A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods.

Computational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a standardized process for evaluating new methods. Additionally, previous studies have only compared several methods, with conflicting results. In this context, we conducted a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods on 11 existing datasets. We developed a comprehensive framework to evaluate their performance, scalability and usability. Our study revealed that methods such as HGIMC, ITRPCA and BNNR exhibit the best overall performance, as they rely on matrix completion or factorization. HINGRL, MLMC, ITRPCA and HGIMC demonstrate the best performance, while NMFDR, GROBMC and SCPMF display superior scalability. For usability, HGIMC, DRHGCN and BNNR are the top performers. Building on these findings, we developed an online tool called HN-DREP (http://hn-drep.lyhbio.com/) to facilitate researchers in viewing all the detailed evaluation results and selecting the appropriate method. HN-DREP also provides an external drug repositioning prediction service for a specific disease or drug by integrating predictions from all methods. Furthermore, we have released a Snakemake workflow named HN-DRES (https://github.com/lyhbio/HN-DRES) to facilitate benchmarking and support the extension of new methods into the field.

DFBP: a comprehensive database of food-derived bioactive peptides for peptidomics research.

MOTIVATION: Food-derived bioactive peptides (FBPs) have demonstrated their significance in pharmaceuticals, diets and nutraceuticals, benefiting public health and global ecology. While significant efforts have been made to discover FBPs and to elucidate the underlying bioactivity mechanisms, there is lack of a systemic study of sequence-structure-activity relationship of FBPs in a large dataset. RESULTS: Here, we construct a database of food-derived bioactive peptides (DFBP), containing a total of 6276 peptide entries in 31 types from different sources. Further, we develop a series of analysis tools for function discovery/repurposing, traceability, multifunctional bioactive exploration and physiochemical property assessment of peptides. Finally, we apply this database and data-mining techniques to discover new FBPs as potential drugs for cardiovascular diseases. The DFBP serves as a useful platform for not only the fundamental understanding of sequence-structure-activity of FBPs but also the design, discovery, and repurposing of peptide-based drugs, vaccines, materials and food ingredients. AVAILABILITY AND IMPLEMENTATION: DFBP service can be accessed freely via http://www.cqudfbp.net/. All data are incorporated into the article and its online supplementary material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification and molecular mechanism of a tri-peptide inhibitor targeting iNOS from duck embryo protein hydrolysates by experimental and bioinformatics studies.

Duck embryonic proteins are a promising source of food-derived functional peptides. Using a combination of experiments and bioinformatics approaches, a tri-peptide inhibitor YPW targeting iNOS was identified from duck embryo protein hydrolysates. Our results indicated that YPW could significantly inhibit LPS-induced NO generation in macrophages in a dose-dependent manner. YPW also significantly inhibited the expression of IL-6 and iNOS. Molecular simulations revealed that YPW could interact strongly with (iNOS) with a binding energy of -45.71 ± 17.75 kJ/mol. The stability of YPW-iNOS was maintained by the hydrogen bonds of amino acid residues Ile195, Gly196, Gly365, Glu371, Asn364, and Trp366, and the hydrophobic interactions by Trp188, Phe363, and Val346. In conclusion, our study provides a new idea for broadening the utilization of duck embryo proteins, and a strategy for the discovery of food-derived bioactive peptides.

Antioxidant and α-amylase inhibitory activities of tannic acid.

Tannic acid widely exists in plants, which forms a part of human diet. The antioxidant activity of tannic acid was evaluated by the chemical and cellular antioxidant assays. And its α-amylase inhibitory activity and behavior were also investigated. It was found that hydrogen- and electron donating capacities of tannic acid were higher than those of tertiary butylhydroquinone (TBHQ) based on reducing power, ABTS and DPPH radical scavenging assays. But for its low hydrophobic property, the antioxidant activity of tannic acid in linoleic acid system was inferior to that of TBHQ. In the cellular antioxidant assay, tannic acid showed the higher activity than gallic acid in the "PBS wash" protocol, which could attribute to its high binding capacity of cell membrane. Compared with acarbose, tannic acid possessed the stronger α-amylase inhibitory capacity. And the static fluorescence quenching of α-amylase in the presence of tannic acid could be also observed, which was caused by their binding interaction.

A computational study of self-assembled hexapeptide inhibitors against amyloid-ß (Aß) aggregation.

The fibrillation and deposition of amyloid-ß (Aß) peptides in human brains are pathologically linked to Alzheimer's disease (AD). Development of different inhibitors (peptides, organic molecules, and nanoparticles) to prevent Aß aggregation becomes a promising therapeutic strategy for AD treatment. We recently propose a "like-interacts-like" design principle to computationally design/screen and experimentally validate a new set of hexapeptide inhibitors with completely different sequences from the Aß sequence. These hexapeptide inhibitors inhibit Aß aggregation and reduce Aß-induced cytotoxicity. However, inhibitory mechanisms of these hexapeptides and the underlying interactions between hexapeptides and Aß remain unclear. Herein we apply multi-scale computational methods (quantum-chemical calculations, molecular docking and explicit-solvent molecular dynamic simulation) to explore the structure, dynamics, and interaction between 3 identified hexapeptides (CTLWWG, GTVWWG, and CTIYWG) and different Aß-derived fragments and an Aß17-42 pentamer. When interacting with 6 Aß-derived fragments, 3 hexapeptide inhibitors show stronger interactions with two lysine-included fragments (16KLVFFA21 and 27NKGAII33) than other fragments, indicating different sequence-specific interactions with Aß. When interacting with the Aß17-42 pentamer, the 3 peptides show similar binding modes and interaction mechanisms by preferentially binding to the edge of the Aß17-42 pentamer to potentially block the Aß elongation pathway. This work provides structural-based binding information on further modification and optimization of these peptide inhibitors to experimentally enhance their inhibitory abilities against Aß aggregation.

Polymorphic cross-seeding amyloid assemblies of amyloid-ß and human islet amyloid polypeptide.

Epidemiological studies have shown that the development of Alzheimer's disease (AD) is associated with type 2 diabetes (T2D), but it still remains unclear how AD and T2D are connected. Heterologous cross-seeding between the causative peptides of Aß and hIAPP may represent a molecular link between AD and T2D. Here, we computationally modeled and simulated a series of cross-seeding double-layer assemblies formed by Aß and hIAPP peptides using all-atom and coarse-gained molecular dynamics (MD) simulations. The cross-seeding Aß-hIAPP assemblies showed a wide range of polymorphic structures via a combination of four ß-sheet-to-ß-sheet interfaces and two packing orientations, focusing on a comparison of different matches of ß-sheet layers. Two cross-seeding Aß-hIAPP assemblies with different interfacial ß-sheet packings exhibited high structural stability and favorable interfacial interactions in both oligomeric and fibrillar states. Both Aß-hIAPP assemblies displayed interfacial dehydration to different extents, which in turn promoted Aß-hIAPP association depending on interfacial polarity and geometry. Furthermore, computational mutagenesis studies revealed that disruption of interfacial salt bridges largely disfavor the ß-sheet-to-ß-sheet association, highlighting the importance of salt bridges in the formation of cross-seeding assemblies. This work provides atomic-level information on the cross-seeding interactions between Aß and hIAPP, which may be involved in the interplay between these two disorders.

Experimental and Theoretical Investigations on the Supermolecular Structure of Isoliquiritigenin and 6-O-α-D-Maltosyl-ß-cyclodextrin Inclusion Complex.

Isoliquiritigenin (ILTG) possesses many pharmacological properties. However, its poor solubility and stability in water hinders its wide applications. The solubility of bioactive compounds can often be enhanced through preparation and delivery of various cyclodextrin (CD) inclusion complexes. The 6-O-α-D-maltosyl-ß-CD (G2-ß-CD), as one of the newest developments of CDs, has high aqueous solubility and low toxicity, especially stable inclusion characteristics with bioactive compounds. In this work, we for the first time construct and characterize the supermolecular structure of ILTG/G2-ß-CD by scanning electron microscopy (SEM), ultraviolet-visible spectroscopy (UV), Fourier transform infrared spectroscopy (FT-IR), and X-ray diffractometry (XRD). The solubility of ILTG in water at 25 °C rises from 0.003 to 0.717 mg/mL by the encapsulation with G2-ß-CD. Our experimental observations on the presence of the ILTG/G2-ß-CD inclusion complex are further supported by the ONIOM(our Own N-layer Integrated Orbital molecular Mechanics)-based QM/MM (Quantum Mechanics/Molecular Mechanics) calculations, typically substantiating these supermolecular characteristics, such as detailed structural assignments, preferred binding orientations, selectivity, solvent effects, interaction energies and forces of the ILTG/G2-ß-CD inclusion complex. Our results have elucidated how ILTG interacts with G2-ß-CD, demonstrating the primary host-guest interactions between ILTG and G2-ß-CD, characterized by hydrogen bonds, hydrophobic interactions, electrostatic forces, and conformational effects, are favored for the formation of the ILTG/G2-ß-CD inclusion.

[Application of an R-group search strategy into three-dimensional quantitative structure-activity relationship of HEA beta-secretase inhibitors and molecular virtual screening].

The beta-secretase is one of prospective targets against Alzheimer's disease (AD). A three-dimensional quan titative structure-activity relationship (3D-QSAR) model of Hydroethylamines (HEAs) as beta-secretase inhibitors was established using Topomer CoMFA. The multiple correlation coefficient of fitting, cross validation and external validation were r2 = 0.928, q(loo)2 = 0.605 and r(pred)2 = 0.626, respectively. The 3D-QSAR model was used to search R groups from ZINC database as the source of structural fragments. As a result, a series of R groups with relatively high activity contribution was obtained to design a total of 15 new compounds, with higher activity than that of the template molecule. The molecular docking was employed to study the interaction mode between the new compounds as ligands and beta-secretase as receptors, displaying that hydrogen bond and hydrophobicity played important roles in the binding affinity between the new compounds and beta-secretase. The results showed that Topomer CoMFA and To pomer Search could be effectively used to screen and design new molecules of HEAs as beta-secretase inhibitors, and the designed compounds could provide new candidates for drug design targeting AD.

Exploring novel lead scaffolds for SGLT2 inhibitors: Insights from machine learning and molecular dynamics simulations.

Sodium-glucose cotransporter 2 (SGLT2) plays a pivotal role in mediating glucose reabsorption within the renal filtrate, representing a well-known target in type 2 diabetes and heart failure. Recent emphasis has been directed toward designing SGLT2 inhibitors, with C-glycoside inhibitors emerging as front-runners. The architecture of SGLT2 has been successfully resolved using cryo-electron microscopy. However, comprehension of the pharmacophores within the binding site of SGLT2 remains unclear. Here, we use machine learning and molecular dynamics simulations on SGLT2 bound with its inhibitors in preclinical or clinical development to shed light on this issue. Our dataset comprises 1240 SGLT2 inhibitors amalgamated from diverse sources, forming the basis for constructing machine learning models. SHapley Additive exPlanation (SHAP) elucidates the crucial fragments that contribute to inhibitor activity, specifically Morgan_3, 162, 310, 325, 366, 470, 597, 714, 926, and 975. Furthermore, the computed binding free energies and per-residue contributions for SGLT2-inhibitor complexes unveil crucial fragments of inhibitors that interact with residues Asn-75, His-80, Val-95, Phe-98, Val-157, Leu-274, and Phe-453 in the binding site of SGLT2. This comprehensive investigation enhances understanding of the binding mechanism for SGLT2 inhibitors, providing a robust framework for evaluating and discovering novel lead scaffolds within this domain.

The mechanism of epigallocatechin-3-gallate inhibiting the antigenicity of ß-lactoglobulin under pH 6.2, 7.4 and 8.2: Multi-spectroscopy and molecular simulation methods.

The antigenicity of ß-lactoglobulin (ß-LG) can be influenced by pH values and reduced by epigallocatechin-3-gallate (EGCG). However, a detailed mechanism concerning EGCG decreasing the antigenicity of ß-LG at different pH levels lacks clarity. Here, we explore the inhibition mechanism of EGCG on the antigenicity of ß-LG at pH 6.2, 7.4 and 8.2 using enzyme-linked immunosorbent assay, multi-spectroscopy, mass spectrometry and molecular simulations. The results of Fourier transform infrared spectroscopy (FTIR) and circular dichroism (CD) elucidate that the noncovalent binding of EGCG with ß-LG induces variations in the secondary structure and conformations of ß-LG. Moreover, EGCG inhibits the antigenicity of ß-LG the most at pH 7.4 (98.30 %), followed by pH 6.2 (73.18 %) and pH 8.2 (36.24 %). The inhibitory difference is attributed to the disparity in the number of epitopes involved in the interacting regions of EGCG and ß-LG. Our findings suggest that manipulating pH conditions may enhance the effectiveness of antigenic inhibitors, with the potential for further application in the food industry.

Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) have led to groundbreaking advancements in life sciences. To develop bioinformatics tools for scRNA-seq and SRT data and perform unbiased benchmarks, data simulation has been widely adopted by providing explicit ground truth and generating customized datasets. However, the performance of simulation methods under multiple scenarios has not been comprehensively assessed, making it challenging to choose suitable methods without practical guidelines. RESULTS: We systematically evaluated 49 simulation methods developed for scRNA-seq and/or SRT data in terms of accuracy, functionality, scalability, and usability using 152 reference datasets derived from 24 platforms. SRTsim, scDesign3, ZINB-WaVE, and scDesign2 have the best accuracy performance across various platforms. Unexpectedly, some methods tailored to scRNA-seq data have potential compatibility for simulating SRT data. Lun, SPARSim, and scDesign3-tree outperform other methods under corresponding simulation scenarios. Phenopath, Lun, Simple, and MFA yield high scalability scores but they cannot generate realistic simulated data. Users should consider the trade-offs between method accuracy and scalability (or functionality) when making decisions. Additionally, execution errors are mainly caused by failed parameter estimations and appearance of missing or infinite values in calculations. We provide practical guidelines for method selection, a standard pipeline Simpipe ( https://github.com/duohongrui/simpipe ; https://doi.org/10.5281/zenodo.11178409 ), and an online tool Simsite ( https://www.ciblab.net/software/simshiny/ ) for data simulation. CONCLUSIONS: No method performs best on all criteria, thus a good-yet-not-the-best method is recommended if it solves problems effectively and reasonably. Our comprehensive work provides crucial insights for developers on modeling gene expression data and fosters the simulation process for users.

Comparative molecular dynamics study of human islet amyloid polypeptide (IAPP) and rat IAPP oligomers.

Human islet amyloid polypeptide (hIAPP or amylin) is a causative agent in pancreatic amyloid deposits found in patients with type 2 diabetes. The aggregation of full-length hIAPP(1-37) into small oligomeric species is increasingly believed to be responsible for cell dysfunction and death. However, rat IAPP (rIAPP(1-37)), which differs from hIAPP in only six of 37 residues, loses its aggregation ability to form toxic amyloid species. Atomic details of the effect of sequence on the structure and toxicity between the amyloidogenic, toxic hIAPP peptide and the nonamyloidogenic, nontoxic rIAPP peptide remain unclear. Here, we probe sequence-induced differences in structural stability, conformational dynamics, and driving forces between different hIAPP and rIAPP polymorphic forms from monomer to pentamer using molecular dynamics simulations. Simulations show that hIAPP forms from trimer to pentamer exhibit high structural stability with well-preserved in-register parallel ß-sheet and the U-bend conformation. The hIAPP trimer appears to be a smallest minimal seed in solution. The stabilities of parallel hIAPP oligomers increase with the number of peptides. Conversely, replacement of hIAPP sequence by rIAPP sequence causes a significant loss of favorable interpeptide interactions in all rIAPP oligomers, destabilizing the C-terminal ß-sheet, turn conformation, and overall stability. A less ß-sheet-rich structure and a disturbed U-shaped topology exert a large energy penalty on the self-assemble of the rIAPP peptides into highly ordered, in-register ß-sheet-rich protofibrils and fibrils, which explains the nonamyloidogenic activity of rIAPP. Moreover, the absence of interior water within the U-turn region in the well-packed higher-order hIAPP oligomers, not in the poorly packed rIAPP oligomers, also stabilizes peptide association. This work provides atomic details of the sequence-structure relationship between the amyloidogenic hIAPP and its analogues such as the nonamyloidogenic rIAPP and some mutants, which could help in the development of novel therapeutic agents to block the formation of toxic hIAPP oligomeric species for type 2 diabetes.

Engineering antimicrobial peptides with improved antimicrobial and hemolytic activities.

The rapid rise of antibiotic resistance in pathogens becomes a serious and growing threat to medicine and public health. Naturally occurring antimicrobial peptides (AMPs) are an important line of defense in the immune system against invading bacteria and microbial infection. In this work, we present a combined computational and experimental study of the biological activity and membrane interaction of the computationally designed Bac2A-based peptide library. We used the MARTINI coarse-grained molecular dynamics with adaptive biasing force method and the umbrella sampling technique to investigate the translocation of a total of 91 peptides with different amino acid substitutions through a mixed anionic POPE/POPG (3:1) bilayer and a neutral POPC bilayer, which mimic the bacterial inner membrane and the human red blood cell (hRBC) membrane, respectively. Potential of mean force (PMF, free energy profile) was obtained to measure the free energy barrier required to transfer the peptides from the bulk water phase to the water-membrane interface and to the bilayer interior. Different PMF profiles can indeed identify different membrane insertion scenarios by mapping out peptide-lipid energy landscapes, which are correlated with antimicrobial activity and hemolytic activity. Computationally designed peptides were further tested experimentally for their antimicrobial and hemolytic activities using bacteria growth inhibition assay and hemolysis assay. Comparison of PMF data with cell assay results reveals a good correlation of the peptides between predictive transmembrane activity and antimicrobial/hemolytic activity. Moreover, the most active mutants with the balanced substitutions of positively charged Arg and hydrophobic Trp residues at specific positions were discovered to achieve the improved antimicrobial activity while minimizing red blood cell lysis. Such substitutions provide more effective and cooperative interactions to distinguish the peptide interaction with different lipid bilayers. This work provides a useful computational tool to better understand the mechanism and energetics of membrane insertion of AMPs and to rationally design more effective AMPs.

Application of Molecular Simulation Methods in Food Science: Status and Prospects.

Molecular simulation methods, such as molecular docking, molecular dynamic (MD) simulation, and quantum chemical (QC) calculation, have become popular as characterization and/or virtual screening tools because they can visually display interaction details that in vitro experiments can not capture and quickly screen bioactive compounds from large databases with millions of molecules. Currently, interdisciplinary research has expanded molecular simulation technology from computer aided drug design (CADD) to food science. More food scientists are supporting their hypotheses/results with this technology. To understand better the use of molecular simulation methods, it is necessary to systematically summarize the latest applications and usage trends of molecular simulation methods in the research field of food science. However, this type of review article is rare. To bridge this gap, we have comprehensively summarized the principle, combination usage, and application of molecular simulation methods in food science. We also analyzed the limitations and future trends and offered valuable strategies with the latest technologies to help food scientists use molecular simulation methods.

Reducing Antigenicity and Improving Antioxidant Capacity of ß-Lactoglobulin through Covalent Interaction with Six Flavonoids.

ß-lactoglobulin (ß-LG) is a pivotal nutritional and functional protein. However, its application is limited by its antigenicity and susceptibility to oxidation. Here, we explore the impact of covalent modification by six natural compounds on the antigenicity and antioxidant characteristics of ß-LG to explore the underlying interaction mechanism. Our findings reveal that the covalent interaction of ß-LG and flavonoids reduces the antigenicity of ß-LG, with the following inhibition rates: epigallocatechin-3-gallate (EGCG) (57.0%), kaempferol (42.4%), myricetin (33.7%), phloretin (28.6%), naringenin (26.7%), and quercetin (24.3%). Additionally, the ß-LG-flavonoid conjugates exhibited superior antioxidant capacity compared to natural ß-LG. Our results demonstrate that the significant structural modifications from α-helix to ß-sheet induced by flavonoid conjugation elicited distinct variations in the antigenicity and antioxidant activity of ß-LG. Therefore, the conjugation of ß-LG with flavonoids presents a prospective method to reduce the antigenicity and enhance the antioxidant capacity of ß-LG.

Insight into covalent conjugates of ß-lactoglobulin with rutin: Characterizing allergenicity, digestibility, and antioxidant properties in vitro.

ß-lactoglobulin (ß-LG) is an essential nutrient in milk, but it is the primary allergen causing dairy allergy in humans. Currently, researchers are focusing on using flavonoids to covalently modify ß-LG for improving its functionality. However, the impact and underlying mechanisms of rutin covalent modification on the functional properties and allergenicity of ß-LG remain unclear. Here, we aim to investigate the changes in allergenicity, digestive characteristics, and antioxidant properties of ß-LG after covalent modification using a combination of spectroscopy, enzyme-linked immunosorbent assay (ELISA), simulated digestion, and antioxidant assays. The results indicate that rutin forms covalent bonds with the free amino group, sulfhydryl group, and tryptophan of ß-LG, leading to alterations in the secondary structure of ß-LG. Furthermore, the modified ß-LG exhibits improved antioxidant capacity and decreased allergenicity, along with reduced resistance to pancreatin digestion in vitro. This study provides novel insights and strategies to expand the functional application of ß-LG.

Prediction of antioxidant peptides using a quantitative structure-activity relationship predictor (AnOxPP) based on bidirectional long short-term memory neural network and interpretable amino acid descriptors.

Antioxidant peptides can protect against free radical-mediated diseases, especially food-derived antioxidant peptides are considered as potential competitors among synthetic antioxidants due to their safety, high activity and abundant sources. However, wet experimental methods can not meet the need for effectively screening and clearly elucidating the structure-activity relationship of antioxidant peptides. Therefore, it is particularly important to build a reliable prediction platform for antioxidant peptides. In this work, we developed a platform, AnOxPP, for prediction of antioxidant peptides using the bidirectional long short-term memory (BiLSTM) neural network. The sequence characteristics of peptides were converted into feature codes based on amino acid descriptors (AADs). Our results showed that the feature conversion ability of the combined-AADs optimized by the forward feature selection method was more accurate than that of the single-AADs. Especially, the model trained by the optimal descriptor SDPZ27 significantly outperformed the existing predictor on two independent test sets (Accuracy = 0.967 and 0.819, respectively). The SDPZ27-based AnOxPP learned four key structure-activity features of antioxidant peptides, with the following importance as steric properties > hydrophobic properties > electronic properties > hydrogen bond contributions. AnOxPP is a valuable tool for screening and design of peptide drugs, and the web-server is accessible at http://www.cqudfbp.net/AnOxPP/index.jsp.

Insight into the Interaction Mechanism of Vitamin D against Metabolic Syndrome: A Meta-Analysis and In Silico Study.

As a dietary supplement or functional food additive, vitamin D (VD) deficiency may impact extra-skeletal functions associated with metabolic syndrome (MetS) risk factors. However, the precise effects and mechanisms of VD supplementation on dyslipidemia and insulin resistance in MetS subjects remain controversial. Here, we investigate potential therapeutic targets, pathways and mechanisms of VD against MetS through a comprehensive strategy including meta-analysis, network pharmacology analysis, molecular docking, dynamics simulations, and quantum chemical calculations. Our results reveal that VD supplementation significantly reduces triglyceride levels, fasting glucose, and insulin concentrations in subjects, thereby improving insulin homeostasis to some extent. We theoretically identify 14 core MetS-associated targets. Notably, VD exhibits substantial interactions with three targets (PPARγ, FABP4, and HMGCR) in the PPAR signaling pathway, indicating that VD can modulate this pathway. Van der Waals forces predominantly stabilize the complexes formed between VD and the three targets. Nonetheless, to provide valuable insights for personalized MetS management, further research is necessary to confirm our findings, emphasizing the importance of exploring genetic variability in VD response. In conclusion, our study contributes insights into the mechanisms of VD in preventing and treating MetS through dietary supplementation, promoting the development of VD-based functional foods or nutritious diets.

An investigation on pickering nano-emulsions stabilized by dihydromyricetin/high-amylose corn starch composite particles: Preparation conditions and carrier properties.

With dihydromyricetin (DMY)/high-amylose corn starch (HCS) composite particles as the emulsifier, Pickering nano-emulsions were fabricated by combining high-speed shearing and high-pressure homogenization. The effect of particle properties and processing conditions on the formation and physicochemical properties of the Pickering nano-emulsions was then investigated systematically. The results showed that the DMY content of the composite particles, the oil phase volume fraction of the emulsion, and the homogenization conditions had obvious effects on the droplet size of the emulsion, where appropriate DMY content in the composite particles (5-20%) contributed to the formation of stable Pickering nano-emulsions. The oil phase of the obtained emulsions exhibited good stability during high-temperature storage, and their ß-carotene protecting performance against UV irradiation was superior to the emulsion stabilized by Tween 20. The in vitro simulated digestion analysis indicated that the nano-emulsions developed by the composite particles could enhance the bioaccessibility of ß-carotene and inhibit starch hydrolysis.

Clustering ensemble in scRNA-seq data analysis: Methods, applications and challenges.

With the rapid development of single-cell RNA-sequencing techniques, various computational methods and tools were proposed to analyze these high-throughput data, which led to an accelerated reveal of potential biological information. As one of the core steps of single-cell transcriptome data analysis, clustering plays a crucial role in identifying cell types and interpreting cellular heterogeneity. However, the results generated by different clustering methods showed distinguishing, and those unstable partitions can affect the accuracy of the analysis to a certain extent. To overcome this challenge and obtain more accurate results, currently clustering ensemble is frequently applied to cluster analysis of single-cell transcriptome datasets, and the results generated by all clustering ensembles are nearly more reliable than those from most of the single clustering partitions. In this review, we summarize applications and challenges of the clustering ensemble method in single-cell transcriptome data analysis, and provide constructive thoughts and references for researchers in this field.