Search Results (1,678)

This study aimed to develop a functional powder using whey and milk matrices, leveraging the protective capacity of chia–alginate hydrogels and the advantages of electrohydrodynamic spraying (EHDA), a non-thermal technique suitable for encapsulating probiotic cells under stress conditions commonly encountered in food processing. A hydrogel matrix composed of chia seed mucilage and sodium alginate was used to form a biopolymeric network that protected probiotic cells during processing. The encapsulation efficiency reached 99.0 ± 0.01%, and bacterial viability remained above 9.9 log₁₀ CFU/mL after lyophilization, demonstrating the excellent protective capacity of the hydrogel matrix. Microstructural analysis using confocal laser scanning microscopy (CLSM) revealed well-retained cell morphology and homogeneous distribution within the hydrogel matrix while, in contrast, scanning electron microscopy (SEM) showed spherical, porous microcapsules with distinct surface characteristics influenced by the encapsulation method. Encapsulates were incorporated into beverages flavored with red fruits and pear and subsequently freeze-dried. The resulting powders were analyzed for moisture, protein, lipids, carbohydrates, fiber, and color determinations. The results were statistically analyzed using ANOVA and response surface methodology, highlighting the impact of ingredient ratios on nutritional composition. Raman spectroscopy identified molecular features associated with casein, lactose, pectins, anthocyanins, and other functional compounds, confirming the contribution of both matrix and encapsulants maintaining the structural characteristics of the product. The presence of antioxidant bands supported the functional potential of the powder formulations. Chia–alginate hydrogels effectively encapsulated L. reuteri, maintaining cell viability and enabling their incorporation into freeze-dried beverage powders. This approach offers a promising strategy for the development of next-generation functional food gels with enhanced probiotic stability, nutritional properties, and potential application in health-promoting dairy systems. Full article

Recent advancements have been made in the precise prediction of protein structures within the Protein Folding Problem (PFP), particularly in relation to minimizing the energy function to achieve stable and biologically relevant protein structures. This problem is classified as NP-hard within computational theory, necessitating the development of various techniques and algorithms. Bio-inspired algorithms have proven effective in addressing NP-hard challenges in practical applications. This study introduces a novel hybrid algorithm, termed GRSABio, which integrates the strategies of Jumping Spider Algorithm (JSOA) with the Golden Ratio Simulated Annealing (GRSA) for peptide prediction. Furthermore, the GRSABio algorithm incorporates a Convolutional Neural Network for fragment prediction (FCNN), forms an enhanced methodology called GRSABio-FCNN. This integrated framework achieves improved structure refinement based on energy for protein prediction. The proposed enhanced GRSABio-FCNN approach was applied to a dataset of 60 peptides. The Wilcoxon and Friedman statistics test were employed to compare the GRSABio-FCNN results against recent state-of-the-art-approaches. The results of these tests indicate that the GRSABio-FCNN approach is competitive with state-of-the-art methods for peptides up to 50 amino acids in length and surpasses leading PFP algorithms for peptides with up to 30 amino acids. Full article

Community detection in complex networks plays a pivotal role in modern scientific research, including in social network analysis and protein structure analysis. Traditional community detection methods face challenges in integrating heterogeneous multi-source information, capturing global semantic relationships, and adapting to dynamic network evolution. This paper proposes a novel unsupervised multimodal community detection algorithm (UMM) based on fractal iteration. The core idea is to design a dual-channel encoder that comprehensively considers node semantic features and network topological structures. Initially, node representation vectors are derived from structural information (using feature vectors when available, or singular value decomposition to obtain feature vectors for nodes without attributes). Subsequently, a parameter-free graph convolutional encoder (PFGC) is developed based on fractal iteration principles to extract high-order semantic representations from structural encodings without requiring any training process. Furthermore, a semantic–structural dual-channel encoder (DC-SSE) is designed, which integrates semantic encodings—reduced in dimensionality via UMAP—with structural features extracted by PFGC to obtain the final node embeddings. These embeddings are then clustered using the K-means algorithm to achieve community partitioning. Experimental results demonstrate that the UMM outperforms existing methods on multiple real-world network datasets. Full article

Anthocyanins, a subclass of flavonoid pigments, impart vivid red, purple, and blue coloration to horticultural plants, playing essential roles in ornamental enhancement, stress resistance, and pollinator attraction. Recent studies have identified B-box (BBX) proteins as a critical class of transcription factors (TFs) involved in anthocyanin biosynthesis. Despite these advances, comprehensive reviews systematically addressing BBX proteins are urgently needed, especially given the complexity and diversity of their roles in regulating anthocyanin production. In this paper, we provide an in-depth overview of the fundamental structures, biological functions, and classification of BBX TFs, along with a detailed description of anthocyanin biosynthetic pathways and bioactivities. Furthermore, we emphasize the diverse molecular mechanisms through which BBX TFs regulate anthocyanin accumulation, including direct activation or repression of target genes, indirect modulation via interacting protein complexes, and co-regulation with other transcriptional regulators. Additionally, we summarize the known upstream regulatory signals and downstream target genes of BBX TFs, highlighting their significance in shaping anthocyanin biosynthesis pathways. Understanding these regulatory networks mediated by BBX proteins will not only advance fundamental horticultural science but also provide valuable insights for enhancing the aesthetic quality, nutritional benefits, and stress adaptability of horticultural crops. Full article

Membrane transporters play a crucial role in any cell. Identifying the substrates they translocate across membranes is important for many fields of research, such as metabolomics, pharmacology, and biotechnology. In this study, we leverage recent advances in deep learning, such as amino acid sequence embeddings with protein language models (pLMs), highly accurate 3D structure predictions with AlphaFold 2, and structure-encoding 3Di sequences from FoldSeek, for predicting substrates of membrane transporters. We test new deep learning features derived from both sequence and structure, and compare them to the previously best-performing protein encodings, which were made up of amino acid k-mer frequencies and evolutionary information from PSSMs. Furthermore, we compare the performance of these features either using a previously developed SVM model, or with a regularized feedforward neural network (FNN). When evaluating these models on sugar and amino acid carriers in A. thaliana, as well as on three types of ion channels in human, we found that both the DL-based features and the FNN model led to a better and more consistent classification performance compared to previous methods. Direct encodings of 3D structures with Foldseek, as well as structural embeddings with ProstT5, matched the performance of state-of-the-art amino acid sequence embeddings calculated with the ProtT5-XL model when used as input for the FNN classifier. Full article

Protein–polyelectrolyte nanostructures assembled via electrostatic interactions offer versatile applications in biomedicine, tissue engineering, and food science. However, several open questions remain regarding their intermolecular interactions and the influence of external conditions—such as temperature and pH—on their assembly, stability, and responsiveness. This study explores the formation and stability of networks between poly(acrylic acid) (PAA) and lysozyme (LYZ) at the nanoscale upon thermal treatment, using a combination of experimental and simulation measures. Experimental techniques of static and dynamic light scattering (SLS and DLS), Fourier transform infrared spectroscopy (FTIR), and circular dichroism (CD) are combined with all-atom molecular dynamics simulations. Model systems consisting of multiple PAA and LYZ molecules explore collective assembly and complexation in aqueous solution. Experimental results indicate that electrostatic complexation occurs between PAA and LYZ at pH values below LYZ’s isoelectric point. This leads to the formation of nanoparticles (NPs) with radii ranging from 100 to 200 nm, most pronounced at a PAA/LYZ mass ratio of 0.1. These complexes disassemble at pH 12, where both LYZ and PAA are negatively charged. However, when complexes are thermally treated (TT), they remain stable, which is consistent with earlier findings. Atomistic simulations demonstrate that thermal treatment induces partially reversible structural changes, revealing key microscopic features involved in the stabilization of the formed network. Although electrostatic interactions dominate under all pH and temperature conditions, thermally induced conformational changes reorganize the binding pattern, resulting in an increased number of contacts between LYZ and PAA upon thermal treatment. The altered hydration associated with conformational rearrangements emerges as a key contributor to the stability of the thermally treated complexes, particularly under conditions of strong electrostatic repulsion at pH 12. Moreover, enhanced polymer chain associations within the network are observed, which play a crucial role in complex stabilization. These insights contribute to the rational design of protein–polyelectrolyte materials, revealing the origins of association under thermally induced structural rearrangements. Full article

Pancreatic cancer is frequently accompanied by cancer-associated cachexia, a debilitating metabolic syndrome marked by progressive skeletal muscle wasting and systemic metabolic dysfunction. This study presents a systems biology framework to simultaneously identify therapeutic targets for both pancreatic ductal adenocarcinoma (PDAC) and its associated cachexia (PDAC-CX), using cell-specific genome-scale metabolic models (GSMMs). The human metabolic network Recon3D was extended to include protein synthesis, degradation, and recycling pathways for key inflammatory and structural proteins. These enhancements enabled the reconstruction of cell-specific GSMMs for PDAC and PDAC-CX, and their respective healthy counterparts, based on transcriptomic datasets. Medium-independent metabolic biomarkers were identified through Parsimonious Metabolite Flow Variability Analysis and differential expression analysis across five nutritional conditions. A fuzzy multi-objective optimization framework was employed within the anticancer target discovery platform to evaluate cell viability and metabolic deviation as dual criteria for assessing therapeutic efficacy and potential side effects. While single-enzyme targets were found to be context-specific and medium-dependent, eight combinatorial targets demonstrated robust, medium-independent effects in both PDAC and PDAC-CX cells. These include the knockout of SLC29A2, SGMS1, CRLS1, and the RNF20–RNF40 complex, alongside upregulation of CERK and PIKFYVE. The proposed integrative strategy offers novel therapeutic avenues that address both tumor progression and cancer-associated cachexia, with improved specificity and reduced off-target effects, thereby contributing to translational oncology. Full article

Background and objectives: Adducins are cytoskeletal proteins essential for membrane stability, actin–spectrin network organization, and cell signaling. Mutations in the genes ADD1, ADD2, and ADD3 have been linked to hypertension, neurodevelopmental disorders, and cancer. However, no comprehensive in silico saturation mutagenesis study has systematically evaluated the pathogenic potential and structural consequences of all possible missense mutations in adducins. This study aimed to identify high-risk variants and their potential impact on protein stability and function. Methods: We performed computational saturation mutagenesis for all possible single amino acid substitutions across the adducin proteins family. Pathogenicity predictions were conducted using four independent tools: AlphaMissense, Rhapsody, PolyPhen-2, and PMut. Predictions were validated against UniProt-annotated pathogenic variants. Predictive performance was assessed using Cohen’s Kappa, sensitivity, and precision. Mutations with a prediction probability ≥ 0.8 were further analyzed for structural stability using mCSM, DynaMut2, MutPred2, and Missense3D, with particular focus on functionally relevant domains such as phosphorylation and calmodulin-binding sites. Results: PMut identified the highest number of pathogenic mutations, while PolyPhen-2 yielded more conservative predictions. Several high-risk mutations clustered in known regulatory and binding regions. Substitutions involving glycine were consistently among the most destabilizing due to increased backbone flexibility. Validated variants showed strong agreement across multiple tools, supporting the robustness of the analysis. Conclusions: This study highlights the utility of multi-tool bioinformatic strategies for comprehensive mutation profiling. The results provide a prioritized list of high-impact adducin variants for future experimental validation and offer insights into potential therapeutic targets for disorders involving ADD1, ADD2, and ADD3 mutations. Full article

Prognostic markers such as overall survival (OS) and tertiary lymphoid structure (TLS) ratios, alongside diagnostic signatures like primary cancer-type classification, provide critical information for treatment selection, risk stratification, and longitudinal care planning across the oncology continuum. However, extracting these signals solely from sparse, high-dimensional multi-omics data remains a major challenge due to heterogeneity and frequent missingness in patient profiles. To address this challenge, we present SeNMo, a self-normalizing deep neural network trained on five heterogeneous omics layers—gene expression, DNA methylation, miRNA abundance, somatic mutations, and protein expression—along with the clinical variables, that learns a unified representation robust to missing modalities. Trained on more than 10,000 patient profiles across 32 tumor types from The Cancer Genome Atlas (TCGA), SeNMo provides a baseline that can be readily fine-tuned for diverse downstream tasks. On a held-out TCGA test set, the model achieved a concordance index of 0.758 for OS prediction, while external evaluation yielded 0.73 on the CPTAC lung squamous cell carcinoma cohort and 0.66 on an independent 108-patient Moffitt Cancer Center cohort. Furthermore, on Moffitt’s cohort, baseline SeNMo fine-tuned for TLS ratio prediction aligned with expert annotations (p < 0.05) and sharply separated high- versus low-TLS groups, reflecting distinct survival outcomes. Without altering the backbone, a single linear head classified primary cancer type with 99.8% accuracy across the 33 classes. By unifying diagnostic and prognostic predictions in a modality-robust architecture, SeNMo demonstrated strong performance across multiple clinically relevant tasks, including survival estimation, cancer classification, and TLS ratio prediction, highlighting its translational potential for multi-omics oncology applications. Full article

The shikimate pathway is the fundamental metabolic route for aromatic amino acid biosynthesis in bacteria, plants, and fungi, but is absent in mammals. This review explores how multi-enzyme synergy and allosteric regulation coordinate metabolic flux through this pathway by focusing on three key enzymes: 3-deoxy-d-arabino-heptulosonate-7-phosphate synthase, chorismate mutase, and tryptophan synthase. We examine the structural diversity and distribution of these enzymes across evolutionary domains, highlighting conserved catalytic mechanisms alongside species-specific regulatory adaptations. The review covers directed evolution strategies that have transformed naturally regulated enzymes into standalone biocatalysts with enhanced activity and expanded substrate scope, enabling synthesis of non-canonical amino acids and complex organic molecules. Industrial applications demonstrate the pathway’s potential for sustainable production of pharmaceuticals, polymer precursors, and specialty chemicals through engineered microbial platforms. Additionally, we discuss the therapeutic potential of inhibitors targeting pathogenic organisms, particularly their mechanisms of action and antimicrobial efficacy. This comprehensive review establishes the shikimate pathway as a paradigmatic system where understanding allosteric networks enables the rational design of biocatalytic platforms, providing blueprints for biotechnological innovation and demonstrating how evolutionary constraints can be overcome through protein engineering to create superior industrial biocatalysts. Full article

Using common corn stalks as raw materials, a functional dense-structured carbon microsphere with good elastic deformation and certain rigid support was modified from biomass through a step-by-step hydrothermal method. The composition, thermal stability, fluid-loss reduction performance, and reservoir protection performance of the modified carbon microspheres were studied. Research indicates that after hydrothermal treatment, under the multi-level structural action of a small amount of proteins in corn stalks, the naturally occurring cellulose, polysaccharide organic compounds, and part of the ash in the stalks are adsorbed and encapsulated within the long-chain network structure formed by proteins and cellulose. By attaching silicate nanoparticles with certain rigidity from the ash to the relatively stable chair-type structure in cellulose, functional dense-structured carbon microspheres were ultimately prepared. These carbon microspheres could still effectively reduce fluid loss at 200 °C. The permeability recovery value of the cores treated with modified biomass carbon microspheres during flowback reached as high as 88%, which was much higher than that of the biomass itself. With the dense network-like chain structure supplemented by small-molecule aldehydes and silicate ash, the subsequent invasion of drilling fluid was successfully prevented, and a good sealing effect was maintained even under high-temperature and high-pressure conditions. Moreover, since this functional dense-structured carbon microsphere achieved sealing through a physical mechanism, it did not cause damage to the formation, showing a promising application prospect. Full article

This study explored the potential of chitosan (CH)/bacterial cellulose (BC) complexes (0.5% w/v) as novel emulsifiers to stabilize oil-in-water (o/w) Pickering emulsions (20% v/v sunflower oil), with a focus on their gel-like behavior. Emulsions were prepared using CH combined with BNC derived via H₂SO₄ (BNC1) or H₂SO₄-HCl (BNC2) hydrolysis. Increasing BNC content improved stability by reducing phase separation and enhancing viscosity, while CH contributed interfacial activity and electrostatic stabilization. CH/BNC1_25:75 emulsions showed the highest stability, maintaining an emulsion stability index (ESI) of up to 100% after 3 days, with minimal change in droplet size (R_h ~8.5–8.8 μm) and a positive ζ-potential (15.1–29.8 mV), as confirmed by dynamic/electrophoretic light scattering. pH adjustment to 4 and 10 had little effect on their ESI, while ionic strength studies showed that 0.1 M NaCl caused only a slight increase in droplet size combined with the highest ζ-potential (−35.2 mV). Higher salt concentrations led to coalescence and disruption of their gel-like structure. Rheological analysis of CH/BNC1_25:75 emulsions revealed shear-thinning behavior and dominant elastic properties (G′ > G″), indicating a soft gel network. Incorporating sunflower-seed protein isolates into CH/BNC1 (25:75) emulsions led to coacervate formation (three-layer system), characterized by a decrease in droplet size and an increase in ζ-potential (up to 32.8 mV) over 7 days. These findings highlight CH/BNC complexes as sustainable stabilizers for food-grade Pickering emulsions, supporting the development of biopolymer-based emulsifiers aligned with bioeconomy principles. Full article

Background: Neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD), are genetically complex and often linked to structural genomic variations such as copy number variants (CNVs). Current diagnostic strategies face challenges in interpreting the clinical significance of such variants. Methods: We developed a customized, gene-oriented chromosomal microarray (CMA) targeting 6026 genes relevant to neurodevelopment, aiming to improve diagnostic yield and candidate gene prioritization. A total of 39 patients with unexplained developmental delay, intellectual disability, and/or ASD were analyzed using this custom platform. Systems biology approaches were employed for downstream interpretation, including protein–protein interaction networks, centrality measures, and tissue-specific functional module analysis. Results: Pathogenic or likely pathogenic CNVs were identified in 31% of cases (9/29). Network analyses revealed candidate genes with key topological properties, including central “hubs” (e.g., NPEPPS, PSMG1, DOCK8) and regulatory “bottlenecks” (e.g., SLC15A4, GLT1D1, TMEM132C). Tissue- and cell-type-specific network modeling demonstrated widespread gene involvement in both prenatal and postnatal developmental modules, with glial and astrocytic networks showing notable enrichment. Several novel CNV regions with high pathogenic potential were identified and linked to neurodevelopmental phenotypes in individual patient cases. Conclusions: Customized CMA offers enhanced detection of clinically relevant CNVs and provides a framework for prioritizing novel candidate genes based on biological network integration. This approach improves diagnostic accuracy in NDDs and identifies new targets for future functional and translational studies, highlighting the importance of glial involvement and immune-related pathways in neurodevelopmental pathology. Full article

Background: Parvovirus B19 (B19V) can cause severe relapsing episodes of pure red cell aplasia in immunocompromised individuals, which are commonly treated with intravenous immunoglobulins (IVIGs). Few data are available on B19V intra-host evolution and the role of humoral immune selection. Here, we report the dynamics of genomic mutations and subsequent protein changes during relapsing infection. Methods: Longitudinal plasma samples from immunocompromised patients with relapsing B19V infection in the period 2011–2019 were analyzed using whole-genome sequencing to evaluate intra-host evolution. The impact of mutations on the 3D viral protein structure was predicted by deep neural network modeling. Results: Of the three immunocompromised patients with relapsing infections for 3 to 9 months, one patient developed two consecutive nonsynonymous mutations in the VP1/2 region: T372S/T145S and Q422L/Q195L. The first mutation was detected in multiple B19V IgG-seropositive follow-up samples and resolved after IgG seroreversion. Computational prediction of the VP1 3D structure of this mutant showed a conformational change in the proximity of the antibody binding domain. No conformational changes were predicted for the other mutations detected. Discussion: Analysis of relapsing B19V infections showed mutational changes occurring over time. Resulting amino acid changes were predicted to lead to a conformational capsid protein change in an IgG-seropositive patient. The impact of humoral response and IVIG treatment on B19V infections should be further investigated to understand viral evolution and potential immune escape. Full article

In order to achieve the efficient utilization of agricultural by-products and overcome the bottleneck of animal feed shortages in dry seasons, this study utilized corn stover (CS; Zea mays L.) as a material to systematically investigate the dynamic changes in the fermentation quality, bacterial community structure, and pathogenic risk of silage under different fermentation times (0, 3, 7, 15, and 30 days). CS has high nutritive value, including crude protein and sugar, and can serve as a carbon source and a nitrogen source for silage fermentation. After ensiling, CS silage (CSTS) exhibited excellent fermentation quality, characterized by relatively high lactic acid content, low pH, and ammonia nitrogen content within an acceptable range. In addition, neither propionic acid nor butyric acid was detected in any of the silages. CS exhibited high α-diversity, with Serratia marcescens being the dominant bacterial species. After ensiling, the α-diversity significantly (p < 0.05) decreased, and Lactiplantibacillus plantarum was the dominant species during the fermentation process. With the extension of fermentation days, the relative abundance of Lactiplantibacillus plantarum significantly (p < 0.05) increased, reaching a peak and stabilizing between 15 and 30 days. Ultimately, lactic acid bacteria dominated and constructed a microbial symbiotic network system. In the bacterial community of CSTS, the abundance of “potential pathogens” was significantly (p < 0.01) lower than that of CS. These results provide data support for establishing a microbial regulation theory for silage fermentation, thereby improving the basic research system for the biological conversion of agricultural by-products and alleviating feed shortages in dry seasons. Full article