: mid-sized cities).</p>
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<p>Methodological flowchart of the study.</p>
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<p>Built (black)/unbuilt (green) map with a corresponding heat map for both a megacity and its respective mid-sized city in a low-latitude region (Lagos and Colombo).</p>
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<p>Built (black)/unbuilt (green) map with a corresponding heat map for both a megacity and its respective mid-sized city in mid-latitude region (Mumbai and Port-au-Prince).</p>
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<p>Built (black)/unbuilt (green) map with a corresponding heat map for both a megacity and its respective mid-sized city in a high-latitude region (New York and Naples).</p>
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<p>Temperature range and built-up percentage for each city and megacity.</p>
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<p>Recorded daily temporal data for the surface temperatures of various built-up materials in Mumbai.</p>
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<p>Visualization of volumetric analysis of UHP classes of megacities.</p>
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<p>Built/unbuilt maps with corresponding heat maps for both megacities and their respective mid-sized cities, as per the latitudinal reference (<b>top</b>: Ho Chi Minh City and Barranquilla) (<b>middle</b>: Manila and Dakar) (<b>bottom</b>: Karachi and Miami).</p>
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<p>Built/unbuilt maps with corresponding heat maps for both megacities and their respective mid-sized cities, as per the latitudinal reference (<b>top</b>: Shanghai and Tijuana) (<b>middle</b>: Tokyo and Tunis) (<b>bottom</b>: Vancouver and London).</p>
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14 pages, 46065 KiB  
Article
Whole Genome Resequencing Identifies Single-Nucleotide Polymorphism Markers of Growth and Reproduction Traits in Zhedong and Zi Crossbred Geese
by Guojun Liu, Zhenhua Guo, Xiuhua Zhao, Jinyan Sun, Shan Yue, Manyu Li, Zhifeng Chen, Zhigang Ma and Hui Zhao
Genes 2023, 14(2), 487; https://doi.org/10.3390/genes14020487 - 14 Feb 2023
Cited by 3 | Viewed by 3129
Abstract
The broodiness traits of domestic geese are a bottleneck that prevents the rapid development of the goose industry. To reduce the broodiness of the Zhedong goose and thus improve it, this study hybridized it with the Zi goose, which has almost no broody [...] Read more.
The broodiness traits of domestic geese are a bottleneck that prevents the rapid development of the goose industry. To reduce the broodiness of the Zhedong goose and thus improve it, this study hybridized it with the Zi goose, which has almost no broody behavior. Genome resequencing was performed for the purebred Zhedong goose, as well as the F2 and F3 hybrids. The results showed that the F1 hybrids displayed significant heterosis in growth traits, and their body weight was significantly greater than those of the other groups. The F2 hybrids showed significant heterosis in egg-laying traits, and the number of eggs laid was significantly greater than those of the other groups. A total of 7,979,421 single-nucleotide polymorphisms (SNPs) were obtained, and three SNPs were screened. Molecular docking results showed that SNP11 located in the gene NUDT9 altered the structure and affinity of the binding pocket. The results suggested that SNP11 is an SNP related to goose broodiness. In the future, we will use the cage breeding method to sample the same half-sib families to accurately identify SNP markers of growth and reproductive traits. Full article
(This article belongs to the Special Issue Genetic Variation in Biological Traits)
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<p>Schematic diagram of Zhedong and Zi crossbred goose experiment. Purebred Zhedong geese were introduced in Xiangshan County in 2016 and 2019. The Zi goose is a local variety in Heilongjiang. The F1, F2 and F3 hybrids were obtained through hybridization, and some experimental populations were selected to measure body weight and laying rate as well as for whole genome resequencing analysis. If the primary generation Zhedong goose was homozygous for site AA, then gene type AB and AA could appear in the F2 and F3 generations. However, BB type was not possible.</p>
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<p>Changes in the goose egg-laying trait and body weight in different generations. (<b>A</b>) Heat map of goose egg-laying data of different generations. The different colors represent different laying rates, and the abscissa is initially dated 21 February. The end time is 3 July; the total number of days is 133. On 29 February 2020, the experimental group did not start laying eggs. (<b>B</b>) Growth data of different generations of geese (0–4 weeks) (<b>C</b>) Growth data of different generations of geese (6–8 weeks) (<b>D</b>) Growth data of different generations of geese (10–12 weeks). The body weight of the F3 hybrids shows significant differences at 2 weeks and 12 weeks. The different letters indicate significant differences. The green bar represents SE. Body-weight unit: g.</p>
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<p>Changes in the goose egg-laying trait and body weight in different generations. (<b>A</b>) Heat map of goose egg-laying data of different generations. The different colors represent different laying rates, and the abscissa is initially dated 21 February. The end time is 3 July; the total number of days is 133. On 29 February 2020, the experimental group did not start laying eggs. (<b>B</b>) Growth data of different generations of geese (0–4 weeks) (<b>C</b>) Growth data of different generations of geese (6–8 weeks) (<b>D</b>) Growth data of different generations of geese (10–12 weeks). The body weight of the F3 hybrids shows significant differences at 2 weeks and 12 weeks. The different letters indicate significant differences. The green bar represents SE. Body-weight unit: g.</p>
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<p>Changes in the goose egg-laying trait and body weight in different generations. (<b>A</b>) Heat map of goose egg-laying data of different generations. The different colors represent different laying rates, and the abscissa is initially dated 21 February. The end time is 3 July; the total number of days is 133. On 29 February 2020, the experimental group did not start laying eggs. (<b>B</b>) Growth data of different generations of geese (0–4 weeks) (<b>C</b>) Growth data of different generations of geese (6–8 weeks) (<b>D</b>) Growth data of different generations of geese (10–12 weeks). The body weight of the F3 hybrids shows significant differences at 2 weeks and 12 weeks. The different letters indicate significant differences. The green bar represents SE. Body-weight unit: g.</p>
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<p>Changes in the goose egg-laying trait and body weight in different generations. (<b>A</b>) Heat map of goose egg-laying data of different generations. The different colors represent different laying rates, and the abscissa is initially dated 21 February. The end time is 3 July; the total number of days is 133. On 29 February 2020, the experimental group did not start laying eggs. (<b>B</b>) Growth data of different generations of geese (0–4 weeks) (<b>C</b>) Growth data of different generations of geese (6–8 weeks) (<b>D</b>) Growth data of different generations of geese (10–12 weeks). The body weight of the F3 hybrids shows significant differences at 2 weeks and 12 weeks. The different letters indicate significant differences. The green bar represents SE. Body-weight unit: g.</p>
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<p>Screening and analysis of candidate SNPs. (<b>A</b>) Gene Ontology (GO) analysis for 3.370 candidate SNPs. (<b>B</b>) Level two GO analysis of candidate SNPs. (<b>C</b>) KEGG enrichment of candidate SNPs. (<b>D</b>) Distribution statistics of candidate SNPs on chromosome positions. (<b>E</b>) Classified statistics of candidate SNPs distributed in the exon.</p>
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<p>Screening and analysis of candidate SNPs. (<b>A</b>) Gene Ontology (GO) analysis for 3.370 candidate SNPs. (<b>B</b>) Level two GO analysis of candidate SNPs. (<b>C</b>) KEGG enrichment of candidate SNPs. (<b>D</b>) Distribution statistics of candidate SNPs on chromosome positions. (<b>E</b>) Classified statistics of candidate SNPs distributed in the exon.</p>
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<p>Screening and analysis of candidate SNPs. (<b>A</b>) Gene Ontology (GO) analysis for 3.370 candidate SNPs. (<b>B</b>) Level two GO analysis of candidate SNPs. (<b>C</b>) KEGG enrichment of candidate SNPs. (<b>D</b>) Distribution statistics of candidate SNPs on chromosome positions. (<b>E</b>) Classified statistics of candidate SNPs distributed in the exon.</p>
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<p>Screening and analysis of candidate SNPs. (<b>A</b>) Gene Ontology (GO) analysis for 3.370 candidate SNPs. (<b>B</b>) Level two GO analysis of candidate SNPs. (<b>C</b>) KEGG enrichment of candidate SNPs. (<b>D</b>) Distribution statistics of candidate SNPs on chromosome positions. (<b>E</b>) Classified statistics of candidate SNPs distributed in the exon.</p>
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<p>SNP3 and SNP4 affect the NUP37 protein. The structure of the NUP37 protein was unchanged due to SNP3 and SNP4. The 304th amino acid of SNP3 in the upper right corner is Ile, and in the lower right corner is Met. The 298th amino acid of SNP4 in the upper left corner is Phe, and in the lower left corner is Val.</p>
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<p>SNP11 affects NUDT9 molecular docking. SNP11 does not affect the structure of the NUDT9 protein. To display the docking of amino acids, different angles were rotated. The left side shows 218 Thr combined with ADPR. The right side shows 218 Ala combined with ADPR. The combination box has changed.</p>
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14 pages, 2280 KiB  
Article
Comparative Proteomics and Genome-Wide Druggability Analyses Prioritized Promising Therapeutic Targets against Drug-Resistant Leishmania tropica
by Sara Aiman, A. Khuzaim Alzahrani, Fawad Ali, Abida, Mohd. Imran, Mehnaz Kamal, Muhammad Usman, Hamdy Khamees Thabet, Chunhua Li and Asifullah Khan
Microorganisms 2023, 11(1), 228; https://doi.org/10.3390/microorganisms11010228 - 16 Jan 2023
Cited by 4 | Viewed by 4242
Abstract
Leishmania tropica is a tropical parasite causing cutaneous leishmaniasis (CL) in humans. Leishmaniasis is a serious public health threat, affecting an estimated 350 million people in 98 countries. The global rise in antileishmanial drug resistance has triggered the need to explore novel therapeutic [...] Read more.
Leishmania tropica is a tropical parasite causing cutaneous leishmaniasis (CL) in humans. Leishmaniasis is a serious public health threat, affecting an estimated 350 million people in 98 countries. The global rise in antileishmanial drug resistance has triggered the need to explore novel therapeutic strategies against this parasite. In the present study, we utilized the recently available multidrug resistant L. tropica strain proteome data repository to identify alternative therapeutic drug targets based on comparative subtractive proteomic and druggability analyses. Additionally, small drug-like compounds were scanned against novel targets based on virtual screening and ADME profiling. The analysis unveiled 496 essential cellular proteins of L. tropica that were nonhomologous to the human proteome set. The druggability analyses prioritized nine parasite-specific druggable proteins essential for the parasite’s basic cellular survival, growth, and virulence. These prioritized proteins were identified to have appropriate binding pockets to anchor small drug-like compounds. Among these, UDPase and PCNA were prioritized as the top-ranked druggable proteins. The pharmacophore-based virtual screening and ADME profiling predicted MolPort-000-730-162 and MolPort-020-232-354 as the top hit drug-like compounds from the Pharmit resource to inhibit L. tropica UDPase and PCNA, respectively. The alternative drug targets and drug-like molecules predicted in the current study lay the groundwork for developing novel antileishmanial therapies. Full article
(This article belongs to the Special Issue Leishmaniasis: Interventions Used to Control Infection)
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<p>Flowchart diagram of the methodological steps pursued during this study. ‘n’ represents the number of proteins shortlisted in each step.</p>
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<p>The subcellular localization prediction of the <span class="html-italic">L. tropica</span> essential, human nonhomolog proteins.</p>
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<p>(<b>A</b>) The pharmacophore design based on the active site of LtUGPase. (<b>B</b>) The molecular interaction of top hit compound docked in the active site of LtUGPase. The nature of protein–ligand interactions is represented with different colors.</p>
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<p>(<b>A</b>) The pharmacophore model designed based on the active site of LtPCNA. (<b>B</b>) The molecular interaction of top hit compound docked in the active site of LtPCNA. The nature of protein–ligand interactions is represented with different colors.</p>
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11 pages, 2692 KiB  
Article
Modeling Undrained Shear Strength of Sensitive Alluvial Soft Clay Using Machine Learning Approach
by Mohamed B. D. Elsawy, Mohammed F. Alsharekh and Mahmoud Shaban
Appl. Sci. 2022, 12(19), 10177; https://doi.org/10.3390/app121910177 - 10 Oct 2022
Cited by 10 | Viewed by 6014
Abstract
Soft soils are commonly located in many regions near seas, oceans, and rivers all over the world. These regions are vital and attractive for population and governments development. Soft soil is classified as problematic soil owing to sustaining low shear strength and high [...] Read more.
Soft soils are commonly located in many regions near seas, oceans, and rivers all over the world. These regions are vital and attractive for population and governments development. Soft soil is classified as problematic soil owing to sustaining low shear strength and high settlement under structures. Constructing structures and/or infrastructures on soft soil is a considerable risk that needs great attention from structural engineers. The bearing capacity of structure foundations on soft soil depends mainly on their undrained shear strength. This soil feature strongly influences the selection of appropriate soil improvement methods. However, determining undrained shear strength is very difficult, costly, and time-consuming, especially for sensitive clay. Consequently, extracting undisturbed samples of sensitive clay faces several difficulties on construction sites. In this research, accurate field-tested data were fed to advanced machine learning models to predict the undrained shear strength of the sensitive clay to save hard effort, time, repeated laboratory testing, and costs. In this context, a dataset of 111 geotechnical testing points were collected based on laboratory and field examinations of the soil’s key features. These features included the water content, liquid limit, dry unit weight, plasticity index, consistency index, void ratio, specific gravity, and pocket penetration shear. Several machine learning algorithms were adopted to provide the soft clay modeling, including the linear, Gaussian process regression, ensemble and regression trees, and the support vector regression. The coefficient of determination was mainly used to assess the performance of each predictive model. The achieved results revealed that the support vector regression model attained the most accurate prediction for soil undrained shear strength. These outcomes lay the groundwork for evaluating soil shear strength characteristics in a practical, fast, and low-cost way. Full article
(This article belongs to the Special Issue The Application of Machine Learning in Geotechnical Engineering)
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<p>Plot of (<b>a</b>) PL, (<b>b</b>) W<sub>n</sub>, and (<b>c</b>) LL versus depth.</p>
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<p>Field vane shear equipment.</p>
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<p>Sensitivity of the soft soil.</p>
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<p>Framework of soft clay ML modeling.</p>
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<p>Correlation chart of soft clay features.</p>
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<p>Comparison results of the trained model, predicted versus actual output data of vane-tested USS. The straight line denotes to line of equality.</p>
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<p>Soft clay USS plotted against sample number of the measured, predicted, and tested dataset.</p>
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15 pages, 6539 KiB  
Article
Structural Insights into Alphavirus Assembly Revealed by the Cryo-EM Structure of Getah Virus
by Ming Wang, Zhenzhao Sun, Chenxi Cui, Shida Wang, Decheng Yang, Zhibin Shi, Xinyu Wei, Pengfei Wang, Weiyao Sun, Jing Zhu, Jiaqi Li, Bingchen Du, Zaisi Liu, Lili Wei, Chunguo Liu, Xijun He, Xiangxi Wang, Xinzheng Zhang and Jingfei Wang
Viruses 2022, 14(2), 327; https://doi.org/10.3390/v14020327 - 5 Feb 2022
Cited by 8 | Viewed by 3328
Abstract
Getah virus (GETV) is a member of the alphavirus genus, and it infects a variety of animal species, including horses, pigs, cattle, and foxes. Human infection with this virus has also been reported. The structure of GETV has not yet been determined. In [...] Read more.
Getah virus (GETV) is a member of the alphavirus genus, and it infects a variety of animal species, including horses, pigs, cattle, and foxes. Human infection with this virus has also been reported. The structure of GETV has not yet been determined. In this study, we report the cryo-EM structure of GETV at a resolution of 3.5 Å. This structure reveals conformational polymorphism of the envelope glycoproteins E1 and E2 at icosahedral 3-fold and quasi-3-fold axes, which is believed to be a necessary organization in forming a curvature surface of virions. In our density map, three extra densities are identified, one of which is believed a “pocket factor”; the other two are located by domain D of E2, and they may maintain the stability of E1/E2 heterodimers. We also identify three N-glycosylations at E1 N141, E2 N200, and E2 N262, which might be associated with receptor binding and membrane fusion. The resolving of the structure of GETV provides new insights into the structure and assembly of alphaviruses and lays a basis for studying the differences of biology and pathogenicity between arthritogenic and encephalitic alphaviruses. Full article
(This article belongs to the Topic Veterinary Infectious Diseases)
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<p>Cryo-EM reconstruction of GETV particles. (<b>A</b>) Radially colored 3D reconstruction of GETV particles. The 5-fold (i5) axis and 2-fold (i2) axis are indicated with a pentagon and a circle, respectively. The icosahedral 3-fold (i3) axis and quasi-3-fold (q3) axis are indicated with black triangles. (<b>B</b>) The central slice of the GETV virion structure. The lipid bilayer is marked with black dashed circles. (<b>C</b>) The 3D reconstruction of inner capsid shell. (<b>D</b>) Each asymmetric unit (ASU) is composed of four E1/E2 heterodimers. Left panel, one of the ASU is indicated with a black dashed quadrilateral. Right panel, a top view (left) and a side view (right) of one ASU. Each E1/E2/CP unit within an ASU is distinguished by different colors (pink, wheat, green, and blue).</p>
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<p>Structure of E1/E2 heterodimer. (<b>A</b>) Linear diagrams show the subdomain distributions in E1, E2, and CP with different colors (DI, light green; DII, light blue; DIII, purple; FL, peach puff; TM of E1, olive; domain A, Indian red; domain B, orchid; domain C, medium purple; domain D, orange; TM of E2, slate blue. CP, turquoise). (<b>B</b>,<b>C</b>) The atomic models of GETV heterodimer displayed with edged ribbon. Subdomains of E1 and E2 are color coded in the same way as (<b>A</b>). (<b>D</b>–<b>G</b>) The primary hydrogen bonds formed between E1 and E2 are labeled with dashed lines.</p>
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<p>Assembly of envelope glycoproteins. (<b>A</b>,<b>B</b>) Five types of contacts between two adjacent E1. (<b>A</b>) Locations of the five contacts are labeled on the top view of three heterotrimers, indicated by i, ii, iii, iv, and v, respectively. The red dashed lines represent the interacting edges between two adjacent E1. Subdomains of E1 are color coded in the same way as <a href="#viruses-14-00327-f002" class="html-fig">Figure 2</a>A. (<b>B</b>) Conformational changes between E1 from i3 and q3. We overlapped the two E1 from q3, and structural differences between the E1 from i3 (colored) and q3 (grey) are observed. Three contacts (R1, R2, and R3) are enlarged, showing the movement (indicated with dashed arrows) of the major residues. (<b>C</b>) Contacts between E2 from i3 and q3. Top view of E2 in the heterotrimers. E2 from q3 are labeled α (light blue), β (light green), and γ (wheat), and from the corresponding position of i3 are labeled α’ (pink), β’ (pink), and γ’ (pink), respectively. Interactions of α/β, β/γ, α/γ, and α’/β’ (β’/γ’ and α’/γ’ are identical with α’/β’) are enlarged. Dashed lines indicated hydrogen bonds.</p>
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<p>Structure and assembly of CP. (<b>A</b>) Interaction between CP and E2 is mediated by the cytoplasmic tail of E2 inserted into a hydrophobic pocket on the surface of CP. (<b>B</b>) The main residues involved in the interaction between CP and the cytoplasmic tail of glycoproteins (E1 and E2). The dashed lines represent hydrogen bonds. (<b>C</b>) Surface potential of CP. The enlarged images show that the electrostatic interaction between a pentamer and a hexamer is mediated by the complementary charged amino acids. The electrostatic potential ranges from negative (red) to positive (blue). (<b>D</b>) The overall atomic model of capsid. The four copies of CPs in each of the ASUs is colored light blue, light green, wheat, and pink, respectively. A pentamer and a hexamer are labeled with a pentagon and a hexagon, respectively. The enlarged image shows the interactions between CPs. The distances between D127 from pentamer/ hexamer and K158 from hexamer are labeled.</p>
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<p>Hydrophobic pocket and three extra densities. (<b>A</b>) Three extra densities located by the E1/E2 transmembrane helix. The density responses to the previously identified “pocket factor” is showed as light green surface with an 18C model fitted in. The two newly identified extra densities are presented as wheat and olive surfaces with two 14C models fitted in, respectively. (<b>B</b>) The hydrophobic pocket with an 18C molecule modelled in. (<b>C</b>) A “lid-like” structure formed by P351 from E2 and W409 from E1 at the posterior of the pocket. (<b>D</b>) Two putative fatty acids are fitted in the positions of the two extra densities, showing they attach to two hydrophobic grooves on the side surface of domain D of E2 and transmembrane helix (TM), respectively. The two putative fatty acid with 14C are displayed as stick. (<b>E</b>,<b>F</b>) Major amino acids from E2 participated in the interaction with the two densities.</p>
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<p>The N-linked glycans in E1 and E2. (<b>A</b>) Top view and side view of an asymmetric unit (ASU) showing the locations of the N-linked glycans. The E1/E2 heterodimers are colored blue, green, wheat, and pink, respectively. (<b>B</b>–<b>D</b>) Local density and structure of the three N-linked glycans. The glycan linked on N141 from E1 is shown in (<b>B</b>); The glycan linked on N200 from E2 has a close contact with E99 by forming a hydrogen bond (<b>C</b>); The glycans linked on N262 from two adjacent E2 form a “hand shake”-like structure (<b>D</b>).</p>
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21 pages, 3591 KiB  
Article
Mechanism of the Conformational Change of the Protein Methyltransferase SMYD3: A Molecular Dynamics Simulation Study
by Jixue Sun, Zibin Li and Na Yang
Int. J. Mol. Sci. 2021, 22(13), 7185; https://doi.org/10.3390/ijms22137185 - 2 Jul 2021
Cited by 9 | Viewed by 3829
Abstract
SMYD3 is a SET-domain-containing methyltransferase that catalyzes the transfer of methyl groups onto lysine residues of substrate proteins. Methylation of MAP3K2 by SMYD3 has been implicated in Ras-driven tumorigenesis, which makes SMYD3 a potential target for cancer therapy. Of all SMYD family proteins, [...] Read more.
SMYD3 is a SET-domain-containing methyltransferase that catalyzes the transfer of methyl groups onto lysine residues of substrate proteins. Methylation of MAP3K2 by SMYD3 has been implicated in Ras-driven tumorigenesis, which makes SMYD3 a potential target for cancer therapy. Of all SMYD family proteins, SMYD3 adopt a closed conformation in a crystal structure. Several studies have suggested that the conformational changes between the open and closed forms may regulate the catalytic activity of SMYD3. In this work, we carried out extensive molecular dynamics simulations on a series of complexes with a total of 21 ?s sampling to investigate the conformational changes of SMYD3 and unveil the molecular mechanisms. Based on the C-terminal domain movements, the simulated models could be depicted in three different conformational states: the closed, intermediate and open states. Only in the case that both the methyl donor binding pocket and the target lysine-binding channel had bound species did the simulations show SMYD3 maintaining its conformation in the closed state, indicative of a synergetic effect of the cofactors and target lysine on regulating the conformational change of SMYD3. In addition, we performed analyses in terms of structure and energy to shed light on how the two regions might regulate the C-terminal domain movement. This mechanistic study provided insights into the relationship between the conformational change and the methyltransferase activity of SMYD3. The more complete understanding of the conformational dynamics developed here together with further work may lay a foundation for the rational drug design of SMYD3 inhibitors. Full article
(This article belongs to the Section Physical Chemistry and Chemical Physics)
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<p>Overall structure of the SAM-binding pocket and the lysine-binding channel in SMYD3. (<b>A</b>) Crystal structure of SMYD3 in complex with substrate peptide and cofactor SAH (PDB: 5EX0). The SET, MYND, post-SET, and C-terminal domains of SMYD3 are shown in green, blue, yellow, and pink, respectively. SAH and the MAP3K2 peptide are shown as cyan and magenta sticks, respectively. Zinc ions are shown as grey spheres. The SAM-binding pocket and the lysine-binding channel are highlighted in the red box. (<b>B</b>) Magnified view of the SAM-binding pocket and the lysine-binding channel. GSK2807 is shown as yellow sticks. (<b>C</b>) Binding mode of SAH in the SAM-binding pocket. Residues in SMYD3 are shown as green sticks. The hydrogen bond is depicted as a dashed line. (<b>D</b>) Binding mode of the target lysine residue in the lysine-binding channel.</p>
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<p>Conformational descriptors characterizing the dynamics of the CTD in SMYD3. (<b>A</b>) Distributions of RoG. The simulated systems are shown in cyan, red, blue, magenta, green and orange. The same scheme is used in the following figures unless otherwise specified. (<b>B</b>) RMSF analyses of the Apo and SAM_MAP3K2 systems. Two distinct differences are highlighted in the red and black boxes. (<b>C</b>) Dynamic cross-correlation maps for the Apo (upper left triangle) and SAM_MAP3K2 (lower right triangle) systems. The color scale is shown on the right changing from red (highly positive correlations) to blue (highly negative correlations). (<b>D</b>) Definition of distance D1 and D2 in the structure of SMYD3. Residues 42–48, 298–302, 209 and 227, and 363–365 are shown in green, cyan, yellow and orange, respectively. (<b>E</b>) Time evolutions of distance D1. The color scale is shown on the right changing from red (long distance) to blue (short distance). (<b>F</b>) Time evolutions of distance D2.</p>
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<p>Conformational states of SMYD3 during MD simulations. (<b>A</b>) PMF calculated for the distance D1 vs. the RMSD of SMYD3. (<b>B</b>) Representative structures of the closed (red), intermediate (yellow), and open (green) states. Only the CTD is colored for comparison purposes.</p>
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<p>Distinct binding states of hydrogen bonds between the Apo and SAM_MAP3K2 systems. (<b>A</b>) P1–P5 positions labeled in the structure of SMYD3. (<b>B</b>–<b>F</b>) Binding states of hydrogen bonds at the P1–P5 positions in the Apo (cyan) and SAM_MAP3K2 (magenta) systems. Hydrogen bonds are depicted as dashed lines.</p>
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<p>Structural details of the lysine-binding channel. (<b>A</b>) Definition of distance D3 in the structure of SMYD3. Residues in SMYD3 and the MAP3K2 peptide are shown as green and grey sticks, respectively. (<b>B</b>) Distributions of distance D3. The initial value in the crystal structure is labeled using a black dash. Conformational state of Y239 in the representative structures of the (<b>C</b>) Apo (cyan) and (<b>D</b>) SAM_MAP3K2 (magenta) systems. (<b>E</b>) Distributions of dihedral Y239. The initial value in the crystal structure is labeled using a black dash. (<b>F</b>) Two different conformational states of Y239 in the SAM system. Y239 in the initial and flipped conformational states are shown as magenta and cyan sticks, respectively. SAM is shown as grey sticks and with a grey surface.</p>
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<p>The decomposed binding free energy values of the SET and MYND domains toward the CTD. The representative structures in the (<b>A</b>) SAM_MAP3K2 and (<b>B</b>) Apo systems are colored by the decomposed binding free energy levels of the key residues. The color scale is shown on the right changing from red (high decomposed binding free energy) to blue (low decomposed binding free energy).</p>
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<p>Conformational states of SMYD3 during MD simulations from the open conformation. PMF calculated for the distance D1 vs. the RMSD of SMYD3 for the (<b>A</b>) Apo_open and (<b>B</b>) SAM_MAP3K2_open systems. (<b>C</b>,<b>D</b>) Free energy surface associated with the conformational change of SMYD3 as a function of the distance D1 for the (<b>C</b>) Apo (black) and Apo_open (red), and (<b>D</b>) SAM_MAP3K2 (black) and SAM_MAP3K2_open (red) systems.</p>
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20 pages, 2923 KiB  
Article
Design, Synthesis, and Evaluation of Dihydropyranopyrazole Derivatives as Novel PDE2 Inhibitors for the Treatment of Alzheimer’s Disease
by Yan Zhou, Jinjian Li, Han Yuan, Rui Su, Yue Huang, Yiyou Huang, Zhe Li, Yinuo Wu, Haibin Luo, Chen Zhang and Ling Huang
Molecules 2021, 26(10), 3034; https://doi.org/10.3390/molecules26103034 - 19 May 2021
Cited by 9 | Viewed by 2812
Abstract
Phosphodiesterase 2 (PDE2) has been regarded as a novel target for the treatment of Alzheimer’s disease (AD). In this study, we obtained (R)-LZ77 as a hit compound with moderate PDE2 inhibitory activity (IC50 = 261.3 nM) using a high-throughput [...] Read more.
Phosphodiesterase 2 (PDE2) has been regarded as a novel target for the treatment of Alzheimer’s disease (AD). In this study, we obtained (R)-LZ77 as a hit compound with moderate PDE2 inhibitory activity (IC50 = 261.3 nM) using a high-throughput virtual screening method based on molecular dynamics. Then, we designed and synthesized 28 dihydropyranopyrazole derivatives as PDE2 inhibitors. Among them, compound (+)-11h was the most potent PDE2 inhibitor, with an IC50 value of 41.5 nM. The molecular docking of PDE2-(+)-11h reveals that the 4-(trifluoromethyl)benzyl)oxyl side chain of the compound enters the H-pocket and forms strong hydrophobic interactions with L770/L809/F862, which improves inhibitory activity. The above results may provide insight for further structural optimization of highly potent PDE2 inhibitors and may lay the foundation for their use in the treatment of AD. Full article
(This article belongs to the Section Medicinal Chemistry)
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<p>Chemical structures of several reported PDE2 inhibitors.</p>
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<p>Structure and putative binding mode of (<b><span class="html-italic">R</span></b>)-<b>LZ77</b> with PDE2 according to molecular docking studies and the design strategy for PDE2 inhibitors.</p>
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<p>Summarized SARs of synthesized compounds.</p>
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<p>(<b>A</b>) Putative binding mode of compound (<b><span class="html-italic">+</span></b>)-<b>11h</b> with PDE2 in molecular docking studies. (<b>B</b>) Putative binding mode of compound (<b><span class="html-italic">+</span></b>)-<b>11h</b> and (<b><span class="html-italic">R</span></b>)-<b>LZ77</b> with PDE2 in molecular docking studies. (<b>C</b>) Putative binding mode of compound (<b><span class="html-italic">−</span></b>)-<b>11h</b> with PDE2 in molecular docking studies. (<b>D</b>) Decomposition of important residue contributions to the total binding free energies for (<b><span class="html-italic">R</span></b>)-<b>LZ77</b> and (<b><span class="html-italic">+</span></b>)-<b>11h</b> with PDE2.</p>
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<p>Reagents and conditions: (<b>a</b>) K<sub>2</sub>CO<sub>3</sub>, acetonitrile, 80 °C, 5 h; (<b>b</b>) malononitrile triethylamine, 3-methyl-5-pyrazolone, ethanol, 80 °C, 15 min.</p>
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<p>Reagents and conditions: (<b>a</b>) HATU, DIPEA, anhydrous CH<sub>2</sub>Cl<sub>2</sub>, room temperature, overnight; (<b>b</b>) malononitrile, 3-methyl-5-pyrazolone, <span class="html-italic">N</span>-methylmorpholine, ethanol, room temperature, overnight; (<b>c</b>) K<sub>2</sub>CO<sub>3,</sub> acetonitrile, 80 °C, 5 h; (<b>d</b>) malononitrile, triethylamine, pyrazolinone, ethanol, 80 °C, 15 min.</p>
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<p>Reagents and conditions: (<b>a</b>) LiAlH<sub>4</sub>, THF, 85 °C, 6 h; (<b>b</b>) triphosgene, triethylamine, CH<sub>2</sub>Cl<sub>2</sub>, room temperature, 6 h.</p>
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<p>Reagents and conditions: (<b>a</b>) DMAP, (Boc)<sub>2</sub>O, THF, room temperature, 8 h; (<b>b</b>) CH<sub>3</sub>I, K<sub>2</sub>CO<sub>3</sub>, CH<sub>3</sub>CN, 60 °C, 5 h; (<b>c</b>) trifluoroacetic acid, DCM, room temperature, 1 h.</p>
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14 pages, 3482 KiB  
Article
Structural and Functional Analysis of the Only Two Pyridoxal 5?-Phosphate-Dependent Fold Type IV Transaminases in Bacillus altitudinis W3
by Lixin Zhai, Zihao Xie, Qiaopeng Tian, Zhengbing Guan, Yujie Cai and Xiangru Liao
Catalysts 2020, 10(11), 1308; https://doi.org/10.3390/catal10111308 - 12 Nov 2020
Cited by 1 | Viewed by 2057
Abstract
Aminotransferases are employed as industrial biocatalysts to produce chiral amines with high enantioselectivity and yield. BpTA-1 and BpTA-2 are the only two pyridoxal 5′-phosphate-dependent fold type IV transaminase enzymes in Bacillus altitudinis W3. Herein, we compared the structures and biochemical characteristics of BpTA-1 [...] Read more.
Aminotransferases are employed as industrial biocatalysts to produce chiral amines with high enantioselectivity and yield. BpTA-1 and BpTA-2 are the only two pyridoxal 5′-phosphate-dependent fold type IV transaminase enzymes in Bacillus altitudinis W3. Herein, we compared the structures and biochemical characteristics of BpTA-1 and BpTA-2 using bioinformatic analysis, circular dichroism spectroscopy, atomic force microscopy and other approaches. BpTA-1 and BpTA-2 are similar overall; both form homodimers and utilize a catalytic lysine. However, there are distinct differences in the substrate cofactor-binding pocket, molecular weight and the proportion of the secondary structure. Both enzymes have the same stereoselectivity but different enzymatic properties. BpTA-2 is more active under partial alkaline and ambient temperature conditions and BpTA-1 is more sensitive to pH and temperature. BpTA-2 as novel enzyme not only fills the building blocks of transaminase but also has broader industrial application potential for (R)-α-phenethylamines than BpTA-1. Structure-function relationships were explored to assess similarities and differences. The findings lay the foundation for modifying these enzymes via protein engineering to enhance their industrial application potential. Full article
(This article belongs to the Section Biocatalysis)
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<p>Amino acid sequence alignment of the transaminase BpTA-2, BpTA-1 and ATA-117. The amino acid residues in the black box are the active site residues of these three enzymes.</p>
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<p>Homology modelling structure of BpAT-2 showing conserved domains. The 10 residues in red (Y66, F71, R95, V151, K197, Y202, E233, I260, T261 and T303) constitute the catalytic site and the cofactor-binding pocket of BpAT-2.</p>
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<p>Sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) analysis of BpTA-1 and BpTA-2. Lane 1. Protein marker (14.3–97.2 kDa); Lane 2. Crude fractions of BpTA-1; Lane 3. Purification fractions of BpTA-1; Lane 4. Crude fractions of BpTA-2; Lane 5. Purification fractions of BpTA-2.</p>
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<p>The size-exclusion chromatography elution curves of BpTA-1 and BpTA-2.</p>
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<p>Ultraviolet circular dichroism (UV-CD) spectrum of purified BpTA-1 and BpTA-2 at room temperature and pH 7.0.</p>
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<p>2D/3D Atomic force microscopy (AFM) images of BpTA-1 and BpTA-2. (<b>A</b>) Enzyme BpTA-1 diluted 20-fold; (<b>B</b>) Enzyme BpTA-2 diluted 20-fold; (<b>C</b>) Enzyme BpTA-1 diluted 200-fold and the size of its single particles; (<b>D</b>) Enzyme BpTA-2 diluted 200-fold and the size of its single particles.</p>
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<p>Effects of pH (<b>A</b>) and temperature (<b>B</b>) on BpTA-2 activity. Maximum activity in A (3.53 ± 0.11 U/mg) and B (3.50 ± 0.15 U/mg) was set as 100% and relative activity was calculated by comparison with the maximal activity.</p>
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<p>Effects of temperature (<b>A</b>) and pH (<b>B</b>) on BpTA-2 stability. Maximum activity in A (3.62 ± 0.19 U/mg) and B (3.30 ± 0.17 U/mg) was set as 100% and relative activity was calculated by comparison with the maximal activity.</p>
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22 pages, 2339 KiB  
Article
Retrieval of Evapotranspiration from Sentinel-2: Comparison of Vegetation Indices, Semi-Empirical Models and SNAP Biophysical Processor Approach
by Nieves Pasqualotto, Guido D’Urso, Salvatore Falanga Bolognesi, Oscar Rosario Belfiore, Shari Van Wittenberghe, Jesús Delegido, Alejandro Pezzola, Cristina Winschel and José Moreno
Agronomy 2019, 9(10), 663; https://doi.org/10.3390/agronomy9100663 - 22 Oct 2019
Cited by 42 | Viewed by 7339
Abstract
Remote sensing evapotranspiration estimation over agricultural areas is increasingly used for irrigation management during the crop growing cycle. Different methodologies based on remote sensing have emerged for the leaf area index (LAI) and the canopy chlorophyll content (CCC) estimation, essential biophysical parameters for [...] Read more.
Remote sensing evapotranspiration estimation over agricultural areas is increasingly used for irrigation management during the crop growing cycle. Different methodologies based on remote sensing have emerged for the leaf area index (LAI) and the canopy chlorophyll content (CCC) estimation, essential biophysical parameters for crop evapotranspiration monitoring. Using Sentinel-2 (S2) spectral information, this study performed a comparative analysis of empirical (vegetation indices), semi-empirical (CLAIR model with fixed and calibrated extinction coefficient) and artificial neural network S2 products derived from the Sentinel Application Platform Software (SNAP) biophysical processor (ANN S2 products) approaches for the estimation of LAI and CCC. Four independent in situ collected datasets of LAI and CCC, obtained with standard instruments (LAI-2000, SPAD) and a smartphone application (PocketLAI), were used. The ANN S2 products present good statistics for LAI (R2 > 0.70, root mean square error (RMSE) < 0.86) and CCC (R2 > 0.75, RMSE < 0.68 g/m2) retrievals. The normalized Sentinel-2 LAI index (SeLI) is the index that presents good statistics in each dataset (R2 > 0.71, RMSE < 0.78) and for the CCC, the ratio red-edge chlorophyll index (CIred-edge) (R2 > 0.67, RMSE < 0.62 g/m2). Both indices use bands located in the red-edge zone, highlighting the importance of this region. The LAI CLAIR model with a fixed extinction coefficient value produces a R2 > 0.63 and a RMSE < 1.47 and calibrating this coefficient for each study area only improves the statistics in two areas (RMSE ≈ 0.70). Finally, this study analyzed the influence of the LAI parameter estimated with the different methodologies in the calculation of crop potential evapotranspiration (ETc) with the adapted Penman–Monteith (FAO-56 PM), using a multi-temporal dataset. The results were compared with ETc estimated as the product of the reference evapotranspiration (ETo) and on the crop coefficient (Kc) derived from FAO table values. In the absence of independent reference ET data, the estimated ETc with the LAI in situ values were considered as the proxy of the ground-truth. ETc estimated with the ANN S2 LAI product is the closest to the ETc values calculated with the LAI in situ (R2 > 0.90, RMSE < 0.41 mm/d). Our findings indicate the good validation of ANN S2 LAI and CCC products and their further suitability for the implementation in evapotranspiration retrieval of agricultural areas. Full article
(This article belongs to the Special Issue Remote Sensing of Agricultural Monitoring)
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<p>Test site locations. (<b>a</b>) Caserta test site located in the south of Italy from the S2 image of 7 March 2019, (<b>b</b>) Tarquina test area located in the center of Italy from the S2 image of 15 March, 2017, (<b>c</b>) Bahía Blanca test site located in the center south of Argentina from S2 image of 18 November, 2018 and (<b>d</b>) Valencia test site situated in the center south of Spain from S2 image of 3 October, 2018.</p>
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<p>Land European Remote-Sensing Instruments (VALERI) sampling approach for each elementary sampling unit (ESU).</p>
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<p>LAI validation for the different LAI retrieval methods. (<b>a</b>) ANN S2 LAI product, (<b>b</b>) LAI obtained with CLAIR model with a fixed α* and (<b>c</b>) the normalized Sentinel-2 LAI index (SeLI) index method. All graphs present the 1:1 line.</p>
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<p>CCC validation for both CCC retrieval methods. (<b>a</b>) ANN S2 CCC product and (<b>b</b>) CI<sub>red-edge</sub> vegetation index method. Both graphs present the 1:1 line.</p>
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<p>Short-seasonal evolution of ET<sub>c</sub> (mm/d) estimated with the commonly used method (ET<sub>o</sub> * Kc), with LAI in situ data, with ANN S2 LAI product, with LAI obtained with CLAIR model and with LAI from SeLI index, using TAR16-IT temporal dataset of <b>a</b>) wheat crop type and <b>b</b>) tomato crop type. Vertical bars correspond to the standard deviation on ET<sub>c</sub> estimation for each ESU.</p>
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