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24 pages, 7292 KiB  
Article
The Impact of Temperature and Pressure on the Structural Stability of Solvated Solid-State Conformations of Bombyx mori Silk Fibroins: Insights from Molecular Dynamics Simulations
by Ezekiel Edward Nettey-Oppong, Riaz Muhammad, Ahmed Ali, Hyun-Woo Jeong, Young-Seek Seok, Seong-Wan Kim and Seung Ho Choi
Materials 2024, 17(23), 5686; https://doi.org/10.3390/ma17235686 - 21 Nov 2024
Viewed by 482
Abstract
Bombyx mori silk fibroin is a promising biopolymer with notable mechanical strength, biocompatibility, and potential for diverse biomedical applications, such as tissue engineering scaffolds, and drug delivery. These properties are intrinsically linked to the structural characteristics of silk fibroin, making it essential to [...] Read more.
Bombyx mori silk fibroin is a promising biopolymer with notable mechanical strength, biocompatibility, and potential for diverse biomedical applications, such as tissue engineering scaffolds, and drug delivery. These properties are intrinsically linked to the structural characteristics of silk fibroin, making it essential to understand its molecular stability under varying environmental conditions. This study employed molecular dynamics simulations to examine the structural stability of silk I and silk II conformations of silk fibroin under changes in temperature (298 K to 378 K) and pressure (0.1 MPa to 700 MPa). Key parameters, including Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), and Radius of Gyration (Rg) were analyzed, along with non-bonded interactions such as van der Waals and electrostatic potential energy. Our findings demonstrate that both temperature and pressure exert a destabilizing effect on silk fibroin, with silk I exhibiting a higher susceptibility to destabilization compared to silk II. Additionally, pressure elevated the van der Waals energy in silk I, while temperature led to a reduction. In contrast, electrostatic potential energy remained unaffected by these environmental conditions, highlighting stable long-range interactions throughout the study. Silk II’s tightly packed β-sheet structure offers greater resilience to environmental changes, while the more flexible α-helices in silk I make it more susceptible to structural perturbations. These findings provide valuable insights into the atomic-level behavior of silk fibroin, contributing to a deeper understanding of its potential for applications in environments where mechanical or thermal stress is a factor. The study underscores the importance of computational approaches in exploring protein stability and supports the continued development of silk fibroin for biomedical and engineering applications. Full article
(This article belongs to the Special Issue Advances in Bio-Polymer and Polymer Composites)
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Figure 1
<p>Schematic representation of <span class="html-italic">Bombyx mori</span> silk structure. During the pupa stage of their metamorphosis into moths, silkworms spin silk fibers to construct protective cocoons. Each silk fiber is composed of two core fibroin filaments encased by sericin (depicted in purple), an adhesive glycoprotein that facilitates fiber cohesion. At the molecular level, each fibroin filament consists of numerous assemblies of nanofibrils, which can adopt either silk I or silk II structural conformations. These structural forms are determined by the specific arrangement of secondary protein structures within the fibroin. The silk I structure predominantly features type II β-turns and α-helices, while the silk II structure is mainly characterized by β-turns and β-sheets. Both of these secondary structures contribute to the fiber’s mechanical properties and functional versatility across various applications.</p>
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<p>Schematic of the secondary structures of <span class="html-italic">Bombyx mori</span> silk fibroin. The silk fibroin protein comprises several distinct secondary structures that play a critical role in determining the material’s mechanical strength and biological properties. These secondary structures include the following: (<b>a</b>) α-helix, a right-handed coiled structure that contributes to flexibility; (<b>b</b>) β-sheet, an extended conformation that forms the crystalline regions, providing mechanical robustness; and (<b>c</b>) random coils, which are unstructured regions that contribute to the amorphous domains of the protein. The schematic also illustrates the specific amino acid residues associated with each of these secondary structures, alongside the corresponding all-atom models, providing a molecular-level perspective on silk fibroin’s hierarchical organization.</p>
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<p>Schematic representation of the primary structure of <span class="html-italic">Bombyx mori</span> silk fibroin. The heavy chain of silk fibroin consists of alternating crystalline (R1 to R12) and amorphous phases (L1 to L11), along with N terminus (N) and C terminus (C), each having distinct structural and functional roles. The crystalline regions, indicated by the R domains, are primarily composed of β-sheet structures that impart rigidity and strength to the fiber. In contrast, the amorphous regions, denoted as L domains, serve as flexible linkers that provide elasticity and contribute to the material’s overall mechanical performance. This schematic highlights the representative molecular structure used for simulations, where domain R6 (crystalline) is flanked by domains L5 and L6 (amorphous), providing a balanced model for studying both phases of the fibroin.</p>
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<p>Molecular dynamics equilibration simulation of silk fibroin protein structures. This figure illustrates the evolution of both potential energy and kinetic energy over time during the equilibration phase for hydrated silk fibroin systems. Equilibration was performed under the NPT ensemble for the silk I (<b>a</b>) and silk II (<b>b</b>) structures. The gradual decrease and stabilization of the total potential energy throughout the simulation indicates that the system achieves a stable configuration as the atoms settle into their equilibrium positions. The kinetic energy, in contrast, remains relatively constant, reflecting consistent thermal motion within the system. This energy evolution demonstrates the successful stabilization of silk fibroin structures under the specified conditions, preparing them for further simulation analyses.</p>
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<p>Volume changes of the simulation cells during equilibration of silk fibroin structures. The figure presents the observed reduction in the volume of the simulation cells during equilibration. For the silk I structure (<b>a</b>), the cubic cell size decreased from an initial length of 105 Å to 102.7 Å, while for the silk II structure (<b>b</b>), the cell size reduced from 89 Å to 86.9 Å. This volume contraction is indicative of the system reaching equilibrium, as the protein and water molecules reorganize into a more compact and energetically favorable configuration. The simulations ensured complete hydration of the protein by maintaining a 10 Å buffer between the protein structures and the cell boundaries. A visual snapshot on the far right provides a cross-sectional view of the hydrated silk I and silk II proteins, illustrating the distribution of water molecules around the protein structures and confirming that the proteins are fully solvated within the simulation cells.</p>
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<p>Root Mean Square Deviation (RMSD) of silk I and silk II structures under different pressure and temperature conditions. (<b>a</b>) RMSD values for the backbone atoms of silk I across a pressure range of 0.1 MPa to 700 MPa, showing how the structural deviation increases with pressure. (<b>b</b>) RMSD values for the backbone atoms of silk II under the same pressure range. Both structures exhibit increasing deviation with pressure. (<b>c</b>) The average RMSD values for silk I and silk II as a function of pressure. Silk I has a minimum deviation of 0.804 Å at 0.1 MPa, increasing to a maximum of 1.454 Å at 700 MPa. Similarly, silk II exhibits a minimum deviation of 0.772 Å at 0.1 MPa and a maximum of 1.285 Å at 700 MPa. These results demonstrate that pressure induces structural instability, with silk I experiencing a more pronounced deviation than silk II. (<b>d</b>) RMSD of silk I backbone atoms across a temperature range of 298 K to 378 K, indicating how thermal agitation affects structural deviation. (<b>e</b>) RMSD values for silk II at the same temperature range, illustrating the temperature-dependent structural perturbations. (<b>f</b>) The average RMSD values for silk I and silk II as a function of temperature. The temperature increase caused a rise in RMSD for both structures, with silk I showing a deviation from 0.764 Å at 298 K to 0.871 Å at 378 K, and silk II deviating from 0.742 Å to 0.843 Å over the same temperature range. This increase indicates that thermal agitation leads to greater atomic movement and structural deviations, particularly for silk I.</p>
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<p>Root Mean Square Fluctuation (RMSF) of silk I and silk II structures under different pressure and temperature conditions. (<b>a</b>) RMSF values for silk I backbone atoms across a pressure range of 0.1 MPa to 700 MPa, showing per-residue fluctuations and the effect of pressure on protein flexibility. (<b>b</b>) RMSF values for silk II under the same pressure range, highlighting differences in flexibility between the two structures. (<b>c</b>) The average RMSF values for silk I and silk II as a function of pressure. The minimum fluctuation for silk I was 0.470 Å at 0.1 MPa, increasing to a maximum of 0.507 Å at 700 MPa. For silk II, the fluctuation values ranged from 0.454 Å to 0.453 Å over the same pressure range. The observed fluctuations are relatively low, indicating minimal atomic mobility under pressure for both structures. (<b>d</b>) RMSF values for silk I backbone atoms across a temperature range of 298 K to 378 K, showing increased fluctuations with rising temperature. (<b>e</b>) RMSF values for silk II at the same temperature range, reflecting a similar trend of increased fluctuations with temperature. (<b>f</b>) The average RMSF values for silk I and silk II as a function of temperature. The fluctuations increase with temperature, with silk I showing a rise from 0.498 Å at 298 K to 0.592 Å at 378 K, and silk II increasing from 0.468 Å to 0.536 Å. These results suggest that temperature-induced thermal agitation leads to increased per-residue flexibility, particularly for silk I, which demonstrates greater fluctuation than silk II.</p>
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<p>Radius of Gyration (R<sub>g</sub>) of silk I and silk II structures under different pressure and temperature conditions. (<b>a</b>) R<sub>g</sub> values for silk I across a pressure range of 0.1 MPa to 700 MPa, illustrating how pressure affects the overall compactness of the structure. (<b>b</b>) R<sub>g</sub> values for silk II under the same pressure range, showing a similar trend but with lower values compared to silk I. (<b>c</b>) The average R<sub>g</sub> values for silk I and silk II as a function of pressure. An increase in pressure leads to a reduction in R<sub>g</sub>, indicating increased compaction of both structures. Silk I shows a maximum R<sub>g</sub> of 22.485 Å at 0.1 MPa, decreasing to 21.520 Å at 700 MPa. Silk II exhibits a maximum R<sub>g</sub> of 20.635 Å at 0.1 MPa, reducing to 19.929 Å at 700 MPa. These results confirm that pressure induces compaction, with silk I showing a greater reduction in compactness than silk II. (<b>d</b>) R<sub>g</sub> values for silk I across a temperature range of 298 K to 378 K, indicating how thermal effects impact protein packing. (<b>e</b>) R<sub>g</sub> values for silk II under the same temperature range, showing minimal changes in compactness. (<b>f</b>) The average R<sub>g</sub> values for silk I and silk II as a function of temperature. Both structures show minimal alterations in compactness with temperature, with R<sub>g</sub> values of 22.762 Å for silk I and 20.874 Å for silk II at 298 K, slightly decreasing to 22.711 Å and 20.937 Å, respectively, at 378 K. The small changes in R<sub>g</sub> suggest that the temperature range studied has a negligible effect on the compactness of the silk structures.</p>
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<p>Non-bonded interactions of silk I and silk II structures under different pressure and temperature conditions. (<b>a</b>) The average van der Waals energy as a function of pressure, from 0.1 MPa to 700 MPa. For both silk I and silk II, the van der Waals energy increased with rising pressure, reflecting the compression of inter-atomic distances and the strengthening of long-range non-bonded interactions. (<b>b</b>) The average electrostatic potential energy as a function of pressure. The electrostatic potential energy remained constant for both silk I and silk II, with values of −1.97 × 10<sup>6</sup> kcal/mol and −1.16 × 10<sup>6</sup> kcal/mol, respectively, indicating that pressure has no significant effect on electrostatic interactions. (<b>c</b>) The average van der Waals energy as a function of temperature, from 298 K to 378 K. In contrast to pressure, the van der Waals energy decreased with increasing temperature for both silk structures, due to the expansion of inter-atomic distances and the weakening of non-bonded interactions. (<b>d</b>) The average electrostatic potential energy as a function of temperature. Similar to pressure, the electrostatic potential energy remained constant with temperature for both silk I and silk II, highlighting the stability of long-range electrostatic interactions under thermal fluctuations.</p>
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27 pages, 3104 KiB  
Review
Developments in the Dry Fractionation of Plant Components: A Review
by Ganapathy Subramanian Meenakshi Sundaram, Divyapratim Das, Tolu Emiola-Sadiq, Abdullah Sajeeb Khan, Lifeng Zhang and Venkatesh Meda
Separations 2024, 11(12), 332; https://doi.org/10.3390/separations11120332 - 21 Nov 2024
Viewed by 334
Abstract
Over the years, pulses and cereals have been identified as promising sources of plant proteins. The intensive production of these crops and concerns about food security and malnutrition worldwide have intensified research into their separation. While wet extraction remains the standard protein isolation [...] Read more.
Over the years, pulses and cereals have been identified as promising sources of plant proteins. The intensive production of these crops and concerns about food security and malnutrition worldwide have intensified research into their separation. While wet extraction remains the standard protein isolation method, the search for more sustainable extraction methods is still ongoing. Two dry fractionation techniques, air classification and tribo-electrostatic separation, have been discussed in this review. This review highlights the design aspects of air classifiers including the cut-off point and flow rate, and for electrostatic separators, factors such as charger materials, the nature of the flow in charger tubes, and the strength of the electric field potential have been discussed in detail. Our analysis revealed that cascading the two techniques should help enhance the concentration and purity of the separated fractions. While limitations such as low purity and low yield exist, current research studies are focused on overcoming such drawbacks. Dry fractionation exhibits potential as a sustainable processing method while also preserving the native functionality of the proteins, making it easier to incorporate the fractions in commercial scale processes. Full article
(This article belongs to the Special Issue Extraction, Purification and Application of Bioactive Compounds)
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<p>Seeded acreage of peas and lentils over the years [<a href="#B16-separations-11-00332" class="html-bibr">16</a>].</p>
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<p>Some of the common sources of plant proteins and their uses [<a href="#B26-separations-11-00332" class="html-bibr">26</a>].</p>
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<p>An overview of wet and dry fractionation techniques [<a href="#B40-separations-11-00332" class="html-bibr">40</a>].</p>
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<p>Schematics of an air classifier that fractionates using a classifier wheel with labels [<a href="#B38-separations-11-00332" class="html-bibr">38</a>].</p>
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<p>Schematics of a centrifugal force-based air classifier [<a href="#B70-separations-11-00332" class="html-bibr">70</a>].</p>
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<p>Mechanism of the charging of particles inside a tribo-charger [<a href="#B75-separations-11-00332" class="html-bibr">75</a>].</p>
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<p>Triboelectric series of various materials applied in the separation process [<a href="#B83-separations-11-00332" class="html-bibr">83</a>].</p>
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16 pages, 4459 KiB  
Article
Novel Spectroscopic Studies of the Interaction of Three Different Types of Iron Oxide Nanoparticles with Albumin
by Silviya Abarova, Tsenka Grancharova, Plamen Zagorchev, Boris Tenchov and Bissera Pilicheva
Nanomaterials 2024, 14(23), 1861; https://doi.org/10.3390/nano14231861 - 21 Nov 2024
Viewed by 415
Abstract
In the present work, we studied the interactions of three types of iron oxide nanoparticles (IONPs) with human serum albumin (HSA) by fluorescence and UV-Vis spectroscopy. The determined binding parameters of the reactions and the thermodynamic parameters, including ΔHo, ΔSo, and ΔGo indicated [...] Read more.
In the present work, we studied the interactions of three types of iron oxide nanoparticles (IONPs) with human serum albumin (HSA) by fluorescence and UV-Vis spectroscopy. The determined binding parameters of the reactions and the thermodynamic parameters, including ΔHo, ΔSo, and ΔGo indicated that electrostatic forces play a major role in the interaction of IONPs with HSA. These measurements indicate a fluorescent quenching mechanism based on IONPs-HSA static complex formation. Our study shows that the interaction between HSA and IONPs depends on the nanoparticle structure. The interaction between IONPs and HSA was found to be spontaneous, exothermic, and entropy-driven. HSA was shown to interact moderately with IONPs obtained with plant extracts of Uncaria tomentosa L. (IONP@UT) and Clinopodium vulgare L. (IONP@CV), and firmly with IONPs prepared with Ganoderma lingzhi (Reishi) extract (IONP@GL), via ground-state association. Analysis by modified Stern-Volmer approximation indicates that the quenching mechanism is static. Our study significantly improves our understanding of the mechanisms of interaction, distribution, and transport involved in the interaction between proteins and IONPs. It provides crucial insights into the functional perturbations of albumin binding capacity and the effects of IONPs on the stability and structural modifications of plasma carrier proteins. Full article
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<p>FTIR spectra of iron oxide nanoparticles without bioactive corona (IONP@) and samples obtained by green synthesis using aqueous extracts from Uncaria tomentosa (IONP@UT), Clinopodium vulgare (IONP@CV), and Ganoderma lingzhi (IONP@GL).</p>
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<p>FTIR spectra of iron oxide nanoparticles without bioactive corona (IONP@) and samples obtained by green synthesis using aqueous extracts from Uncaria tomentosa (IONP@UT), Clinopodium vulgare (IONP@CV), and Ganoderma lingzhi (IONP@GL).</p>
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<p>SEM and EDX analysis of iron oxide nanoparticles without Bioactive corona (<b>A</b>) and samples obtained by green synthesis using aqueous extracts from <span class="html-italic">Uncaria tomentosa</span> (<b>B</b>), <span class="html-italic">Clinopodium vulgare</span> (<b>C</b>), and <span class="html-italic">Ganoderma lingzhi</span> (<b>D</b>).</p>
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<p>DLS distribution curves of nanoparticles of differen batches.</p>
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<p>Fluorescence spectra of an HSA solution (0.3 mg/mL) before and after titration with naked nanoparticles IONP@ at 15, 25, and 37 °C. Excitation wavelength: 286 ± 1 nm.</p>
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<p>Fluorescence spectra of HSA solution (0.3 mg/mL) before and after titration with IONP@UT (<b>A</b>), IONP@CV (<b>B</b>), and IONP@GL (<b>C</b>) at 15, 25, and 37 °C. Excitation wavelength: 286 ± 1 nm.</p>
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<p>Stern–Volmer plot for the binding of IONP@ (<b>A</b>), IONP@UT (<b>B</b>), IONP@CV (<b>C</b>) and IONP@GL (<b>D</b>) nanoparticles with HSA at 15, 25, and 37 °C, (HSA) = 0.3 mg/mL.</p>
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<p>Double logarithmic plots of the interaction between HSA and IONP@ (<b>A</b>), IONP@UT (<b>B</b>), IONP@CV (<b>C</b>), and IONP@GL (<b>D</b>) nanoparticles at 15, 25, and 37 °C.</p>
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<p>Absorbance spectra of HSA and IONP@UT (<b>A</b>), IONP@CV-HSA (<b>B</b>), and IONP@GL-HSA (<b>C</b>) complexes. HSA concentration was 2.3 mg/mL, and the IONPs concentration for the IONPs-HSA complex was 10.5 μg/mL.</p>
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22 pages, 2861 KiB  
Article
Molecular Determinants for Guanine Binding in GTP-Binding Proteins: A Data Mining and Quantum Chemical Study
by Pawan Bhatta and Xiche Hu
Int. J. Mol. Sci. 2024, 25(22), 12449; https://doi.org/10.3390/ijms252212449 - 20 Nov 2024
Viewed by 372
Abstract
GTP-binding proteins are essential molecular switches that regulate a wide range of cellular processes. Their function relies on the specific recognition and binding of guanine within their binding pockets. This study aims to elucidate the molecular determinants underlying this recognition. A large-scale data [...] Read more.
GTP-binding proteins are essential molecular switches that regulate a wide range of cellular processes. Their function relies on the specific recognition and binding of guanine within their binding pockets. This study aims to elucidate the molecular determinants underlying this recognition. A large-scale data mining of the Protein Data Bank yielded 298 GTP-binding protein complexes, which provided a structural foundation for a systematic analysis of the intermolecular interactions that are responsible for the molecular recognition of guanine in proteins. It was found that multiple modes of non-bonded interactions including hydrogen bonding, cation–π interactions, and π–π stacking interactions are employed by GTP-binding proteins for binding. Subsequently, the strengths of non-bonded interaction energies between guanine and its surrounding protein residues were quantified by means of the double-hybrid DFT method B2PLYP-D3/cc-pVDZ. Hydrogen bonds, particularly those involving the N2 and O6 atoms of guanine, confer specificity to guanine recognition. Cation–π interactions between the guanine ring and basic residues (Lys and Arg) provide significant electrostatic stabilization. π–π stacking interactions with aromatic residues (Phe, Tyr, and Trp) further contribute to the overall binding affinity. This synergistic interplay of multiple interaction modes enables GTP-binding proteins to achieve high specificity and stability in guanine recognition, ultimately underpinning their crucial roles in cellular signaling and regulation. Notably, the NKXD motif, while historically considered crucial for guanine binding in GTP-binding proteins, is not universally required. Our study revealed significant variability in hydrogen bonding patterns, with many proteins lacking the NKXD motif but still effectively binding guanine through alternative arrangements of interacting residues. Full article
(This article belongs to the Special Issue Latest Advances in Protein-Ligand Interactions)
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Graphical abstract
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<p>(<b>a</b>) The guanine base of the guanine nucleotide (GTP/GDP/GMP), where the symbol R represents ribose and phosphate groups. The inward arrow shows the hydrogen bond acceptor, and the outward arrow shows the hydrogen bond donor. All the atoms are labeled according to the IUPAC naming system. (<b>b</b>) Structure of a representative GTP-binding protein: a p21-ras protein bound to GTP (PDB ID:1QRA).</p>
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<p>Representative hydrogen bond patterns: (<b>a</b>) <b><span style="color:red">N<sub>i</sub>-K<sub>i+1</sub></span></b>-X<sub>i+2</sub>-<b><span style="color:red">D<sub>i+3</sub></span></b> in the p21-ras protein (PDB ID: 1QRA); (<b>b</b>) <b><span style="color:red">N<sub>i</sub></span></b>-K<sub>i+1</sub>-X<sub>i+2</sub>-<b><span style="color:red">D<sub>i+3</sub> </span></b>in the Human Ras-like, family 12 protein (PDB ID: 3C5C); (<b>c</b>) N<sub>i</sub>-<b><span style="color:red">K</span><sub><span style="color:red">i+1</span></sub></b>-X<sub>i+2</sub>-<b><span style="color:red">D<sub>i+3</sub></span></b> in the human adenylosuccinate synthetase isozyme 2 (PDB ID: 2V40); (<b>d</b>) N<sub>i</sub>-K<sub>i+1</sub>-X<sub>i+2</sub>-<b><span style="color:red">D<sub>i+</sub><sub>3</sub></span></b> in the Plasmodium falciparum rab6 protein (PDB ID: 1D5C); (<b>e</b>) “D<sub>i</sub>/E<sub>i</sub> plus” in PnrA from Treponema pallidum (PDB ID: 2FQX); and (<b>f</b>) the “Others” pattern in Murray Valley encephalitis virus methyltransferase domain (PDB ID: 2PXA). C, N, O, and S atoms are colored in cyan, blue, red and yellow, respectively.</p>
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<p>A 3D stereographic drawing of a guanine base surrounded by positively charged residues. All the 258 complexes that contain cation–π interactions are aligned by the superimposition of the guanine base.</p>
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<p>Representative cation–π interactions between the positively charged residues and the guanine ring. PDB IDs for the cation–π interacting motifs are displayed. C, N, and O atoms are colored in cyan, blue, and red, respectively. The red dashed line indicates the coexistence of hydrogen bond interactions.</p>
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<p>A 3D stereographic drawing of a guanine base surrounded by aromatic residues. All of the 162 complexes that contain π–π stacking interactions are aligned by the superimposition of the guanine base.</p>
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<p>Representative π–π stacking interactions between the aromatic residue and the guanine ring. PDB IDs for the π–π stacking interacting motifs are displayed. C, N, and O atoms are colored in cyan, blue, and red, respectively.</p>
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<p>(<b>a</b>) A schematic intermolecular interaction map between the guanine and its interacting residues in a GTP-binding protein p21-ras (PDB ID: 1QRA). The interatomic distances (in Å) are indicated along the dashed lines. The red, blue, and gray dashed lines represent hydrogen bond interactions, cation–π interactions, and π–π stacking interactions, respectively. (<b>b</b>) The 3D structure of the residues surrounding the guanine. For clarity, only side-chain interactions are shown. The color codes of the dashed lines are the same as in (<b>a</b>).</p>
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25 pages, 4113 KiB  
Article
Rabbit and Human Angiotensin-Converting Enzyme-2: Structure and Electric Properties
by Svetlana H. Hristova, Trifon T. Popov and Alexandar M. Zhivkov
Int. J. Mol. Sci. 2024, 25(22), 12393; https://doi.org/10.3390/ijms252212393 - 19 Nov 2024
Viewed by 352
Abstract
The angiotensin-converting enzyme-2 (ACE2) is a transmembrane glycoprotein, consisting of two segments: a large carboxypeptidase catalytic domain and a small transmembrane collectrin-like segment. This protein plays an essential role in blood pressure regulation, transforming the peptides angiotensin-I and angiotensin-II (vasoconstrictors) into angiotensin-1-9 and [...] Read more.
The angiotensin-converting enzyme-2 (ACE2) is a transmembrane glycoprotein, consisting of two segments: a large carboxypeptidase catalytic domain and a small transmembrane collectrin-like segment. This protein plays an essential role in blood pressure regulation, transforming the peptides angiotensin-I and angiotensin-II (vasoconstrictors) into angiotensin-1-9 and angiotensin-1-7 (vasodilators). During the COVID-19 pandemic, ACE2 became best known as the receptor of the S-protein of SARS-CoV-2 coronavirus. The purpose of the following research is to reconstruct the 3D structure of the catalytic domain of the rabbit enzyme rACE2 using its primary amino acid sequence, and then to compare it with the human analog hACE2. For this purpose, we have calculated the electric properties and thermodynamic stability of the two protein globules employing computer programs for protein electrostatics. The analysis of the amino acid content and sequence demonstrates an 85% identity between the two polypeptide chains. The 3D alignment of the catalytic domains of the two enzymes shows coincidence of the α-helix segments, and a small difference in two unstructured segments of the chain. The electric charge of the catalytic domain of rACE2, determined by 70 positively chargeable amino acid residues, 114 negatively chargeable ones, and two positive charges of the Zn2+ atom in the active center exceeds that of hACE2 by one positively and four negatively chargeable groups; however, in 3D conformation, their isoelectric points pI 5.21 coincide. The surface electrostatic potential is similarly distributed on the surface of the two catalytic globules, but it strongly depends on the pH of the extracellular medium: it is almost positive at pH 5.0 but strongly negative at pH 7.4. The pH dependence of the electrostatic component of the free energy discloses that the 3D structure of the two enzymes is maximally stable at pH 6.5. The high similarity in the 3D structure, as well as in the electrostatic and thermodynamic properties, suggests that rabbit can be successfully used as an animal model to study blood pressure regulation and coronavirus infection, and the results can be extrapolated to humans. Full article
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<p>Renin-angiotensin system. <span class="html-italic">Subsection</span> (<b>a</b>) on the left: Renin and angiotensin-converting enzyme (ACE) action. The precursor α-2-globulin angiotensinogen is produced by hepatocytes. The renal enzyme renin cleaves the covalent peptide bond after the first 10 amino acids from the N-terminus of angiotensinogen, leading to the formation of angiotensin-I (first reaction). This decapeptide is converted by the pulmonary angiotensin-converting enzyme (ACE) to the octapeptide angiotensin-II by the cleavage of the last two amino acid residues, resulting in the emergence of a carboxylic group on the C-terminus (second reaction). <span class="html-italic">Subsection</span> (<b>b</b>) on the right: Angiotensin-converting enzyme-2 (ACE2) action. The angiotensin-converting enzyme-2 (ACE2) cleaves one amino acid residue from the C-terminus of both angiotensin peptides, which leads to the formation of the nonapeptide angiotensin-1–9 and the heptapeptide angiotensin-1–7, respectively. The cleaved peptide bonds (in the two subsections) are shown by dotted lines colored according to the corresponding enzyme: blue (ACE) or red (ACE2). The amino acid residues are colored according to their charge and hydrophilicity: green (uncharged hydrophilic), blue (positively charged hydrophilic), red (negatively charged hydrophilic), and yellow (uncharged hydrophobic); the charges are determined at neutral pH. The end-side residues are marked by double color considering the protonated α-amino group (NH<sub>3</sub><sup>+</sup>–) on the N-terminus and the deprotonated carboxylic group (–COO<sup>−</sup>) on the C-terminus. The N-end aspartic acid (Asp) residue bears one positive (NH<sub>3</sub><sup>+</sup>–) and one negative (COO<sup>−</sup>–) charge. The C-end histidine (His) residue of the angiotensin-1-9 bears one negative charge (the deprotonated carboxyl group) and one positive charge at acid pH (the protonated imidazole group), which disappears at basic pH. The C-ends of the remaining three peptides have only negative charge (–COO<sup>−</sup>).</p>
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<p>Primary amino acid sequence of the polypeptide chains of human (the rows beginning with <b>h</b>) and rabbit (<b>r</b>) ACE2. The numbers at the end of every row indicate the first and the last amino acid residue on the corresponding row. The amino acid residues are denoted according to the standard one-letter code. The cells of the different residues are colored according to the electric charge at pH 7.0 and the hydrophilicity of the given residue: red (negatively charged hydrophilic), blue (positively charged hydrophilic), green (uncharged hydrophilic), and yellow (uncharged hydrophobic). The polypeptide chain is divided (indicated by vertical lines) into three segments: signal peptide (amino acid residues 1–18, colored in bright orange), catalytic domain (19–615, colored in bright gray), and transmembrane segment (616–805 colored in bright purple). The five residues included in the zinc-binding motif HEMGH of the active center of the enzyme are denoted by the red rectangle.</p>
Full article ">Figure 2 Cont.
<p>Primary amino acid sequence of the polypeptide chains of human (the rows beginning with <b>h</b>) and rabbit (<b>r</b>) ACE2. The numbers at the end of every row indicate the first and the last amino acid residue on the corresponding row. The amino acid residues are denoted according to the standard one-letter code. The cells of the different residues are colored according to the electric charge at pH 7.0 and the hydrophilicity of the given residue: red (negatively charged hydrophilic), blue (positively charged hydrophilic), green (uncharged hydrophilic), and yellow (uncharged hydrophobic). The polypeptide chain is divided (indicated by vertical lines) into three segments: signal peptide (amino acid residues 1–18, colored in bright orange), catalytic domain (19–615, colored in bright gray), and transmembrane segment (616–805 colored in bright purple). The five residues included in the zinc-binding motif HEMGH of the active center of the enzyme are denoted by the red rectangle.</p>
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<p>(<b>a</b>–<b>d</b>) 3D structure models of the human (red, hACE2) and rabbit (green, rACE2) catalytic domains of the angiotensin-converting enzyme-2 (the upper two models (<b>a</b>,<b>b</b>)); the α-helix segments are depicted as ribbon spirals. The model of the rabbit ACE2 (<b>b</b>) is reconstructed by replacement of the different amino acid residues in the hACE2 (PDB: 1r42) model (<b>a</b>). The violet spherical object (Zn<sup>2+</sup>) is the zinc atom in the enzyme active center. The low two models (<b>c</b>,<b>d</b>) present the aliment (hACE2+rACE2) of the human and rabbit ACE2 in two different projections: the 3D volume of the protein globules (the ribbon model on the left) (<b>c</b>) and the 2D surface of the globules (the atomic model on the right) (<b>d</b>). The right bottom model (<b>d</b>) presents the atoms exposed on the surface of the aligned two protein globules; the atoms are colored fully in red (hACE2) or green (rACE2) when they are entirely protruded above the others, or in mixed color when their coordinates partially coincide. The brightness, shade, and color nuance of the atomic images give the impression for a quasi 3D surface of the protein globules.</p>
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<p>pH dependences of the net electric charge <span class="html-italic">nz</span> + 2 of the globular catalytic domain of human (PDB: 1r42, hACE2, red curve 1) and reconstructed rabbit (rACE2, green curve 2) angiotensin-converting enzyme-2 in 3D conformation of the polypeptide chain in aqueous medium. The net charge of the two globular domains is the algebraic sum of the positive and negative coulombic charges of the polypeptide chain with the addition of two positive charges of the Zn<sup>2+</sup> atom in the catalytic center. <span class="html-italic">Insert</span>: pH dependence of hACE2 (red curve 1) and rACE2 (green curve 2) with denoted isoelectric point (<span class="html-italic">nz</span> = 0): pI 5.21 (human) and pI 5.21 (rabbit) ACE2.</p>
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<p>Electrostatic potential on the 3D surface of the catalytic domain of human (hACE2, two upper models, PDB: 1r42) and reconstructed rabbit (rACE2, two lower models) of angiotensin-converting enzyme-2 at pH 5.0 (two left models) and at pH 7.0 (two right models). The surfaces of the models are colored according to the electrostatic potential (negative—red, positive—blue), computed at pH 5.0 or pH 7.0, ionic strength 0.0001 mol/L, and temperature 20 °C, and visualized in the range <span class="html-italic">kT</span>/<span class="html-italic">e</span> = ±6 J/C (the scale on the right); 1 <span class="html-italic">kT</span>/<span class="html-italic">e</span> = 25.26 mV at 20 °C or 26.73 mV at 37 °C.</p>
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<p>Amino acid sequence of the polypeptide chains of human (rows beginning with <b>h</b>) and rabbit (<b>r</b>) ACE2. The numbers at the end of every row indicate the first and the last amino acid residue on the corresponding row. The cells of the amino acid residues (denoted by the standard one-letter code) are colored according to their affinity to the water molecules: hydrophilic (green) or hydrophobic (yellow). The vertical lines and the red rectangle denote the beginning and the end of the catalytic domain and the amino acid residues from the zinc-binding motif included in the enzyme active center, respectively.</p>
Full article ">Figure 6 Cont.
<p>Amino acid sequence of the polypeptide chains of human (rows beginning with <b>h</b>) and rabbit (<b>r</b>) ACE2. The numbers at the end of every row indicate the first and the last amino acid residue on the corresponding row. The cells of the amino acid residues (denoted by the standard one-letter code) are colored according to their affinity to the water molecules: hydrophilic (green) or hydrophobic (yellow). The vertical lines and the red rectangle denote the beginning and the end of the catalytic domain and the amino acid residues from the zinc-binding motif included in the enzyme active center, respectively.</p>
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<p>pH dependences of the electrostatic component Δ<span class="html-italic">G</span><sub>el</sub> of the folding energy Δ<span class="html-italic">G</span><sub>fold</sub> of the human (hACE2, PDB: 1r42, curve 1) and reconstructed rabbit (rACE2, curve 2) polypeptide chains of angiotensin-converting enzyme-2 at the transformation of the polypeptide chain from fully unfolded (random coil) to folded (globular 3D structure) conformation. The two 3D models are optimized by the program YASARA.</p>
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<p>Amino acid sequence of the polypeptide chains of transmembrane collectrin-like segment of the human ACE2 (the rows denoted by <b>A</b>) and human collectrin (<b>C</b>). The numbers at the end of every row indicate the first and the last amino acid residue on the corresponding row (the numbering corresponds to that in the hACE2 shown in <a href="#ijms-25-12393-f006" class="html-fig">Figure 6</a>). The cells of the amino acid residues (denoted by the standard one-letter code) are colored according to their affinity to water: hydrophilic (green) or hydrophobic (yellow). The absent amino acid residues are denoted by dashes.</p>
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<p>pH dependences of the electrostatic component Δ<span class="html-italic">G</span><sub>el</sub> of the folding energy of the catalytic domain of rabbit ACE2 polypeptide chain at transformation from random coil to 3D structure for four models: reconstructed on the base of hACE2 (PDB: 1r42, curve 1) and optimized by Chimera (curve 2) or YASARA (curve 3), and created by AlphaFold2 on the base of amino acid sequence of rACE2 (curve 4).</p>
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<p>pH dependences of the net electric charge <span class="html-italic">nz</span> of the catalytic domain of human (hACE2, curves 1, 2, and 3) and rabbit (rACE2, curves 4, 5, and 6) angiotensin-converting enzyme-2 in aqueous medium when the polypeptide chain is in unfolded conformation (random coil, curves 1 and 4), folded in 3D globule without Zn<sup>2+</sup> (curves 2 and 5) and when the Zn<sup>2+</sup> cation is bound in the enzyme active center (curves 3 and 6). The folded 3D conformations correspond to the crystallographic model of hACE2 (PDB:1r42, red curve 3) and to the reconstructed model of rACE2 (green curve 6). The isoelectric points are denoted by open cycles. The net charge <span class="html-italic">nz</span> is the algebraic sum of the positive and negative coulombic charges of the polypeptide chain without (curves 1, 2, 4, 5) or with the attached Zn<sup>2+</sup> cation (curves 3 and 6).</p>
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<p>pH dependences of the electrostatic component Δ<span class="html-italic">G</span><sub>el</sub> of the folding energy (the main figure) and the net electric charge <span class="html-italic">nz</span> (the inserted figure) of horse myoglobine according to the original 3D model (PDB: 1AZI, curves 1) and its reconstructed analog (curves 2). The reconstruction of the 3D model of the horse myoglobine is performed on the base of human myoglobine (PDB: 3RGK) considering the difference in the amino acid sequences of the human and horse myoglobine without optimization.</p>
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16 pages, 15805 KiB  
Article
Assessing Protein Surface-Based Scoring for Interpreting Genomic Variants
by Nikita R. Dsouza, Neshatul Haque, Swarnendu Tripathi and Michael T. Zimmermann
Int. J. Mol. Sci. 2024, 25(22), 12018; https://doi.org/10.3390/ijms252212018 - 8 Nov 2024
Viewed by 357
Abstract
Clinical genomics sequencing is rapidly expanding the number of variants that need to be functionally elucidated. Interpreting genetic variants (i.e., mutations) usually begins by identifying how they affect protein-coding sequences. Still, the three-dimensional (3D) protein molecule is rarely considered for large-scale variant analysis, [...] Read more.
Clinical genomics sequencing is rapidly expanding the number of variants that need to be functionally elucidated. Interpreting genetic variants (i.e., mutations) usually begins by identifying how they affect protein-coding sequences. Still, the three-dimensional (3D) protein molecule is rarely considered for large-scale variant analysis, nor in analyses of how proteins interact with each other and their environment. We propose a standardized approach to scoring protein surface property changes as a new dimension for functionally and mechanistically interpreting genomic variants. Further, it directs hypothesis generation for functional genomics research to learn more about the encoded protein’s function. We developed a novel method leveraging 3D structures and time-dependent simulations to score and statistically evaluate protein surface property changes. We evaluated positive controls composed of eight thermophilic versus mesophilic orthologs and variants that experimentally change the protein’s solubility, which all showed large and statistically significant differences in charge distribution (p < 0.01). We scored static 3D structures and dynamic ensembles for 43 independent variants (23 pathogenic and 20 uninterpreted) across four proteins. Focusing on the potassium ion channel, KCNK9, the average local surface potential shifts were 0.41 kBT/ec with an average p-value of 1 × 10−2. In contrast, dynamic ensemble shifts averaged 1.15 kBT/ec with an average p-value of 1 × 10−5, enabling the identification of changes far from mutated sites. This study demonstrates that an objective assessment of how mutations affect electrostatic distributions of protein surfaces can aid in interpreting genomic variants discovered through clinical genomic sequencing. Full article
(This article belongs to the Section Biochemistry)
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Figure 1
<p>Assessment of structure and dynamics of protein surfaces. (<b>A</b>) In this work, we analyzed the surface changes of proteins with groups ranging from global to local changes caused by variants. (<b>B</b>) We ran MD simulations in triplicate, calculated the electrostatic potential for each replicate, and measured the difference in the dynamic ensemble for each structure.</p>
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<p>Thermophilic and mesophilic enzymes demonstrate significant shifts in surface electrostatics. We consider these comparisons as a baseline for defining a considerable change in protein surface electrostatics. We compared 3D models and surface representations for three (<b>A</b>,<b>D</b>,<b>G</b>) mesophilic enzymes (blue), their (<b>B</b>,<b>E</b>,<b>J</b>) thermophilic orthologs (salmon), and their (<b>C</b>,<b>F</b>,<b>K</b>) electrostatic potential probability distributions. These three enzymes are (<b>A</b>–<b>C</b>), acyl phosphatase, (<b>D</b>–<b>F</b>) adenylate kinase, and (<b>G</b>–<b>K</b>) malate dehydrogenase. Malate dehydrogenase has hybrid and moderate thermophile structures that show small changes intermediate between the mesophile and thermophile. Protein cartoon surface representations are as in <a href="#ijms-25-12018-f001" class="html-fig">Figure 1</a>.</p>
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<p>Mutations that increase protein solubility induce significant local changes in electrostatic potential. (<b>A</b>,<b>D</b>) Wild-type crystal structure with the electrostatic surface shown and zoomed to show the local distribution of charges. (<b>B</b>,<b>E</b>) 3D structure with the variant shown as sticks in orange followed by the surface showing the change in the local surface compared to the WT. (<b>C</b>,<b>F</b>) The electrostatic potential distribution shows a significant change between the WT and structure with the variant. Colors are as in <a href="#ijms-25-12018-f002" class="html-fig">Figure 2</a>.</p>
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<p>Static local surface potential shows a nuanced change in the variants when compared to the WT. The local surface potential displayed as a violin plot for the static structures of (<b>A</b>) UROD and (<b>B</b>) PIK3R1 show small to no difference in the distribution and median with the exception of charged variants.</p>
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<p>TBL1XR1 dynamic ensemble helps show a distinguishable change in the surface potential compared to the static data. The graph of the median potential difference between the WT and the variant versus the <span class="html-italic">p</span>-value obtained from the AD test for the static and dynamic ensemble shows that the local surface potential for the ensemble was statistically more significant compared to the static structures. The labels in the graph are assigned to the dynamic points, and the connecting lines show the change in the <span class="html-italic">p</span>-value of static versus dynamic.</p>
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<p>We evaluated 12 KCNK9 variants using static structures and dynamic ensembles and demonstrate that the latter show more significant changes to local surface potentials. The graph represents the distinguishable change in the <span class="html-italic">p</span>-value from low to high (static to dynamic) for 12 variants and 4 variants change from a high <span class="html-italic">p</span>-value to low. The thicker lines connecting the static and dynamic variants show the <span class="html-italic">p</span>-value is more significant for the dynamic ensembles. The thinner lines are the 4 variants that had greater statistical significance in static structures than dynamic ensembles. Gray shading links the abscissa to zoom in on the region encompassing most of the genetic variants.</p>
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<p>Electrostatic potential for each TBL1XR1 variant dynamic ensemble shows the change in the distribution and median when compared to the WT ensemble. (<b>A</b>) We show the electrostatic representation of the binding interface of TBL1XR1 with surface color representing electrostatic potential as in previous figures. (<b>B</b>) We observe the distribution and median of the local surface potential distribution shifts significantly for all three case variants and one additional disease variant (D370Y) as compared to the WT. The variants shift the potential toward the positive direction.</p>
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<p>Shift in median surface potential for KCNK9 in both the electropositive and electronegative direction as compared to the WT. (<b>A</b>) We show the electrostatic potential distribution of the pore of the KCNK9 membrane protein with surface color representing electrostatic potential as in previous figures. (<b>B</b>) The variants move the potential in both directions compared to the WT. The shape fill color is based on the median values ranging from light gray for more electropositive to dark gray for more electronegative, and boxplots overlaid.</p>
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17 pages, 3873 KiB  
Article
Calcium Transport Activity of UV/H2O2-Degraded Fucoidans and Their Structural Characterization
by Biyang Zhu, Jiacheng Wang, Lijun You, Lianzhu Lin, Kuncheng Lin and Kseniya Hileuskaya
Mar. Drugs 2024, 22(11), 499; https://doi.org/10.3390/md22110499 - 4 Nov 2024
Viewed by 588
Abstract
Calcium-chelated polysaccharides have been increasingly considered as promising calcium supplements. In this study, degraded fucoidans (DFs) with different molecular weights (Mws) were prepared after UV/H2O2 treatment; their calcium-chelating capacities and intestinal absorption properties were also investigated. The results showed that [...] Read more.
Calcium-chelated polysaccharides have been increasingly considered as promising calcium supplements. In this study, degraded fucoidans (DFs) with different molecular weights (Mws) were prepared after UV/H2O2 treatment; their calcium-chelating capacities and intestinal absorption properties were also investigated. The results showed that the calcium-chelating capacities of DFs were improved with a decrease in Mw. This was mainly ascribed to the increased carboxyl content, which was caused by free-radical-mediated degradation. Meanwhile, the conformation of DF changed from a rod-like chain to a shorter and softer chain. The thermodynamic analysis demonstrated that DF binding to calcium was spontaneously driven by electrostatic interactions. Additionally, DF-Ca chelates with lower Mw showed favorable transport properties across a Caco-2 cell monolayer and could effectively accelerate the calcium influx through intestinal enterocytes. Furthermore, these chelates also exhibited a protective effect on the epithelial barrier by alleviating damage to tight junction proteins. These findings provide an effective free-radical-related approach for the development of polysaccharide-based calcium supplements with improved intestinal calcium transport ability. Full article
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<p>Effects of degradation time on the contents of carbonyl and carboxyl groups. (<b>A</b>) Carbonyl content. (<b>B</b>) Carboxyl content. Different letters (a–e) represent significant differences (<span class="html-italic">p</span> &lt; 0.05). If two variables have different letters, they are significantly different.</p>
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<p>Characterization of different DFs. (<b>A</b>,<b>B</b>) Molecular weight distributions of standard dextrans and DFs. (<b>C</b>) Hydrodynamic radius (R<sub>h</sub>) distributions. (<b>D</b>,<b>E</b>) SAXS profiles.</p>
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<p>Preparation and characterization of DF-Ca chelates. (<b>A</b>) The calcium-binding capacity of different DF samples. (<b>B</b>,<b>C</b>) Thermodynamics of binding interaction between DF<sub>T120</sub> and Ca<sup>2+</sup>. (<b>D</b>) SEM images of Fuc, DF<sub>T120</sub>, and DF<sub>T120</sub>-Ca samples.</p>
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<p>Transport capacities of different DFs and DF-FITC samples across Caco-2 cell monolayers. (<b>A</b>,<b>B</b>) Effects of DFs and DF-FITC on the cell viability, respectively. (<b>C</b>) DAPI staining of the cell nucleus. (<b>D</b>) The permeability of standard sodium fluorescein. (<b>E</b>) The P<sub>app</sub> values of different samples from AP to BL side.</p>
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<p>Effects of different Fuc-Ca and DF-Ca samples on the intracellular calcium level of Caco-2 cells. (<b>A</b>) Representative CLSM images at 0, 100, 200, and 400 s, with an image scale of 638.9 μm × 638.9 μm and magnification of 20×. (<b>B</b>) Changes in the intracellular calcium level.</p>
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<p>Effects of different DF-Ca chelates on gene transcription and protein expression levels related to the intestinal barrier. (<b>A</b>) Representative immunofluorescence images. (<b>B</b>) The transcription level of the ZO-1 gene. (<b>C</b>) The expression of the claudin-1 protein.</p>
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13 pages, 4291 KiB  
Article
Diffusion and Spectroscopy of H2 in Myoglobin
by Jiri Käser, Kai Töpfer and Markus Meuwly
Oxygen 2024, 4(4), 389-401; https://doi.org/10.3390/oxygen4040024 - 31 Oct 2024
Viewed by 362
Abstract
The diffusional dynamics and vibrational spectroscopy of molecular hydrogen (H2) in myoglobin (Mb) is characterized. Hydrogen has been implicated in a number of physiologically relevant processes, including cellular aging or inflammation. Here, the internal diffusion through the protein matrix was characterized, [...] Read more.
The diffusional dynamics and vibrational spectroscopy of molecular hydrogen (H2) in myoglobin (Mb) is characterized. Hydrogen has been implicated in a number of physiologically relevant processes, including cellular aging or inflammation. Here, the internal diffusion through the protein matrix was characterized, and the vibrational spectroscopy was investigated using conventional empirical energy functions and improved models able to describe higher-order electrostatic moments of the ligand. Depending on the energy function used, H2 can occupy the same internal defects as already found for Xe or CO (Xe1 to Xe4 and B-state). Furthermore, four additional sites were found, some of which had been discovered in earlier simulation studies. Simulations using a model based on a Morse oscillator and distributed charges to correctly describe the molecular quadrupole moment of H2 indicate that the vibrational spectroscopy of the ligand depends on the docking site it occupies. This is consistent with the findings for CO in Mb from experiments and simulations. For H2, the maxima of the absorption spectra cover ∼20 cm−1 which are indicative of a pronounced Stark effect of the surrounding protein matrix on the vibrational spectroscopy of the ligand. Electronic structure calculations show that H2 forms a stable complex with the heme iron (stabilized by ∼−12 kcal/mol), but splitting of H2 is unlikely due to a high activation energy (∼50 kcal/mol). Full article
(This article belongs to the Special Issue Interaction of Oxygen and Other Gases with Haem Containing Proteins)
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<p>Pocket representation in Mb. Shown is the secondary structure of Mb (eight helices) with the heme-unit in ball-and-stick representation together with the pockets determined for H<sub>2</sub> found in the present simulations. The pockets are Xe1 to Xe4, B-state, and pockets 6 to 9, which were found in addition to the experimentally known ligand-binding sites [<a href="#B13-oxygen-04-00024" class="html-bibr">13</a>,<a href="#B30-oxygen-04-00024" class="html-bibr">30</a>].</p>
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<p>H<sub>2</sub> ESP fit. Panel (<b>A</b>): reference ESP contour plot of H<sub>2</sub> along the <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>z</mi> <mo>)</mo> </mrow> </semantics></math>—plane going through the molecule. H<sub>2</sub> is at equilibrium bond length, and the ESP is only shown for for grid points with distances larger than <math display="inline"><semantics> <mrow> <mn>1.44</mn> </mrow> </semantics></math> Å of the closest hydrogen atom. Panel (<b>B</b>): ESP difference contour plot between reference and model ESP. The open black circles mark the positions of the hydrogen atoms.</p>
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<p>PES scan of H<sub>2</sub> interacting with the Heme–Histidine active site of myoglobin. Calculations were carried out at the rPBE/def2-TZVP level of theory. The coordinate system for scanning this potential energy surface is shown in <a href="#app1-oxygen-04-00024" class="html-app">Figure S1</a>.</p>
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<p>Pocket dynamics of H<sub>2</sub> in Mb. Panels (<b>A</b>,<b>B</b>) report the pocket occupied by H<sub>2</sub> as a function of the simulation time from simulations using the CGenFF and MDCM/Morse energy functions, respectively. Panels (<b>C</b>,<b>D</b>) show the separation between H<sub>2</sub> and each of the pocket centers (Xe1 to Xe4, B-state, and 6 to 9). For better visualization, only 1 ns out of the 5 ns trajectory is shown, specifically in panels (<b>A</b>,<b>B</b>). Each color corresponds to a particular separation between H<sub>2</sub> and the respective pocket center. In panel D, between 150 and 300 ps, the distance between H<sub>2</sub> and any other pocket is ∼5 Å, which points to one or several other uncharacterized docking sites. Simulations for panels (<b>A</b>–<b>D</b>) were started from identical initial structures, each with five H<sub>2</sub> molecules located at sites B-state (3) and Xe4 (2) to increase sampling. Results are reported for one out of the five H<sub>2</sub> molecules.</p>
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<p>Vibrational spectra of H<sub>2</sub> in Mb from unconstrained MD simulations. Panel (<b>A</b>): simulations using CGenFF and Panel (<b>B</b>): simulations using the Morse potential and MDCM for H<sub>2</sub>. The black trace is the total spectrum as it would, for example, be measured from an experiment. Filled circles indicate the mean of each spectrum, the mean frequencies are given in the legend, and the simulated frequency of gas phase H<sub>2</sub> is shown as a vertical dashed line at (<b>A</b>) <math display="inline"><semantics> <mrow> <mn>4062.4</mn> </mrow> </semantics></math> cm<sup>−1</sup> and (<b>B</b>) <math display="inline"><semantics> <mrow> <mn>4465.4</mn> </mrow> </semantics></math> cm<sup>−1</sup>. The differences between panels (<b>A</b>,<b>B</b>) are both due to using a harmonic bond (<b>A</b>) versus a Morse oscillator (<b>B</b>) and a point charge description for H<sub>2</sub> (<b>A</b>) versus an MDCM model (<b>B</b>).</p>
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<p>Vibrational spectra of H<sub>2</sub> in Mb from pocket-constrained MD simulations. Panel (<b>A</b>): simulations using the CGenFF energy function. Panel (<b>B</b>): using the MDCM model for H<sub>2</sub> but a conventional harmonic bond potential. Panel (<b>C</b>): using the MDCM model and the Morse potential for H<sub>2</sub>. The weighted average position of the maximum intensity (in cm<sup>−1</sup>) for each spectra is given in brackets in the legend. The vibrational frequency from the MD simulation of H<sub>2</sub> in the gas phase is marked as a vertical dashed line.</p>
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14 pages, 3601 KiB  
Article
Regulation on Aggregation Behavior and In Vitro Digestibility of Phytic Acid–Whey Protein Isolate Complexes: Effects of Heating, pH and Phytic Acid Levels
by Yaqiong Pei, Ziyu Deng and Bin Li
Foods 2024, 13(21), 3491; https://doi.org/10.3390/foods13213491 - 31 Oct 2024
Viewed by 784
Abstract
The impact of heat treatment, pH and phytic acid (PA) concentration on the aggregation behavior and digestibility of whey protein isolate (WPI) was investigated. The experimental results indicated that below the isoelectric point of WPI, heat treatment and elevated PA levels significantly increased [...] Read more.
The impact of heat treatment, pH and phytic acid (PA) concentration on the aggregation behavior and digestibility of whey protein isolate (WPI) was investigated. The experimental results indicated that below the isoelectric point of WPI, heat treatment and elevated PA levels significantly increased turbidity and particle size, leading to the aggregation of WPI molecules. No new chemical bonds were formed and the thermodynamic parameters ΔH < 0, ΔS > 0 and ΔG < 0 suggested that the interaction between PA and WPI was primarily a spontaneous electrostatic interaction driven by enthalpy. After the small intestine stage, increasing phytic acid levels resulted in a significant decrease in hydrolysis degree from 16.2 ± 1.5% (PA0) to 10.9 ± 1.4% (0.5% PA). Conversely, above isoelectric point of WPI, there was no significant correlation between the presence of PA and the aggregation behavior or digestion characteristics of WPI. These results were attributed to steric hindrance caused by PA-WPI condensates, which prevented protease binding to hydrolysis sites on WPI. In summary, the effect of PA on protein aggregation behavior and digestive characteristics was not simply dependent on its presence but largely on the aggregation degree of PA-WPI induced by heat treatment, pH and PA concentration. The findings obtained here suggested that phytic acid may be utilized as an agent to modulate the digestion characteristics of proteins according to production requirements. Additionally, the agglomerates formed by heating phytic acid and protein below the isoelectric point could also be utilized for nutrient delivery. Full article
(This article belongs to the Section Food Physics and (Bio)Chemistry)
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<p>Turbidity curves (<b>A</b>) and zeta potential (<b>B</b>) of PA-WPI mixture solution as a function of pH at various PA concentrations (from 0 to 0.01% <span class="html-italic">w</span>/<span class="html-italic">w</span>).</p>
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<p>FTIR spectra of WPI and PA-WPI mixtures containing 0.2% <span class="html-italic">w</span>/<span class="html-italic">w</span> PA and 1.0% <span class="html-italic">w</span>/<span class="html-italic">w</span> WPI at pH 3.5 and pH 7.0.</p>
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<p>Thermograms and binding isotherms corresponding to the titration of PA with β-Lg at varying pH values: pH 5.5 (<b>A</b>), pH 4.0 (<b>B</b>) and pH 3.5 (<b>C</b>).</p>
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<p>Thermodynamic parameters of binding between PA and WPI at varying temperatures.</p>
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<p>Microstructure (<b>A</b>) and particle size (<b>B</b>) of PA-WPI mixtures with varying PA (0, 0.05%, 0.1%, 0.2%, 0.5% <span class="html-italic">w</span>/<span class="html-italic">w</span>) levels before and after heat treatment at pH 7.0 and pH 3.5. The scale bar is 50 μm.</p>
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<p>Impact of heat treatment on hydrolysis degree (<b>A</b>) and SDS-PAGE (<b>B</b>) of WPI and PA-WPI mixtures containing 0.2% <span class="html-italic">w</span>/<span class="html-italic">w</span> PA and 1.0% <span class="html-italic">w</span>/<span class="html-italic">w</span> WPI. Different capital letters (A, B) indicate significant difference between heated and non-heated samples. Different lower-case letters (a, b, c) denote significant differences between WPI and PA-WPI (LSD, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Hydrolysis degree of protein within PA-WPI mixtures containing 1.0% <span class="html-italic">w</span>/<span class="html-italic">w</span> WPI and different PA levels (0, 0.05%, 0.1%, 0.2%, 0.5% <span class="html-italic">w</span>/<span class="html-italic">w</span>) at pH 7.0 (<b>A</b>), pH 3.5 (<b>B</b>), and the final degree of hydrolysis after digestion (<b>C</b>). Different capital letters (A, B) indicate significant difference (LSD, <span class="html-italic">p</span> &lt; 0.05) between samples heated at pH 7.0 and pH 3.5. Different lower-case letters (a, b) mean significant differences (LSD, <span class="html-italic">p</span> &lt; 0.05) between different phytic acid levels .</p>
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<p>SDS-PAGE of WPI and PA-WPI mixtures containing 1.0% <span class="html-italic">w</span>/<span class="html-italic">w</span> WPI and different PA levels (0, 0.05%, 0.1%, 0.2%, 0.5% <span class="html-italic">w</span>/<span class="html-italic">w</span>) at the end of digestion: heated at pH 7.0 (<b>A</b>), heated at pH 3.5 (<b>B</b>). ND WPI represented the undigested WPI molecule.</p>
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18 pages, 3296 KiB  
Article
Improving the Gelation Properties of Pea Protein Isolates Using Psyllium Husk Powder: Insight into the Underlying Mechanism
by Qiongling Chen, Jiewen Guan, Zhengli Wang, Yu Wang, Xiaowen Wang and Zhenjia Chen
Foods 2024, 13(21), 3413; https://doi.org/10.3390/foods13213413 - 26 Oct 2024
Viewed by 938
Abstract
The industrial application of pea protein is limited due to its poor gelation properties. This study aimed to evaluate the effects of psyllium husk powder (PHP) on improving the rheological, textural, and structural properties of heat-induced pea protein isolate (PPI) gel. Scanning electron [...] Read more.
The industrial application of pea protein is limited due to its poor gelation properties. This study aimed to evaluate the effects of psyllium husk powder (PHP) on improving the rheological, textural, and structural properties of heat-induced pea protein isolate (PPI) gel. Scanning electron microscopy (SEM), intermolecular forces analysis, the quantification of the surface hydrophobicity and free amino groups, and Fourier transform infrared spectroscopy (FTIR) were conducted to reveal the inner structures of PPI-PHP composite gels, conformational changes, and molecular interactions during gelation, thereby clarifying the underlying mechanism. The results showed that moderate levels of PHP (0.5–2.0%) improved the textural properties, water holding capacity (WHC), whiteness, and viscoelasticity of PPI gel in a dose-dependent manner, with the WHC (92.60 ± 1.01%) and hardness (1.19 ± 0.02 N) peaking at 2.0%. PHP significantly increased surface hydrophobicity and enhanced hydrophobic interactions, hydrogen bonding, and electrostatic interactions in PPI-PHP composite gels. Moreover, the electrostatic repulsion between anionic PHP and negatively charged PPI in a neutral environment prevented the rapid and random aggregation of proteins, thereby promoting the formation of a well-organized gel network with more β-sheet structures. However, the self-aggregation of excessive PHP (3.0%) weakened molecular interactions and disrupted the continuity of protein networks, slightly reducing the gel strength. Overall, PHP emerged as an effective natural gel enhancer for the production of pea protein gel products. This study provides technical support for the development of innovative plant protein-based foods with strong gel properties and enriched dietary fiber content. Full article
(This article belongs to the Section Food Physics and (Bio)Chemistry)
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<p>Schematic diagram of molecular interactions between protein and polysaccharide.</p>
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<p>Optical (<b>A</b>) and scanning electron microscopy (<b>B</b>) images of heat-induced PPI-PHP composite gels.</p>
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<p>Changes of the storage modulus (G′), loss modulus (G″), and loss angle (tan δ) in temperature sweep for the dispersions of PPI-PHP mixtures.</p>
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<p>The surface hydrophobicity (<b>A</b>) and the content of free amino groups (<b>B</b>) of heat-induced PPI-PHP composite gels. Different letters at the top of the columns represent significant differences (<span class="html-italic">p</span> &lt; 0.05) in varying amounts of PHP.</p>
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<p>Molecular interaction forces of heat-induced PPI-PHP composite gels. Different letters at the top of the columns represent significant differences (<span class="html-italic">p</span> &lt; 0.05) in varying amounts of PHP.</p>
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<p>Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) pattern of heat-induced PPI-PHP composite gels. CV stands for con-vicilin, V for vicilin, L<sub>α</sub> for legumin acidic subunit, and L<sub>β</sub> for legumin basic subunit.</p>
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<p>Fourier-transform infrared spectra of PPI−PHP composite gels in the range of 400−4000 cm<sup>−1</sup> (<b>A</b>) and the relative proportion of secondary structures (<b>B</b>). Different letters in the columns represent significant differences (<span class="html-italic">p</span> &lt; 0.05) in varying amounts of PHP.</p>
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<p>A schematic diagram of the influence mechanism of PHP on the heat-induced PPI gelation.</p>
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11 pages, 1606 KiB  
Article
Identifications of False Positives Amongst Sodium(I) Cations in Protein Three-Dimensional Structures—A Validation Approach Extendible to Any Alkali or Alkaline Earth Cation and to Any Monoatomic Anion
by Oliviero Carugo
Crystals 2024, 14(11), 918; https://doi.org/10.3390/cryst14110918 - 24 Oct 2024
Viewed by 524
Abstract
Validation of the data deposited in the Protein Data Bank is of the upmost importance, since many other databases, data mining processes, and artificial intelligence tools are strictly grounded on them. The present paper is divided into two parts. The first part describes [...] Read more.
Validation of the data deposited in the Protein Data Bank is of the upmost importance, since many other databases, data mining processes, and artificial intelligence tools are strictly grounded on them. The present paper is divided into two parts. The first part describes and analyzes validation methods that have been designed and used by the structural biology community. Everything began with the Ramachandran plot, with its allowed and disallowed types of backbone conformations, and evolved in different directions, with the inclusion of additional stereochemical features, distributions’ analyses of structural moieties, and scrutiny of structure factor amplitudes across the reciprocal lattice. The second part of the paper is focused on the largely unexplored problem of the high number of false positives amongst the sodium(I) cations observed in protein crystal structures. It is demonstrated that these false positives, which are atoms wrongly identified with sodium, can be identified by using electrostatic considerations and it is anticipated that this approach can be extended to other alkali and alkaline earth cations or to monoatomic anions. In the end, I think a global initiative, accessible to all volunteers and possibly overseen by the Protein Data Bank, should take the place of the numerous web servers and software applications by providing the community with a select few reliable and widely accepted tools. Full article
(This article belongs to the Special Issue Protein Crystallography: The State of the Art)
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<p>(<b>a</b>) Distribution of the Coulombic potential energy values in the 10 subsets extracted from the Protein Data Bank. (<b>b</b>) Average distribution, with standard errors in parentheses, of the Coulombic potential energy.</p>
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<p>Three examples of anomalous sodium cations: NA 504 A of entry 6TDS [<a href="#B60-crystals-14-00918" class="html-bibr">60</a>] (<b>a</b>), NA 202 B of entry 6bgk [<a href="#B61-crystals-14-00918" class="html-bibr">61</a>] (<b>b</b>), and NA 405 A of entry 8by5 [<a href="#B62-crystals-14-00918" class="html-bibr">62</a>] (<b>c</b>). For each example, an image on the left displays atoms within 3.5 Å of the cation. These atoms, along with their distances from the cation, are listed in the center. This information allows for the calculation of the Coulomb potential (V). On the right, the percentage of sodium cations with a Coulomb potential greater than V is shown for each of the ten subsets of the PDB. The average percentage is provided at the conclusion of each example.</p>
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<p>Two examples of sodium cations: NA 403 A of entry 6MR8 (<b>a</b>) and NA 202 A of entry 6ont (<b>b</b>). For each example, an image on the left displays atoms within 3.5 Å of the cation (in italic when present in symmetry related asymmetric units). These atoms, along with their distances from the cation, are listed in the center. This information allows for the calculation of the Coulomb potential (V). On the right, the percentage of sodium cations with a Coulomb potential greater than V is shown for each of the 10 subsets of the PDB. The average percentage is provided at the conclusion of each example.</p>
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15 pages, 5266 KiB  
Article
Cationic Surface Charge Engineering of Recombinant Transthyretin Remarkably Increases the Inhibitory Potency Against Amyloid β-Protein Fibrillogenesis
by Xiaoding Lin, Ting Xu, Wenqi Hou, Xiaoyan Dong and Yan Sun
Molecules 2024, 29(21), 5023; https://doi.org/10.3390/molecules29215023 - 24 Oct 2024
Viewed by 603
Abstract
The deposition of amyloid β-protein (Aβ) in the brain is the main pathogenesis of Alzheimer’s disease (AD). The development of potent inhibitors against Aβ aggregation is one of the effective strategies to combat AD. Endogenous transthyretin (TTR) can inhibit Aβ fibrillization via hydrophobic [...] Read more.
The deposition of amyloid β-protein (Aβ) in the brain is the main pathogenesis of Alzheimer’s disease (AD). The development of potent inhibitors against Aβ aggregation is one of the effective strategies to combat AD. Endogenous transthyretin (TTR) can inhibit Aβ fibrillization via hydrophobic interactions, but its weak inhibitory potency hinders its application in AD therapy. Here, different recombinant TTRs were designed by cationic surface charge engineering. Compared with TTR, all positively charged recombinant TTRs showed enhanced capability in inhibiting Aβ aggregation, especially the recombinant protein obtained by mutating the acidic amino acid in TTR to arginine (TTR-nR) exhibited excellent inhibitory effect. Among them, TTR-7R remarkably increased the inhibitory potency against Aβ, which could effectively inhibit Aβ40 fibrillization at a very low concentration (0.5 μM). In addition, TTR-7R increased cultured cell viability from 62% to 89%, scavenged amyloid plaques in AD nematodes, and prolonged nematode lifespan by 5 d at 2 μM. Thermodynamic studies demonstrated that TTR-7R, enriching in positive charges, presented hydrophobic interactions and enhanced electrostatic interactions with Aβ40, leading to a significantly enhanced inhibitory capacity of TTR-7R. The research provided insights into the development of efficient recombinant protein inhibitors for AD treatment. Full article
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<p>Characterization of TTR and TTR-5R/7R/9R proteins. (<b>a</b>) Nine acidic amino acid residues with the highest SASA in TTR. 1–9 represent acidic amino acid residues of TTR in descending order of SASA. (<b>b</b>) The position of nine acidic amino acid residues with the highest SASA in TTR (marked in red). (<b>c</b>) Tryptophan fluorescence. (<b>d</b>) ANS fluorescence. (<b>e</b>) CD spectra. (<b>f</b>) Secondary structure content. The concentrations of the four proteins were 10 μM.</p>
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<p>Inhibition of Aβ<sub>40</sub> fibrillization by TTR-7R. (<b>a</b>) ThT fluorescence kinetic assay of Aβ<sub>40</sub> monomers incubated with TTR-7R. (<b>b</b>) Normalized ThT fluorescence intensity of Aβ<sub>40</sub> incubated with different inhibitors for 100 h. *** <span class="html-italic">p</span> &lt; 0.001, compared to the control group. (<b>c</b>) AFM images of Aβ<sub>40</sub> incubated with different concentrations of TTR-7R for 100 h. Scale bar, 1 μm. (<b>d</b>) Surface coverage of Aβ<sub>40</sub> incubated without or with various agents (0.5–2 μM) for 100 h. (<b>e</b>) Far-UV circular dichroism spectra of Aβ<sub>40</sub> incubated with different agents (2 μM) at 100 h. (<b>f</b>) Secondary structure content of Aβ<sub>40</sub> alone or with different inhibitors. The inhibitor concentration was 2 μM. The Aβ<sub>40</sub> concentration was 25 μM in all experiments.</p>
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<p>Calorimetric data for titration of (<b>a</b>,<b>b</b>) TTR, (<b>c</b>,<b>d</b>) TTR-5R, (<b>e</b>,<b>f</b>) TTR-7R, and (<b>g</b>,<b>h</b>) TTR-9R into Aβ<sub>40</sub> (20 μM) at 25 °C. The concentrations of TTR and TTR-5R/7R/9R were all 40 μM.</p>
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<p>Structures of (<b>a</b>) TTR monomer (PDB ID: 1DVQ), (<b>b</b>) TTR-5R monomer, (<b>c</b>) TTR-7R monomer, and (<b>d</b>) TTR-9R monomer.</p>
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<p>SH-SY5Y cell assays with various agents. (<b>a</b>) Viability of SH-SY5Y cells incubated with TTR and TTR-5R/7R/9R. (<b>b</b>) Detoxification of Aβ<sub>40</sub>-induced SH-SY5Y cytotoxicity. The viability of cells treated with PBS buffer only was defined as 100%. The data are presented as mean ± SD (<span class="html-italic">n</span> = 6). ### <span class="html-italic">p</span> &lt; 0.001, compared to the control group. Values of <span class="html-italic">p</span> &lt; 0.01 and <span class="html-italic">p</span> &lt; 0.001 for the pairs of data sets are marked with ** and ***, respectively. (<b>c</b>) Observation of the protection of TTR-7R on SH-SY5Y cells by FDA/PI double staining. FDA-positive cells present viable cells, while PI-positive cells present dead cells. Scale bar, 100 μm. The aged Aβ<sub>40</sub> concentration was 25 μM.</p>
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<p>In vivo assays with nematodes. Representative fluorescence images of Aβ deposits in (<b>a</b>) N2 and (<b>b</b>) CL2006 nematodes co-stained with ThT. Fluorescence images of inhibiting Aβ deposits in CL2006 nematodes with (<b>c</b>) TTR, (<b>d</b>) TTR-5R, (<b>e</b>) TTR-7R, or (<b>f</b>) TTR-9R. The white arrow in the figures represents Aβ deposits in <span class="html-italic">C. elegans</span>. Scale bar, 50 μm. (<b>g</b>) Survival curves of N2 or CL2006 strains after co-incubation with TTR and TTR-5R/7R/9R. The concentrations of TTR and TTR-5R/7R/9R were all 2 μM.</p>
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<p>Schematic illustration of the design of recombinant TTR for the enhanced Aβ inhibition.</p>
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17 pages, 802 KiB  
Systematic Review
Effectiveness of Lyoprotectants in Protein Stabilization During Lyophilization
by Vinoothini Karunnanithy, Nur Hazirah Binti Abdul Rahman, Nur Atiqah Haizum Abdullah, Mh Busra Fauzi, Yogeswaran Lokanathan, Angela Ng Min Hwei and Manira Maarof
Pharmaceutics 2024, 16(10), 1346; https://doi.org/10.3390/pharmaceutics16101346 - 21 Oct 2024
Viewed by 941
Abstract
Background: Proteins are commonly used in the healthcare industry to treat various health conditions, and most proteins are sensitive to physical and chemical changes. Lyophilization, also known as freeze-drying, involves sublimating water in the form of ice from a substance at low pressure, [...] Read more.
Background: Proteins are commonly used in the healthcare industry to treat various health conditions, and most proteins are sensitive to physical and chemical changes. Lyophilization, also known as freeze-drying, involves sublimating water in the form of ice from a substance at low pressure, forming a freeze-dried powder that increases its shelf life. Extreme pressure and varying temperatures in the freeze-drying process may damage the protein’s structural integrity. Lyoprotectants are commonly used to protect protein conformations. It is important to choose a suitable lyoprotectant to ensure optimal effectiveness. Method: Twenty articles screened from Scopus, Web of Science, and PubMed were included in this review that discussed potential lyoprotectants and their effectiveness with different protein models. Results: Lyoprotectants were categorized into sugars, polyols, surfactants, and amino acids. Lyoprotectants can exhibit significant protective effects towards proteins, either singularly or in combination with another lyoprotectant. They exert various interactions with the protein to stabilize it, such as hydrogen bonding, hydrophobic interactions, electrostatic interactions, and osmoprotection. Conclusions: This review concludes that disaccharides are the most effective lyoprotectants, while other groups of lyoprotectants are best used in combination with other lyoprotectants. Full article
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<p>The PRISMA flow diagram of study selection.</p>
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15 pages, 1910 KiB  
Article
Effects of Chitosan and N-Succinyl Chitosan on Metabolic Disorders Caused by Oral Administration of Olanzapine in Mice
by Balzhima Shagdarova, Viktoria Melnikova, Valentina Kostenko, Mariya Konovalova, Vsevolod Zhuikov, Valery Varlamov and Elena Svirshchevskaya
Biomedicines 2024, 12(10), 2358; https://doi.org/10.3390/biomedicines12102358 - 16 Oct 2024
Viewed by 758
Abstract
Background: The issue of human mental health is gaining more and more attention nowadays. However, most mental disorders are treated with antipsychotic drugs that cause weight gain and metabolic disorders, which include olanzapine (OLZ). The search for and development of natural compounds for [...] Read more.
Background: The issue of human mental health is gaining more and more attention nowadays. However, most mental disorders are treated with antipsychotic drugs that cause weight gain and metabolic disorders, which include olanzapine (OLZ). The search for and development of natural compounds for the prevention of obesity when taking antipsychotic drugs is an urgent task. The biopolymer chitosan (Chi) and its derivatives have lipid-lowering and anti-diabetic properties, which makes them potential therapeutic substances for use in the treatment of metabolic disorders. The purpose of this work was to analyze the effect of the natural biopolymer Chi, its derivative N-succinyl chitosan (SuChi), and Orlistat (ORL) as a control on the effects caused by the intake of OLZ in a mouse model. Methods: Mice were fed with pearl barley porridge mixed with OLZ or combinations OLZ + Chi, OLZ + SuChi, or OLZ + ORL for 2 months. The weight, lipid profile, blood chemokines, expression of genes associated with appetite regulation, and behavior of the mice were analyzed in dynamics. Results: For the first time, data were obtained on the effects of Chi and SuChi on metabolic changes during the co-administration of antipsychotics. Oral OLZ increased body weight, food and water intake, and glucose, triglyceride, and cholesterol levels in blood. ORL and SuChi better normalized lipid metabolism than Chi, decreasing triglyceride and cholesterol levels. OLZ decreased the production of all chemokines tested at the 4th week of treatment and increased CXCL1, CXCL13, and CCL22 chemokine levels at the 7th week. All of the supplements corrected the level of CXCL1, CXCL13, and CCL22 chemokines but did not recover suppressed chemokines. SuChi and ORL stimulated the expression of satiety associated proopiomelanocortin (POMC) and suppressed the appetite-stimulating Agouti-related protein (AgRP) genes. All supplements improved the locomotion of mice. Conclusions: Taken collectively, we found that SuChi more than Chi possessed an activity close to that of ORL, preventing metabolic disorders in mice fed with OLZ. As OLZ carries positive charge and SuChi is negatively charged, we hypothesized that SuChi’s protective effect can be explained by electrostatic interaction between OLZ byproducts and SuChi in the gastrointestinal tract. Full article
(This article belongs to the Special Issue Advanced Research in Metabolic Syndrome)
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<p>Stack of <sup>1</sup>H NMR spectra of (<b>a</b>) chitosan and (<b>b</b>) <span class="html-italic">N</span>-succinyl chitosan.</p>
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<p>Effects of olanzapine (OLZ) and food supplements on body weight, glucose level, and water and food intake. Mice were fed with pearl barley porridge mixed with dry OLZ only or supplemented with chitosan (Chi), <span class="html-italic">N</span>-succinyl chitosan (SuChi), or orlistat (ORL). (<b>a</b>) Body weight was calculated as a ratio to the weight at the start of the experiment for each mouse and averaged. (<b>b</b>) Glucose level was estimated in the blood at the 4th and 7th weeks of the experiment. (<b>c</b>,<b>d</b>) Water (<b>c</b>) and food (<b>d</b>) intake were measured once a week from the 1st to 4th (4 weeks) and 5th to 8th weeks (7 weeks) per group of five mice and averaged. Data are shown as the mean ± SEM. Significant differences (<span class="html-italic">p</span> &lt; 0.05, Mann–Whitney) are shown by brackets.</p>
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<p>Effects of olanzapine (OLZ) and food supplements on lipid metabolism. Triglyceride (<b>a</b>), total cholesterol (<b>b</b>), high density lipoproteins (HDL) (<b>c</b>), low density lipoproteins (LDL) (<b>d</b>), and LDL to HDL ratios at the 4th (<b>e</b>) and 7th (<b>f</b>) weeks of the treatment in the blood of mice fed with dry OLZ only or supplemented with chitosan (Chi), <span class="html-italic">N</span>-succinyl chitosan (SuChi), or orlistat (ORL). Data are shown as mean ± SEM. Significant differences (<span class="html-italic">p</span> &lt; 0.05, Mann–Whitney) are shown by brackets.</p>
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<p>Effects of olanzapine (OLZ) and food supplements on blood chemokines. (<b>a</b>–<b>c</b>) Intact blood homeostatic chemokine concentrations (<b>a</b>) and the effect of OLZ and the combination of OLZ with chitosan (Chi), <span class="html-italic">N</span>-succinyl chitosan (SuChi), or orlistat (ORL) at week 4 (<b>b</b>) or 7 (<b>c</b>). (<b>d</b>–<b>f</b>) Intact blood inducible chemokine concentrations (<b>d</b>) and OLZ and OLZ plus the supplements at weeks 4 (<b>e</b>) and 7 (<b>f</b>) of the treatment. Data are shown and mean ± SEM (<b>a</b>) or as the ratios of experimental sample concentrations to the control intact blood ones. Significant differences (<span class="html-italic">p</span> &lt; 0.05, Mann–Whitney) are shown by brackets only for the increased by OLZ chemokines.</p>
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<p>Expression of appetite-associated genes in the hypothalamus of olanzapine-treated mice. (<b>a</b>) Scheme of hypothalamic isolation. Incisions were made rostrally (r) at the level of the optic chiasma and caudally (c) along the pituitary pedicle, dorsally along the border of the third ventricle (3v). The drawing was created using the BioRender program (<a href="https://app.biorender.com" target="_blank">https://app.biorender.com</a>). (<b>b</b>–<b>f</b>) An analysis of gene expression associated with metabolic changes in the hypothalamus of mice treated orally with olanzapine (OLZ) alone and in combination with orlistat (OLZ-ORL), chitosan (OLZ-Chi), or <span class="html-italic">N</span>-succinyl chitosan (OLZ-SuChi). Data are shown as the relative expression calculated as DeltaCt<sub>experiment</sub>/DeltaCt<sub>control</sub>, where experiment corresponds to OLZ-treated samples and control corresponds to intact mice. Significant differences (<span class="html-italic">p</span> &lt; 0.05, Mann–Whitney) are shown by brackets.</p>
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<p>Effect of olanzapine (OLZ) and food supplementation on the locomotion and rearings of mice in an open field test (OFT). Mice fed with OLZ alone or supplemented with chitosan (Chi), <span class="html-italic">N</span>-succinyl chitosan (SuChi), or orlistat (ORL) were run for 4 min in an OFT (3 min in the light and the last min in the dark, shown by the gray bar). (<b>a</b>,<b>b</b>) Number of sectors crossed at the 4th (<b>a</b>) and 7th (<b>b</b>) weeks. (<b>c</b>,<b>d</b>) Total number of rearing at the 4th (<b>c</b>) and 7th (<b>d</b>) weeks. Significant differences (<span class="html-italic">p</span> &lt; 0.05, Mann–Whitney) are shown by brackets (* in (<b>a</b>)).</p>
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18 pages, 5253 KiB  
Article
Targeted PHA Microsphere-Loaded Triple-Drug System with Sustained Drug Release for Synergistic Chemotherapy and Gene Therapy
by Shuo Wang, Chao Zhang, Huandi Liu, Xueyu Fan, Shuangqing Fu, Wei Li and Honglei Zhang
Nanomaterials 2024, 14(20), 1657; https://doi.org/10.3390/nano14201657 - 16 Oct 2024
Viewed by 884
Abstract
The combination of paclitaxel (PTX) with other chemotherapy drugs (e.g., gemcitabine, GEM) or genetic drugs (e.g., siRNA) has been shown to enhance therapeutic efficacy against tumors, reduce individual drug dosages, and prevent drug resistance associated with single-drug treatments. However, the varying solubility of [...] Read more.
The combination of paclitaxel (PTX) with other chemotherapy drugs (e.g., gemcitabine, GEM) or genetic drugs (e.g., siRNA) has been shown to enhance therapeutic efficacy against tumors, reduce individual drug dosages, and prevent drug resistance associated with single-drug treatments. However, the varying solubility of chemotherapy drugs and genetic drugs presents a challenge in co-delivering these agents. In this study, nanoparticles loaded with PTX were prepared using the biodegradable polymer material poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBHHx). These nanoparticles were surface-modified with target proteins (Affibody molecules) and RALA cationic peptides to create core-shell structured microspheres with targeted and cationic functionalization. A three-drug co-delivery system (PTX@PHBHHx-ARP/siRNAGEM) were developed by electrostatically adsorbing siRNA chains containing GEM onto the microsphere surface. The encapsulation efficiency of PTX in the nanodrug was found to be 81.02%, with a drug loading of 5.09%. The chemogene adsorption capacity of siRNAGEM was determined to be 97.3%. Morphological and size characterization of the nanodrug revealed that PTX@PHBHHx-ARP/siRNAGEM is a rough-surfaced microsphere with a particle size of approximately 150 nm. This nanodrug exhibited targeting capabilities toward BT474 cells with HER2 overexpression while showing limited targeting ability toward MCF-7 cells with low HER2 expression. Results from the MTT assay demonstrated that PTX@PHBHHx-ARP/siRNAGEM exhibits high cytotoxicity and excellent combination therapy efficacy compared to physically mixed PTX/GEM/siRNA. Additionally, Western blot analysis confirmed that siRNA-mediated reduction of Bcl-2 expression significantly enhanced cell apoptosis mediated by PTX or GEM in tumor cells, thereby increasing cell sensitivity to PTX and GEM. This study presents a novel targeted nanosystem for the co-delivery of chemotherapy drugs and genetic drugs. Full article
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<p>SEM (<b>a</b>), TEM (<b>b</b>), and FTIR (<b>c</b>) analysis.</p>
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<p>Release curves of PTX and siRNA<sub>GEM</sub> in PTX@PHBHHx-ARP/siRNA<sub>GEM</sub>. (<b>a</b>) The PTX drug release curve. (<b>b</b>) The release curve of siRNA<sub>GEM</sub>.</p>
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<p>Drug uptake by BT474 and MCF-7 cells. (<b>a</b>) CLSM analysis of BT474 cells treated with PTX@PHBHHx-ARP/siRNA<sub>GEM</sub> for different times. Panel (<b>b</b>) CLSM analysis of MCF-7 cells treated with PTX@PHBHHx-ARP/siRNA<sub>GEM</sub> for different times. Scale bar: 20 μm.</p>
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<p>Survival of BT474 and MCF-7 cells treated with PTX, GEM, physical mixture of PTX/GEM/siRNA and PTX@PHBHHx-ARP/siRNA<sub>GEM</sub>. (<b>a</b>) The survival rates of the two cell types after 48 h incubation with PTX monotherapy. (<b>b</b>) The survival rates of the two cell types after 48 h incubation with GEM monotherapy. (<b>c</b>) The survival rates of the two cell types after 48 h of incubation with the physical mixture of PTX/GEM/siRNA. (<b>d</b>) The survival rates of the two cell types after 48 h incubation with PTX@PHBHHx-ARP/siRNA<sub>GEM</sub>.</p>
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<p>Western blot of Bcl-2 in (<b>a</b>) BT474 cells and (<b>b</b>) MCF-7 cells treated with free PTX, GEM, PTX/GEM/siRNA, and PTX@PHBHHx-ARP/siRNA<sub>GEM</sub>, respectively. (<b>c</b>) Significance analysis of Bcl-2 in BT474 cells. (<b>d</b>) Significance analysis of Bcl-2 in MCF-7 cells. Statistical analysis: * <span class="html-italic">p</span> &lt; 0.05, **** <span class="html-italic">p</span> &lt; 0.0001 and ns (No significant difference).</p>
Full article ">Figure 6
<p>Scatter plots of apoptosis in (<b>a</b>) MCF-7 cells and (<b>b</b>) BT474 cells treated with free PTX, GEM, PTX/GEM/siRNA, and PTX@PHBHHx-ARP/siRNA<sub>GEM</sub>, respectively.</p>
Full article ">Scheme 1
<p>The assembly process of PTX@PHBHHx-ARP/siRNA<sub>GEM</sub> and their synergistic cancer therapy.</p>
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