Antidiabetic peptides (ADPs), peptides with potential antidiabetic activity, hold significant importance in the treatment and control of diabetes. Despite their therapeutic potential, the discovery ...and prediction of ADPs remain challenging due to limited data, the complex nature of peptide functions, and the expensive and time-consuming nature of traditional wet lab experiments. This study aims to address these challenges by exploring methods for the discovery and prediction of ADPs using advanced deep learning techniques. Specifically, we developed two models: a single-channel CNN and a three-channel neural network (CNN + RNN + Bi-LSTM). ADPs were primarily gathered from the BioDADPep database, alongside thousands of non-ADPs sourced from anticancer, antibacterial, and antiviral peptide datasets. Subsequently, data preprocessing was performed with the evolutionary scale model (ESM-2), followed by model training and evaluation through 10-fold cross-validation. Furthermore, this work collected a series of newly published ADPs as an independent test set through literature review, and found that the CNN model achieved the highest accuracy (90.48%) in predicting the independent test set, surpassing existing ADP prediction tools. Finally, the application of the model was considered. SeqGAN was used to generate new candidate ADPs, followed by screening with the constructed CNN model. Selected peptides were then evaluated using physicochemical property prediction and structural forecasts for pharmaceutical potential. In summary, this study not only established robust ADP prediction models but also employed these models to screen a batch of potential ADPs, addressing a critical need in the field of peptide-based antidiabetic research.
•Proposed a novel data preprocessing strategy to address extreme class imbalance issues.•Employed the ESM-2 model for dataset preprocessing to enhance dataset quality.•Expanded the anti-diabetic peptide dataset by collecting a new set of anti-diabetic peptides from recent literature.•Introduced two deep learning-based ADP prediction models, namely a CNN model and a three-channel neural network model consisting of CNNs, RNNs, and Bi-LSTMs. The CNN model demonstrated superior accuracy in predictions on an independent test set compared to all other ADP prediction tools.•Integrated the CNN model, physicochemical property prediction, and molecular dynamics simulations to screen a set of potential anti-diabetic peptides.
Many proteins can be used to fabricate nanocarriers for encapsulation, protection, and controlled release of nutraceuticals. This review examined the protein-based nanocarriers from microscopic ...molecular characteristics to the macroscopical structural and functional attributes. Structural, physical, and chemical properties of protein-based nanocarriers were introduced in detail. The spatial size, shape, water dispersibility, colloidal stability, etc. of protein-based nanocarriers were largely determined by the molecular physicochemical principles of protein. Different preparative techniques, including antisolvent precipitation, pH-driven, electrospray, and gelation methods, among others, can be used to fabricate different protein-based nanocarriers. Various modifications based on physical, chemical, and enzymatic approaches can be used to improve the functional performance of these nanocarriers. Protein is a natural resource with a wide range of sources, including plant, animal, and microbial, which are usually used to fabricate the nanocarriers. Protein-based nanocarriers have many advantages in aid of the application of bioactive ingredients to the medical, food, and cosmetic industries.
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This study aimed to enhance both the dispersion stability and recyclability of micron-sized ceria slurry in glass polishing by incorporating polyacrylic acid (PAA). PAA with a ...molecular weight of 240 k was very effective as a dispersant for ceria slurry due to its unique surface adsorption and its characteristics as an anionic polyelectrolyte. Using a 240 k-PAA concentration of 1.0 wt% led to significant improvements in polishing performances, as demonstrated by consistent glass removal rates across recycling cycles. Additionally, the study investigated the physico-chemical properties of polishing slurries, such as specific gravity, particle size, zeta potential, and pH. These properties were effectively maintained with the use of PAA as the dispersant, particularly when KOH was used as a pH adjuster. A 100 µm bag-type filtration system was also introduced for handling the micron-sized ceria slurry, highlighting its practical benefits in slurry management. In conclusion, employing 240 k-PAA as a dispersant in micron-sized ceria slurries for Gorilla glass polishing not only maintains quality and stability across recycling cycles but also provides notable economic advantages by enabling the recycling of the slurry.
This Review offers a comprehensive review of the colloidal synthesis, mechanistic understanding, physicochemical properties, and applications of one-dimensional (1D) metal nanostructures. After a ...brief introduction to the different types of 1D nanostructures, we discuss major concepts and methods typically involved in a colloidal synthesis of 1D metal nanostructures, as well as the current mechanistic understanding of how the nanostructures are formed. We then highlight how experimental studies and computational simulations have expanded our knowledge of how and why 1D metal nanostructures grow. Following specific examples of syntheses for monometallic, multimetallic, and heterostructured systems, we showcase how the unique structure–property relationships of 1D metal nanostructures have enabled a broad spectrum of applications, including sensing, imaging, plasmonics, photonics, display, thermal management, and catalysis. Throughout our discussion, we also offer perspectives with regard to the future directions of development for this class of nanomaterials.
Extracellular vesicles (EVs) are secreted by cells with a membrane structure and complex components such as DNA, RNA and proteins. These biomolecules play an important role in cell communication, ...cell proliferation, cell migration, vascularization, immune response and other physiological and pathological processes. Most current research on EVs focused on populations of EVs. Heterogeneity of EVs is neglected. Considering the heterogeneity of single EVs may offer critical molecular insights into cell-cell interactions, it is necessary to enhance our understanding about molecular characteristics from EVs derived from cell population to a single EV of derived from a single cell. This transformation is expected to provide a new insight into the understanding of cellular biology and the accurate description of the law of disease progress.
In this article, we review the current research progress of single EV analysis technology for single EVs derived from cell population (SECP) and discuss its main applications in biological and clinical medicine research. After that, we propose the development direction, main difficulties and application prospect of single EV analysis technology for single EVs derived from single cells (SESC) according to our own research work, to provide new perspectives for the field of EV research.
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•Recent advances in single-EV detection technologies are comprehensively reviewed.•Current biological and clinical applications of single-EV assays are presented.•The current limitations of single-EV research and its possible directions of development are discussed.
Nanomaterials (NMs) tailored via conventional physicochemical routes play havoc with the environment that has led to the evolution of competent green routes for the actualization of a circular ...economy on an industrial-scale. Algae belonging to the class Cyanophyceae, Chlorophyceae, Phaeophyceae and Rhodophyceae have been harnessed as nano-machineries through intracellular and extracellular synthesis of gold (Au), silver (Ag) and several other metallic nanoparticles. Algae are an appealing platform for the production of diverse NMs, primarily due to the presence of bioactive compounds such as pigments and antioxidants in their cell extracts that act as biocompatible reductants. Chlorella spp. and Sargassum spp. have been extensively explored for the synthesis of nanoparticles having antimicrobial properties, which can potentially substitute conventional antibiotics. Characterization of nanoparticles (NPs) synthesised from algae has been done using advanced spectroscopic, diffractographic and microscopic techniques such as UV-Vis FT-IR, DLS, XPS, XRD, SEM, TEM, AFM, HR-TEM, and EDAX. The present paper reviews the information available on algae-mediated biosynthesis of various NPs, their characterization and applications in different domains.
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•Comprehensive review on algae mediated biosynthesis of metallic nanoparticles (NPs).•Mechanism of synthesis of NPs from micro and macroalgae and their characterization.•Chorella spp and Sargassum spp have been extensively explored to fabricate diverse NPs such as Au-NPs, Ag-NPs and ZnO-NPs.•Characterization of NPs synthesised from algae is done using different advanced techniques.•Application of metallic NPs in different domains such as antimicrobial and antifouling agents.
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•AAontology is the first ontology of amino acid scales.•Classifies 586 physicochemical scales into 8 categories and 67 subcategories.•Detailed descriptions and visualized ...relationships in each subcategory.•Part of the AAanalysis framework for interpretable protein prediction.•Enables linking amino acid properties to protein functions and protein embeddings.
Amino acid scales are crucial for protein prediction tasks, many of them being curated in the AAindex database. Despite various clustering attempts to organize them and to better understand their relationships, these approaches lack the fine-grained classification necessary for satisfactory interpretability in many protein prediction problems.
To address this issue, we developed AAontology—a two-level classification for 586 amino acid scales (mainly from AAindex) together with an in-depth analysis of their relations—using bag-of-word-based classification, clustering, and manual refinement over multiple iterations. AAontology organizes physicochemical scales into 8 categories and 67 subcategories, enhancing the interpretability of scale-based machine learning methods in protein bioinformatics. Thereby it enables researchers to gain a deeper biological insight. We anticipate that AAontology will be a building block to link amino acid properties with protein function and dysfunctions as well as aid informed decision-making in mutation analysis or protein drug design.
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