Structural Features Research Articles

Therapeutic Peptides, Proteins and their Nanostructures for Drug Delivery and Precision Medicine.

Peptide and protein nanostructures with tunable structural features, multifunctionality, biocompatibility and biomolecular recognition capacity enable development of efficient targeted drug delivery tools for precision medicine applications. In this review article, we present various techniques employed for the synthesis and self-assembly of peptides and proteins into nanostructures. We discuss design strategies utilized to enhance their stability, drug-loading capacity, and controlled release properties, in addition to the mechanisms by which peptide nanostructures interact with target cells, including receptor-mediated endocytosis and cell-penetrating capabilities. We also explore the potential of peptide and protein nanostructures for precision medicine, focusing on applications in personalized therapies and disease-specific targeting for diagnostics and therapeutics in diseases such as cancer.

Predicting the tensile and compressive modulus of electrospun fiber mat‐reinforced hydrogels using the Halpin–Tsai equations

AbstractThe reinforcement of mechanically‐weak hydrogels to yield composites with increased stiffness, strength, or toughness is a well‐established approach. In particular, introducing electrospun nanofibers into hydrogels is a common strategy for biomedical applications, as the resulting hierarchical structure mimics biology and allows for control over fiber diameter and alignment and tuning of mechanical properties. However, further study of the link between the constituent materials and the mechanical properties of the composite is uncommon. One potential model to understand the mechanical properties of fiber‐reinforced hydrogels involves the Halpin–Tsai equations, which relate the modulus values of the fibers and hydrogel matrix and the fiber volume fraction, to the modulus of the composite. To assess the application of this model to fiber‐reinforced hydrogels, predicted values were compared with experimental values from mechanical testing of a poly(ethylene glycol) (PEG) matrix reinforced with an electrospun polycaprolactone (PCL) fiber mat. Although the equations described these systems well in tension, providing a facile approach to identify a fiber volume fraction that will achieve a desired modulus, the Halpin–Tsai approach was less successful under compression. This study motivates additional investigation of the role of structural features of hydrogel composites in determining mechanical properties to enable design of materials for specific applications.

Colloidal Photonic Composites with a Long-Range Order by Hot-Pressing Polymer Brush-Grafted Silica Colloids.

Colloidal photonic composites (CPCs) are unique optical materials that combine flexible and responsive polymers with colloidal photonic crystals, and they have promising applications in colorful displays, optical anticounterfeiting, and visual sensors. However, conventional self-assembly strategies for constructing CPCs via solvent evaporation have faced limitations due to the meticulous regulation required during the evaporation process and typically long preparation durations. Here, we present an external force method to achieve a long-range ordered arrangement in CPCs by hot-pressing poly(2-[[(butylamino)carbonyl]oxy]ethyl acrylate (PBCOE)) brush-grafted silica colloidal particles (SiO2-g-PBCOE). We show that the hot-pressing conditions (i.e., temperature and pressure) and the silica volume fraction (φsilica) of the SiO2-g-PBCOE colloidal particles play crucial roles in determining their ordering and optical properties. By optimization of the hot-pressing temperature up to 100 °C and pressure of 5 MPa, a long-range ordered arrangement of SiO2-g-PBCOE colloidal particles with a φsilica of 20.3% can be achieved. For the effect of structural features, our findings reveal that SiO2-g-PBCOE colloidal particles featuring a higher φsilica are more prone to obtain a long-range ordered arrangement compared to a lower φsilica under hot-pressing conditions at relatively low temperature and pressure (50 °C and 5 MPa), which is mainly attributed to the chain entanglement and hydrogen bonding interactions induced by grafted longer polymer brushes, leading to additional energy inputs and weakening the ordering. Significantly, the critical φsilica (φc) of SiO2-g-PBCOE colloidal particles is discerned, strongly influencing the optical properties of the hot-pressed films. Specifically, a hot-pressed SiO2-g-PBCOE film with a critical φsilica of 29.3% displays enhanced optical properties characterized by intensified reflection peaks, narrowed full width at half-maximum (FWHM), and brilliant structural colors. Notably, in this work, we reveal the mechanism of hot-pressing-driven core-shell colloidal particle ordering and the key factors affecting the ordering of colloidal particles, i.e., chain entanglement and hydrogen-bonding interactions, which play a crucial role in obtaining CPCs with controllable structures. Moreover, angle-dependent structural color is observed in the hot-pressed SiO2-g-PBCOE film with a φsilica content of 29.3% due to the unique attributes of the highly ordered arrangement, while the films exhibit mechanochromic properties due to chain entanglement and hydrogen bonding interactions. This work provides valuable insights into the rapid construction of highly ordered CPCs and establishes a solid foundation for external force-assisted ordering of colloidal particles.

Prediction of Metal-Organic Frameworks with Phase Transition via Machine Learning.

Metal-organic frameworks (MOFs) possess a virtually unlimited number of potential structures. Although the latter enables an efficient route to control the structure-related functional properties of MOFs, it still complicates the prediction and searching for an optimal structure for specific application. Next to prediction of the MOFs for gas sorption/separation and catalysis via machine learning (ML), we report on ML to find MOFs demonstrating a phase transition (PT). On the basis of an available QMOF database (7463 frameworks), we create and train the autoencoder followed by training the classifier of MOFs from a unique database with experimentally confirmed PT. This makes it possible to identify MOFs with a high potential for PT and evaluate the most likely stimulus for it (guest molecules or temperature/pressure). The formed list of available MOFs for PT allows us to discuss their structural features and opens an opportunity to search for phase change MOFs for diverse physical/chemical application.

Agencement of onlife and phygital: smart tech–enabled value co-creation practices

PurposeIn this article, we reflect on how smart technology is transforming service research discourses about service innovation and value co-creation. We adopt the concept of technology smartness’ to refer to the ability of technology to sense, adapt and learn from interactions. Accordingly, we seek to address how smart technologies (i.e. cognitive and distributed technology) can be powerful resources, capable of innovating in relation to actors’ agency, the structure of the service ecosystem and value co-creation practices.Design/methodology/approachThis conceptual article integrates evidence from the existing theories with illustrative examples to advance research on service innovation and value co-creation.FindingsThrough the performative utterances of new tech words, such as onlife and materiality, this article identifies the emergence of innovative forms of agency and structure. Onlife agency entails automated, relational and performative forms, which provide for new decision-making capabilities and expanded opportunities to co-create value. Phygital materiality pertains to new structural features, comprised of new resources and contexts that have distinctive intelligence, autonomy and performativity. The dialectic between onlife agency and phygital materiality (structure) lies in the agencement of smart tech–enabled value co-creation practices based on the notion of becoming that involves not only resources but also actors and contexts.Originality/valueThis paper proposes a novel conceptual framework that advances a tech-based ecology for service ecosystems, in which value co-creation is enacted by the smartness of technology, which emerges through systemic and performative intra-actions between actors (onlife agency), resources and contexts (phygital materiality and structure).

Topology Controlled All-(Meth)acrylic Thermoplastic Elastomers by Multi-Functional Lewis Pairs-Mediated Polymerization.

It remains challenging to synthesize all-(meth)acrylic triblock thermoplastic elastomers (TPEs), due to the drastically different reactivities between the acrylates and methacrylates and inevitable occurrence of side reactions during polymerization of acrylates. By taking advantage of the easy structural modulation features of N-heterocyclic olefins (NHOs), we design and synthesize strong nucleophilic tetraphenylethylene-based NHOs varying in the number (i.e. mono-, dual- and tetra-) of initiating functional groups. Its combination with bulky organoaluminum [iBuAl(BHT)2] (BHT=bis(2,6-di-tBu-4-methylphenoxy)) constructs Lewis pair (LP) to realize the living polymerization of both acrylates and methacrylates, furnishing polyacrylates with ultrahigh molecular weight (Mn up to 2174 kg ⋅ mol-1) within 4 min. Moreover, these NHO-based LPs enable us to not only realize the control over the polymers' topology (i.e. linear and star), but also achieve triblock star copolymers in one-step manner. Mechanical studies reveal that the star triblock TPEs exhibit better mechanical properties (elongation at break up to 1863 % and tensile strength up to 19.1 MPa) in comparison with the linear analogs. Moreover, the presence of tetraphenylethylene group in the NHOs entitled the triblock TPEs with excellent AIE properties in both solution and solid state.

Comment on "Three New Dimers and Two Monomers of Phenolic Amides from the Fruits of Lycium barbarum and Their Antioxidant Activities".

This Comment critically addresses the article by Gao et al. (Gao, K., et al. J. Agric. Food Chem. 2015, 63, 1067-1075), providing the structural elucidation of three phenolamide dimers (neolignanamides) from the fruits of Lycium barbarum. A more recent article published by Chen et al. (Chen, H., et al. J. Agric. Food Chem. 2023, 71, 11080-11093) incorporates these structures into further research on the bioactivity of these compounds. Although the analytical techniques used by Gao et al. are adequate, in our opinion, the nuclear magnetic resonance (NMR) spectroscopic data have not been interpreted correctly, resulting in incorrect structures for three neolignanamides from the fruits of L. barbarum. In this Comment, an alternative interpretation of the NMR spectroscopic data and the corresponding structures are proposed. The proposed structures feature linkage types that are much more common for neolignanamides than the linkage types in the originally reported structures of these compounds.

Cortical cell assemblies and their underlying connectivity: An in silico study.

Recent developments in experimental techniques have enabled simultaneous recordings from thousands of neurons, enabling the study of functional cell assemblies. However, determining the patterns of synaptic connectivity giving rise to these assemblies remains challenging. To address this, we developed a complementary, simulation-based approach, using a detailed, large-scale cortical network model. Using a combination of established methods we detected functional cell assemblies from the stimulus-evoked spiking activity of 186,665 neurons. We studied how the structure of synaptic connectivity underlies assembly composition, quantifying the effects of thalamic innervation, recurrent connectivity, and the spatial arrangement of synapses on dendrites. We determined that these features reduce up to 30%, 22%, and 10% of the uncertainty of a neuron belonging to an assembly. The detected assemblies were activated in a stimulus-specific sequence and were grouped based on their position in the sequence. We found that the different groups were affected to different degrees by the structural features we considered. Additionally, connectivity was more predictive of assembly membership if its direction aligned with the temporal order of assembly activation, if it originated from strongly interconnected populations, and if synapses clustered on dendritic branches. In summary, reversing Hebb's postulate, we showed how cells that are wired together, fire together, quantifying how connectivity patterns interact to shape the emergence of assemblies. This includes a qualitative aspect of connectivity: not just the amount, but also the local structure matters; from the subcellular level in the form of dendritic clustering to the presence of specific network motifs.

Evaluation of the Potential for CO2 Storage and Saline Water Displacement in Huaiyin Sag, Subei Basin, East China

CO2 geological storage combined with deep saline water recovery technology (CO2-EWR) is one of the most effective ways to reduce carbon emissions. Due to the complex structural features, it is difficult to use CO2-EWR technology in Huaiyin Sag, Subei basin, East China. In this study, the multi-source information superposition evaluation technology of GIS was utilized for the selection of CO2 storage sites and water displacement potential target areas in this area, which mainly focused on the sandstone reservoirs of Cretaceous Pukou Formation. Based on the results, a three-dimensional injection–extraction model was established. Various scenarios with different production/injection well ratios (PIR) were simulated. Research has shown that the suitability of the surrounding site of Huaiyin Power Plant can be divided into two levels: relatively suitable and generally suitable; the area in the generally suitable level accounts for more than 80%. At a PIR of 1, CO2 is distributed asymmetrically, whereas at PIRs of 2 or 4, CO2 is distributed symmetrically. When the number of production wells is constant, a higher injection rate results in a faster expansion rate of the CO2 plume. This means that the time taken for the CO2 plume to reach the production wells is shorter. Reservoir pressure increases rapidly after more than 60 years of CO2 injection at lower PIR values, while at higher PIRs, reservoir pressure eventually stabilizes. Higher PIR values correspond to higher gas saturation, indicating a greater capacity for CO2 sequestration with more producing wells. When PIR = 4, the total CO2 injection increased by 55.73% compared to PIR = 1. However, the extraction of saline decreases with an increase in the number of producing wells, resulting in a decrease in replacement efficiency. This study provides a theoretical basis and technical support for the implementation of large-scale CO2-EWR engineering and technology demonstration in this region.

Physicochemical, Structural, and Functional Properties of Fructans from Single-Clove Garlic and Multiclove Garlic: A Comparison.

This study aimed to compare the structural features and functional properties of polysaccharides from single-clove garlic (SGPs) and multiclove garlic (MGPs) and to establish their structure-function relationships. Both SGPs and MGPs were identified as fructans consisting mainly of →1)-β-d-Fruf (2→ and →6)-β-d-Fruf (2→ residues but differed in average molecular weights (6.76 and 5.40 kDa, respectively). They shared similar thermodynamic properties, X-ray diffraction patterns, and high gastrointestinal digestive stability. These two purified fructans could dose-dependently scavenge free radicals, reduce oxidized metals, and effectively alleviate metronidazole-induced oxidative stress and CuSO4-induced inflammation in zebrafish via inhibiting the overexpression of inflammation-related proteins and cytokines. SGPs showed lower free radical scavenging activity in vitro than MGPs but higher antioxidant and anti-inflammatory activities in vivo. Taken together, the molecular weight was the main structural difference between the two garlic fructans of different varieties, which is a potential reason for their differences in biological activities.

Tuning of the flat band and its impact on superconductivity in Mo5Si3−xPx

The superconductivity in systems containing dispersionless (flat) bands is seemingly paradoxical, as traditional Bardeen-Cooper-Schrieffer theory requires an infinite enhancement of the carrier masses. However, the combination of flat and steep (dispersive) bands within the multiple band scenario might boost superconducting responses, potentially explaining high-temperature superconductivity in cuprates and metal hydrides. Here, we report on the magnetic penetration depths, the upper critical field, and the specific heat measurements, together with the first-principles calculations for the Mo5Si3−xPx superconducting family. The band structure features a flat band that gradually approaches the Fermi level as a function of phosphorus doping x, reaching the Fermi level at x ≃ 1.3. This leads to an abrupt change in nearly all superconducting quantities. The superfluid density data placed on the ’Uemura plot‘ results in two separated branches, thus indicating that the emergence of a flat band enhances correlations between conducting electrons.

Electron-translucency and partial defects of synaptic basal lamina in the electrocyte synapse of an electric ray (Narke japonica) in 3D embedment-free section electron microscopy.

The synaptic basal lamina of the electrocytes was disclosed to be electron-translucent to some extent when viewed in an en-face direction in embedment-free section transmission electron microscopy (EFS-TEM), and synaptic vesicles located close to the presynaptic membrane were seen through the synaptic basal lamina together with the presynaptic and postsynaptic membranes. This feature of translucency has the potential to analyze possible spatial interrelations in situ between bioactive molecules in the synaptic basal lamina and the synaptic vesicles in further studies. The synaptic basal lamina, appearing as an electron-dense line sandwiched by two parallel lines representing the presynaptic and postsynaptic membranes in ultrathin sections cut right to the synaptic junctional plane in conventional TEM, was not fully continuous but randomly intermittent along its trajectory. Compatible with the intermittent line appearance, the en-face 3D view in embedment-free section TEM revealed for the first time partial irregular defects of the synaptic basal lamina. Considering the known functional significance of several molecules contained in the synaptic basal lamina in the maintenance and exertion of the synapse, its partial defects may not represent its rigid structural features, but its immature structure under remodeling or its dynamic changes in consistency such as the sol/gel transition, whose validity needs further examination. RESEARCH HIGHLIGHTS: In embedment-free section TEM, a 3D en-face view of synaptic basal lamina in situ is reliably possible. The basal lamina en-face is electron-translucent, which makes it possible to analyze spatial interrelation between pre- and post-synaptic components. Partial irregular defects in the basal lamina are revealed in Torpedo electrocytes, suggesting its remodeling or dynamic changes in consistency.

Backbone and Side Group Interchain Correlations Govern Wide-Angle X-ray Scattering of Poly(3-hexylthiophene).

Identifying the origin of scattering from polymer materials is crucial to infer structural features that can relate to functional properties. Here, we use our recently developed virtual-site coarse graining to accelerate atomistic simulations and show how various molecular features govern wide-angle X-ray scattering from a conjugated polymer, poly(3-hexylthiophene) (P3HT). The efficient molecular dynamics simulations can represent the structure and capture the emergence of crystalline order from amorphous melts upon cooling while retaining atomistic details of chain configurations. The scattering extracted from simulations shows good agreement with wide-angle X-ray scattering experiments. Amorphous P3HT exhibits broad scattering peaks: a high-q peak from interchain side-group correlations and a low-q peak from interchain backbone-backbone correlations. During amorphous to crystalline phase transitions, the distance between backbones along the side-group direction increases because of lack of interdigitation in the crystalline phase. Scattering from π-π stacking emerges only after crystallization takes place. Intrachain correlations contribute negligibly to the scattering from the amorphous and crystalline phases.

First report on chemometrics-driven multilayered lead prioritization in addressing oxysterol-mediated overexpression of G protein-coupled receptor 183.

Contemporary research has convincingly demonstrated that upregulation of G protein-coupled receptor 183 (GPR183), orchestrated by its endogenous agonist, 7α,25-dihydroxyxcholesterol (7α,25-OHC), leads to the development of cancer, diabetes, multiple sclerosis, infectious, and inflammatory diseases. A recent study unveiled the cryo-EM structure of 7α,25-OHC bound GPR183 complex, presenting an untapped opportunity for computational exploration of potential GPR183 inhibitors, which served as our inspiration for the current work. A predictive and validated two-dimensional QSAR model using genetic algorithm (GA) and multiple linear regression (MLR) on experimental GPR183 inhibition data was developed. QSAR study highlighted that structural features like dissimilar electronegative atoms, quaternary carbon atoms, and CH2RX fragment (X: heteroatoms) influence positively, while the existence of oxygen atoms with a topological separation of 3, negatively affects GPR183 inhibitory activity. Post assessment of true external set prediction capability, the MLR model was deployed to screen 12,449 DrugBank compounds, followed by a screening pipeline involving molecular docking, druglikeness, ADMET, protein-ligand stability assessment using deep learning algorithm, molecular dynamics, and molecular mechanics. The current findings strongly evidenced DB05790 as a potential lead for prospective interference of oxysterol-mediated GPR183 overexpression, warranting further in vitro and in vivo validation.

Thermophilic β-mannanases from bacteria: production, resources, structural features and bioengineering strategies.

β-mannanases are pivotal enzymes that cleave the mannan backbone to release short chain mannooligosaccharides, which have tremendous biotechnological applications including food/feed, prebiotics and biofuel production. Due to the high temperature conditions in many industrial applications, thermophilic mannanases seem to have great potential to overcome the thermal impediments. Thus, structural analysis of thermostable β-mannanases is extremely important, as it could open up new avenues for genetic engineering, and protein engineering of these enzymes with enhanced properties and catalytic efficiencies. Under this scope, the present review provides a state-of-the-art discussion on the thermophilic β-mannanases from bacterial origin, their production, engineering and structural characterization. It covers broad insights into various molecular biology techniques such as gene mutagenesis, heterologous gene expression, and protein engineering, that are employed to improve the catalytic efficiency and thermostability of bacterial mannanases for potential industrial applications. Further, the bottlenecks associated with mannanase production and process optimization are also discussed. Finally, future research related to bioengineering of mannanases with novel protein expression systems for commercial applications are also elaborated.

On the Specificity of Nanozymes: A Perspective

Comprehensive SummaryWe have compiled eight promising strategies for enhancing the specificity and selectivity of nanozymes, as depicted in the comprehensive summary above. Enzymes exhibit intricate and sophisticated structures, including substrate channels and active sites, which can inform the design of nanozymes. Replication of these structural features and the application of facet engineering/doping techniques can significantly enhance the catalytic specificity of nanozymes. Alternatively, the use of Molecularly Imprinted Polymers (MIPs) to coat nanozymes represents an effective approach to impart substrate specificity. Furthermore, several straightforward stopgap strategies have been devised to improve nanozyme specificity for analytical applications, such as the integration of biorecognition elements and nanozyme sensor arrays through surface modification.imageKey Scientistsimage

Isolation and genomic characterization of Staphylococcus aureus bacteriophages from Chennai, India.

We isolated and characterized two lytic bacteriophages against Staphylococcus aureus named TANUVAS_MVC-VPHSA1 and TANUVAS_MVC-VPHSA2, with the aim of investigating their genomic and structural features. The bacteriophages belong to the Caudoviricetes, and their genomes have sizes of 50,505 and 50,516 base pairs with a GC content of 41.4%.

GraphsformerCPI: Graph Transformer for Compound-Protein Interaction Prediction.

Accurately predicting compound-protein interactions (CPI) is a critical task in computer-aided drug design. In recent years, the exponential growth of compound activity and biomedical data has highlighted the need for efficient and interpretable prediction approaches. In this study, we propose GraphsformerCPI, an end-to-end deep learning framework that improves prediction performance and interpretability. GraphsformerCPI treats compounds and proteins as sequences of nodes with spatial structures, and leverages novel structure-enhanced self-attention mechanisms to integrate semantic and graph structural features within molecules for deep molecule representations. To capture the vital association between compound atoms and protein residues, we devise a dual-attention mechanism to effectively extract relational features through .cross-mapping. By extending the powerful learning capabilities of Transformers to spatial structures and extensively utilizing attention mechanisms, our model offers strong interpretability, a significant advantage over most black-box deep learning methods. To evaluate GraphsformerCPI, extensive experiments were conducted on benchmark datasets including human, C. elegans, Davis and KIBA datasets. We explored the impact of model depth and dropout rate on performance and compared our model against state-of-the-art baseline models. Our results demonstrate that GraphsformerCPI outperforms baseline models in classification datasets and achieves competitive performance in regression datasets. Specifically, on the human dataset, GraphsformerCPI achieves an average improvement of 1.6% in AUC, 0.5% in precision, and 5.3% in recall. On the KIBA dataset, the average improvement in Concordance index (CI) and mean squared error (MSE) is 3.3% and 7.2%, respectively. Molecular docking shows that our model provides novel insights into the intrinsic interactions and binding mechanisms. Our research holds practical significance in effectively predicting CPIs and binding affinities, identifying key atoms and residues, enhancing model interpretability.

Image reconstruction method for incomplete CT projection based on self-guided image filtering.

In some fields of medical diagnosis or industrial nondestructive testing, it is difficult to obtain complete computed tomography (CT) data due to the limitation of radiation dose or other factors. Therefore, image reconstruction of incomplete projection data is the focus of this paper. In this paper, a new image reconstruction model based on self-guided image filtering (SGIF) term is proposed for few-view and segmental limited-angle (SLA) CT reconstruction. Then the alternating direction method (ADM) is used to solve this model. For simplicity, we call it ADM-SGIF method. The key idea of ADM-SGIF method is to use the reconstructed image itself as a reference and utilize its structural features to guide CT reconstruction. This method can effectively preserve image structures and remove shading artifacts. To validate the effectiveness of the proposed reconstruction method, we conduct digital phantom and real CT data experiments. The results indicate that ADM-SGIF method outperforms competing methods, including total variation (TV), relative total variation (RTV), and L0-norm minimization solved by ADM (ADM-L0) methods, in both subjective and objective evaluations.

Construction of Reconfigurable and Polymorphic DNA Origami Assemblies with Coiled-Coil Patches and Patterns.

DNA origami nanodevices achieve programmable structure and tunable mechanical and dynamic properties by leveraging the sequence-specific interactions of nucleic acids. Previous advances have also established DNA origami as a useful building block to make well-defined micron-scale structures through hierarchical self-assembly, but these efforts have largely leveraged the structural features of DNA origami. The tunable dynamic and mechanical properties also provide an opportunity to make assemblies with adaptive structures and properties. Here the integration of DNA origami hinge nanodevices and coiled-coil peptides are reported into hybrid reconfigurable assemblies. With the same dynamic device and peptide interaction, it is made multiple higher-order assemblies (i.e., polymorphic assembly) by organizing clusters of peptides into patches or arranging single peptides into patterns on the surfaces of DNA origami to control the relative orientation of devices. The coiled-coil interactions are used to construct circular and linear assemblies whose structure and mechanical properties can be modulated with DNA-based reconfiguration. Reconfiguration of linear assemblies leads to micron scale motions and ≈2.5-10-fold increase in bending stiffness. The results provide a foundation for stimulus-responsive hybrid assemblies that can adapt their structure and properties in response to nucleic acid, peptide, protein, or other triggers.